ORCID Profile
0000-0002-8279-9666
Current Organisations
University of South Australia
,
The University of Adelaide Faculty of the Professions
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Building | Housing Markets, Development, Management | Land Use and Environmental Planning | Structural engineering | Civil engineering | Building Science and Techniques | Design Innovation | Building Construction Management and Project Planning | Infrastructure engineering and asset management
ANZSRC Socio-Economic Objective (SEO)
Management of Solid Waste from Construction Activities | Climate Change Adaptation Measures | Environmental Policy, Legislation and Standards not elsewhere classified | Residential Construction Design | Commercial Construction Design | Commercial Construction Planning | Health Related to Ageing |
Related Links
Publications
Characterization of brominated flame retardants in construction and demolition waste components: HBCD and PBDEs
Publisher: Elsevier BV
Date: 12-2016
DOI: 10.1016/J.SCITOTENV.2016.07.165
Abstract: The vast majority of construction material is inert and can be managed as nonhazardous. However, structures may have either been built with some environmentally unfriendly substances such as brominated flame retardants (BFRs), or have absorbed harmful elements such as heavy metals. This study focuses on end-of-life construction materials, i.e. construction and demolition (C&D) waste components. The aim was to characterize the concentration of extremely harmful substances, primarily BFRs, including hexabromocyclododecane (HBCD) and polybrominateddiphenyl ethers (PBDEs). Results revealed extremely high contents of HBCD and PBDEs in typical C&D waste components, particularly polyurethane foam materials. Policies should therefore be developed for the proper management of C&D waste, with priority for POP-containing debris. The first priority is to develop a classification system and procedures to separate out the harmful materials for more extensive processing. Additionally, identification and quantification of the environmental implications associated with dumping-dominated disposal of these wastes are required. Finally, more sustainable materials should be selected for use in the construction industry.
Characterizing the generation and flows of construction and demolition waste in China
Publisher: Elsevier BV
Date: 04-2017
The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding
Publisher: Emerald
Date: 02-11-2023
DOI: 10.1108/ECAM-08-2020-0603
Abstract: Unbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry. The identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance. The proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage. The data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.
Time Series Visualization and Forecasting from Australian Building and Construction Statistics
Publisher: MDPI AG
Date: 25-02-2022
DOI: 10.3390/APP12052420
Abstract: The Australian Bureau of Statistics (ABS) regularly releases statistical information, for the whole of Australia, for public access. Building- and construction-related statistics are important to reflect the status of this pillar industry of Australia and help researchers, practitioners, and investors with decision-making. Due to complex retrieval hierarchy of ABS’s website and irregular update frequency, it is usually time-consuming to find relevant information. Moreover, browsing the raw data from ABS’s webpages could not provide the insights to the future. In this work, we applied techniques from computer science to help users in the building and construction domain to better explore the ABS statistics and forecast the future trends. Specifically, we built an integrated Web application that could help collect, sort, and visualize the ABS statistics in a user-friendly and customized way. Our Web application is publicly accessible. We further injected our insights into the Web application, based on the existing data by providing online forecasting on user’s interested information. To achieve this, we identified a series of related economic factors as features and adjusted a multi-variant, LSTM-based time series forecasting model by considering the most informative factors. We also compared our approach with the most widely used SARIMA-based forecasting model to show the effectiveness of the deep learning-based models. The forecast values are depicted at the end of the time series plots, selected by the users.
How to evaluate the efforts on reducing CO2 emissions for megacities? Public building practices in Shenzhen city
Publisher: Elsevier BV
Date: 10-2019
Challenges of responding to sustainability with implications for affordable housing
Publisher: Elsevier BV
Date: 10-2009
Do investment and improvement demand outweigh basic consumption demand in housing market? Evidence from small cities in Jiangsu, China
Publisher: Elsevier BV
Date: 08-2017
The future of sustainable building assessment tools: A case study in Australia
Publisher: Springer Berlin Heidelberg
Date: 18-06-2014
Developing an Assessment Framework for Affordable and Sustainable Housing
Publisher: University of Technology, Sydney (UTS)
Date: 02-07-2010
Sustainable Education Buildings in Australia: A Green Star Review
Publisher: Springer Berlin Heidelberg
Date: 04-06-2013
Incentive Model Based on Cooperative Relationship in Sustainable Construction Projects
Publisher: MDPI AG
Date: 06-07-2017
DOI: 10.3390/SU9071191
Approaches for Transitions Towards Sustainable Development: Status Quo and Challenges
Publisher: Wiley
Date: 06-01-2017
DOI: 10.1002/SD.1661
Hourly operation strategy of a CCHP system with GSHP and thermal energy storage (TES) under variable loads: A case study
Publisher: Elsevier BV
Date: 04-2015
Effects of structure characteristics of project network on conflicts and project success
Publisher: Emerald
Date: 09-12-2019
DOI: 10.1108/ECAM-03-2019-0129
Abstract: The purpose of this paper is to investigate the relationships between structure characteristics of project network, types of conflicts and project success. Network density and centrality were used to reflect the structure characteristics of project network. This study collected 254 valid responses from construction professionals (including project managers, department managers and project engineers) via a questionnaire survey and analyzed the data using structural equation modeling and bootstrapping techniques. The results showed that network centrality of project stakeholders negatively affected project success, whereas the effect of network density on project success was non-significant. The network density was positively related to task conflict, whereas negatively related to process and relationship conflict. Network centrality was positively related to relationship conflict and had negative effects on task and process conflict. Project conflicts served as the mediator, weakening the relationship between network structure characteristics and project success. This study provides direction for project managers and other stakeholders (e.g. owners, contractors and subcontractors) to appropriately establish social ties and manage conflicts to achieve project success. However, the potential influence of conflict transformation on project success, the dynamic nature of project networks and the network diagram were not addressed in the context of erse culture. The future research should cover different stakeholders in order to get an integrative understanding of project networks and collect data from different cultural and industrial characteristics, extending and verifying the results. The outcomes of the study provide evidence in regard to social network ties governance, which is comprised by the important or representative stakeholders, being a part of the effective strategy in improving project success. This study also contributes to the knowledge of conflict management in the project context, revealing the positive and negative of project conflicts and enriching the current understandings of the underlying mechanism of the project network characteristics on project success.
Exploring the determinants of residential satisfaction in public rental housing in China: a case study of Chongqing
Publisher: Springer Science and Business Media LLC
Date: 04-07-2019
Erratum: Corrigendum: Common variants in ACYP2 influence susceptibility to cisplatin-induced hearing loss
Publisher: Springer Science and Business Media LLC
Date: 27-03-2015
DOI: 10.1038/NG0415-423
Study of transient voltage stability with transient stability probing method in hunan power grid
Publisher: IEEE
Date: 11-2017
A bibliometric review of green building research 2000–2016
Publisher: Informa UK Limited
Date: 18-06-2018
A computational fluid dynamic (CFD) simulation of PM10 dispersion caused by rail transit construction activity: A real urban street canyon model
Publisher: MDPI AG
Date: 09-03-2018
Living environment, heating-cooling behaviours and well-being: Survey of older South Australians
Publisher: Elsevier BV
Date: 06-2019
Impacts of renewable energy regulations on the structure of power generation in China – A critical analysis
Publisher: Elsevier BV
Date: 2011
Adopting global virtual engineering teams in AEC Projects
Publisher: Emerald
Date: 07-04-2015
Abstract: – This study aims to present an integrated conceptual model in order to highlight the major aspects of diffusion of innovations in the architecture, engineering and construction (AEC) context. To this end, a critical review of literature is conducted, accompanied by synthesising the findings of previous studies. The driving force behind this study is stemmed from the fragmentation of literature on innovation diffusion, and paucity of research on diffusion of Global Virtual Engineering Teams (GVETs) as the platform for many technological innovations in relevant literature. Thus, the present study is intended to facilitate filling the gap in GVETs literature. That is, the proposed model will offer a foundation for academia for grounding studies on any innovation including GVETs in the literature on innovation diffusion in the AEC context. – This paper draws upon the qualitative meta-analysis approach encompassing a critical review of the relevant literature. To this end, the review builds upon studies found within 15 prestigious journals in AEC. The domain of this review was confined to areas described as “innovation”, “innovation diffusion” and “innovation adoption”, along with keywords used within a broad review of recently published GVETs literature. The rigour of review is augmented by incorporating 35 authoritative works from other disciplines published in 21 well-known journals in the manufacturing, business and management fields. Moreover, the study deploys the peer-debriefing approach through conducting unstructured interviews with five Australian scholars to verify a model presenting an aggregated summary of previous studies. – The key findings of the study include the following items: synthesising the fragmented studies on innovation diffusion in the AEC context. In doing so, a model capturing the major aspects affecting diffusion of an innovation in AEC projects is presented providing a foundation to address the drawbacks of previous studies within the sphere of GVETs, based on the developed model. – The developed model was only enhanced using a small s le size of academics, as such not empirically validated. – As possibly, the first literature review of innovation in the AEC context, this paper contributes to the sphere by sensitising the AEC body of knowledge on innovation diffusion as a concise conceptual model, albeit verified through the peer-debriefing approach. This study will also further establish the research field in AEC on GVETs along with other methods reliant on virtual working such as building information modelling (BIM) through providing an expanded foundation for future inquiries and creation of knowledge.
Attitudinal and behavioural approaches to improving waste management on construction projects in Australia: benefits and limitations
Publisher: Informa UK Limited
Date: 03-04-2015
Factors influencing the retrofitting of existing office buildings using Adelaide, South Australia as a case study
Publisher: Emerald
Date: 11-05-2015
Abstract: – The purpose of this paper is to explore the barriers preventing investment in the re-use of low-grade multi-storey building stock in order to identify attributes that determine whether an existing building is suitable for retrofitting. – Semi-structured interviews were undertaken with key industry practitioners to investigate existing practices and barriers facing low-grade building retrofits and what “ideal” multi-storey building features represent a successful investment opportunity. – The findings showed that tenant commitment is necessary before any project goes ahead and that there exist many barriers influencing the investment decision. These include: high levels of asbestos found in existing buildings changes in the National Construction Code necessitating enhanced fire safety and disability access heritage listing lack of awareness overestimation of costs involved on simple and effective energy efficiency upgrades and change in tenant demands towards modern and efficient open plan offices. Many low-grade structures are privately owned inherited assets where the owners lack the expertise and capital to undertake retrofitting effectively. – The study is focused on the Adelaide CBD in South Australia but the findings are relevant to other Australian cities. – There is room in the market for more positive and influential schemes such as the Green Building Fund that encourage more energy efficiency upgrading of these buildings. – The greater occurrence of retrofitting and re-use of older buildings, rather than demolition and rebuilding, has advantages with regard to reducing the impact of buildings on the environment and promoting sustainability. – The research has indicated certain features of older buildings which render them as suitable candidates for retrofitting and refurbishment.
Corporate social responsibility for construction contractors: a China study
Publisher: Emerald
Date: 04-07-2016
DOI: 10.1108/JEDT-07-2014-0043
Abstract: Corporate social responsibility (CSR) practice and research regarding construction contractors are comparatively limited. The purpose of this research is to identify a series of CSR issues that reflect the major components of CSR, and to determine the perceived importance of these factors in the context of construction contractors. A CSR indicator framework was developed based on stakeholder theory. CSR stakeholders and their corresponding CSR performance issues in construction contractors are classified into two levels, i.e. project level and organizational level. This is followed by a questionnaire survey to investigate the perceptions on relative importance of CSR issues of four key stakeholders in typical construction projects in China, i.e. construction contractors, clients, design and engineering consultancy and supervision firms. The study highlighted a number of factors, e.g. “quality and safety of construction”, “occupational health and safety” and “supplier artner relationship” were highly regarded however, their relative importance varied according to the type of responding organization. The findings indicated the major concerns of the different parties in construction projects, thereby providing a pathway for construction contractors to improve their CSR practice. The priorities of various stakeholders described in this paper provide a useful reference for construction contractors in the selection and adoption of criteria for CSR performance. A better understanding of perceived priorities of CSR factors from different participating parties also serves useful inputs to construction contractors in their stakeholder management process.
Low-carbon technology diffusion in the decarbonization of the power sector: Policy implications
Publisher: Elsevier BV
Date: 05-2018
A novel copper(II) complex with schiff base derived from o-vanillin and L-methionine: Syntheses and crystal structures
Publisher: Pleiades Publishing Ltd
Date: 03-2011
Dynamic interactions between sustainability and competitiveness in construction firms
Publisher: Emerald
Date: 18-09-2017
DOI: 10.1108/ECAM-01-2016-0025
Abstract: Sustainability and competitiveness have received extensive attentions. Despite a large number of studies on sustainability and competitiveness in the construction industry, little research has been conducted to holistically explore the interactions between these two concepts. From a dynamic transition perspective, the purpose of this paper is to link sustainability and competitiveness of construction firms by developing a Sustainability-Competitiveness Dynamic Interaction Framework (SCDIF). Conceptual theory-building approach was adopted to develop the conceptual framework. It is an iterative analysis and synthesis process, which involves reading literature, identifying commonalities and differences, synthesizing, proposing an initial framework, collecting additional literature, and revisiting and revising the framework. There are complex interactions between sustainability and competitiveness of construction firms. This leads to uncertain relationships between sustainability and competitiveness, which is context dependent. Under evolving economic and socio-political environments, sustainability and competitiveness of construction firms could transition from mutually exclusive to mutually supportive, and finally merge into “sustainable competitiveness.” A SCDIF proposed in this study demonstrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies, and thus the relationships and interactions between sustainability and competitiveness are context dependent. This framework helps corporate managers to understand how corporate sustainability and competitiveness interact with each other, thereby informing their decision-making of sustainability strategy. Similarly, the framework provides useful references for policymakers to understand the mechanisms of transitioning industries toward sustainable competitiveness. The proposed framework offers a new perspective for understanding sustainability and competitiveness. From the dynamic transition perspective, this study effectively illustrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies. Compared to existing approaches, the dynamic and holistic approach proposed in this paper provides the capacity to capture the complexity of sustainability and competitiveness.
A review of the first twenty-three years of articles published in the Journal of Cleaner Production: With a focus on trends, themes, collaboration networks, low/no-fossil carbon transformations and th
Publisher: Elsevier BV
Date: 10-2017
Does project culture matter? A comparative study of two major hospital projects
Publisher: Emerald
Date: 30-09-2014
Abstract: – This research aims to investigate the impacts of project culture on the performance of construction projects. Cultural issues in the construction industry have attracted growing attention from both practitioners and academia. However, there are few studies on culture issues at the project level. The influence of project culture has not traditionally been on the research radar. – A case study approach, utilising questionnaire surveys, in-depth interviews and review of project documents, was used to investigate project culture and its associated impacts in two major hospital projects. – The results indicated that project culture played an important role in achieving harmonious relationships between project participants and better project outcomes in terms of schedule, functionality, satisfaction with the process, satisfaction with the relationships, environmental issues addressed commercial success, further business opportunities and overall performance. Case 1 outperformed Case 2 in these performance indicators. Similarly, it became clear that the project’s culture should be developed from the outset and sustained during the project period. Furthermore, it was also highlighted that the project culture should be translated to all levels of the supply chain, i.e. sub-contractors and suppliers. – The findings enabled the client to understand the role of project culture and actively commit towards the development and maintenance of the project culture from very early on. It also helps project teams to understand how to deal with cultural issues at the project level. – This study is one of limited empirical studies that offer in-depth insights of how project culture affects the performance of construction projects. It is also the first study of hospital projects on the research topic.
Effective approaches to reduce greenhouse gas emissions from waste to energy process: A China study
Publisher: Elsevier BV
Date: 11-2015
Factors influencing the success of BOT power plant projects in China: A review
Publisher: Elsevier BV
Date: 06-2013
3-Pyrrolyl-oxindoles as efficient nucleophiles for organocatalytic asymmetric synthesis of structurally diverse 3,3′-disubstituted oxindole derivatives
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C4CC08364D
Abstract: An organocatalytic asymmetric reaction of 3-pyrrolyl-oxindoles to nitroalkenes was developed to afford a range of 3-pyrrolyl-3,3′-disubstituted oxindoles in excellent yields.
Major Participants in the Construction Industry and Their Approaches to Risks: A Theoretical Framework
Publisher: Elsevier BV
Date: 2017
The effects of trust network among project participants on project performance based on SNA approach: a case study in China
Publisher: Informa UK Limited
Date: 10-2018
Synthesis, crystal structure, and DNA interaction of magnesium(II) complexes with Schiff bases
Publisher: Informa UK Limited
Date: 13-05-2013
Lewis acid catalyzed formation of 3-amino-3-carboxy-tetrahydroquinoline derivatives via tandem 1,5-hydride transfer/cyclization process
Publisher: Elsevier BV
Date: 08-2013
Identification and Prevention of Unbalanced Bids Using the Unascertained Model
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2018
Dust pollution control on construction sites: Awareness and self-responsibility of managers
Publisher: Elsevier BV
Date: 11-2017
What leads to the high capital cost of prefabricated construction in China: perspectives of stakeholders
Publisher: Emerald
Date: 19-01-2022
DOI: 10.1108/ECAM-06-2021-0538
Abstract: Prefabricated construction (PC) will play a vital role in the transformation and upgrading of the construction industry in the future. However, high capital cost is currently one of the biggest obstacles to the application and promotion of PC in China. Clarifying the factors that affect the PC cost from the perspectives of stakeholders and exploring key cost control paths help to achieve effective cost management, but few studies have paid enough attention to this. Therefore, this research aims to explore the critical cost influencing factors (CIFs) and critical stakeholders of PC based on stakeholder theories and propose corresponding strategies for different stakeholders to reduce the cost of PC. Based on the stakeholder theory and social network theory, literature review and two rounds of expert interviews were used to obtain the stakeholder-associated CIFs and their mutual effects, then the consistency of the data was tested. After that, social network analysis was applied to identify the critical CIFs, critical interaction and key stakeholders in PC cost control and mine the influence conduction paths between CIFs. The results reveal that the cognition and attitude of developer and relevant standards and codes are the most critical CIFs while the government, developer and contractor are crucial to the cost control of PC. The findings further suggest that measures should be taken to reduce the transaction costs of the developer, and the contractor ought to efficiently apply information technology. Moreover, the collaborative work between designer and manufacturer can avoid unnecessary cost consumption. This research combines stakeholder management and cost management in PC for the first time and explores the effective cost control paths. The research results can contribute to clarifying the key points of cost management for different stakeholders and improving the cost performance of PC projects.
Critical success factors for the implementation of urban regeneration REITs in China: a TISM–MICMAC based approach
Publisher: Emerald
Date: 22-09-2022
DOI: 10.1108/ECAM-03-2022-0220
Abstract: Real estate investment trusts (REITs) have shown great potential in addressing the current contradiction between underinvestment and sustainable development of urban regeneration in China, as well as in further facilitating the transformation and upgrading of China's urban development. In this regard, this study aims to investigate critical success factors (CSFs) and explore the relationships among these factors, and serve as a reference to provide recommendations and strategies for the successful implementation and sustainable development of urban regeneration REITs. In this study, an integrated total interpretive structural modeling–matriced impact croises multiplication applique (TISM–MICMAC) approach using the TISM technique and MICMAC analysis is then implemented to explore the relationships among CSFs, demonstrate the hierarchical structure and classify these factors into clusters based on calculated driving powers and dependence. This study has determined a final list of 11 CSFs through literature review and expert survey. The TISM model demonstrates a six-level hierarchical structure encompassing the influence transmission paths of CSFs, in which the most significant factors and links are established, while the MICMAC analysis further classifies CSFs into four clusters as a complement for the findings of the TISM technique. This study offers practical implications for governments, in idual and institutional investors, REITs and property managers, and other stakeholders concluded in urban regeneration REITs. The final list of determined CSFs can serve as the decision points for management and control of the implementation processes, while the findings of the TISM–MICMAC approach can be a significant reference to provide strategies for optimization and enhancement of urban regeneration REITs. This study is a novel attempt to use both the TISM technique and MICMAC analysis to investigate CSFs for the implementation of urban regeneration REITs, and to address the theoretical and methodological research gaps in the existing literature.
Scientometric Review of Articles Published in ASCE’sJournal of Construction Engineering and Managementfrom 2000 to 2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2019
Mitigation of carbon dioxide by accelerated sequestration in concrete debris
Publisher: Elsevier BV
Date: 2020
Effects of contractual flexibility on conflict and project success in megaprojects
Publisher: Emerald
Date: 12-12-2017
DOI: 10.1108/IJCMA-06-2017-0051
Abstract: This study aims to investigate the influence of contractual flexibility on different types of conflict, determine if contractual flexibility is significantly correlated with project success between contracting parties, verify the mediating effect of project conflicts on the relationship between contractual flexibility and project success and examine the relationship between different types of conflicts and project success in megaprojects. A theoretical model was developed and a structured questionnaire survey was conducted with 468 professionals. The structural equation modeling technique was used to analyze the data. The results showed that both types of contractual flexibility – term and process flexibility – were correlated with and significantly positively affected project success, and term flexibility was found to have a greater influence. The introduction of project conflicts significantly weakened the relationship between contractual flexibility and project success, verifying the partial mediating effect of conflicts. All types of project conflicts play a destructive role in achieving project success relationship conflict had the largest negative effect. Contractual flexibility affects two paths with respect to project success: the direct path (contractual flexibility → project success) and the indirect path (contractual flexibility → conflict → project success). The direct effect of contractual flexibility on project success is positive the corresponding indirect effect is negative. The direct effect is greater than the corresponding indirect effect. Different types of conflicts may mutually transform to extent certain degree. However, this study did not address the potential influence of conflict transformation on project success. The results implied that more emphasis should be placed on contractual terms, particularly on developing flexible terms in the contractual document, when implementing megaprojects. Meanwhile, this study reveals the effects of conflicts on project success in megaprojects, which provides a useful reference for project stakeholders to avoid the negative effect of conflicts. This study provides a better understanding of the relationship between contractual flexibility, types of conflicts in megaprojects and a reliable reference for the project manager to effectively deal with these related issues. This implies the contracting parties strengthen communication and cooperation to establish a trust mechanism, while reducing the negative influence of project conflicts and enhancing the positive effect of contractual flexibility. Few studies have investigated the effects of contractual flexibility on conflict and project success in megaprojects this study contributes significant theoretical and practical insights to contract management and conflict management and provides a reliable reference to achieve project success.
Flexibility of wind power industry chain for environmental turbulence: A matching model study
Publisher: Elsevier BV
Date: 11-2015
Transformation of water resource management: a case study of the South-to-North Water Diversion project
Publisher: Elsevier BV
Date: 10-2017
The impact of international forces on the Chinese wind power industry
Publisher: Elsevier BV
Date: 08-2013
Barriers to the transition towards off-site construction in China: An Interpretive structural modeling approach
Publisher: Elsevier BV
Date: 10-2018
Soft skills of construction project management professionals and project success factors
Publisher: Emerald
Date: 16-04-2018
DOI: 10.1108/ECAM-01-2016-0016
Abstract: The purpose of this paper is twofold: first, to identify the soft skills of construction project management and second, to investigate the influence of these soft skills on project success factors in the Vietnamese construction industry. A questionnaire survey was conducted with 108 project management professionals from the Vietnamese construction industry. Partial least square structural equation modelling was employed in data analysis. Four-dimensional structure of project success factors was confirmed in this study. Results also showed that soft skills of project managers significantly contributed to project success factors and hence the project success. There may be geographical limitation on the conclusions drawn from the findings. Similarly, the s le size was still small, despite a relatively high response rate. In addition, the majority of the respondents were contractors and clients as other project players were reluctant to respond to the survey. This study provides an understanding of the relationship between soft skills and project success factors. Although there have been studies focused on soft skills of project management and project success factors, few have attempted to analyse the effects of these soft skills on critical success factors. Thus, this study adds significantly to the existing research on both project management skills and project success factors.
Way forward for alternative energy research: A bibliometric analysis during 1994–2013
Publisher: Elsevier BV
Date: 08-2015
Green building evaluation from a life-cycle perspective in Australia: A critical review
Publisher: Elsevier BV
Date: 04-2017
Identifying the critical factors for green construction – An empirical study in China
Publisher: Elsevier BV
Date: 10-2013
Project Management Personnel Turnover in Public Sector Construction Organizations in China
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2020
A critical assessment of the Higher Education For Sustainable Development from students' perspectives – a Chinese study
Publisher: Elsevier BV
Date: 06-2013
A system dynamic model for assessing the level of BIM implementation in construction phase: a China case study
Publisher: Emerald
Date: 21-12-2021
DOI: 10.1108/ECAM-10-2021-0895
Abstract: Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase. An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation. The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM. This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.
Competitiveness assessment of the biomass power generation industry in China: A five forces model study
Publisher: Elsevier BV
Date: 04-2016
Social acceptance of solar energy technologies in China—End users’ perspective
Publisher: Elsevier BV
Date: 03-2011
The index system for project selection in ecological industrial park: A China study
Publisher: Elsevier BV
Date: 06-2017
Characteristics and application of road absorbing solar energy
Publisher: Springer Science and Business Media LLC
Date: 18-09-2013
Gold(III)-Dithiocarbamato Peptidomimetics in the Forefront of the Targeted Anticancer Therapy: Preclinical Studies against Human Breast Neoplasia
Publisher: Public Library of Science (PLoS)
Date: 02-01-2014
Assessment of the Sustainable Development Capacity with the Entropy Weight Coefficient Method
Publisher: MDPI AG
Date: 10-2015
DOI: 10.3390/SU71013542
Critical success factors for BOT electric power projects in China: Thermal power versus wind power
Publisher: Elsevier BV
Date: 06-2010
On the management of social risks of hydraulic infrastructure projects in China: A case study
Publisher: Elsevier BV
Date: 04-2015
Characteristics and trends of research on waste-to-energy incineration: A bibliometric analysis, 1999–2015
Publisher: Elsevier BV
Date: 12-2016
Synthesis, crystal structure, and DNA interaction and cleavage studies of a novel dinuclear Cu(II) complex with 3-indolylacetic acid
Publisher: Pleiades Publishing Ltd
Date: 08-2012
Characterizing the essential materials and energy performance of city buildings: A case study of Macau
Publisher: Elsevier BV
Date: 09-2018
Effects of Building Form on Energy Use for Buildings in Cold Climate Regions
Publisher: Elsevier BV
Date: 2016
Exploring the Direct Rebound Effect of Energy Consumption: A Case Study
Publisher: MDPI AG
Date: 19-01-2018
DOI: 10.3390/SU10010259
Analysis of inductive filtering technology based on multi-winding transformer and its typical practical application
Publisher: IEEE
Date: 11-2017
Quinine-catalyzed asymmetric domino Michael-cyclization reaction for the synthesis of spirocyclic oxindoles bearing two spiro quaternary centers and three consecutive stereocenters
Publisher: Elsevier BV
Date: 03-2014
Sustainable development of the wind power industry in a complex environment: a flexibility study
Publisher: Elsevier BV
Date: 12-2014
Objective conflicts in green buildings projects: A critical analysis
Publisher: Elsevier BV
Date: 02-2016
The designer's role in workplace health and safety in the construction industry: Post-harmonized regulations in South Australia
Publisher: Informa UK Limited
Date: 02-10-2015
Sectoral linkage analysis of three main air pollutants in China's industry: Comparing 2010 with 2002
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.JENVMAN.2017.07.038
Abstract: To investigate the driving forces of air pollution in China, the changes in linkages amongst inter-industrial air pollutant emissions were analyzed by hypothetical extraction method under the input-output framework. Results showed that the emissions of SO
Confucian principles and performance: a contractors’ leadership model
Publisher: Informa UK Limited
Date: 02-04-2019
Dealing with cultural diversity in global virtual engineering teams: Lessons for construction managers
Publisher: Unpublished
Date: 2013
Hydrological alteration of the upper Yangtze River and its possible links with large-scale climate indices
Publisher: IWA Publishing
Date: 10-04-2019
DOI: 10.2166/NH.2019.112
Abstract: The relationship between hydrological alteration and climate variability in the upper Yangtze River is not fully understood. In this paper, the periodicity features and the intercorrelation of annual and seasonal eco-flow metrics at the Yichang gauge station are analyzed for the period 1882 to 2013. Analysis is carried out to explore the formation of the eco-flow metrics and the possible linkages between eco-flow metrics and selected climate indices, using the cross-wavelet and wavelet coherence methods on data from 1948 to 2013. The results show that the variation of eco-flow metrics correlates well with some selected climate indices, but changes in different eco-flow metrics are complex. Most annual and seasonal eco-flow metrics correlate well with the Northern Hemisphere (N.H) and Indian Ocean Dipole (IOD) and have a significant common power in the two to four years band. In addition, most annual eco-flow metrics have an obvious phase relationship with the selected climate indices. However, the seasonal eco-flow metrics have no significant phase relationship with the selected climate indices. These findings provide a better understanding of how hydrological alterations of the streamflow and better water resource management can ensure ecosystem sustainability for the Yangtze River.
Improving waste management in construction projects: An Australian study
Publisher: Elsevier BV
Date: 08-2015
Searching for niche market for engineering consultants
Publisher: Emerald
Date: 19-09-2016
DOI: 10.1108/ECAM-09-2015-0132
Abstract: A growing number of foreign consulting firms have been engaged in the Chinese construction market where the supervision system, as a kind of engineering consultant service has been widely implemented. However, the performance of supervision system varies significantly across regions. Therefore, foreign consulting firms are in desperate need of related performance benchmarking statistics to determine the niche market. A major issue is lack of a quantitative method to objectively evaluate regional supervision systems’ performance. The paper aims to discuss these issues. A new regional construction supervision system benchmarking model was developed via the principal component analysis method. This model is based on key performance indicators drawn from annual official statistics. This list of key indicators was refined by means of a focus group with selected experts. Consequently the performances of all 31 regional supervision systems were calculated and ranked. Results indicate a general gap between coastal and inland regions. The various development trends of top 10 regions are analyzed and the underlying reasons are explored. Furthermore, the regions deserving more attention or currently lacking in development are identified. The supervision industry in China is shifting from a labor-intensive industry to a knowledge-intensive industry. The proposed model provides a single quantitative parameter to conveniently benchmark the performance of various regions. The findings are vital for the benchmarking and clarification of future endeavor of the regional supervision systems and for foreign enterprises that are planning to enter the real-estate market in China.
Organocatalytic asymmetric Michael addition of 3-substituted oxindoles to α,β-unsaturated acyl phosphonates for the synthesis of 3,3′-disubstituted oxindoles with chiral squaramides
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C5OB00317B
Abstract: The first Michael addition of 3-monosubstituted oxindoles to α,β-unsaturated acyl phosphonates is investigated to give 3,3′-disubstituted oxindoles with excellent results.
Effectiveness of pavement-solar energy system – An experimental study
Publisher: Elsevier BV
Date: 2015
Ensuring environmental performance in green leases: the role of facilities managers
Publisher: Emerald
Date: 07-2019
Abstract: A green lease incorporates sustainability practices to reduce a building’s negative impact on the environment. Facilities managers play an important role in ensuring these best practices are implemented during the operational stage of a building however, green leasing is an under-researched area in the emerging field of sustainable facilities management (SFM). This paper aims to investigate the common barriers encountered in ensuring environmental performance when a green lease agreement is in operation between a landlord and tenant. This research was conducted in three stages using the principal-agent problem as the theoretical foundation for data collection. Stages 1 and 2 used semi-structured interviews to collect data with policy/corporate-level professionals, landlord and facilities management representatives who have considerable experience in green leases. Stage 3 used document reviews based on summative content analysis to further evaluate the extent of the contextual use of green leasing concepts as used within the facilities management community. The study confirmed a strong incentive gap and information asymmetry between the landlord and facilities manager, forming a typical double principal-agent problem when the split incentives between the landlord and tenants are also taken into consideration, which results in agents acting on their own self-interest rather than the interests of the principal. Goal alignment is found to be key for the successful operation and management of a building throughout its life when present, these goal conflicts can lead to disharmony between the parties to the contract. The study proposes a few practical measures to close the gaps in incentive and information asymmetry that create the principal-agent problem, while providing recommendations to the facilities management professional community. These recommendations could be included in future revisions of the SFM guidelines or code of practices used by the industry. Although this study exposed a rather neglected area of the facilities manager’s role in green leases, the findings are limited by the relatively small s le size used for the interviews. This study contributes to the SFM body of knowledge from a green lease perspective, and the theoretical framework in the double principal-agent problem introduced in the study could be used in future research endeavours.
Environmental emissions influencing solar photovoltaic waste management in Australia: An optimised system network of waste collection facilities
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.JENVMAN.2022.115007
Abstract: The Australian urban construction electricity sector has witnessed a transformational effect in the use of small-scale solar photovoltaic (PV) systems in the past decade. Currently, Australia has one of the highest rates of rooftop solar PV users with over 20% of households connected. This will see a rapid growth in the volume of PV waste in the coming years when these PV systems come to their end-of-life or require replacement. The collection and transportation involved in solar PV waste treatment has a significant impact on the environmental sustainability of Australian cities while designing a holistic reverse logistic (RL) network may play an essential role in the reduction of the associated cost and environmental impacts. In this study, the Weibull distribution model is employed to forecast the PV waste in the next three decades in South Australia. The study further estimates the pollutant emission associated with the collection and transportation of the waste for recycling and recovery using hotspot analysis, location allocation modelling and vehicle routing problem. Generation of pollutants - Particulate Matter (PM), Carbon Monoxide (CO), Carbon dioxide (CO
An expert-based evaluation on end-of-life solar photovoltaic management: An application of Fuzzy Delphi Technique
Publisher: Elsevier BV
Date: 10-2022
MAJOR FACTORS IMPEDING THE IMPLEMENTATION OF WASTE MANAGEMENT IN AUSTRALIAN CONSTRUCTION PROJECTS
Publisher: College Publishing
Date: 06-2018
DOI: 10.3992/1943-4618.13.3.101
Abstract: This article aims to identify barriers to implementing waste management practices in construction projects and their interrelationship, based on the particular context of Australia. Interviews and a questionnaire survey were conducted as the primary data collection methods supported by the findings of a charrette. The findings reveal twenty critical barriers to implementing waste management practices in Australian construction projects. Four underlying factors that impede waste management practices are extracted based on results of an exploratory factor analysis. These include rigidity of construction practices, construction project characteristics, awareness, experience and commitment, and the nascent nature of waste management. The study also finds that while both human factors and technical factors act as barriers to implementing waste management practices in Australian construction projects, human factors are more dominant. Thus, it is essential to address all these barriers in the early stage of construction projects for reducing waste generation.
Environmental and economic impact assessment of the alumina–carbon refractory production in China
Publisher: Springer Science and Business Media LLC
Date: 05-08-2019
Building Energy Assessment Based on a Sequential Sensitivity Analysis Approach
Publisher: Elsevier BV
Date: 2017
Social capital and social integration after project-induced displacement and resettlement: exploring the impact on three life stages in the Three Gorges Project
Publisher: Informa UK Limited
Date: 09-03-2020
Preparation of a Thermo-Sensitive Poly(N-Acryloyl Alanine Methyl Ester) for Controlled Drug Release
Publisher: Trans Tech Publications, Ltd.
Date: 08-2011
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.306-307.675
Abstract: A novel thermo-sensitive poly( N -acryloyl alanine methyl ester) (PAAME) was synthesized by radical polymerization. The structures of the corresponding monomer and polymer have been confirmed by 1 H NMR and FTIR measurements. The thermo-sensitivity of PAAME was investigated by measuring their lower critical solution temperatures (LCST) in pure water and at different pH solution. The results indicated that PAAME exhibited a reversible thermo-sensibility in its aqueous solutions at 17.7 °C. The drug release study showed that 72% caffeine was released from hydrogel at 37 °C after 6 hours, while more than 94% caffeine was gradually diffused into the medium at 18 °C.
Life cycle assessment and the willingness to pay of waste polyester recycling
Publisher: Elsevier BV
Date: 10-2019
Environmental and economic impacts assessment of prebaked anode production process: A case study in Shandong Province, China
Publisher: Elsevier BV
Date: 09-2018
Optical properties of ZnO nanowires grown by thermal evaporation
Publisher: SPIE
Date: 25-07-2009
DOI: 10.1117/12.840031
Pressure Effect on Optical Properties and Structure Stability of LaPO<SUB>4</SUB>:Eu<SUP>3+</SUP> Microspheres
Publisher: American Scientific Publishers
Date: 11-2010
Abstract: Uniform monoclinic core-shell and hexagonal urchin-like LaPO4:Eu(3+) spheres are synthesized via an attractive hydrothermal method owing to the higher yield and simplicity. Photoluminescence and Raman spectra of two s les have been investigated under high pressure up to 28 GPa using diamond anvil cells. At ambient pressure, both s les exhibit same luminescent properties with that of bulk monazite LaPO4:Eu(3+). With the increase of pressure, the emission intensity of Eu(3+) decreases and the half-widths of transition lines increase for both s les, while emission peaks show a red shift toward longer wavelengths due to increase in both the crystal-field strength and the covalency. Monoclinic core-shell LaPO4:Eu(3+) becomes amorphous finally while hexagonal urchin-like one transforms to monoclinic structure at lower pressure of 3.2 GPa and turns into amorphous structure at higher pressures, which are presented based on the analysis of high pressure Raman spectra.
Improving clinical practice guidelines with implementation science
Publisher: Springer Science and Business Media LLC
Date: 19-11-2022
(E)-2-[2-(4-Chlorobenzylidene)hydrazinyl]-4-[3-(morpholin-4-ium-4-yl)propylamino]quinazolin-1-ium bis(perchlorate)
Publisher: International Union of Crystallography (IUCr)
Date: 19-05-2012
DOI: 10.1107/S1600536812021678
Abstract: In the title compound, C 22 H 27 ClN 6 O 2 2+ ·2ClO 4 − , the molecule adopts an E conformation about the C=N double bond. The quinazoline ring is approximately planar, with an r.m.s. deviation of 0.0432 Å, and forms a dihedral angle of 5.77 (4)° with the chlorophenyl ring. The crystal packing features N—H...O hydrogen bonds.
Way to accomplish low carbon development transformation: A bibliometric analysis during 1995–2014
Publisher: Elsevier BV
Date: 02-2017
Mapping the scientific research on life cycle assessment: a bibliometric analysis
Publisher: Springer Science and Business Media LLC
Date: 28-01-2015
How national policies facilitate low carbon city development: A China study
Publisher: Elsevier BV
Date: 10-2019
Ways to improve the project management efficiency in a centralized public procurement system: A structural equation modeling approach
Publisher: Emerald
Date: 30-08-2019
DOI: 10.1108/ECAM-12-2018-0560
Abstract: The purpose of this paper is to investigate driving factors that improve the project management efficiency (PME) in centralized public procurement systems. Employees in four public-sector organizations in China were surveyed. The structural equation modeling was employed to examine the relationship amongst those variables. Organizational culture (OC) is an effective method to improve PME, and employee quality is the most critical factor of OC in this system. Job satisfaction (JS) is another significant contributor to PME and satisfaction with fairness of salary in this system being the key factor of JS. Job analysis (JA) has indirect influence on PME through JS and OC, whereas the job structure in this system is the most critical factor for JA. An operational way to improve PME is to implement it from the perspectives of employee, organization and technique. At the organizational level, it is imperative to strengthen the OC by a well-structured recruitment system and to improve PME via well-design training. At the person level, both financial (i.e. income and welfare) and career incentives (i.e. promotion opportunities and a sense of belonging) are proposed to achieve employees’ JS to improve PME. At the technique level, JA approach (i.e. job rotation) is recommended to enlarge the positive influence of OC and JS on PME. These can not only ensure the management professionalism in a centralized public procurement system but can also motivate employees and maximize PME. PME in a centralized public procurement system will be improved by addressing these key factors and their interrelationships. This provides detailed pathways for the centralized public procurement system to achieve better PME via optimal OC and JS and reasonable JA in China. In addition, the institutional and administrative traditions may vary significantly across cities, regions and countries. Therefore, such contextual differences should be taken into consideration for the improvement of PME in a centralized public procurement system.
(E)-2-[2-(4-Chlorobenzylidene)hydrazin-1-yl]-4-{[3-(dimethylazaniumyl)propyl]amino}quinazolin-1-ium bis(perchlorate)
Publisher: International Union of Crystallography (IUCr)
Date: 28-04-2012
How secure are national energy systems: A dynamic assessment approach
Publisher: Elsevier BV
Date: 2020
1,10-Phenanthroline promotes copper complexes into tumor cells and induces apoptosis by inhibiting the proteasome activity
Publisher: Springer Science and Business Media LLC
Date: 09-10-2012
Relationship between built form and energy performance of office buildings in a severe cold Chinese region
Publisher: Springer Science and Business Media LLC
Date: 06-09-2016
Inter-regional and sectoral linkage analysis of air pollution in Beijing–Tianjin–Hebei (Jing-Jin-Ji) urban agglomeration of China
Publisher: Elsevier BV
Date: 11-2017
Research on the Influence of Indoor Relative Humidity on PM2.5 Concentration in Residential Buildings
Publisher: IOP Publishing
Date: 07-2019
DOI: 10.1088/1757-899X/585/1/012086
Abstract: This study presented an experimental analysis to investigate the influence of the RH on PM2.5 concentration in a typical residential building in Tianjin, China. PM2.5 concentrations were measured using an aerosol monitor in different conditions of three RH scenarios, two pollutant sources and two initial pollutant concentrations. It was observed that about 95% of the size of particles produced by cigarette and wormwood are smaller than 1μm, and the particulate matters produced by wormwood has smaller particle size than that produced by cigarette. Results shows humidification is a practicable method to accelerate the deposition rate of PM2.5. Furthermore, the larger the particle size, and the higher initial pollutant concentration, the more significant influences of the RH on PM2.5 concentration. Considering the requirement of human comfort, the RH is recommended to be controlled at the range of 60%-70% when the indoor PM2.5 pollution is serious. Although humidification can reduce indoor PM2.5 concentration to a certain extent, it cannot reduce the PM2.5 concentration to the permissible range in a short period of time. Therefore, it is recommended to use RH control together with purification device.
Greenhouse Gas Emissions Embodied in the Chinese International Trade of Computer Products
Publisher: MDPI AG
Date: 18-05-2018
DOI: 10.3390/SU10051623
Effects of inter-organizational conflicts on construction project added value in China
Publisher: Emerald
Date: 09-10-2017
DOI: 10.1108/IJCMA-03-2017-0025
Abstract: The purpose of this research is to investigate the effects of inter-organizational conflicts on the project added value in the Chinese construction industry, and also to examine the mediating effect of conflicts on project added value and the moderating effect of conflict management strategies. A conceptual model was developed, and a structured questionnaire survey was conducted with 667 professionals. The structural equation modeling technique was used to analyze the data. The results showed that task conflict, relationship conflict and process conflict were influenced by subject characteristics of project participants, bilateral relationship characteristics and project characteristics. Similarly, these three types of conflicts interact with each other. Meanwhile, these three types of conflicts influence the added value in construction projects, which are moderated by conflict management strategies. Under a collaborating strategy, task conflict and process conflict were positively associated with project added value, and relationship conflict was negatively associated with project added value. Under a competing strategy, task conflict, process conflict and relationship conflict were negatively associated with added value in construction projects. Therefore, the constructive and destructive effects of conflicts on project added value under different conflict management strategies are verified in Chinese construction projects. The variables may not be exhaustive for construction projects and most of them were applied in construction projects for the first time. As a result, their rationality and effectiveness could be further improved. The results implied that inter-organizational conflicts had a constructive effect on project added value and should attract broad attention for future research. Additionally, different driving factors had different influences on these conflicts, and even the driving factors can be ided into different dimensions. This study provides a better understanding of the relationship between inter-organizational conflicts and added value in construction projects, and a reliable reference for the project manager to effectively deal with these conflicts. In addition, this research reveals the effects of conflicts on project added value and the path of conflicts transformation. This provides a useful reference for project managers to take advantage of the positive effect of task conflict and process conflict, and to avoid the negative effect of relationship conflict. Very few studies attempted to examine the effects of inter-organizational conflicts on project added value in construction projects. Therefore, this research makes significant theoretical and practical contributions to the existing body of knowledge on the conflict management and project added value. This research provides an empirical evidence to support the viewpoint that different types of conflicts can be mutually transformed. Similarly, this study explains how conflicts present functional and dysfunctional effects in construction projects. Both of them are potential theoretical contributions to the existing body of knowledge.
Ability or morality? Exploring the multiple dimensions of social trust on public acceptance of urban transport infrastructure projects
Publisher: Emerald
Date: 30-01-2023
DOI: 10.1108/IJMPB-07-2022-0152
Abstract: The public's trust in the authorities has a great impact on people's perception and cognition on development of different types of urban transport infrastructure projects (UTIPs). Given the importance of public acceptance for the efficient construction and operation of UTIPs, this study aims at investigating the personal and environmental factors that influence public acceptance behavior from the perspective of stakeholder management. Based on social cognitive theory (SCT), this study explores the multiple dimensions of social trust on public acceptance in the development of UTIPs by a comparative case study. Two types of UTIPs, a metro railway and a bridge in the Wuhan City, China, were selected as cases, with a questionnaire distributed among the public to collect their sense of trust towards the development of these projects. The data were analyzed through structural equation modeling (SEM). This study reveals that social trust positively influences public acceptance, directly or indirectly through perceived benefit and -risks and self-efficacy. However, the emphasis on social trust about competence and integrity of the authorities varies with the types of projects. Self-efficacy worked as the “mirror of trust” reflecting people's attitude towards social trust in the authorities on their ability and morality. The value of the paper lies in discussing social trust from multiple dimensions in the field of urban infrastructures, which provides new insights into specific mechanisms for shaping public acceptance in project management towards the development of UTIPs.
Evolution of international trade for photovoltaic cells: A spatial structure study
Publisher: Elsevier BV
Date: 04-2017
Performance of wind power industry development in China: A DiamondModel study
Publisher: Elsevier BV
Date: 12-2009
Research on the configuration and operation effect of the hybrid solar-wind-battery power generation system based on NSGA-II
Publisher: Elsevier BV
Date: 12-2019
Exploration of public stereotypes of supply-and-demand characteristics of recycled water infrastructure - evidence from an event-related potential experiment in Xi'an, China
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.JENVMAN.2022.116103
Abstract: There is a growing consensus that recycled water, as an alternative and renewable water source, can serve as a vital water supply to alleviate water scarcity problem and in support of water resilience. Accordingly, recycled water infrastructure investment has seen a significant growth in recent years in many regions of the world. However, previous studies found the perceptions of public, the main end user, toward using recycled water for potable or non-potable purposes remain negatively stereotyped. The negative stereotypes led to public rejections to the construction and operation of recycled water infrastructure. Traditionally, public perceptions of recycled water uses are captured through self-reporting interview or survey techniques. To gain a more accurate measurement of the implicit public stereotypes toward recycled water uses, this study employed an event-related potential (ERPs) technique to collect neurophysiological responses with participants and presented a few research findings. Firstly, the negative stereotypes of recycled water still exist. Secondly, the degree of human contact impacts the negative stereotypes of participants toward recycled water uses more significantly on the supply side (referring to the whole supply chain of recycled water) rather than on the demand side (referring to the potential consumers of recycled water) Third, knowledge level significantly impacts the negative stereotypes of participants toward recycled water uses that have close human contact, at both supply and demand sides, and shows a more significant impact on the supply side. The findings of study contributed to the literature through creatively iding the negative stereotypes of recycled water into the "supply-side" and the "demand-side" ones, and meanwhile have managerial implication for policymaking and scheme implementation in the area.
Ultrasonic-Assisted Synthesis of Monodisperse Ag Nanoparticles and Their Applications in Surface Enhanced Raman Scattering and Fluorescence Enhancement
Publisher: AIP Publishing
Date: 08-2012
DOI: 10.1088/1674-0068/25/04/501-506
Abstract: Monodisperse Ag nanoparticles with diameters of about 3.4 nm were synthesized by a facile ultrasonic synthetic route at room temperature with the reduction of borane-tert-butylamine in the presence of oleylamine (OAm) and oleic acid (OA). The reaction parameters of time, the molar ratios of OAm to OA were studied, and it was found that these parameters played important roles in the morphology and size of the products. Meanwhile, surface enhanced Raman spectrum (SERS) property suggested the Ag nanoparticles exhibited high SERS effect on the model molecule Rhodamine 6G. And also, two-photon fluorescence images showed that the silver nanoparticles had high performances in fluorescence enhancement.
Microalgae biodiesel production in China: A preliminary economic analysis
Publisher: Elsevier BV
Date: 04-2019
An innovative approach to managing demolition waste via GIS (geographic information system): a case study in Shenzhen city, China
Publisher: Elsevier BV
Date: 2016
How does the strength of ties influence relationship quality in Chinese megaprojects? The mediating role of contractual flexibility
Publisher: Emerald
Date: 03-2021
Abstract: This study aims to explore the influence of the strength of ties (strong ties and weak ties) on contractual flexibility (term flexibility and process flexibility) and relationship quality among stakeholders in a megaproject network. This study, via a questionnaire survey, collected 380 valid responses from megaproject professionals (including project managers, department managers and project engineers). The data were analyzed using least squares structural equation modeling. The results show that both strong ties and weak ties have positive effects on relationship quality. The introduction of contractual flexibility can help improve relationship quality by combining the positive effects of the strength of ties. Interestingly, the indirect influence of strong ties on relationship quality is mainly due to term flexibility. However, the influence of process flexibility is not significant, while weak ties have an indirect influence through term flexibility and process flexibility. This study, while helpful to megaproject management both in theory and practice, is nevertheless subject to several limitations. First, this study only considers the impact of the strength of ties on contractual flexibility and relationship quality other factors, such as environmental uncertainty, are not explored. Second, the s le data are limited to just a few regions of China. Future research should cover other influencing factors, in order to make the model more substantial data should also be collected from different cultural and industrial sources, thereby extending and further verifying the results. This study makes three contributions to extant megaproject literature. First, this study provides a deep and nuanced understanding of the strength of ties. With the distinction between strong ties and weak ties clearly explained, this research furnishes a subtler understanding of relationship governance than has previously been achieved. Second, by precisely identifying the mechanism of how contract flexibility improves contract control and coordination functions, this research offers a complementary view of how contractual flexibility positively contributes to cooperation and relationship quality. Third, this study identifies which dimension of the strength of ties is more influential. This brings a new explanation for the previous controversy and offers some insight into the determinants of how to improve relationship quality in Chinese megaprojects.
Renewable energy in buildings in China—A review
Publisher: Elsevier BV
Date: 08-2013
What does the grey matter decrease in the medial prefrontal cortex reflect in people with chronic pain?
Publisher: Wiley
Date: 04-09-2019
DOI: 10.1002/EJP.1304
Abstract: Alterations in the grey matter volume of several brain regions have been reported in people with chronic pain. The most consistent observation is a decrease in grey matter volume in the medial prefrontal cortex. These findings are important as the medial prefrontal cortex plays a critical role in emotional and cognitive processing in chronic pain. Although a logical cause of grey matter volume decrease may be neurodegeneration, this is not supported by the current evidence. Therefore, the purpose of this review was to evaluate the existing literature to unravel what the decrease in medial prefrontal cortex grey matter volume in people with chronic pain may represent on a biochemical and cellular level. A literature search for this topical review was conducted using PubMed and SCOPUS library. Search terms included chronic pain, pain, medial prefrontal cortex, anterior cingulate cortex, grey matter, neurochemistry, spectroscopy, magnetic resonance imaging, positron emission tomography, dendrite, neurodegeneration, glia, astrocyte, microglia, neurotransmitter, glutamate, GABA and different combinations of these terms. Adopting a stress model of chronic pain, two major pathways are proposed that contribute to grey matter volume decrease in the medial prefrontal cortex: (a) changes in dendritic morphology as a result of hypothalamic-pituitary axis dysfunction and (b) neurotransmitter dysregulation, specifically glutamate and γ-Aminobutyric acid, which affects local microvasculature. Our model proposes new mechanisms in chronic pain pathophysiology responsible for mPFC grey matter loss as alternatives to neurodegeneration. It is unclear what the decrease in medial prefrontal cortex grey matter volume represents in chronic pain. The most attractive reason is neurodegeneration. However, there is no evidence to support this. Our review reveals nondegenerative causes of decreased medial prefrontal grey matter to guide future research into chronic pain pathophysiology.
Evolving theories of sustainability and firms: History, future directions and implications for renewable energy research
Publisher: Elsevier BV
Date: 05-2017
Research on the impact assessment of urbanization on air environment with urban environmental entropy model: a case study
Publisher: Springer Science and Business Media LLC
Date: 05-2011
A conceptual meta-framework for managing multicultural global virtual teams
Publisher: Inderscience Publishers
Date: 2013
Investigating vulnerability of ecological industrial symbiosis network based on automatic control theory
Publisher: Springer Science and Business Media LLC
Date: 21-07-2018
DOI: 10.1007/S11356-018-2753-0
Abstract: System fluctuations of eco-industrial symbiosis network (EISN) organization due to disturbance are very similar to the controller adjustment in the automatic control theory. Thus, a methodology is proposed in this study to assess the vulnerability of EISN based on the automatic control theory. The results show that the regulator plays a key role to enhance the resilience of the network system to vulnerability. Therefore, it is imperative to strengthen the real-time regulation and control of EISN so that the system stability is improved. In order to further explore the impact of various regulations on the system vulnerability, the influence of system stability is simulated by means of proportional, differential, and integral control. A case study with Guigang eco-industrial park (EIP) was undertaken to test this model. The results showed that when the system was disturbed at different positions, the key nodes which had great influence on system vulnerability could be selected according to the magnitude of simulation curve. By changing the ratio coefficient of proportional, differential, and integral units to adjust the ecological chain network, the system's resilience to vulnerability can be enhanced. Firstly, if basic conditions of EISN organization remain unchanged, the integral control of the policy support and infrastructure sharing should be strengthened. Secondly, the differential regulation should be improved continuously for the technological innovation capability of key node enterprises. Finally, the key chain filling projects should be introduced for proportional control so that the chain network design can be optimized from the source.
Situation and Competitiveness of Foreign Project Management Consultancy Enterprises in China
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2011
Towards a low carbon transition of urban public transport in megacities: A case study of Shenzhen, China
Publisher: Elsevier BV
Date: 07-2018
Notice of Retraction: The Risk Management of Thermal Power Construction Projects in China: A Case Study
Publisher: IEEE
Date: 09-2009
Delivery risk analysis within the context of program management using fuzzy logic and DEA: A China case study
Publisher: Elsevier BV
Date: 02-2014
International Atherosclerosis Society guidance for implementing best practice in the care of familial hypercholesterolaemia
Publisher: Springer Science and Business Media LLC
Date: 15-06-2023
40-Deoxy-40(S)-iodorapamycin
Publisher: International Union of Crystallography (IUCr)
Date: 04-07-2012
DOI: 10.1107/S1600536812027547
Abstract: The title compound, C 51 H 78 INO 12 , contains a 29-membered ring incorporating amide, lactone and ester groups. It contains a total of 15 stereogenic centres. In the crystal, molecules are linked by O—H...O hydrogen bonds, forming C (8) chains propagating in [100]. A weak intramolecular O—H...O interaction also occurs.
Stability of ecological industry chain: an entropy model approach
Publisher: Springer Science and Business Media LLC
Date: 08-04-2016
DOI: 10.1007/S11356-016-6579-3
Abstract: A novel methodology is proposed in this study to examine the stability of ecological industry chain network based on entropy theory. This methodology is developed according to the associated dissipative structure characteristics, i.e., complexity, openness, and nonlinear. As defined in the methodology, network organization is the object while the main focus is the identification of core enterprises and core industry chains. It is proposed that the chain network should be established around the core enterprise while supplementation to the core industry chain helps to improve system stability, which is verified quantitatively. Relational entropy model can be used to identify core enterprise and core eco-industry chain. It could determine the core of the network organization and core eco-industry chain through the link form and direction of node enterprises. Similarly, the conductive mechanism of different node enterprises can be examined quantitatively despite the absence of key data. Structural entropy model can be employed to solve the problem of order degree for network organization. Results showed that the stability of the entire system could be enhanced by the supplemented chain around the core enterprise in eco-industry chain network organization. As a result, the sustainability of the entire system could be further improved.
Comprehensive evaluation and optimization of agricultural system: An emergy approach
Publisher: Elsevier BV
Date: 12-2019
Evolution of online public opinions on social impact induced by NIMBY facility
Publisher: Elsevier BV
Date: 09-2019
Research on the performance of radiative cooling and solar heating coupling module to direct control indoor temperature
Publisher: Elsevier BV
Date: 02-2020
Investigating the relationship between communication-conflict interaction and project success among construction project teams
Publisher: Elsevier BV
Date: 11-2017
What are the Key Indicators of Mega Sustainable Construction Projects? —A Stakeholder-Network Perspective
Publisher: MDPI AG
Date: 18-08-2018
DOI: 10.3390/SU10082939
Abstract: Mega sustainable construction projects (MSCPs) require complex system engineering. There are various indicators available to evaluate sustainable construction, and it is difficult to determine which the key indicators are among them. Existing studies do not adequately consider the stakeholders associated with the indicators of sustainable construction, leading to key decision-makers’ lack of targeted management strategies to improve the sustainability level of MSCPs. Using literature analysis and expert interviews, this study identified the key evaluation indicators of MSCPs from a stakeholder-network perspective. Social network analysis (SNA) was used to explore the relationships between the key evaluation indicators and corresponding stakeholders. The results showed that the government and designers significantly impacted other stakeholders and played as the key stakeholders in MSCPs. Regarding the indicators, applying energy-saving and intelligent technologies plays a key role in the MSCPs. This study links key indicators of MSCPs with the associated stakeholders, which helps decision-makers to develop targeted strategies to improve the sustainability level of MSCPs, thereby not only improving the efficiency and effectiveness of the intervention strategies, but also helping to save decision-makers’ monetary and human resources which are usually limited.
Factors affecting perceived level of virtuality in hybrid construction project teams (HCPTs)
Publisher: Emerald
Date: 03-10-2016
Abstract: Deploying hybrid construction project teams (HCPTs) in which the common pattern of interactions is a blend of face-to-face and virtual communications has been increasingly gaining momentum in the construction context. Evidence has demonstrated that effectiveness of HCPTs is affected by a perceived level of virtuality, i.e. the perception of distance and boundaries between members where teams shift towards working virtually as opposed to purely collocated teams. This study aims to provide an integrated model of the factors affecting perceived virtuality in HCPTs, to address the conspicuous absence of studies on virtuality in the construction context. An a priori list of factors extracted from existing literature on virtuality was subjected to the scrutiny of 17 experts with experiences of working in HCPTs through semi-structured interviews. Nvivo 10 was deployed for analysing the interview transcripts. The findings outline the factors affecting virtuality in HCPTs and map the patterns of their associations as an integrated model. This leads to discovering a number of novel factors, which exert moderating impacts upon perceived virtuality in HCPTs. The findings assist managers and practitioners dealing with any form of HCPTs (including building information modelling-based networks and distributed design teams) in identifying the variables manipulating the effectiveness of their teams. This enables them of designing more effective team arrangements. As the first empirical study on virtuality in the construction context, this paper contributes to the sphere by conceptualising and contextualising the concept of virtuality in the construction industry. The study presents a new typology for the factors affecting perceived virtuality by categorising them into predictors and moderators.
Characterization of post-disaster environmental management for Hazardous Materials Incidents: Lessons learnt from the Tianjin warehouse explosion, China
Publisher: Elsevier BV
Date: 09-2017
DOI: 10.1016/J.JENVMAN.2017.05.021
Abstract: Hazardous Materials Incidents (HMIs) have attracted a growing public concern worldwide. The health risks and environmental implications associated with HMIs are almost invariably severe, and underscore the urgency for sound management. Hazardous Materials Explosion incidents (HMEIs) belong to a category of extremely serious HMIs. Existing studies placed focuses predominately on the promptness and efficiency of emergency responses to HMIs and HMEIs. By contrast, post-disaster environmental management has been largely overlooked. Very few studies attempted to examine the post-disaster environmental management plan particularly its effectiveness and sufficiency. In the event of the Tianjin warehouse explosion (TWE), apart from the immediate emergency response, the post-disaster environmental management systems (P-EMSs) have been reported to be effective and sufficient in dealing with the environmental concerns. Therefore, this study aims to critically investigate the P-EMSs for the TWE, and consequently to propose a framework and procedures for P-EMSs in general for HMIs, particularly for HMEIs. These findings provide a useful reference to develop P-EMSs for HMIs in the future, not only in China but also other countries.
Synthesis and crystal structure of a new nickel(II) coordination polymer based on Schiff base
Publisher: Pleiades Publishing Ltd
Date: 05-2012
A Green Dispatch Model of Power System with Wind Energy Considering Energy-Environmental Efficiency
Publisher: Springer Singapore
Date: 2017
Is cement pavement more sustainable than permeable brick pavement? A case study for Jinan, China
Publisher: Elsevier BV
Date: 07-2019
Correlation data analysis for low-frequency oscillation source identification
Publisher: IEEE
Date: 11-2017
Identification and evaluation of the key social competencies for Chinese construction project managers
Publisher: Elsevier BV
Date: 07-2013
A Framework for Quantifying Carbon Emissions Generated During Demolition Waste Processing
Publisher: Springer Singapore
Date: 19-12-2017
Critical Uncertainty Factors for Efficient Risk Allocation in Privately Financed Public Infrastructure Projects in Australia
Publisher: Informa UK Limited
Date: 2011
Transition to low carbon energy policies in China—from the Five-Year Plan perspective
Publisher: Elsevier BV
Date: 06-2011
Life cycle assessment of autoclaved aerated fly ash and concrete block production: a case study in China
Publisher: Springer Science and Business Media LLC
Date: 23-06-2019
DOI: 10.1007/S11356-019-05708-8
Abstract: With the rapid development of construction industry, consumption of concrete block has increased rapidly in China. As a kind of green building material and resource comprehensive utilization product, autoclaved aerated fly ash and concrete block have better performance in terms of heat preservation, sound insulation, and fire resistance. However, some typical issues are associated with autoclaved aerated fly ash and concrete block production process such as energy and material consumption as well as pollutant emissions. To examine the environmental and economic impacts of its production process is imperative. Choosing 1 m
Building life cycle assessment research: A review by bibliometric analysis
Publisher: Elsevier BV
Date: 09-2017
Harmonic Modeling and Experimental Validation of the Converters of DFIG-Based Wind Generation System
Publisher: Hindawi Limited
Date: 03-11-2019
DOI: 10.1155/2019/7968914
Abstract: The double-fed induction wind generator- (DFIG-) based wind generation system contains power electronic converters and filter capacitor and inductor, which will bring about high-frequency harmonics under the influence of controllers. Aiming at this problem, this paper studies the relation between the output current and the harmonic source at grid-side and rotor-side converters based on their control features in the DFIG system. Furthermore, the harmonic equivalent models of these two converters are built, and the influence of different factors on harmonic features is explored from four perspectives, i.e., modulation method, altering controller parameters, altering output power, and the unbalance of three-phase voltage. Finally, the effectiveness of the proposed model is verified through the 2 MW DFIG real-time hardware-in-the-loop test platform by StarSim software and real test data, respectively.
Green buildings for greying people
Publisher: Emerald
Date: 28-04-2014
Abstract: – This paper aims to identify the critical issues to be considered by developers and practitioners when embarking on their first green residential retirement project in Australia. With an increasingly ageing population and widespread acceptance of the need for sustainable development in Australia, the demand for green retirement villages is increasing. – In view of the lack of adequate historical data for quantitative analysis, a case study approach is used to examine the successful delivery of green retirement villages. Face-to-face interviews and document analyses were conducted for data collection. – The findings of the study indicate that one of the major obstacles to the provision of affordable green retirement villages is the higher initial costs involved. However, positive aspects were identified, the most significant of which relate to the innovative design of site and floor plans adoption of thermally efficient building materials orientation of windows installation of water harvesting and recycling systems, water conservation fittings and appliances and waste management during the construction stage. With the adoption of these measures, it is believed that sustainable retirement development can be achieved without significant additional capital costs. – The research findings serve as a guide for developers in decision-making throughout the project life-cycle when introducing green features into the provision of affordable retirement accommodation. – This paper provides insights into the means by which affordable green residential retirement projects for aged people can be successfully completed.
Preparation of 3-Sulfonylated 3,3-Disubstituted Oxindoles by the Addition of Sulfinate Salts to 3-Halooxindoles
Publisher: American Chemical Society (ACS)
Date: 08-12-2014
DOI: 10.1021/JO502270A
Abstract: An efficient method for the preparation of 3-sulfonylated 3,3-disubstituted oxindole derivatives has been developed. The protocol involves a base-catalyzed addition of sulfinate salts to 3-halooxindoles, affording a wide range of 3-sulfonylated 3,3-disubstituted oxindoles in good to excellent yields under mild conditions. A preliminary trial of asymmetric catalytic version was conducted and gave promising enantioselectivity. The mechanism for the reaction was tentatively explored with the help of mass spectrometric analysis.
From “Traditional” to “Low carbon” urban land use: Evaluation and obstacle analysis
Publisher: Elsevier BV
Date: 11-2019
Social acceptance of wind power: a case study of Shandong Province, China
Publisher: Elsevier BV
Date: 04-2015
Evaluation of Design-Builder Qualifications through the Analysis of Requests for Qualifications
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2012
Dynamics of Collaborative Networks for Green Building Projects: Case Study of Shanghai
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2021
Environmental impact analysis and process optimization of batteries based on life cycle assessment
Publisher: Elsevier BV
Date: 02-2018
Air pollutant emissions from economic sectors in China: A linkage analysis
Publisher: Elsevier BV
Date: 06-2017
Indoor daylight distribution in a room with integrated dynamic solar concentrating facade
Publisher: Elsevier BV
Date: 2018
Optimal Dispatch Modes of Tie Line and Early Warning Strategy
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICEI.2017.29
China's approach to nuclear safety — From the perspective of policy and institutional system
Publisher: Elsevier BV
Date: 2015
Past, current and future of biomass energy research: A bibliometric analysis
Publisher: Elsevier BV
Date: 12-2015
Challenges of developing sustainable neighborhoods in China
Publisher: Elsevier BV
Date: 11-2016
A pH/Temperature-Sensitive Semi-IPN Bead for Drug Release Carrier
Publisher: Trans Tech Publications, Ltd.
Date: 10-2010
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.148-149.1449
Abstract: A series of pH/temperature sensitive hydrogel beads with semi-interpenetrating polymer network (semi-IPN), composed of sodium alginate and poly(N-acryloylglycinate) were prepared as drug delivery carrier. In pH=2.3 phosphate buffer solution (PBS), the release amount of indomethacin incorporated into the beads was about 9% within 610 min, while this value approached to 68% in pH=7.4 PBS. The release rate of indomethacin was higher at 37 than that at 20 . In addition, the release amount of indomethacin was increased with increasing poly(N-acryloylglycinate) content. These results suggest that the stimuli-sensitive beads have the potential to be used as an effective pH/temperature delivery system in bio-medical fields.
Prediction System for Change Management in Construction Project
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2010
Green building research–current status and future agenda: A review
Publisher: Elsevier BV
Date: 02-2014
Construction waste reduction behavior of contractor employees: An extended theory of planned behavior model approach
Publisher: Elsevier BV
Date: 2018
Practices, drivers and barriers of implementing green leases: lessons from South Australia
Publisher: Emerald
Date: 03-04-2017
DOI: 10.1108/JCRE-04-2016-0018
Abstract: This paper aims to investigate the practices, drivers and barriers which influence the implementation of green leases in South Australia. Despite some efforts on legal aspects of green leases, only a few studies have examined these aspects from an operational perspective. In addition, very little empirical evidence was presented in previous studies to show how green leases work in real-life settings. Data were collected using semi-structured interviews with landlord and tenant representatives who have considerable experience in green leases. These interviewees were selected via a purposive s ling technique that identified buildings which use green leases in South Australia. The concept of interface management (IM) was used to operationalize this research. The green leases were found to be mainly initiated by tenants while government involvement, economic and environmental benefits are the main drivers in South Australia. Drivers such as staff retention, well-being and corporate social responsibility are found to be more relevant to tenants. Lack of awareness and transaction costs are the main barriers to the implementation of green leases. This study focuses on the South Australian context and mainly covers dark green leases. There are implications for the government’s continued involvement and the promotion of lighter shades of green leases to overcome operational issues and barriers identified in this study. This study contributes to the body of knowledge on the subject of green lease implementation from an operational perspective. In addition, the study introduces a conceptual framework via IM that could be used in future research endeavours.
Understanding Factors Influencing Project Managers’ Behavioral Intentions to Reduce Waste in Construction Projects
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2018
Timing of surgery following SARS‐CoV‐2 infection: an international prospective cohort study
Publisher: Wiley
Date: 09-03-2021
DOI: 10.1111/ANAE.15458
Abstract: Peri‐operative SARS‐CoV‐2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS‐CoV‐2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre‐operative SARS‐CoV‐2 infection were compared with those without previous SARS‐CoV‐2 infection. The primary outcome measure was 30‐day postoperative mortality. Logistic regression models were used to calculate adjusted 30‐day mortality rates stratified by time from diagnosis of SARS‐CoV‐2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre‐operative SARS‐CoV‐2 diagnosis. Adjusted 30‐day mortality in patients without SARS‐CoV‐2 infection was 1.5% (95%CI 1.4–1.5). In patients with a pre‐operative SARS‐CoV‐2 diagnosis, mortality was increased in patients having surgery within 0–2 weeks, 3–4 weeks and 5–6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3–4.8), 3.9 (2.6–5.1) and 3.6 (2.0–5.2), respectively). Surgery performed ≥ 7 weeks after SARS‐CoV‐2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9–2.1)). After a ≥ 7 week delay in undertaking surgery following SARS‐CoV‐2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2–8.7) vs. 2.4% (95%CI 1.4–3.4) vs. 1.3% (95%CI 0.6–2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS‐CoV‐2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.
Relationship between Project’s Added Value and the Trust–Conflict Interaction among Project Teams
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2017
Strategic route map of sulphur dioxide reduction in China
Publisher: Elsevier BV
Date: 09-2013
Managing the high capital cost of prefabricated construction through stakeholder collaboration: a two-mode network analysis
Publisher: Emerald
Date: 08-2023
DOI: 10.1108/ECAM-04-2023-0392
Abstract: Prefabricated construction (PC) can benefit construction industry due to its high efficiency, energy saving, consumption reduction and safety. However, the high capital cost is a critical challenge hindering its development in China. The collaboration of PC stakeholders is conducive to improving cost management efficiency and optimizing resource allocation which has been ignored in previous studies. Therefore, this study aims to explore the collaboration paths of stakeholders in the process of solving critical cost influencing factors (CIFs) of PC to reduce costs. Firstly, 25 CIFs and five main stakeholders that affect PC capital cost were identified through literature research and expert interviews. Then, questionnaires were used to investigate the relationship between stakeholders and CIFs from the perspectives of three stakeholder attributes of proximity, attitude and power, respectively. Finally, based on the survey data, three stakeholder-CIF networks from three attributes perspective and a comprehensive network were constructed and used for subsequent social network analysis. (1) Stakeholders mainly show willingness and potential to collaborate on organization and management factors (2) More stakeholders pay attention to incentive policies and the setting of prefabrication rates and assembly rates, while all stakeholders have the right to facilitate information and resource sharing in the PC supply chain (3) The comprehensive network shows a core-periphery structure. As core stakeholders, contractor, designer and manufacturer are more likely to actively manage the 14 core CIFs. This paper innovatively combined stakeholder and cost management in PC, and used two-mode network based on three stakeholder perspectives to reveal the collaboration potential and motivation of stakeholders in PC cost management. The findings not only provide guidance for stakeholders to find potential partners and optimize resource allocation in solving specific cost issues, but also facilitate stakeholders' sustainable collaboration to achieve PC's cost performance.
Willingness to pay for higher construction waste landfill charge: A comparative study in Shenzhen and Qingdao, China
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.WASMAN.2018.09.043
Abstract: High landfill charge presents an effective approach to ert construction waste from landfill. The stakeholders' willingness to pay (WTP) for the disposal of construction waste in landfill provide the useful information to set a reasonable charge level. Considering the ersity in stakeholder groups and regional socioeconomic conditions, contingent valuation method (CVM) was employed in this study to investigate the WTP of two major stakeholder groups in two typical Chinese cities. In addition, the perception of stakeholders towards landfill charge policy was measured and the impact of various factors on WTP was explored. The results indicated that there were statistically significant disparities of WTP between cities and stakeholder groups. Stakeholders from Shenzhen were willing to pay more than their counterparts in Qingdao. Contractors were willing to pay less than owners. Respondents who evaluate the policy as effective in reducing construction waste landfill were willing to pay more. However, firm size, ownership, position of respondent and perceived equity factors did not show statistically significant effect on WTP. These findings highlight the necessity to customize landfill charge policy according to local socioeconomic conditions.
Bayesian inference of synthetic daily rating curves by coupling Chebyshev polynomials and the GR4J model
Publisher: Copernicus GmbH
Date: 22-09-2022
DOI: 10.5194/IAHS2022-2
Abstract: & & Building Rating Curves is complex since it depends on the availability of direct instantaneous stage-velocity/area measurements (gaugings) and variable hydraulic conditions. This work proposes a methodology to estimate synthetic (non-gauged) daily rating curves. A regional rainfall-runoff model (GR4J) is coupled with an instantaneous/stage - daily/discharge relationship based on third-order Chebyshev polynomials. Bayesian inference with Delayed Rejection Adaptive Metropolis algorithm is used to optimize the parameters and to quantify the uncertainty in the joint daily rating curve and the regional rainfall-runoff model. The likelihood function based on the model residuals is assumed to be gaussian, homoscedastic, and independent. As a study area, a region with 4 gauging sites located in New South Wales, Australia was chosen, and periods with no changes in the stage-discharge relationship were selected. Then, the method is implemented 4 times across the gauging sites, where 3 sites are supposed to be gauged and 1 site is supposed to be partially gauged (with only instantaneous water level). The proposed approach can increase the spatial representation of hydrological data when used in combination with satellite river altimetry. Another extension would be to identify the error if fewer gaugings are available.& Future works could address how to incorporate heteroscedastic, autocorrelation, and zero-inflation of model residuals as well as how to account for the variation in the range of model residuals across the gauging sites.& & & & & & & & & & img src=& quot data:image ng base64, 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 Ke/fu1WpbPz8/Wrt2rdrrSyQSsra2pk8//VSu/McffyQAdOfOHZXb//HHH2RhYaFVrNqSSqWUnZ2tUF5QUKC0vCwXFxdycXFRKJ8+fTo9ffpUrkwikci9Li4upvz8fOF1VlYWde/enYiI/v33X5o3bx7FxMQo7NvDw4P27dunMi5VysdR/jWR8r+L8mXqbOfn50dubm5yZcr6+2Xqaz8/P63/nrSVkZFBAOjMmTM1Wi/TDa2+WYWEhCA/Px+FhYXYt28fDh8+jPfff1+4HMCqxsjICA8ePMDRo0flyiMjI2Fubo7WrVur3P758+c1PgrTwMAAFhYWCuUmJiYK5ZcuXcKCBQsAlFwi9fT0VHov0MXFRbjkVKr0clkpsVgs94ndzMwMjRs3xrlz53DhwgXk5eUhPDxcbhuJRIILFy5g6tSpmjVSRRzlXwNQem+xfJm625WnrL9f1b7WlXr16sHa2hrJycm1HQrTglYDLPz8/GBqagpzc3PhxI+IiMCOHTuwdevWKgc1a9YsPHz4UK11xWIxDh06BCsrqyrXq0/q1q0r9/rJkyc4c+YMVq9eXenllPT09EpHk9WmDh06ID4+Hh4eHsjIyICPjw9sbW0V1mvZsiVGjRqFs2fPYvTo0WrtWywW48yZM8jPz8fIkSNRUFCg0F/r16/HTz/9JPwG6WVw+fJl1K9fX+NBM/rY135+fjh79izs7e01aosu1K1blx+99JLSOllNmzZNbvjrs2fP0Lx5c50EtWvXLrUf1SQSiar86JqXwYwZM/D5558LN81VSU9PR506dWogKu00adIEc+fOVWtdBwcH5OTkaFxHafvLv3kSERYtWlTtj5/SpXfffRcWFhawtLTUeFt97OtWrVphypQpOnu/0IS5uTkyMjJqvF5WdRonq+LiYgQGBmLRokVCGRHh6tWr2LFjh06CEolEOv3U6+/vr/bcNqNHj8bs2bO1rmvOnDl48OCBXFl6ejpevHiBwMBAuXJDQ0O4u7tX+q1ww4YNaNmyJbZt26bWQI+MjIxKk5Wrq6vc8GXGXnarV68WfgtXEXNzc/5h8EtK42QVEhICiUQiN0ooICAAmZmZwm9FqioxMRG5ublqrWtgYIB27dqpfBPv169fjY1c/OOPPxTK/P39ERgYiBUrVmi8Pw8PDxQUFAgfBHJyciAWi1UmI0NDQ+Tn56vcr6ura6V/2NUlOjoa69evh729vdyTxHWNiODr66vw2yBtyWQyBAYG4oMPPhDKDh48CF9fXzg5OcmV66vXve+Li4v1+qoDq5jGycrX1xc9evSAmZmZUHb06FGMGDECFhYW8PDwwIQJEwCU/Ibk2rVrGDVqlPA8MU9PTwwZMkTlfZfDhw8jLS1NrXgMDAzg4uJS6z84rA5XrlxBWloaVq1aJZSdOXMGAwcOVPkHZ2FhgadPn9ZEiFopnfJi2rRpAEqmu1iwYAF69Oih0297rq6ucpfAVqxYgdOnT+Pu3btaXTouO+VF6UNfp06diubNmyM2NrZWkpW7uzvGjRun9rDw2up7oGr9r6u+z8/PR/369TWqm+kHjZOVsic9X716Fd999x0kEonwC/zHjx8jIiICDRs2xO+//47t27ejuLgYTk5OlT6iSZtvINWpqKgIxcXFlX5b0aWwsDD8/PPP+PTTT7Fr1y4QEYqKivDXX39hypQpKretW7euVvceaktNTS8yaNAgBAYGVuke56BBg+Ds7Iw+ffrAzs5OF6FqrXS6kc8++0zrfdT01C5V6X9d9H1ubi4nq5eUxskqPDxc7pM+AAwbNgxpaWnYsmWL8KDLgoICODk5YcqUKfjkk08AlFxCbNeundIhzvooNjYWu3fvxsOHD2Fubo49e/bgwYMHsLe3r9ahuIWFhRg2bBhSUlIUnnyhzlOjGzVqVGvzB2nLz88PGzZs0Mm+6P9PeREQECBXrovpLoD/m/Li999/r/K+qqJ0upGqqom+B3Q73Yg27ZbJZEhJSeFk9ZLSOFlFRkbCxsZGruyXX35BcnKy3HQFrVu3Rl5eHi5duiQ8gkfZk7n1WZs2bXT2R6wJExOTKl3G69ixI5KTk5GXlyd3uVYZbaa6AP7vEVJisRidO3dGkyZN0KBBAwAlH1QOHz4MiUSC4cOH4/r168jJycGnn36qdDBJTU0voovpLgDdT3lRm9ONvE5Tuzx9+hRFRUVKnzDP9J/G38fLJ6pS5U92oOQNrUmTJsIb5oULF+Dg4IA//vhD6+fsvYw6dOgg9yDR6tapUycAQFxcnMr1tJnqAih5Q5o/fz4mTZqESZMmYfTo0XIjHV1cXDBhwgS8//776Nq1K7p06YJ//vkHvr6+SuOoielFdDndBaC7KS9qe7qR12lql7t378LY2BjvvvuuVvWy2qWzyReVeeutt9CsWTOcOnUKiYmJaNq0KcLCwtCsWbMqPWvvZWNra6v0h5jVpXnz5rC2tsatW7dUPh+wdIqKn3/+GStXrpSb6uL7779XOtUFUPKbr19++QWmpqYgIuTn5wtveKX3Jc3MzPDo0SN07doVb7/9Ntzd3Su8V+Hn56fwY9fJkydj+fLlclNebN68GcuXL1dryovyb5hlp7sQiURVmu4C+L8pL5Q9KeTSpUsVJmag5Jtz+elGNDkGpdONTJw4EZ9//rnK/VQ23UhN9D2g2/5X1feq+Pn54d1334WJiYlW9bLaVa3JytDQEOfPn8fz58/x6aefAih5IrQ+P13hVSASiTBy5EhcuXIFkydPrnA9baa6ePToEaKiooQhybdu3ULTpk2F+wCGhoZo3749gJKBN6VP7VZ1U70mphdRZ7oLQDdTXqg71QhQ+9ONvApTu6jL19dX6FP28qn2Rz+IRCK5S4ecqGrGp59+WuEUE2VpOtVFUVER3nzzTeH+xdGjR+Hg4ICbN28iNDQUT58+xc6dOwGUvCGVvjns3r1b6eSVNTW9SGXTXQC6m/JCk6lGtDkGgG6mG3lVpnZRR05ODm7evFmjl+OZblXrNytWewYPHozs7GyEhoaqvEavyVQXpT8iHTx4MPbv3w+ZTIb69evj4cOH8PLygqurKyIiInDlyhUUFhbi448/ho+PD2JjY/Hmm28qfVxQ2ekuxo4dC0B+ehF7e3v4+/srTHmxYcMGZGVl4dq1a0qnvHB3d5erR9V0FwCEKS9OnjxZ41NeaHMMGjVqhKioKBw5cgRLlixRuh87OztERETA29sb3377rUK9NdX3gHrTjVRn31+5cgXNmzfX2Y+UWS2o6ce8s5qzZMkSmjJlisp1tJ3q4sWLFySTyYiIKDs7m6RSqbAsLy9P2E9ubq7CPstPeVET04tUNt0FkfZTXlR1uovanG7kZZ/aRd2+79OnD/3888+Vrsf016v/BNjX2NKlS+Hj46NySgRtp7qwsrISLhVZWFjI3ZOqU6eOsB8zMzOl+yyrJqYXqWy6i9JYa2PKi9qcbuR1mNrF19cX8fHxOv25Aat5IqLXaAz5a2jTpk2Ij4/H9u3bazsUgb+/P/bu3Yvx48drPOXFo0ePcO7cOVhZWSEjIwNjx46tcKTljRs38PTpU7WnvCiVn5+POnXqKJ3yYs2aNfjf//6Hxo0bA5Cf7uJ///ufRvXUhtex7wcMGIApU6YoTazs5cHJ6hVXWFiIPn36YNu2bXI3wmtTTk4OoqOjYWlpiTZt2lR7Xbp6YgoRIS8vT+7bSlJSkjA9TkW/QdQnr1vfHzhwAEePHsX58+dfq5/LvIo4Wb0G7t+/j88//xxeXl41+nsvxmpTaGgoJk+ejMDAwCr9po7pB75n9Rpo2bIldu3ahalTp75UD7hlTFtpaWlYsGABvL29OVG9Ivib1WvkyZMnsLKy4vl82CsvNzcXRUVFlU5syl4enKwYY4zpPb4MyBhjTO9xsmKMMab3OFkxxhjTe5ysGGOM6T1OVowxxvQeJyvGGGN6j5MVY4wxvcfJijHGmN7jZMUYY0zvcbJijDGm9zhZMcYY03ucrBhjjOk9TlaMMcb0Hicrxhhjeo+TFWOMMb3HyYoxxpje42TFGGNM73GyYowxpvc4WTHGGNN7nKwYY4zpPU5WjDHG9B4nK8YYY3qPkxVjjDG9x8mKMcaY3uNkxRhjTO9xsmKMMab3OFkxxhjTe5ysGGOM6T1OVowxxvQeJyvGGGN6j5MVY4wxvcfJijHGmN7jZMUYY0zvcbJijDGm9zhZMcYY03ucrBirogMHDoCIajuMGuXu7g6ZTFbbYbDXiNbJ6v79+/j666/x+eef48svv8S8efPg6ekJANi5cycePXoEALh58yYWLFiAyZMnIyYmRutA/fz88NVXX2HixIl4/Pix1vupjK7i1TehoaH4+uuvMXnyZNy7d6+2w6lx1dX+H374AQ0bNoRIJNLZPssLDg6u8XMyOzsbkZGRyMrKUrq8a9eu+Pbbb2skFsYAAKSFffv2kb29PXl6esqVnz59mhYtWkR16tShO3fuEBFRQUEBXb16lQDQjRs3tKmOiIjy8vLo7NmzBICioqK03k9ldBWvvikoKKCgoCACQIGBgbUdTpUEBASQjY0NeXl5qb1NdbT/ypUrtHTpUp3sS5WCggLy9/evkXOysLCQlixZQr/99ht5enrS+PHj6bPPPqOsrCyFdXfs2EGHDx+u1ngYK6VxstqzZw9ZWFhQQkKC0uXu7u4EQEhWpcRicZX/0F68eFHtyaqULuLVRyYmJi99soqIiKB3332XAgICNN5WV+2XSqXUo0cPSktLq/K+1FUT5+Tq1atp/vz5wuvCwkKqW7cuOTk5KaxbWFhI3bp1o5ycnGqNiTEiIo0uAz5+/BjffvstFi5ciJYtWypdZ/LkyejYsaNCuS4uk1TnpZbarKsmvQrt6ty5M0JCQvD+++9rvK2u2h8UFIQOHTrgjTfe0Mn+1FETx+7+/fvYs2cPcnNzAQDGxsZo1KgRQkNDFdY1NjbG8OHDcfTo0WqPizGNktUvv/yCzMxMjB07VuV6Q4cOVbm8oKAAUqm0wuX5+fmIi4ur9AZufn6+yuWVyc7Oxt27d5GVlSX8cSpTWbylsagTszby8vKE+gsKClSuK5VKERsbi8LCQp3HkZ+fL8RR9v/axFF+X+X7TSKRoKioSGUs5ZeX1pmamqpWe0qpex6UdebMGQwZMqTC5eoch9zcXGFghqp+qoyqc0+dOsrauXMnYmNjYW5uDqCkbx48eFDhB4MBAwbg8OHDle6XsarSKFldvnwZIpEIHTp0ULnekiVL0KpVK4XynJwc/Prrrzh+/Djmz5+PuXPnyi3Py8vDnDlzsH37dkRHR2P8f/WjbQAAIABJREFU+PE4fvy4wn6CgoJw6NAhnD59GkuWLMHKlSuFN679+/djwIABaNu2LUaPHo309HQAwG+//YY2bdrA0dERt2/fhqurKy5duoTi4mJ4e3uje/fuSExM1CjeymI+cOAABg0apBDLpk2b0LZtWzg6OuLOnTsV9uOFCxfQu3dvmJubw93dHb/++iv27duHqVOnYunSpXJv1lKpFK6urvjxxx+RmJiIbdu2wdnZWeWbrzp9defOHQQEBKB///4wMzNDREQENm/ejGPHjintk8riqGhfCxYswJw5c1BcXIwtW7bAw8MDK1aswLhx4+RG2v33339wdHSEmZkZ/v33X6H8xo0bWLt2LRITE3Hu3DmMHDkS8fHxFbYdAIhIrfNAGU9PT/To0UOhXJ3jcOHCBTg4OMDCwgKhoaFYt24dHBwccPDgQWGdEydOYPbs2XB3d8eePXtw7NgxhbpUnXvq1KGMmZkZmjVrJrzes2cPWrVqBVdXV6XrOzg4ICQkRO5cvHfvHhwcHHD9+nWVdTGmEU2uGdarV4+sra21ut5oaGhIM2bMIKlUSkREOTk5JBKJ6ObNm8I648aNoyVLlgivX7x4QTY2NsL9sYyMDAJA69atk9v3vHnzaMqUKcLr+Ph4EovFCvcmxowZQ4WFhXTx4kVatGiR3DJXV1eKi4vTKF51Yr537x6JRCK6fv263HYjR46kwsJCVV1GRERJSUkEgJYvXy6USaVS+uijj2jSpElCmbOzM61atUpu2z179tDQoUPlykxNTeX6pbK+KlVUVEQikYiWLl2qsk/UiUPVvpydnSk/P19Y19LSkry9veX2J5VKFWJ2cHCgxYsXC683btxIffr0ofLKtl+d80CZwsJCEovFJJPJFJapexxSUlIIAG3YsEHYzs3NjYhKBi588MEHVFRUJKzv7e2tMMCisnNPVR2VOXbsGE2bNo26d+9OycnJKtdt2LAhJSYmCq+vXbtGVlZWCgOwGKsKjZJVo0aNyNTUVKuKDA0N6Y8/ 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 lXv5HL5dSrVy+Fb6alpaU0duxYpTNYVf1GIBCQlZUV5efnE9Hjm/rjx48nLy8vIiL69ttvjSJOIiJvb2/y8/NTKBswYADt27eP6urqaP369Trv59q+B7Q5s9K0/xM9/sx5+jK/Mfy9+PSrpmINDAw0mn5FpPlnqD76lTpm/v7+ 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 KamIjs7G1VVVfDw8OB1HPger3HjxiEtLQ0nT56EmZkZIiIiUFtbi7CwMNy6dQutWrXCSy+9pPbvmJSUhLVr1+LOnTvIzs5GWloaxo8fD1NTUzg7O2PPnj1wcnJCamoqpFIpEhISEBcXB7FYDBMTE96veezYsSgqKsL27dthaWmJS5cuoaSkBD4+PlwsJiYmsLGxwe+//w5ra2vIZDL85z//QWJiotLx7tWrV5PHJy4ujnd8BQUF2LdvHyZOnKhwb41hGONjQpreVNCSRCLBP//8AxcXF7Ru3ZorLywsxIcffogjR46gW7duAB5/K7579y58fHwwZ84czJo1Sy8xSaVS/PPPP+jRo4fCIAx9UncctKknkUiQl5eHF198EQCQmZmJDh06wN7eXulyniZkMhlyc3PRpUsXWFlZITs7G7a2tujQoYPKe0lNaTjO3bt3V7v9o0ePUFNTgx49evDaJ9/jyDDM86HFkpU6u3fvxu3bt7F582alddu3b0deXh5++OEHA0TGMAzDGAuDD5d64403kJSUpHAfA3h8E/7YsWPNHqXHMAzDPPsMfmYFPB4UcPjwYdTU1MDS0pJ7Fmb69Ono1KmTgaNjGIZhDM0okhXDMAzDNMbglwEZhmEYpiksWTEMwzBGjyUrhmEYxuixZMUwDMMYPZasGIZhGKPHkhXDMAxj9FiyYhiGYYweS1YMwzCM0WPJimEYhjF6z1WyIiKsWbMGZWVlhg6FYfROKpWq/N23xhQWFnK/Hccwz5LnKll9+umnsLGxQYcOHQwdCsPo1e7duxEaGoqNGzfC19cXYrGY13bdunWDqakpVq1apecIGUa3nptktWXLFlRVVWHFihWGDoVh9KqkpAR79+7Fu+++i9WrV+P+/fs4f/487+2XL1+OvLw8nDt3To9RMoxuPRcT2aakpGDKlCm4efMmbGxsDB0Ow+idVCqFubk5ZDIZhgwZgoiICDg6OvLeXiAQ4JVXXkFERATs7Oz0GCnD6MZzcWb15ZdfYtmyZSxRMf8a5ubmkEqlWLZsGbZv365RogIAOzs7fPbZZ1iwYIGeImQY3Xrmz6z++usvLF68GDk5OVr95DrDPIsqKyuxc+dO+Pj4wMrKCjdu3ICXl5dG+xCLxXBxcUFoaCjc3Nz0FCnD6MYzn6z++9//Ytq0aZg3b56hQzGoO3fu4OHDhxgzZoyhQwER4dSpU8jKyoKLiwsmT54MCwsLAIBQKMSJEyfg4+Nj4CifHSKRCCEhIcjPz4ePjw9sbW3h7u4OoVAIU1NTVFdXIzIyEoMGDdJ43+vXr0d+fj727Nmjh8gZRofoGZaenk6WlpZUUVFh6FAMqqCggN59912SSCSGDoWIiJYuXUq3bt0iiURCP/74Iw0dOpRqamq49cHBwbRz504DRvjsSEtLo2HDhlF0dDRJpVKd7z8vL4+sra3/9e8hxvg902dWK1euRGpq6r96VBMR4Z133sGWLVvQvXt3Q4eD+/fvY+zYsUhMTISdnR2kUik6d+6MLVu2YPr06Vy9//u//8OGDRvw4osvtmh8PXv2RG5uLjw9PdGqVSuVdWQyGWpra/HgwQMUFBRw5V26dEFBQQFMTVvuVu/gwYPh7u6OCRMmoFOnTvD09NR5G3369MHChQvxxRdf6HzfDKMr5tpsJJPJ4Ofnh6KiIgwdOhSLFy/WdVy8XLlyBW+++abe9h8SEoK8vDzMnz8fFy9exP379yGXyzFnzhyluoY6Jn/88Qe6deumNlHV1taisLAQPXr0aJF7ei+88AK++uortGvXDgBgamoKExMTpYdXV69ejS+//BJhYWF6j+lJmzdvxjvvvIP//Oc/2Lx5c5P1BQIBLl26hG+//RY3btzApUuXMH78+BaIFKioqEBGRgb+/vtvmJmZoU2bNnppx9vbG+fPn2fJijFu2p6SVVZW0sCBAykgIEBnp3maqKuro9atW1NkZKRe9p+Tk0MhISE0Z84cmjJlCuXm5hIR0bhx4+jatWsqt2npYyKXy6lv376UlZWltK62tpZWrVpFixYtog0bNtDAgQNpwYIFCpfjWsKpU6eob9++JBQKldZ5eXlRVFRUi8ZDRDRv3jwyMTGhM2fO8N5GLpfTL7/8Qj4+PnqMTLlNJycnqq6u1ms7hw8fJicnJ722wTDNpXWyqqurIysrK4qPj9dlPLwlJCQQACorK9PL/jMzM0ksFtPIkSMpLCyMK3dxcaG7d++q3Kalj8nZs2dp2LBhKtctW7aMUlNTueXy8nJycHAgX1/fFomNiCg7O5u8vb2puLhY5fqtW7fS7Nmztd5/fn4+nT9/XuPtRCIRDRw4kBwcHKioqEijbZcuXar35PGkCxcu0Pz58+nUqVO0c+dO+ueff7h1crlc5f81lZSURACosLCwWbEyjD5pnayio6PJxsbGYDf1g4KCyNbWVq9tCIVCateuHdXX1xMRUVZWFrm4uKit39LHZMGCBbRgwQKlcrlcTs7OzhQUFKRQvnDhQjI1NaWqqiq9x5aZmUkLFy7kzuSys7OV6ty8eZPat2/PHV9NRUVF0XfffafVtunp6WRlZUXjx4/X6INeIBBQeXm5Vm1qSy6XU3FxscIAi6CgIJoxYwbdvn2bysvLaezYsVrvv6KiggBQSEiILsJlGL3Q+k5xZGQkPDw8YG6u1W2vZisrK4Ozs7Ne24iLi8Pw4cO5YdfHjh2Dj48Pbt26pXJ6Gz7HRCQSNdluXV0dr/hiYmIwcuRIpXKpVIrq6mpcuHBBodzR0RFyuVxh0IA+ZGZmYu/evVixYgWqq6tx6dIlpKamKtXr27cvhEIhMjIy9BqPKgMGDMDPP/+MixcvIjAwkPd27du3b/EZH0xMTNCpUyeYmZkBeHwf8tVXX0VxcTHs7e1hZ2eH119/Xev9t2vXDnZ2digqKtJVyAyjc1pnmqioKHh7e+syFo2Ul5fr/UMjNTUVr732Grfs6OiIGzdu4PTp01i6dKlS/caOSXBwMHJycvDiiy/i+vXr6N+/v9JAjfT0dPzyyy8YOnQoqqur4erqiocPH6q88S2RSHDz5k307NlTaZ2FhQXy8/NhZWWlUH779m3Y2NhwI/DKysowf/58JCQkQCqVKu3HxMQEx48fx4gRI7iyc+fOYcuWLcjMzFTYpqFuz549MWbMGDx8+FBhdu+0tDSVcbq6uiItLQ2DBw9Wedz0ae7cuQgPD8eaNWswduzYZ+bBWGtrazg4OEAsFsPR0RGVlZUYOHBgs/bZpk0bCAQCHUXIMLqnVbISi8VISEjA999/32TdOXPmoLCwkNd+zczMcPDgQbRv377JuuXl5Uofxrq2aNEi7qwKAD755BMIhUKV0zo1dkzmzJkDS0tLbNu2DQBQX1+Pjh07YsiQIdwHZFxcHGbOnInIyEh06dIFEokEnTt3xty5c1XGVl5eDrlcrvZYPT1y7MGDBwgJCcG6devQunVrSKVSLFy4EG+++SZWrlyJ7du3Y+HChcjOzkZOTg5ef/11mJubo3fv3tw+Nm7ciFOnTmHRokVwdHTEN998AysrKwQGBnJ1TUxM8ODBgyaO7P/XoUMH5Obm8q6va3/88QeGDBmCDz/8ENevX0fbtm0NFosmbGxs0LdvX5w/fx5FRUXNfsjaxsYGFRUVOoqOYXRPq2QVHx8PU1NTDB8+vMm6O3fuhFwu57VfExMT3s+wVFRU6D1ZPZmoGqibf1DdMTlw4ADCwsIUPpAtLS3Rr18/nD17Fm5ubpBIJJg9ezYWLFiALl26AHg85Lu+vl7tjBQNv9nF9+xy1qxZmDp1KpYtWwYAiI2Nxfr16+Hq6oqSkhI4OTlhwIABuHbtGtzc3DBgwACF7cPCwnDkyBEkJiZyx33t2rWYOHEi+vfvDxMTE15xPK1Nmzaorq7WaltdsLOzw8GDBzFu3DjMnTsXR44cMVgsmgoKCkJdXZ3a58U0YWNjg8rKSh1ExTD6oVWyioqKgru7u8oP86eZmJhw19p1ydzcvMn7P9HR0bx/aG7y5MkKZzH+/v5Yv359o9usW7cO/v7+ANQfk1WrVmHatGlKHyi5ubncc08nT57EnTt38P7773Prk5OTIRaL4e7urrLt2tpaAEDr1q2bfG0//PADevTogW3btnFJ5cl55I4fP47Ro0cDAJKSklTOMbd48WL4+fkpfEGwsLAAPR6ko3WysrS0VHkJ8mmfffYZ8vPzFcrKy8shEAhw5coVhXJzc3McOHCA1xk6AIwePRpLlizBmTNnIJPJ9NJfm8Knv2nryX6qjlQq1fuXP4ZpDq2SVWRkJF599VVedfPy8iAUCnnVNTU1Rd++fXl98Nna2uLhw4eN1vHy8tJ4cs8G/v7+Tb7Bn6TqmBQWFuLevXsYN26cQvm1a9fw6NEjbnBEUlISevfujc6dO3N1oqKiMHz4cLVncra2tgCAmpoa7gFcVY4cOQKxWIzffvuNq29mZqbwwXT8+HGcPn0aAJCdna3UZm5uLvLz8zFlyhSF8hs3bmDYsGHNmtGhuroa9vb2TdYLCgpSKouOjsaVK1fg5+endfvA42MSHx/PPXxrCJr2N1V2794Ne3t73L59G3fv3sWOHTt4fZkBHg/84fN3YBhD0ThZiUQiJCYmYuPGjSgrK0NWVhZGjRqltv6hQ4dQWlrKa9+mpqZYu3Ztox++Ddq0aYOamhrecWuLGpmUtYG6Y9Jww/rJ+z4AsHfvXowcOZK7xFdZWYl+/fop1Ll48WKjk9I2/BpyVVUVd+nwaRERESgtLcWaNWu4spCQELzyyitcskpJSYFMJuOWS0pKcP/+fTg4OHDbFBQUwNnZWSmJ7dq1C/Pnz1cbIx98k5W+EBF8fX2xevVquLq6arWPq1evYtu2bdi/f3+zY2mqr6nT8IOMsbGxePfdd7lZKd566y1e2wuFQpasGKOmcbIqLCxEfX09XnrpJRw+fBhvv/12o/Wb+61XnRdeeAHFxcV62feTli9fDl9fX0ycOBG//PIL3NzcEBMTo/DBre6Y9O3bF126dEFJSQn3QRgfH4+zZ8/i3Llz3BnJ2LFjsXXrVm5/Z86cwcWLF7n7S6o4ODigU6dOyMvLU0p0wOMkFBgYiPfeew87d+4EEUEikSA0NFThZvyWLVuURjBGR0fjpZde4pYHDRqEsrIyVFRUcJfWgoKC0K1bN8yaNUup7bi4OMTGxuKtt95Cnz59cOPGDcTGxqqcGb9hKihD+eabb+Dm5sb7SoEqp0+fVjkqU1N8+po6nTp1QnR0NIDHU38VFRXxHt0ol8u5YfAMY7Q0fTBLIpGQu7s7HTlyhH7++WfdPvWlgVOnThEAldP46Mq9e/eoV69e3EOgEomEOnbsSAcPHlSo19gxuXz5Mk2aNIn++usv2rx5M82YMUNp1gS5XE7Lly+nTZs20b59++j7778nCwuLJqdGmjBhAm3atEmpXCwWk6OjIwFQ+ufm5sbVE4lEZG1trfDA7ieffKJyVonDhw/TjBkz6PTp0+Tv70/+/v4qZwG/d+8e7dixg/bv30+ff/45ERElJyfTiBEjlOoWFxeTqakpVVZWNvo61WnOQ8FERCEhIfTRRx9ptE1VVRUJBAIiIsrIyKCAgADq06cPzZgxgw4fPqx1LHz7WlMkEgktXrxYo2ms7t+/TwAoNjZWo7YYpiVpPYNFQUGBLuPQWG5uLgGgGzdu6K0NuVxOQUFBJJPJiIhIJpORg4MDBQcHq6yv7phIpVLKysoikUjEq92AgAByd3dvst7WrVtp6tSpvPapztNTIVVVVan9AiASiSgrK4s7Hqrk5OSQSCSiKVOm0P79+7nytWvXKtU9f/48vfzyy1pG3rxkdevWLfLy8qLa2lqNtvPz8+OSFdHjLwZt27bVeD9P49PXhEIhHT9+nPz8/GjFihW0YsUKWrVqFfelpqKiggIDA6moqIgEAgHvhHX+/HmytLQksVjcrNfAMPr0zP6elVwuJzs7Ozpw4ECLtdnYpKzaevDgAYWHhyuUeXh4qDxjelpxcTF16NCB6urqdBaPLtTW1lK7du24OfQePHhAJ06cUKq3cOFC+vXXX7Vup6SkhFJSUjTerry8nNzd3TX+wrVnzx565513FMqioqLI09NT4xia8nRfy83NJU9PT/rqq6/oxx9/JFtbW9q8eTOFhISQXC4ngUBA/fr1I2dnZ+revTt16NCBbt68yautVatW0ahRo3T+GhhGl57ZZEVENGPGDJo5c2aLtNXUpKzamjVrFg0ZMoRbDgwMpDFjxvD+pj59+nSjm9MtJyeH+vbtyy3v3LlT6Vt7XV0d9erVq0XmKXySVCqlSZMm0eXLl3nVl8lklJCQQF5eXgSAjh49qrDe39+f1qxZo7RdcyaZVdXXRo8eTRcuXOCWZ86cqfYMX1OjRo2iJUuW6GRfDKMvhpnYT0fee+89fP7553pvJysrC9u3b8eJEydgY2ODnJwcrUeOPa1hyp9t27ahsrISzs7OuHjxIu8h1D/++CN8fHwwefJkncSjC927d8fAgQNx7Ngx3Lt3D2PGjFF6zuyPP/7AvHnz9PYbTeqsXLkSycnJ2Lx5s9rfs5LL5RCJRCgtLUVOTg730LKNjY3S76fFxcVh2bJlqKurw++//44FCxZg586diIuLw4oVK+Dk5IT33nsPly5d4hWfqr5mZmaGkpIShd/RKiwsVBplqo2amhokJSXh22+/bfa+GEafnulk5e3tjerqaiQnJ2PYsGF6aSMzMxP79u3jJmVNTExEWVmZzpLViBEjFObe01TDh+GePXtUjswzBDMzMxw7dgzV1dWwtrZWSry5ubkIDw/HiRMnWjy2SZMmwcPDQ6tt7e3tYW1trVA2atQoXL9+HVevXsXcuXO5SWZDQkI0nmRWXV8bNGgQOnbsyNVLT0+HVCrlNYNMUyIiIuDi4qL0LCDDGB1Dn9o119dff623H8R79OgROTk5KY2oS0tL00t7zeHv70/JycmGDqNJ9fX15OvrS48ePTJ0KDrz9O9b1dTUkJeXFxE9HvTA50cem+prixcvph07dtChQ4doyZIlVFpaqnI/ml5+9PDwoMDAwCbrMYyhmRARGS5VNl9FRQX69euH5ORkvPDCC4YOx6AEAkGL/3yFpiQSCerq6rgZOJ5Xn332Gd59911uklldzIxRXV0NCwsLtbNSaHr5MTIyEtOmTUN2dvZz//dgnn3PfLICgM2bN+Pu3bvYvn27oUNhGI6uJpnlo7a2FiUlJfjss88QHBwMR0dH/PDDD40+WD527Fj4+PgYzeVjhmmM9pO6GZEvvvgCiYmJSEhIMHQoDMNpqUQFaP4bV8HBwWjdujV8fX1bLEaGaY5neoBFg1atWuHYsWOYOnUqwsLCFOa1Y5h/C76/cZWcnIyAgABcuXJF69nyGaalPReXARtcv34dK1euxLFjx9g1eOZfq7HLj6WlpZg8eTL279+vk/kMGaalPFfJCnj8i7jt27dnv83DMCoIhUJIJBLev/XFMMbiuUtWDMMwzPPnuRhgwTAMwzzfWLJiGIZhjB5LVgzDMIzRY8mKYRiGMXosWTEMwzBGjyUrhmEYxuixZMUwDMMYPZasGIZhGKPHkhXDMAxj9FiyYhiGYYweS1YMwzCM0WPJimG0QERYs2YNysrKDB0Kw7QYqVQKmUym8XaFhYUICAhoVtssWTGMFj799FPY2NigQ4cOhg6FYVrE7t27ERoaio0bN8LX1xdisZj3tt26dYOpqSlWrVqldfssWTGMhrZs2YKqqiqsWLHC0KEwTIsoKSnB3r178e6772L16tW4f/8+zp8/r9E+li9fjry8PJw7d06rGNhPhDCMBlJSUjBlyhTcvHkTNjY2hg6HYVqMVCqFubk5ZDIZhgwZgoiICDg6Omq0D4FAgFdeeQURERGws7PTaFt2ZsUwGvjyyy+xbNkylqiYfx1zc3NIpVIsW7YM27dv1zhRAYCdnR0+++wzLFiwQONt2ZkVw/D0119/YfHixcjJyYGlpaWhw2GYFlVZWYmdO3fCx8cHVlZWuHHjBry8vDTej1gshouLC0JDQ+Hm5sZ7O5asGIan//73v5g2bRrmzZtn6FAM7s6dO3j48CHGjBlj6FBARDh16hSysrLg4uKCyZMnw8LCglsvFApx4sQJ+Pj4GDDKZ4tIJEJISAjy8/Ph4+MDW1tbuLu7QygUwtTUFNXV1YiMjMSgQYO02v/69euRn5+PPXv28N+IGIZpUnp6OllaWlJFRYWhQzG4goICevfdd0kikRg6FCIiWrp0Kd26dYskEgn9+OOPNHToUKqpqVGoExwcTDt37jRQhM+WtLQ0GjZsGEVHR5NUKtVLG3l5eWRtba3R+4mdWTFGq2fPnsjNzYWnpydatWqlso5MJkNtbS0ePHiAgoICrrxLly4oKCiAqalubsuuXLkSq 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quot alt=& quot & quot & & & & ul& & li& & em& u& /em& : gauged site& /li& & li& & em& v:& /em& partially gauged site& /li& & li& & em& q& /em& : streamflow (mm/day)& /li& & li& & em& h& /em& : water level (m)& /li& & li& & em& h^& /em& : Transformed & em& h& /em& & /li& & li& & #216 : climatological forcing (mm/day, P & PET)& /li& & li& & #949 : model residuals& /li& & li& x& sub& & /sub& ...x& sub& & /sub& : GR4J parameters& /li& & li& x& sub& & /sub& ...x& sub& & /sub& :Chebyshev parameters& /li& & li& & #963 : variance of residuals& /li& & /ul& & & & & & & & Gauging sites (Figure 1) are located in Lachlan River at Reids Flat & (3742 km& sup& & /sup& ), Lachlan River at Narrawa & (2256 km& sup& & /sup& ), Abercrombie River at Abercrombie (2636 km& sup& & /sup& ), and Abercrombie River at Hadley & (1635 km& sup& & /sup& ).& & & & & img src=& quot data:image ng base64, 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 1q1bOxsniAQoGISoqKiIpluBwBMySQqGTe3evVtBQeHBgwfm5uZQ1IEW4VBfvHgRWR8IDw+HWgiUiyBrU1PTk6dOQawwcuRIERERdI2fdKFgyHn27NmwYcNAwWBwqHLBiQ1Ch4oX0ioNvzJy 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 qaGiomZmZrKysq6srmvsvgAAWSgIKb/GDbXfu3GlkZJSWlhYfHx8SEtK+c15yZQO7Zebj2N9uvVTUN23xKNj+pWgAGbipFTQxNOihgmsKM7V2iIF/9xw/H5WYkpoQe/qA4vjWUSI4BaTDk7OQqLeuusrM4DromUd2f1oBNkzPUAckKCkpSZpi5+vXb6YmZr7ZFUJuhYvtcmzT/lLH+0+fPomJiTk5OcEvBRcA+aMKf4Gqqip1dfX79+8TKBvx68GDB2pqam2C+jYEFNaxvs+SdC8orf/lr9TKxg0OeVOM0g74lVQOhJG74YeAyoGX1xAK14AeKjjG35OdecxKsW1xeahoaosyhTkW4YaPvv3WBclBaChOkxfiHjFt6bfI/nIzpK/BFNwZoGAhIaEXL16g6Z+kVTXKexVo+v+9HgsuLi7wG61du3b16tX6+voU2pAqREZGysjIfPjwAU3/CQMDA0FBQRBxenp6Z13rnvyopDHNME6uIn8cA16X1jXpBhTjnibrhpTW9ftg2NraWkBA4I/Tfwwyeqhgf2cbRtwwhf+ukU8fpyUjMWHCHKfIdDSNUqujvB03YrqjH2U3H4hNtbW11Log6urw+I4G7uuC2uqqvJyczMys/ILCusae7Aim4A4pKCg4c+bMtm3b2k+iUU8gHgssUfYprvuLT3TBpR4YGGhqarpnzx6kc9vfISoqipubGxmGnBIaGxt9fX1lZWXFxcVv375NGlafnHfJ1VPfZbpld3DnzTC2YsyDxAWWmbYZNXB8+20lv6mp6cCBAxoaGqShfIYIPVRw+Ben2bSj+WQ1cut+XjP4Mlm+VaNoGI3df5tfmliVpyaxfiQjm2fIH+daJiTFhN29dV39gMaF2wa+oTH1nVyPRTkZ7i4eWUVdjDLVlJoQY/L0wREtzYNHjr8wt07JbBmL7xfEpsKC/Gr8b3Zuqq/96uF4cN9e/jVrVqxYKSAieuLSVd+IH93VAqbgNkDsFhwcrKysDB5BHiZuQ21jk15k+RrHvLMhJd45+MbWsXH/DkhX4r85AMXly5eXLVvWXekTiUQ/Pz9RUVF4e/s58I8ElMx4lxFc2MGEurFlDQ+iK9R8ipZ+zNnkmvejrJ8OdQRf8M6dO3JycuRT2Q8FeqjgosxEeQFO3BimywbGqekZmWlJxncuTZ5IN2rEiE2qR/MqfhXUzm9fzJpAs0RsR0Ju14+NEr+5WC1fOIdh0jQOTs7Z06dNnbv00XuH9tdiY3XFGXVFHG60iftvcwOTQfRzt1uzdP5YeoZlK1YuX8o+euzYtWKyQT8ykMVFuRkmzx+dPXv6qt4d/9hkJC4gEhrfPb05g4V5Hie3gtJuVdU92+Wk506bMoN9laVXQLfqcJiC25CYmCghIXHw4ME2c2GQU1bfZBBbsd4xd6VtdtzPnlV/BxsbGwgw79+/7+HhkZSU1KeNEnAEIOgmPb/XXZ49ewYRdPvpQrJrGlfa5mxxLSjAdxzmhhbV7/AunGCW7tlRpNxPgPNk0aJFg/tBjPb0UMEQMjq9fTF14jhQ4Vo+/g08nKOGD+MQktJUkh07fsLWA8c+Ort5uLu/undz2fwZOBq6Wya2XTcH5MeHCi5nY2DleGPvVVBY5O/tJLhq6YzFa0LT2hSJTVaP9SbTjcbhxlp8+TV0Ezkl6TFi3MvHT5v/0MwmMzcvKz3h0Q1dRjraTXuOQpBQU1b06MZFlf2Hjc3f6l26qLj/aHhKNrwrNyF05byZq0XlwpJRU4OWvztaLZzONJdfKi6rG82UmILb4O/vD9ENJQMzJlY0SrrkKbvlBxX/vd67UPO1tLSEPYRyQk1N7dq1a0+fPu2L2Rmqq6svXLigqqras7GE6urqdu7cCfuGpsloaGoW9CqaYZUV13mQ65WD53fI9e8oUv7nlJWVRUdHnz59GupJXTykPijpsYKbm/CVNkZPhHjXMjFMYJw8VVRG0SvsR35y9HZhntYOwcNGjRwB/xs+lnH/uVs5JX/w0ffP79lnz7n47NekjbaPLtNPZLlriY5SihDpbc/NuWre3DnjaaeZe/4a25+caB8X3uVsx/Vf/ooHGmr3SPDOXsYdX9UY/s3l1DHt1DL0Cr916vArW5fG5uZAu5eMjJPOPrVF8km8unNlteCm71HduEWAKbgN7u7umzZtysgglW2dklLZyG2XM8s43SnnbwdrJSUlkZGR4GJQsKKiIvyC8Duiy6gE0pIQHByMprsJ1NA3b97cYQSdWdXI9jnPMLGr2XJrGpuWfMh+ndAfT8sHDx5s2bIFCpjBNy/RH+m5ghFyUpJcnR1dPb2zCtGyKzMuTP/KuW3SksKi4nv2HzR+b19a8+eIpiQ/283DI6vgV8+bNzd1xjOxPHf49ZPkp8Rv4eOW2a994/ypyfRTTTtpiKgsK/L1dkvN+tXpsqmh/rCcyOylq2IrGv3dP5/WOUd6bNPw0qnn1o6g4GhPq4n09FJqp4qrf9vb0qKCqJjYsqpu9JfCFNyGhIQEkBolQyIklTewfczd5VdSA3HdvwN+O4hVzc3N0TQ1qK+vv3379r59+9B0N8nKyjp48CAXF1eHCnbMrFnwKSf4T32rL4aXibkVpPezgSOgFiIgILB3717Kn1UZTPRWwZ1RU1FeWFTcg8uooaY68UeM+QuD1UsXC+88mFGKNis3lBfrqm5fvEYwPKvQ2vDmBBqmzhTcntTQL2wzWHi37oZtleVnXNI5euKyfkhklI2ZkexONe+wH7BOTVG2ksi6kcNoxGUVrt++Z+/iSSpUuktNTQ2cUpiCSeDx+CNHjpw8eRJNdw6R2PzoR+XsT7kCLvk3o8rL6v6ZiG1sbPbs2YMmqEFaWtr+/fs/fvyIprvJlStXoFx/9epV+8HVivCE7V8KL0WUVZB1Cu4Q+/Rq3NvML/2pLQL8C19NWFi4BxOIDA76SsFZGUlhEdGFpV3VjDrE39FSlH8t7ahhdNNZje2/oLnNROsnN+fMZzf82NIu8eb+BXoaJjOP37pedEZBWoy88Br6aaxW3mivuJTYsGsXdDU11Q8dPmnrGVDbgN5+SYsKOrBTZsyolvYTJpZpXGv5dqofeu/gXdd50BAWFgYXqoyMzDYyJCQklixZQvk4hIOewMBA0Mf169fRdJdU1jfZZ9eKexRIeRbkUa13Yrfx9/fn4eGh4gDwsbGxu3bt6llFOyQkBA5gZyMT+eXjeR3z/CgQa0ZVo6pf8bd2E2r8QwwNDbv4akOBXim4qQH/1cn2xH86vrFJaFZzc3JkwH6lbYsXLZg1e84yTm7tGw8yirrx9FFSZLClhfmD2zcEeVZxCYpberX03Yn45rycfaHWFQNEhqb3L4KCzb3+3NE4NdJfXoyPjoHFwNiaPEKoKS+JighPy2rb2FdZWhjs6/PC4NbO7VtnsUwGFzOwzDhy/VFR64RM7SkoKHB2dobQxp4MY2Pj1atXD8pJVnpATk6OoqIiVKIpaQsmYRhfKe1ekFTxz6rMUPGHqgzyIF9FRUXvO6sWFhbCQbh16xaapozi4uIHDx5wcnLevHmzwyd3AwrrVn7OVfYpKqqlqMZQjCeU/ylY/muUlpaKiooOpimpekDPFVydl335P00WJgYcbpyZOzoFVn5KjCQfF5hrxOix02dMGzty2LBRY7YdOp1e3N1wmBjsbsvCMFFo58HU9NRdEuvoZsy7oP/w7RtTMzOzfTu30o6kVzt+4Z21Q0XnEzP7uXwQWL1sIsvs+29syYZa7Rg4v8k6nhHLy0tTk5PtLY3WLp8/gpbJzPnXtIN/BOJfrCGCRFBQEC8vL1TD0TRlhBTVLfuUa5XewWMIf4f6+vpLly5BUbp9+3YIh1VUVDQ0NMCDYA10jW5CJBK9vLzk5eUpudgA8D6UXqAnOTm5p0+fdthPAE7ZAwElrB9yQv5iBxIqYmBgICYmBg5C00OSHiqYSKx7qXdu1PDhk6Yv0jpxMTGn9TYaoeGe9gHw73R2rvcu37JzsoO8PkusXT5sBM1dU9uue9YSCI14fB2cpmgacvCV8rwr2TgEbb181i2aTkM7lomZmYGBgZGRcdxY2mHDho8dR7dohXByeUdyJzY5vXmxbM706YtWmn/2+mOv3qaGmhs6R1W1ThbVtg12PC1fTaEdrnrpYTXFoQN2O44cHx+ftWvXdlfBBwNLJdwLoOKMpv8FYWFhMjIyELe6uLiYm5vv2LGDi4vr7du3Pe44DNED+DQ9vc3jox1QVlZ2/vz5pUuXCgsLu7l1MJkTkFjeIOtdON0m2yJlQNa3cnNzpaSk7t+/j6aHKj1UcFp0ID/7DAZWjo9eIfUN6HVSlZ/Gu2g2btRYnYe/biX72lvMph+1asvulC6H6fnmYL1Varvz91/9zOoqCjetXrxgtUhgYvqb5w+vXr165XIL16/fkNskNGYk3dZd6ncNjEp+f8KtFaLXB7PlM2dyCkj7RVN2j7Wp Sy9HiW2XaBbZ/ /mOJdp3L4rjyiPxzAFk6iqqtLR0YEKeHfnDdrpVbjSLudcaFnyv2uLgIAA9h9pgoDX8IM+e/Zs5cqV9vb2yArdAqpZoJtDhw6h6XYUFRWZmpoGBwd7e3uD+o8fPx4SEtLFaGpmqdVsH3Pcsmv/4rOE1OTDhw8SEhKhoRTd0RnE9FDBblZGo3G4vWdvk18f4S7vJowawTx3eWDGrwcZCGVZimLrRkxf6tPlMD3O5objh+E27T2ZXlje1ESsra60e3V/Aj3d1gO6yEeQn2bGd8/T0zCZuv3sEUFsKisqSExOKaloiYhLU2O3rucYxzLXyMYxNTU1JTk5KSkpISEhNT2DdOetPQEOb6czTVgtIRscl1xVWwuXXGNDfWF+zin1XbQMU9+6dWOycUzBJOBQ7N69+/Xr12iaYkKK6rWDS+nfZT0hGwP3nwNnxenTp0+dOtWDhn4ohJ4+fQpuRdO/U1hYeObMGR4eHqiYr169Wl1d/Y+TkCaUN1wILeufj1r8EShv4MQ4e/Ysmh7C9FDB5vev4nC0+sa/jfb05PzhYTjcms3Kvw/QXqOza9uIUTMd/SLRjI6oKcw4vU9h9Eia9eJSFy9dVt+1g5mefrmAhE9MB7W2F3raONyIV64/n7Kvq36go44bz3z1lQ2kzA30cK2MHj2aZtSoka1AcvZizi9JnY74R6iruX/22KQJdMwz5irtVb929erpU0fWcS8fxzxp/6W7BVXdONExBZOora0FZ/W4d9cmj8JdXwoL/l2/iPY4ODgsXbrU17cbRTKCm5sbGPb58+domgww+7179wQEBLKysjIyMmBNCntiwBsJAzMGNjMzW7JkyVB7FrlDeqZgopHeWRyOzuBNyySJKE141U3rcbhhey89QXMQiJUndsoOHz3bOaArBQNVeel3L2qvXc3Fxsa2nINTWeOIT0zHzQgedm93yu/0ivzZDaMB/8n44UY5hfdufpCyM3u5afNm6a1bpbb8YuPGjXsOHonO6bJvBqHu0xujndJSHBwr2BbMX7x0mcAmyXvG78rx3atEYwomx9raetu2bWiim9hn1Io45Cp+KSz5dx2E21BRUaGmpnbo0CE8vm0nGYhzobLVYYBMIBBAskpKSh32rACTwkX44MEDND0E+Pz5s6ioqL+/P5oewvQwCrZ8dhuHG37xyRs03dxclhqxnHXaCNoJbUbPqcqKl+FZMW7uar+YFDSrSypLi2NjYzPz/uVoSfl5ObExUcnpGT277jEFk6ivrz937tymTZvQdPdpIhKlvQp2fS1CRj8gH5L8X5Gamrpq1aorV65YWlo+efLkxo0bEBSHhIRcvHhx586d8Dc5OdnLy+vOnTve3t7Ivbuamho9Pb1Lly4hW2hDbm4uhMCd3XYblOTk5IB3bG3bjgcwBOmhgv1dbFnGjuCX21dQhUaINgaXR48cNn05f8rv/c+c3z6fNG7Eum0a6cVDpZ8spmASUVFRK1as6PEjYQhplY3bvYvEXfPVvAvVvhTG9oPhFp8+fSohIQFR7Z49e0C7CgoKUK2WlJR88+bNqVOnZGVlQalCQkKCgoLI4HBgnF27dhkZGSFvJycoKAjeLiIiMqSGKoerA8pmoP3Am0ONHiq4sjBrn5QAbti4PVpnnVzdbd684mKbDXGx+pUnjb86ljX6e9hvaJlKg/a2ie2/v27+FpiCEaB+bWVlpaKigqZ7QXVj08uEyp0e+WNN0i6G//uJ2WtrayMjIwsKCiDIhUj/69evDx8+RIY4qK6uBkHb2dkVFxfz8fFBPmTm5eWBoP38WhrKSMDxcXZ2FhMT279/P5RVaO6QwcfHB0opJycnND1U6aGCgbBvTqsXs+JwOGaWqXQ0Lfe71m5WQAd1JBLCvzrvU1ZgmzUN8jepHM4q/veXzV8DUzAC1L5Pnjypo6ODpnsNlO1XIsr5nPI+Z/2zRza6xaNHj8C8e/fuPXToEOg4LS0NtIsua262sLBYtGjR4cOH4UChWUMJV1fXrVu3timWhiA9V3BzMyE2yOf0Yc11XJw8vBuOXb4WnvjzCVQC/sWZ/0C+NAwsGtqXUnK7mnlw8IEpGKGurg5qmjt27KCiYmobiep+xRs9CgdERwA8Hu/r63vz5k1VVdUVK1bIyMicOnWKNB6YoqIi+HeoTRJBwt3d/Z9PZd0f6I2CW2iorc5Ia5k3o4Z8mjUiIcr/20tjM9+Q8MpudicYBGAKJmFiYgIVbXAxmu41dY1ErcAS2pdp/30vKsL3o85qXZOXl/f58+cbN25s3rzZ1NQUyZSSkqLueJgDCyifXr58KSIiMsS7pvVWwRjtwRSMAMGvtrZ2dwem6RrbjJpln3K5PuZw2eemVg6w0p1IJN6+ffvgwYMEAgGC3507dw7xllArK6u1a9d+//4dTQ9JqKDgqsrKBjQEJuamJXt7e3l6enp5twB1jbC4hM5m4RysYApGgDDnzJkzu3fv7vHQNu0xjq/EPUtZ7pT3Ib26YaA9lZCdnS0tLT1lypQdO3YoKytraWm1nwVucEMa7K26uvrt27fgXwMDg/r6ATnGELXolYLx1eW25oYi63gNbVxbLgdik/nNcwz0dLRjRo9qhYaGZhr7apvWASeHDpiCSURERKxatar9rPU9xjq5etG7jMUOuSsdcj0GyE05EmZmZvLy8s7OzlAsPX36tLBwCN0jgcvB19f3wYMHJiYmr169gqqAmJiYoaHh0LwVSU7PFVxTkntaQ3k4DjeShvao3vOa1jj41cWjI4YNmzWPfd26dXz8/EsXzsfhcII7hlCnYABTMIni4mI1NbUbN270frxdhJwawpvkKvkvhTiDpLU2Wd/y8XjCwIiFo6OjhYWFHz16BK8bGhrIu0YMerKysuA0EBER2bVrl4KCgri4+J49e3o8h94go4cKbmqoe33rwkgcbhrr4mv3DZOyWu/qEptenj9CP3HS9Vf2DQ0tzQ8J4YFiXOy4URNffHAbOmccpmByQkNDeXl5qXLfqb6J+F9QycJPOROM03EPk+a/zZxnk302tLSy34xB3hlQAb979+72HTvQ9BDjxIkTEJMFBaG14SFV/PyRHio4MzZYdCnrWIZZ5g5kTelNhJfn/qNnmKz/5tc0JBZP9GlH4KQ0zxbUDJWuEZiC22Bvbw/hTw+GtiGnupF4JryM3ib7dEip0OfcEwHFzxKrFlhlL7PLSf+nwwpTAlQCnj17JiUtnZT0a36ZIUJsbCxcDsj8Ixjt6aGCPT6Y0eJw0hq6JeT32n4q+Jbpr75+2dEBfEtmTlohGJaYjWYNdjAFtwEEdOrUqePHj6PpbgIxk18B/khg6Qr73MvRvwZaOhBUinuabDNABiwHEwkJCXUxXvBgRUdHR1lZGbscOqOHCn73WA+HG3X12Vs0jdBEeHR8L+RfeuGA5gD4ogOy4jh6VvfgaDRnsIMpuA1hYWEKCgqenp5oupvgCcSp7zNHvMlIKP+tIvUxvWbBh2zrtAFzP8fa2lpOTm6oVcPFxMQ+fPhtVFsMcnqoYKObZ3A4ugdvfp8+gEiM+ObxwOBxQHQqmtNC7RnlHSOGz3DwjUAzBjuYgtvw8ePHLVu29PgBjYamZr2o8mWfcwPbDU/+KK5yiV22RcrAGOolKSlp69atFy5cGJSza6elpQUGBqanp6ekpCAjeebl5Tk7O69YsWIIjoBBOT1U8NuH1yHa1Xv5Hk13QW2h+lbRYUxsXqFt5wQarGAKbgOcXkpKSsiYYT3DPrNmgUNuUFHbDqQ1jU33YyoWWGdJOubphpfV9O/eERD/+vj4bNu2TUpKqpct4/0HKE7u37+voaEhIyOzbt26TZs2SUtLq6qq qbt68ec2aNQcPHvzjDCBDmR4q2N3adAwOp6x9veZP53xmpB/Pwmkz1m6MSh8qz8JjCm5DQECAsrJyaip53agb1DU1b/tSeD6yvLNHMYziK08Hlky0yvqaj68j9PfeERAnqqiosLGx6evrU/HR7X8CfJdDhw4JCQndvn3bwcHh8+fPdnZ2nz590tLSAim/f/8eouDeFL1DgR4qODXcn2fB1AnzV3mGdTUjHPBa/xLNMJzqmVuVnc/bNsjAFExOZmamvLy8rq5uj3WTX0tYYZvzJecPlff9fiVsH3LWfMzd4F6w/XtxeFH/tRucG6 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 XFRWXKCspGJ9ZTgholS9YOFi2zGXzpjUMKzjXkg8Q8/Lly9afDAdHnnTvKCAgAHJ4eXnLysqQHAxKMDMzk5KSio39VcJh9DVUUzCJhpqqH5FhDnY2d65dkBZaRze85fIQ2nE4JY+iqDDim9vVS+c196txLl8qtXvv1+iW9sSMmGABbs6dRy7VtnZNMH94GRRs8S2mJdGOvNT4rYLrNu3WKmuVvp/dq8ksU64auyBLEQqz050d7YOifqDpn2Qlx781fn1cS4OPh2vcyBGw53PYOe+9+VjVSadmMI6+vv7ly5evkXHy5MmVK1cOTQU/e/ZMWlo6M5kKHUg/2DnIiC5Lfi/QHCoPUTDRf6vl1TXiYiIqe1R1dXVJvbbBGhMnToRfioODgzSCQUhICB0d3caNG8vLexWMDymgANuzZ09vJlrF6AHUV3A9vjY1NtbFwfbGJV0JPm66VpGtllRJyKYoHkEvrUa8vcmT8ePGbVE/nV+Qd0RZmn7GPP0X5t++eHt5eWtr7h43iuGU3pMvPkG1jb/JkVBTev3IXroJU8/fferz7ZuHh/ud80cnMjAo/Xfx23f/YgqnUCLUp6cmebm53buuO386Ey0L68fvf+59QQIq40O2IeLIkSPnz5+vb6DCYzj1DQ179+6/q7Wk/tuW5lC5RAsBGbGV3l99PD09eXh4SI/zmpqaMjAwsLCwgHPt7OyQTEzBPSAoKAgOrK2tLZrG+CtQR8ElRQVgu+cP72hpqouLiiyay0o3bjTNmDEzFyyWVVa99cDAJySyu13WCLXlW9YsXbhKxN7bh3M2A45mzPSZLU/cTZs2bSI93fBhIyYwMC1fvTGl/LcJBEvToiTWLMHhRkyZNn36tJYH9BgZJgwfPnwc/YTl3CJeMR0/hUVsrH11X//S7Qfl+DY72WT9TJ9hJO7AjWdIAE4JQ7lHhIKCwqtXr9BEr/EIjFzHyxFlzEf0k7q4d/FBrSOQicfjdXR0VFRUkGYHRMHwuaysrPLy8sj0E20UDOV6cHDw7du39+/fv23btt27d9+/fz8rKwsWtQFiQGVlZXiXr6/v4cOHt2/fDm+xt7dHF7c2ccAKEHpDxP38+XN4DR995coV0lgK8QkJjx8/Pnjw4I4dOxQVFWERqV4PL6Cab2JigiTb4O7uvnPnTkdHR3gNG7e2tj59+rSSkhLsA+zJp0+f+npmjRs3bsABJH8WHOMv0EMFNxEbs7PTnT59unX+rPIOeW5urulTp44bMwoC3oksU3nFNp+/dOOzs1vUj8TCMorCkNCv7kf+0/aP+NUyUF9TJsu7cj6noHdU/N3Lp9XV1dXUVAG4JNav4Rg1Yiyf2JZj2tfya35rc6wszHp2T2///n17YW01tb171TYJ8tHS0q7kETymczE6o+Oh5YmEOk0ZkQnTF3j9aOvoFH+3RZPHK5+7V4Up+E+ANcAXcXFdtZ53i/rmZuX/dC7sW+p+b7Ww8LqM9Awk38/PD+I1JOYFBU+cOPHYsWNwYsyePTs6OhoyQ0NDyRWckZEhIiICpwEbG9uqVavmzp0Lr4WEhNqPpbly5UoosG/durV48eIJEyYwMzPDKb1161aS/iwtLUeOHAkSB71OamX06NFQBrx+/RqWVlRUqKmpwcbhI+CDFi1aNG7cuGXLljk7tzwZFB4eTkNDIykp2fh71Q2AnKNHj8JSJLp/+/YtIyMjRBuwPytWrEA+CAqePr27qKent2/fPvgKaBrjr9BDBUf5eQpzLIZTf/TIkXCOjh1Lt4qH77D2+XefXaLjEwuKSxq72UfY7rXBGBxO5czt8gb0XA90ej91EqOAwoGaRgK+prrqJzU1NYZ6p+lGMT23/1Zdg29qvTYIhEY4OwlNkGrC19aC/lpWrW7B2dSAedLk0wbv4Y1dTIrr+uYRA/14SbX/cst+DcHVRGh8cPkk7diJzz64Uf59hqaCwb9r1qwBQ1E3WItPSpYQ5mFnZ5l39H7pz841ZWVloNSzZ8/Ca1AwPT39iRMnvn79Cs46f/48ZIaFhZErOD09/eHDhxBmgoshysvMzHz06BGct1Cut2yODAEBgREjRoCpr169CmVJcnKym5ubhYUFyX2gSJZWNmzYAJFpUlISXEKQ6enpCUuLi4tfvnz54cOH1NRUOA1ycnJgEagcVoZIHJaKiYlxcHC0f2gFTk74RvPnzy8sLISkh7v7nTt3EhIScnNzYTvwEcLCwmPHju27GTPhV9u7dy9EwQNxRv0BTQ8VHOBit4Rl0qzFK1W0Dr+0tImJTyouKcX34um4krTYXRvXjx47UUXzmKmZ+c2LuovnzJjGvsLWp4PxSY3vnB2NG2/m+bN9tr7G+NaZ2UtWPbJ0QnPI+Gr1bMJEhvPPf9UlO6SusviEqjwSL5+/ccvC4t1LQwOVHVsnMjJs3H88o6QbPh2CCga5QAAFTmwf3/UeS4v3ezQO7ndPE3QuzK5GNQwVdlAqvAAFg21BweBlCQkJiD1BdhEREeQKbv+ULcgRokuwYZvmCPjhQM0HDx4knxUCNkgqV0CpEJCOGTMGjI/kICBfnHxNBHC3nJwcKBt2CRadPn0aQmZkz2NjY/X19UNCWjr2lJaWQggPgm59Uwc7bG5uDt/ozJkzyPbz8vIgh4oVDigDIK6HY0JqUcH4O/RQwcX5eYmJSTV1dQ09u+Q6CpRy48MPKcnOnT0LYhk4HdeJbrHyDugwoLI1erCGY/Un/589IloUfHbOUq4OFRzkbLF+/YZHli1BStfUlBc9uXZ+zbKlrHPnzJwxbeasmWwrVmpd1MsopqgthcQQVDC4QFlZ2cEBfQSRuoB06urqYisIc21zHDJ+9TMB38FfRMFQi29oaDA2NmZiYoJaf0xMDITG5LfjYAv+/v537949duwYnPEQ/8I5xs7O3mYMcj4+PlBwQEAAmm4HKBg+AlxJ2nIbYK/g058+fXry5EktLa1Dhw4hLQlIW8T79+9h+xApw+ubN2/Ca1gN9B0eHg51SihIWrfRAoTDEE1DqXa4FYhPIQo+cOAAYmcIzyGy3r9/P7XKPDjIEJvDp5iZmaFZGH+FHio4LTHBw9WrsrbbLVN1VeUB37wi4jI7rK0SmxqTfsRALSwoPKoa32nvhSYCAa43pAkCobUhoqHDgYGITU2wMuVjBlVXVkSEh3i4u/j4++eWlHRYBnTNEFQwhHWggz6dUiG7liDslm+Z1raajCj4yJGWO3Vw5FlZWWFPIiMjofpPUjD8BRuCwmbNmrV48eJly5YhTb3wt80AuKDgUaNGdTExpZ2dHShYQ0Ojw06HkHnr1i2Q6dSpU8Hv8EEA+JeZmfnz55ZRSr5//z5+/HgIZsHUO3fuhNcQuUNIC+KDdd6+bXk+HhZBiM3JyQl7uGDBgqVLl8JG5s+fP3LkSCg5EAWDMb28vKSkpL58+dLywVTi/v37mzZtomJwjfFHeqjg8G+ugiuXSiupO38NKq2gqPGouqI02OfLsX3K7CtWWXpTMsjZQGUIKhjiNbBDn06pCyXu/djKZfa53vl15HPHIQqGkxjiQdDTwYMHlyxZAkZjZGQkKRi0OGzYMDk5OQhvkRaGkpIScXFxEFybO3KgYAYGhg47SyCAgmHLELp22HnW1tYWrLp27VqIeZETAHwKgTDoFZkaLi0tDdy6bds2+FwBAQHYDj8/v5+fH8TmM2bMQBoB4DBCJvgXYvbs7Gyk5cHDwwM2smfPHlIbRUVFBRzzK1euULHxp7S0VFVVFXaY/FFDjD6lhwpuqq/5bGHMx8XBxDJLTmmv2Xu7iOjY/JIS8si0BWJjaVFBdHiYndWbfUqyLMxM85dz33xhXvHzgbRByRBU8L1798AOVVW/dRCkOlGlDWs/53G75Gv4lYSWoqcQScGIE+FUnjZtGoSHU6ZMAQUjv4KsrOyIESPgXG99RwvwG82bN4+Nja1nCj5x4kSHCr5w4QIOh/v4+xREYmJisE2kZxsejwfHrVq16uzZs1u2bAkKCpKUlATVwgmzZs0aRKbwFWAjcDxb343y/PlziOLV1NRICgY1v3jxQklJqQett7ARCL1JmwIyMjKg/Dh69CgUBsLCwrB72H25v0MPFYyQkxx3Q/cY+9zZONzwabPnb5Lfpn1a9+5DAyMTY2Mjo8cPHpzTPbF9qyTrNJaWYS1nL9j332m/1qfdBjdDUMFQs4aaPproM+oJRPuMGmnXfNzz1MtRaFMsScHIlBMQx4F56enpIXPz5s1IFyswMg0NTXj4r+drjIyMQHPs7OztGyJ6o2AwF2yWfMrRr1+/QlwMIS3SEAFAcYU0U0DADpqDQwd1fyYmpr179yIB75cvX2AjO3fuRNYH4FwSFBSETFJDBALsJ+RYW1ujaYqBg6aoqAgHDeoHN27cePLkiYKCgoSEBOzSvn37VqxYAX/hSKJrY/QlvVIwQkyw383L50UE+JjoW6aJa8PYiYzcAiLHL+p5+of1oF11IDIEFQxx344dOwIDA9F03wAKPhFcwmKRpRdellmF1r7bKBgq/iYmJsi5B+ZFGiKQG18QdX748MHZ2VlPT2/hwoUsLCxUVzAEkrS0tNzc3G/fvnV1dYXQlZOTE+LxSZMmkZ7vcHBwgI+G/TE0NITkp0+fkCRpfF4ISHl4eEDc586dc3FxsbGxAV3OmTNnzJgxbRRcW1sLi0CjaJoyQPTHjx+HD4X3wgahxNq+ffv9+/fT0tJgaU5ODmzw+/fvyMoYfQ0VFIyQn5vp6epk/OrZjRtXdXVO62jrXr5yzfDlSwc3j+TsofW8zRBUMNSgr1+/DtXkPm1DLK9rkv9SKOnz21RSUBkHf0EISepGlpSUNGNGy0BRAgICyDA92dnZUL8GqYHFwIZLlizR1dXl5+efPHlym2Jj2bJl8EaQIJpuB0S4sMKBAwcQ47cBPk5fX3/atGkjR46EDwJvwtUFhRPE4KRYNSoqCvJhI97e3pAsLi6G3YBkUNCvOWIgdoZTCDKhPACDi4mJQSkCG9m2bRu5gnNzc2HjIHo0TTFxcXFwcJC+zIWFhUPqXO1vUE3BGCSGoIKBkpISiDqNjY3RdB+QXNmw3jnvduxvBxaE++bNm4CAAJKbID718vIyMjJyd3cnPVIBVX7IhAAZomaQHeSDASHABAMiKyA4OjpCANtFMyjYGVaALZCrkBwIw/39/WGX4FDAJ8LOQKD97t07ktZramogEgcjk2r6EBebmZmR90QGUlNTYffgW8BfKNhA7u/fv4cLFWmsQIBYdefOnT0YWzI0NBRqAMgdQox/C6Zg6jM0FQzqkZeXnzt3LtSv8/L6ZFahoromRa+Crc55g2kG+94AhYG0tLSLi0t3TzZkajis20N/AFMw9RmaCoaoEKK/ly9fioiI7Nu3D2kBoDo3I8tX2OVWkHVKG8pkZmbC1QvFHuleX4dAvG9ubp6VlQVxN5ycoGxOTs6vX7+SB9QY/wpMwdRnaCqYhISExI0bNxq6P1n9Hymrb9r/vVg7BLtT/4uCgoJt27Zdu3YNXsfHxz9//vz48eOKioq3b9+OiYkJDAy8evUqBLwADw8PNze3kJAQPz+/qakp8naMfw6mYOozlBUMARdUjfvoMTmnXPzsT7kBBR10RRjKQM1DV1cX/oqLi8vIyGhraz958uTw4cNcXFzgXJDvhQsXUlNTr1+/vmfPHg8Pj6SkJCo+zYHRSzAFU5+hrGATExM+Pj6qz3zT0ES0S6vmd8mX+l7cd7Xn4uLi/jBUY11dXW5uboed3joErCoqKrp48eIHDx5AUIzH45uamuD0gygY4uKysjKkRgKZlG8T46/xlxRMbOmMOFQanoaygmtqaiAQ67ppsgekVzVOMEnb//233gtUR0ND48WLF2jiHwHxKRRjioqK7Qe07Ay4sszMzAwNDbHn2QYiVFEwMeTrVztb59LaDnpKAgFebg+eGqXn9nBK8wHHEG8LhvPJwsICTVCJsvomGe8CCfd8/+K+erQdfi9paWlkoJx/SFZWlpKSkr6+PprGGOxQJwrezbdhOG5aRH6Hc1Lg/5PZiBs21cl/MA/NQ84QV/CJEyd0dXWTk5PBJjk5OR0+wtADfpTXz7HN5nbN76PalKOj44YNG9o8LNdjoMoP372zvsNdACfPrl27Ll68iKYxBjs9VTCRGOr3xcDA4PmLl6Zmz9YuXEgzivm03i0jUxOoyj3/+e+VkfHDGxc55s4cPmG+W0DLjDJDgSGuYC8vr82bN4uIiGhpaYFNQMfITBC9pABP4HHMOxLUV90hvn//vmXLluvXr/d4qMaGhgYwOMTRDx48OH36NHz3mzdvdjimZRcQicQrV64cOHAATWMMdnocBROd371gpsH9Yhj6/w7ZqHoko7BPOor2Q4a4goHAwECwz5s3b+DvmjVr7ty5gy7oBTWNTZfDyhS9C+s7n32qN4BADQ0Nd+7cKSsrCyE8mksxEPCam5vLy8sLCwtLSEhcvnz5/v37UA5BCdStCTHh5FFUVITgBk1jDHZ63hDRUFVm8erRkf+OnDr130KWqTjc2B2qe0+cPHH0yC+OHjmuo3tO3+BxdHKnD90PPjAFk2Nvb8/Pz+/i4oKme0pjU/Pj2HJZ974db6SsrExBQUFTU5Py6DUpKSk0NNTU1BRi/3v37kEgTHJucHCwuLg4bPDYsWMU9hKBLXCvWZOeno6mMQY7VGkLbtJR2b18qUBUDhXqm4MATMHkgHogEO795A61jUStgBIO5zyfPHwdoQ9715w7dw6CWUp6p8XExBw9elRKSmrXrl1KSkqPHz9uM84DAOtAbWD//v2XLl1CszoBPH7ixAleXl7zf31LEONvQp3bcRkpyZFRcdR/HGpggimYnPj4eKhZ+/n5oeme0kRsDilu2OlTNN8m+3JYHzZqvXz5Uk5O7o8Khu8lIiKyZ88eR0dH+HbR0dHt/Uvi/v37HY6nDEF3ZmZmSEgIVBmFhIQ0NDRcXV2bun8TD2PgQh0Fo9TV5WSmJyYmJMJ/rcD/Y2KiXT7bWtg45RX37ZQK/QdMweSUl5efOXPm5s2baLp3hBfX8TjkXg0v67swuKCgQFBQ0Mmpg6lgc3JykpOT6+vrm1qnd5s7dy4lo5TBysbGxuRDsAPwRmVlZZA4VBEkJCRUVFS8vLxKSjrsU4QxmKGOggnVZQaXdVYvZJ81c8aMmTPnzJkza+bMadOmTZ8xfebMmSyTmQVk1eMzh8qj/ZiCySESiebm5qAYNN07iMRmwx+V7LbZEu4Fy6yyrdK719+AQmxtbUGLZ8+e/fz5s5ubW0ZGBoSrpqam/Pz8S5YsAWny8fEJCAiAqduMddkZERERq1evtrS0fPr06eHDh0HHy5Ytg8gXkrD9mJgYTL5DFuoo2ET/AuOY4bjhNNNnzmKgG4vD4ZgmT2WdPWs87Wh4PY1t6fXnFiXVg3m+OHIwBbcB5CUqKlpYWEiVJyRL6whWqdUfM2p47HIO983zcrCfQUFBN27ckJeXB9tycHAICQlJS0sbGhq6uLh8/PgRvAlVPQq7OsDWHBwcICIRFhbW09ODCsGdO3fCw8O7218NY1BCBQUT64pkBdficKPOPjIpKCp5d/f82PEMJ/RfV1VWejtYb1i5mG2NqG/cELrDiym4DXCSiYmJgYWReSKoxYmQMgm3ApesGs+c2viKhkZqt03U19dD8Ovs7KypqRkQEIDMgdQD6urqLl++rK6uXlVVhcfjGxoaevDIBsZghQoKrkwK5VrEyrxkfVpByzka982BbtwEGc2zyFKfj2/nTmbWuHC3unGonHaYgtuD3JRTVlam4qSQIUV1Eq75y2xzOD/lrnHKux1ZTj65PRXpffD+4cOHLVu2UOtBQYzBBBUUXBT+bcW8mcvEVQrLWipWOYnhcxgmcoptR2KGxvIsFVGeqUvWhyZnt2YMfjAFd8jnz58FBQV7MMtOF1Q2NFU0EMG8tuk1Kz5k34oq75vnNnpLbm6ugoLClStX0DQGxk+ooOCatPA17PPm828rqWop5IvzMgTYprNxCsaVIWEvXnf3jhG0rC6BfTKGbD8EU3CHgHx37drl4+ODpqmHUUKltm+xtGcBt1NubV92Ge4NAQEBGzdu7KMpnTAGLlRQcHNdmfx6HpqJsxwDW0aBwJcX7xHlop8+18ovvmUpvnCPxAbc2NkuAZEtySEApuAOSUpKkpGRMTIyQtNUoqaxaemHrDlm6SxWWUeCSgn9dUzUmJgYSUlJZ2dnNI2B0Qo1FNzcbHb7HO1w3JJ1Iqb2Hs2N9aY3T+NwOJEdqp+dXB5fOzttIu3U1eJhKbno2oMdTMEdEhkZqaam1vsnldtQ20jkdsq7Glluk1ZTUNt/55QLCQkREhLq/SMqGIMM6ii4qjBLR33nMBxu8cbdhObmwtQY4ZULwML0DEwj4X+40dp3X1fWY7fjhjRBQUEaGhrwF01Tj1Vu+Xfi/v1sF10DUTBVnhLEGGRQR8FARUGWzfs3b+zdGltrgrGBX7RUFJcvXszDL3jxzpO8kv5+hZBDaGhomf0FTXUbTMHtgaNx5syZAwcO9MUzCBciyjgd8kyTqpr7aSNEC6Wlpbq6uvfu3UPTGBitUE3BJEhtcTXlJfFxcSlpmWiaAtLjo+7p39D677/L+g8DY1LQ3HaU5Wc5OzimdzxC/E8IdX6uny+d1T167NizV2ZZJZQ8Hk3093I+dvCguLAwLy+v1FbZkxevhcR2uhudgSm4PSCgPXv29FGXgLK6podxlQvtcz9m9N+HHRoaGk6cOCEnJ4emMTBaob6C24Kvcra1tLR3L/vT03FfXaz5Vy9imjR57vz5U6dNW8S57plNR+2GTfhrJzSnTZlj49NpF4ummrLb547PmT511hzW+ayzJzEyi29Xjc9Hx3apKS80M7yvrKRwXPdqTNrPFuomwodX95bMnzOLjX3zVnnFnQobRTfMZJm0cBXvp2/B6DqUgSm4PQQC4eTJk8ePH0fT1OZbLp7FJtszt/92vHV2doaz4sOHD2gaA6OVPldwYVyY5JplqzapJGZ39XBRXkLElnUrxs1Y+NraITk52cft05rF8+evFojMahvqfnz9kGUcDQ43wtwrHM1qh/Wzu5PGjt2466BfRGxyUrzB2cOjaWiVTulBgN5YVfxE74rG4VMObq5P9PV2qZ+MSM6CtxQkR3Czz1vOvykg+kdxaXlVZWVRYb67jcliFqblIjsTcrrxICym4PakpKQoKir23eSY+lHlmr7FlQ39tyXC3Nx89erVaWlpaBoDo5U+V3BBdND6hTPn8cn+yOxqgEF3S6OpEyeeuPcaTTc3W9w/N55h0n3rr2i6legvjhtWr545c8Y42ilvvTqe5qu+MG3bBq65q4Qj09BH+BsqiuU3cK8Tky1tbo7wcdc5fioqu+UZrcY6/NUTB03tPeDCDfpkxDSBUdug7byTeic1J81d5h4Ug6YpAFNweywsLDZv3pyR0Vcj92v4FV+P6NfTssCVJi4ubmhoiKYxMFrpewXHBAstnbtIYHt8VldXSE15sY/f98yCX4O+m+ufoWOc/NT+1x3kyuwkBXGBTWpHz2kfmTR+qql7CLrgd0Ld7OZNZTp61xhe11RVFBUX19Y34Guqi4tLmpqbfVzsdU6dLfkZLT29fOq5tWNDc3OkmwXT+PHyhy6WV/42E3hdbXVefj6+vhtjDJWUlGAKboOdnZ2cnBzEwmia2oh4FBz2L8mu7r+9boqLi+EIqKuro2kMjFb6i4JJ1NdWp6WmuH223LBmlZi8WloxKsTGutoLB1Xmc6zz+5FhbXiTfjRTZwr+8OoB03imp+8+fnW23b1Det369aqHT/hFJyJLC1LjLuqcuPHUJDIqys/DSUV5r0dgyzMjlYUZsgJrcDhacVklUwvriJi47Nx8fOMfukU0NjaWl5eDc0vJiI+P5+PjwxRMTmpq6smTJx0dHdE0tQktruP+mMPtkBtU1E8H5IO60ZYtWy5cuICmMTBa6XcKDnGz3SK6gXbkcPrprMZO39Hc5iZrQ/05rPMeW7Y8XGR2/wI9TacKNnlwbcJERnEhYd61a4TEN0lJbpoxeeI0dg57f/T2XVzody11lR2KCvLblUw+eZAu2fjAr9u3SLAwM+JwuFG0dCvWrD924ZqbT3Bt5z2ak5OT9fX14bq6/JNr164dO3aMk5MTG4qQnPDwcFVVVXd3dzTdByRXNG5wzhNyygst6XcWLigoCA0NlZSUxIaJwGhDnyu4KC5UeFk3FBzj7/3wwZ3TJ47wLGPnEhR3an3oOfa7K99qjmO3XyLrWD69PoGG2SYADWzbYHT3Cs0wHOOcpc+tnRF3ulm+ns3MyLNlV1kdKlNCPT49Nbmkom1PtbrqCqePNhd1jktLbWZfMG84DkdDx3L27quKmo5vtWdkZJiamr548eLVT16/fg1S5uLiqq7+rUFjiPPmzRsREZGsrJY7n33Hh7Tq1R9z1jrkvvxRkVnViOb+aywsLDQ1Nbdt27Zjx46AgAA0FwOjlT5XcMmPMOHuRMEkAhzfT2WcsEHhUF5BvqacKP2M+Tcfv/5oZ2tra/efmsLYURO1zt/+7ORZ2dD2SoMoGMLY3Wfv1pBaEYj1OspSU+YudYnqRAFEYmNjI4Hwq9mB0ICPCQkyfKS/in0ubvzkN66+lN9rr6io2LBhA9YQQQ4US8rKymiiL8mvIVwJL8O9TN3v1yejuVMOnAafPn26c+fO8uXLIfh9+fJlfn4+ugwD4yc9VHBGQszdyxd1z5w91yUXL185obl/JhP9ClElihRMNtRgA75Sjm/FwtXCli5ebEyjWp5zJmcYRKi4sQyL4krbPnf38fWj8aOGX3xpiaaBpoZXV08yTJlv5hGK5vwOsRH//PYN3au3y/Ft2xzcLZ4z0g7fd+XxL6H/CaxHRBtycnLU1NQePnyIpvuY4MK6ae8zHyf8y7kKCwoKDh48OHv2bAEBAbAwmouB0Y4eKvjbx3cTEBVSxhLBHXEZXY3V7ef2eZ/qQb+I1sHVWmmoKd+yZtk8TgG/hDTjJ/cuXrhw7txZUP6lK1e2iPCNHkErpbRX3+B1Cb4OfcNPwt3s5jJP2Kl9+5c0mxqvaypOnbfUOarjMYuJTfUaWwRG0E9xCE9Fs36S5OuykHmC6sVHmIJ7DNS+xcTEej+PPYV8TK2ZbZYe8U9bhN3d3VeuXHnv3r3Y2Fg0CwOjI3oeBd86f/bkKUp5YmxZWN7pFN+AtaE+mHrzPu2yelR1bmZPmejHi6scbz83/rtHV+hpmN/7/Dy5icSSgpzouB+FZS0RcU1ptoIYH930Rc6B6Aqp0d9Xss7kFJEv6XzgB5+PJsx0dOvlVCJSf43oRsSXXz2+j2Y8i5H9F8obIjAFtyElJeXAgQO2trZouo9xyKhdbpOV/k/bgl+8eMHNzZ2ePoTm68LoGT1UMJFIJBAIjfAfBTQ2NjY1gfu6klh5UeYh+Y3DRo6VkFO8fPnqfxpqM5mYZi3n9gpPRtcgw0j/LO0wehNSj4iGWpM75xeu4Da0cUMy/Jzes89mmb1o5ZFTp8+d1VnLyc44ldXMkdS/ogMIdVW3dLXGjqZZsHyVxqH/9PT0zp05uVmUf9z48UonLxVVdaN7A6bgNmRlZR09etTc3BxN9yVwkt2JKuf5lFP/7+bPgHN+27Zt586dw27JYvyRPr8dRznF2Sk3Tv+3ZMG8KVOmzmSdJ7v7wNfwOHTZ73jYmSvIKnhF/OwR0YD/8PKu4Mat5k4/v0ZTg7+H/baNIrOmTJvKMo1XaKOFo9cfgyJ8VZmd2autQgKsc+fMmD515pyZqwSFbhoaF/3+sMYfwRTchvDwcDU1tejols4tfU1aZcMU47TVn3Mr+mYeOUqAAOXs2bNbtmzpu6cBMQYN/UjBAKG+Nis97UdcXFJKWpun1Mipq60pKS6uI/WFIBLxNVVFxSU1db81/5WXFCUlJMT/SMgv7MbN8bLiotSU5Li4uITExNzCoh5cx5iCEQoKCmprayESfPv2raKiYk5ODrqgL8mqISy2ylpkmRla3PYmwd/E29ubl5c3IiICTWNgdEL/UvDgAFNwXV2dsbGxoKCghoaGvLw8JycnnGRQPUcX9yW5tQRJ9/zDfsX/MAoGXF1dxcXFY2K6MbQIxtAEUzD1GeIKLi8v19bW5uDguHDhgqGhoaenZ1paGkTE6OK+5350xQLrrKSK9vdx/x4QBa9btw6LgjH+CKZg6jNkFQxf2c/P7/Dhwzt37vz+/TvxX8ykWVBLOORfrOBTVPdPp1Kura3duHEjdWfsxxiUYAqmPkNWwQ8fPuTm5j5//nxpaVd9wPuUVz8qF77Pavh33SEQkpOTeXh4sPmSMf4IpmDqM2QVfOrUKfAvmvhH+OTjeT7mBP/r8dJCQ0M5OTk9PDzQNAZGJ2AKpj5DVsFgHHl5+cDAQDT9L4D490pEuahbwb+dyvP58+eg4Li4jntVYmCQwBRMfYasgsvKylRVVQ0MDND0PyK8pH6ubY5R6r98LOL9+/dbt25NSkpC0xgYnYApmPoMWQUDHz9+FBER+fTpE5r+F+AJxP1+JTzOeTGl9eq+xbu+FUWX/e3eER8+fNi9e3dqatshRzAw2oApmPoMZQVXVFQ8fvx406ZN8fG/Rlz6+yRVNEh6FKx0yNX0LT7oX7zBIdcju6shSqjO9evXubi4sCgY449gCqY+Q1nBCAoKCsbGxq0Dg/wzYsob7LJqaxuJTcTmtZ9zphqlf/1bU9zn5OQICwsfOnQImzkF449gCqY+mIJdXFxkZGT6TzU8pbLxTEDJMgiKA0pC+76zBJhXQ0Nj//79ZWXdm6YAYwiCKZj6YAqGI8DDw2NnZ/dPns7okEZi87vUau3gEq7PeX75dY1NLdFx3xEeHs7Ly/v69Ws0jYHRCfHx8ZiCqQymYODWrVsbNmyAQ4Gm+wdVDUTdkFIZz4IDvsXXI8trQMx9xp07d4SEhPrbEcDobyQnJx85cgRTMDUZ4grG4/GZmZmGhobc3Nx5eXlobr8ho5qw2jlfwr1g1efcZz/68Dd68OABKysrVAUIBALUBuB86D91gt5QVlYWFhbm4+Pji9FrgoKCzM3N9+zZA6/R49s5mIIpZSgruKCg4Pbt2+Li4vz8/Orq6uXl5eiC/kR9U/PXXLywY65LVh/eLouMjNTT05OSkrp58+arV68uXLjw+vXrf/joNrWwsbHh4eGBn1hWVlYGo3fAMRQQEBAREQkODkaPb+dgCqaUoaxgU1PTTZs2gXH+5tBo3YXQ3Lz7WxGHbTZEp2hWn+Hu7g4nw969e+/cubNt27ajR49mZ3c8geFAAX7i06dPl5SUoGmM3pGQkIC1BVOZoazgL1++7Ny5Mzm5g+mm+g+BhXW4F6mbvhb+nSGFSWPVJyUlQbxz9+5dJDlAMTMzO3PmzKBRcFVVVUhISH39PxtUBFMw9RnKCoYQT0JCQldXt6KiZSrV/sn33Dq295miTnnPYv7qThIIhJs3b6qpqZH3mE5NTR1YI7uDguH3LSoqQtMDnB8/fggKClLSFNtHYJ3SqM8Qvx0HgfCGDRuuXbuGx/+lRyG6S31Ts29BnYxXIa/T375bGBkZKSwsrKCg8OrVq9evX0M4CcdqzZo1b968qa39q8/v9Zh+omBkEhaIXuG4hYeHd9bwBdF61zP4paWlLVy4UFlZGcJhNOvvgimY+gxxBQOXLl3auHFjcXE3puz7mzQSm+1Sq1d/zDkS+rcfnSASiU5OTvv27du/f/+hQ4cUFRX19PQePHgAXobqMLpS/6Y/KDguLg4qEwcOHJCXl5eSklq6dKmoqGj7A5iSknL8+PF3796h6Y4AQc+bN4+Njc3a2hrN+rtgCqY+mIJdXV1lZWULCwvRdD+jsqFpz9dCCae8jKo/TqvdV+Tm5kLgRjpJwMU2NjbI637OP1cwxL9Qx8K1wsrKqq6uPmvWLHi9du3a/9s7D7ia3j6AX4qm9qJCKZUREpFIKiTtPaSMIqIS+Zc0iMxQSJGM0lAShQalpKQh0pJKS3vXbXp/995TbouQhvd8P+/n/97nOcftnHPP+T6/5znPgLvO1dU1MTERomMo6qCGAXpNSUlB/uVghIaGKikpWVtbg83HpAMPquCRB1Xw8+fPhYSExq1Tmju6LZOq970Z8oUStrM7pbKtY7SW3mhtbd26dSuoDUmPb8ZcwVCTAMnCMbCzs3Nycuro6LCysoJA7969CzGvlJTU+vXrFRUVubm5wcvk5OS2trZDuRXy5eTkzMzM4uPj4Y6F0AHZMIqgCh55UAXX1tbCg3HixAkk/e1bbGzsnTt3xknrcE1bl9aLih3x39tJqls7c+vbmzu62rtwE0pc+FDP5/PFPWuUfkEI2fT19UEib968QbLGMeOhIaKqqkpLS0tQUPDUqVPe3t4uLi6EhQK6urqys7MTEhK8vLzgfoOCjYKCQl1dfaj5ksrKyvj4+CAEbmlpgWh627Ztoz98BlXwyIMqGICQBOp3GRkZcDXu3bsHIQbUE4dzn40CEORap9SyBBbvfl0dW9bqkF43/2Epf2CJQWylUnQl/4NSJp/CSXcKF4aU1o1KtzV47OFC7dixQ1ZWNi4uDnwxOgv+/x5jruCOjo6DBw8uX748LS3tBxfq48eP4uLiPDw8P4ht8/Pz582b5+fnB59B2RA7w5cTNo0aqIJHHlTBAHjk+PHjYGEREZGNGzdeuXLFwsJCQ0MDLg5sbW9vhwCkvLwckmPSAFfe2umR3bDlZRXf/WL5F5VPiltSa9ouZ9affFf3qhwbVdKSUtOuF1sp/6Li42hN9w5Vh7Nnzy5atEhbWxs+gF/GdrbPoRg1BUPJVF1d/fDhQ4hzicPY5ORk8OahQ4eQ9BBcvHgRg8HAntHR0UPFthEREXJycoQOlCBBqLpBWUjYNGqgCh55UAUTALf6+PiEhYUR3svl5eVBxfDGjRvwwcHBAS6RhITEunXrdHV1IRgh/JNRpqmju6S5s6En1O3q/ka88n0ttgvC5LXPvmI7R0mFUL+GiCwgIMDY2FhKSsrDw2Mc3kWjoGAwJny/s7MzCBTCWH5+fviLhPlGYNPOnTvBWT/tw3ft2jUaGhpSUtJNmzYNtTNEvlD5IES+BQUFu3fv3rNnz6NHj0azgxqq4JEHVfBQ+Pr6SkpKCgsLq6mphYSEgKDhdodwRkVFJScnB9lpPJFd3340pba4abQrp1BLgKILrhJUHSBSG6sialBGQcFgJbhP6OjoxMTEIAoG+cJf3L59O1yKe/fuMTMzHz16FNl1CN6+fbtgwQJLS0u4gE5OTkjuAOD2U1ZWJggaRAyVD15eXgEBAVtb294xjX8bVMEjD6rgoWhqaoK44/r168Rj5+BhVlBQ0NfXR9IoPZSWlh44cGD58uWqqqoJCQnjpIH4ryoYzjEtLQ3iXAwGA/8tKioi5NfU1AgKCrKyss6ZMwc2bdiw4cdDiqEmMWvWLHt7+x8/hk+fPoV7rzdGhurap0+f4FKLioqamJjA7UrI/6ugCh55UAX/EvDUXbhwwdDQEEmjEAGhGdxIxsbGUEGG0BjJHVP+dhS8efNmkOzixYufP3+OZOGfKU1NTahF3bp1y8rKCtSJbBgCuG6enp4SEhIQ5F67dm3QVnXIvHHjhpGR0UCbX716ddu2baMzoRKq4MH5k64pqIJ/FXhIdHR0xufbp/EAqEdLSysyMjIlJSUJzxhOt/aXFIzFYnNzc+/du8fHxzd//vzk5GRkA57m5uYPHz78tP23H+Hh4ZKSkhA4g7UHtnRBkWZmZubl5TXwZV1ISAhocXTaf8aXgmsrSoMD/VyvXPYOCvlcOnTP+aba1OTk8rofOa6zpTEu8tm1a1ev3/CMe5M2TKN+yki7euGCiZHRli1bzQ5aXrtzr/DrL0/wiir4l0hPT9++fbu+vj6q4KH48uXL4cOHVVRU9u7dC9aAGvr+/fvhuiGbR5e/oWA4QVNTUw0NjRkzZtDS0kpJSY3UxMq1tbXBwcEGBgZQzQoMDIyNjbW2tvbz84ObDRQsLy9PHGv3kpiYqKur++NhdSPFOFJwRsorXXkpRnoGBmZmBpbpazdrhMQOegm6PM87LBEUDkkYsvtIbWn+iYN7Z09nY2BiZGGk5547z97Zo6WnJa2rvfnpg3uHDpidueReXPP91eerUD8JkSV0LNMXC4uIiq5ctICfgYFhjbxGYmYessfwQBX8S/j4+AgKCk6IUQljCCgPat9xcXFv376Njo42MTERERE5evTo6E8aOeIKLi0t3bp1KycnJxMTEzk5uY2NzaNHj+AhQjaPBI2NjdevXwcRr169GoPBnD9/HjJBzVD3gr9O2IeYqqoqCJx37do1socxKONFwfUln7crrCOZxmLt5BIeEeF11Zl7OrOgpOLnqv69Q+LD/OYw08B1vPN8yDLK/aQ1OckUdYM9YU+fPgkJVJEQpmGefu1hDG4btsHn2iWDbYbOLhetLA7ssXT8VILrMlVbnCMtvHDmvGW+oc+y8/KLi4sy36fdOGs3g5ZaSnd/SfUv+BRV8C8REBAgIyMzngcjjEPev3+/ZMkSMMjoz+814go+ffo0xPXh4eHz5s1btmzZX5papLu7Oysr69ixYxBXWVhYhISEQKx969YtZPMACgoK4CmOjIxE0n+N8aLgiHse1JMwOv+d7u3+42ZnSs3A6hYSj6TxfElP3Lx6FT0dDTkZi/fzVCS3L921pSoSy1jmr35fiNwl+alR0xkYNu6whM8fk14eMNkXlYqbU7yppuKA4TbvJzHd376lhN2lpWMwPeWF/xe9dFhsUcRQz3jy+hcqfaiCf4mIiIgVK1aMnxXvJwTgKfDvyZMnkfQoMuIKNjQ0BAVjsVi4EwZtFhhBWlpaIPg1NTXV19d3cnL6se7V1dX9/f2RxF9jvCi4ojj/rq93RgHSBwUIuXKChp7pvN/3n6S9pmy/jqKYwpa9u7cxT5t+K2Lwyf26G0vV1y1nXSKdXYo0J1Xnp8ximb5h+0H4HBMWbHXYtrezyc2TVm7+oe3fviU/8mKgpt9ufQnZ0ENFadHrxDdVP2x37gf8rqiCh09dXd3+/fvhLkTSKMMgOTlZTk7uwYMHSHoUGVkFw/fIyspu2bKltnb0Jg4tLy/Pzc398cs9qJbJy8vDnfm3O6KMr9dxQHdXZ2trS2ZavPzalYvENqR/6Z1LpevqscNcAouDX6X7uTnRkTMNpWD4Cq/zjhxs7EaH7N9l53xMSzDWUeSau+B6GC6gLvqYbGm2/2ZIFCiyJOfDji0GD18kQBRcXZi5ZiE/Je0MI3Pr57EJlbV1be0/75APtZuOARQXF6MK/iV8fX0lJCR+3NPzHwMe7D9ZWOT69evCwsJjskDUyCoY7AMxaUlJyXh7GQuP9tWrVzdu3Pj27du/auFxp+CMuGd6mkoz2ZhZuPlvPYlDcr99i37gPZeLy/qCJ+jy7gU7mqmMQyr427fWplr7/QZUlOQsbNNZmRlJ6Zisz3u24X9iUPzrqBBdDWVlVdXNcipnrvs1tBFU2x33yE9kAR8FJRUNLS0P/wJ5jS0XPO9mFZT8oDdFYWGhu7v7+fPnL/bg6upqb2+/dOnSoSZnQukH3N9nzpzR1NT8v+oRceHChRUrVjx69KimpuY3hgCAB5WUlKqqqgZ2qPrbjJSCGxsb79+/r6CgEB/fp7Fx/ACxlIaGxkJBwQMHDhQUFCC5I824U3BSxCPLA/uUN8vMmckho6mfkofr/1j0IUlOfIXmPhvCrerj4kA7lck3bvAeEd0dbb5uznxcM4XEpCwsrSz37V26cJ6AkNiDmJ7Xd93dFaVfkhLiP+bktfUd/l9SkOd+6ZyWwiYBgbmM9PRkZFNn8gld8wtr7xj8ZVFeXh5o9+TJk6d7OHfunJWVlZCQEKrgYQISSUpKkpaWfv36NZL1r1NaWionJ8fPzw918G3btu3Zs+dXZ6oF+ULwqKenN/orVf+JgqGwiYmJefz4sb+/v5mZGfj3yZMnyLZxCdRUCCPFoV4bEBAAUh7x95/jryGCQGf7o5sXmenp5HdZNzbW2RhpMHHP97j/FA43IyPjhKUx9RR6Rw+/rOy8gVOo5LyOFOLiWLJRO6sIaWv/lBq7eM4sIUmlksYhq7rwN5FP8PlbV+XXkuePg8337mBnoqNi53v0KnXQYAP+FcRu/YAHDG2I+CXgRt+9ezfciEj6X8fGxmbDhg05OTm1tbW+vr779+8XFxePiIj4pR5mYLF169aN/twaw1RwB37um87Ozvz8fHgi4MmFA4ZgRVJSEopbqPRAsJKdnU3YeZwDv4udnd3ChQtnz54NNTYkd4QYdwrupbWhUnb5fH7RjcFRL+exUWAwmClTppJMxkE6ZcqkSaSQwzpzaVZt/wa1AA9nGlLyi75hSBro7jh3YDsjJ6//UIFzJ/aB92332769fYd78bt8iop00p5THi3DrvChr+N+FSwW6+TktHbt2sDAwNEZmD/6wJP26dMnwuyLoM6goCBkA/41vYWFhYiIyKFDh3pnRfgpEJ2pqKiMvsWGo2AoU+GnBOeCZ8G2RkZG6urqUKmHsgeuQ2trK3HEM1GAH46GhmbHjh1IeoQYLwrOTEk8d 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 0yeIgpOSkn71GTYxMWFiYuLj4zt37tzoWHhQBUNhq 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gODNUdHR/MDB35jPB6UCuD0EXk/FhcXBwoOCAh4/PgxPJn9XrJDmGxoaDj8xTVGnJ8qGK481Dzg4JH0/weBgYFQNRnOMJx+oAoeeVAFj0PgR4HoDCr47u7u9+7dA4PA0zJwRG98fLy2tvZvNCY8f/5cXV09PT0dSf8BEN5COSEmJgZFhbCwcGxsLLIBDzyxGzZsGJNpKgn8VMGEd5KwG5L+PwCqLBYWFtbW1r/RXRJV8MiDKnjcAhHxjx+S8vJyBQWFY8eOIelhExMTA1H2b/RFG0g3ftLOsrIyCN7Btk+fPkU24IHocunSpV++fEHSo85PFQwFm4mJyblz5351vouJC9xXioqKz549Q9K/AqrgkQdV8IQmJCRk7dq17969+yWD3PPxkZOTG1kzOjo6QmBFPGtEfX39zp079+/fP4Z2+6mCgaCgIA0NjT 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rGx8e7du6EMg8sLwa+8vDxUOH4aX/8YiHlB/UpKSjk5OSkpKbq6ulAYcHFxrVq16k96aHS3d2AfRdWo7K2R312tsLteelu+jfMq4WVnnM8je/wuqIJHHlTB/x5Q63dxcYGH+f79+wkJCU+ePDl58qSwsLC4uHhNTQ2y08gB37lly5bz53/0eB85ckRZWRlJjCI/VXB3dzcEniIiIj4+PkgWHijJrl69CmoGIR4+fNjd3T04OPjBgwc3b968du2al5fXnTt3/nyiSygAKioq4K+D0yUlJQ0NDeGvmJubh4WF/Umloauypm6nTY3C7hoVE/hfh4a5t4iMnrzi56I/7YaIKnjkQRX8r+Ln5wf1WXi8wX0gXzDR3+j6Cjx79kxdXf0HEw6AASHStLe3R9KjyHAaIsrLyx89egQRKES7R48evXjxIpRYcnJymzdvhuTz58+R/f4ap0+fpqOjo6enj4iIsLCwMDMzQzb8Ll3l1XX6h2sUjGuU99apmNSr7tvGxuPi6vLnL/hQBY88qIL/Vdrb29+8eRMbG5uRkQH13N9+A/NjioqKdu7c6ezsjKQHo6CgQFFR8ezZs0h6FBmOgglA1eH69etnzpw5ePCgkpKSnZ1dbm7uj/uWjBQQ8AYGBkIZlpKSMnXqVPjrHR0dUCpAXDzMg+9PR0erbyhOwYp72tXMgsUUlnHxPnr2y1NTDgRV8MiDKhjlTwDHQfz4g/sHavogaAjGoTxAskaR4SuYQGtra01NTWlp6e91m/1DkpKSMBgMBQWFgIDAypUreXh4Zs+e3W/ajWHSjW1rf/m2w+Xu58OntgitPOvsPPzlrn8AquCRB1Uwyp9w7do1HR2dH8zWBkbT19c3MDD4Gx1df8qvKnhsycrKEhMT27Bhg42NDQTFVVVV1tbWEJXDB2SPX6WtPfRBsJam5oeMDCTnz0AVPPKgCkb5E+D+MTU1vXTpEpIewLNnzyAENjY2RtKjy8RSMJRSEKtCAN5bXKWmpiorK/9JP5ZTZ8/obtkyUkE9quCRB1Uwyh9y+fJl0ERjYyOSJqKpqWnv3r3CwsLZ2dlI1ugysRQ8kFevXomLi8fExCDpX6S5uVlPT09XVxdJ/zETWMEd2OYP71KjoqJevUmuqOl3s3aVfy3L/fQpn8Dnz3mf88sqfl71qCwrjgwNu3TurIPD8aseN6JeJdQ2/fIczKiCUf6Erq6us2fPcnFx9T6WhAius7MTcszNzSUlJV1cXAibRp+JruCWlhY7OzuoQ4CLB11l9cOHDxAjE69aUl5e/vDhQycnJzc3N2tr65UrV8IHZNsfM1EVXFdeeMb2AO/smfQMDEzss+U0DGNSvzfNdFQXWZoYLRUSWroU/gf/WbJoifAuu96JVzpTE15e97h2P+RJXev3mZyyk2N1FDYwMLJwzeGdO3cu9ywOJtYZOnss88p+7W5DFYzyJ8DDb2VlBQresWPHp0+fqqurbWxsTp8+ffDgQQjf1NTU7t27h+w6Fkx0BQPt7e1gYX19fRMTEyjtrl+/HhkZSXCul5cXGxsbExOTpaUlYa6JxMRELS0taWlp+DmOHDkC5z6yC4JMTAV3tJy0MJxKTqGst+vqVTf7/yw46WkXrVPILEM6yeclRCzk4WDkmC2yYuUKERGR5cuXLV+x5/gF3LaO1vCge 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& quot alt=& quot & quot & & & & & & & & & & The best fit of the prediction is obtained in the central limb of the rating curves (Figure 2).& & & & & & & & & & img src=& quot data:image ng base64, 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 qqlfQ5Y+iEolEcpsgBV0ikUhuE6SgSyQSyW2CFHSJRD9nz5411NwZpssff/yhrr41Vq5cuWDBAjVQd4hsm+MPY5I6QQq6RKIfb2/vZcuWLV++vGvXriyzAD179lRX3xpr1qz57rvv6lx5RbZqQPL3Qwq6RKIfGxubaRrc3NzMzc2nTp3KspeXl7paIml6SEGXSGrDhAkTRowY8eGHHwYEBLi4uEycODEhIUFdp3n27bnnngsKCrK3t2/RogUjQUpqqlhFYgMDg0WLFh05ckR7SUe77bZt28i5ZcuWbNiqVat///vf2mcODh06RPoffvhh1KhRjo6OrP3xxx/FKqg624ULFw4YMMDT05MNu3XrRjIRD3qz1Z06rF27tl+/fqxiMHv44Yezs7PVFZobiJ944gkOkNJ26tTpp59+0m7ImWFE1D71s2/fPvaya9cuEQT22KVLF7L18PAYOnQoxVBXSG6B+hX0oqIiqpwWkKXh+vXrtM6K00xxMylrRQK2khcBJU0fpPPs2bNbt25duXIlyy+//LK23b7xxhvff/89mr5u3br333/f1NSUPiBWHT58OCIiAonv0KFDeHg4y+Ds7CzWnjhxok2bNqjhqlWrnn322Z9/ m/ut3h7bffRkNPnjw5bNiw+fPnX7lyRcRXne3Ro0fHjRv39ddf//777wwYjz76KAtilaBctlFRUSL+119/FfMSxPqtt97auHEjmq4tz3333Uf8448/TrLOnTvPnj27XLaV8ddff82dO3f48OEcJoMN20ZHR8tef+vU74NFkZGRixcvjomJEc+22tnZ9ezZc8yYMdbW1iKBgLqkOR48eBBZb9eu3ZAhQ3x8fPAX6uoKyAeLJA3J2LFjL2jQbZPIIqYS0bSwsCD42GOPbd68+dy5cyLNyJEjjYyM/vzzz8qa8Zw5c1BPcqiincM777zzySefxMXFkYzEdB9U9fnnnyeYmJjo5ubGsIGqqqlrnC19kDGGcaiKbFFnxIFJhq+vL/MGkSH6O3PmTIYHjDzHHhgY+Oqrr7700ktibffu3Y2NjUNDQwkyJr322mvp6ekYc1bh0Pv27btjxw4mCgTffPPN//73v+xLrK2IfLCoahrnwaLc3FzaIgI9ZcoUxvnx48czL6Mo6mrNi3WoEqScZMwEGbGpSHpOfHy8mqI5QIunBTNKMQ5NmjRp586d6ooag6HTmiktZEi2IPpAPfGvf/2L8quBMvSWp9YIfdm9e7cavl0ICAgwNzcXy+7u7roahC9BwlDJ/fv34z/U2BqAAr7wwgs4VlTVwcEB45yQkKD75iJWcTJZwM3Y2NjExsbWxJPRpxDotm3bOjk5kS3CWk4xK2bL8pkzZy5fvnz33XdrkpRCX+Zwjh07xjKTEv4ydIkNgbWnTp2qyZtJOnbsmJqaOm/evE2bNiUnJ6uxklumHgUdpWbwoXb79OnDPA769+/PgK87qtAWk5KSmN+h8gzg4OHhcVWDmqKZwKExO6YrpqWljR49mnmJuqJmuLq6cmbUQBn0DUwQZkoNNyB6yyMpBxZJXaIjGRrqWkWk/NFHH122bNnAgQPFpecaGkn87y+//PL4449v3LgRq/vEE08gl7oSia1TlxSFSQBdTA1UDopJm0TTGR6QcrIdNGhQOdnVmy19k7/PPvuso6MjwwC0aNGCPnvt2jX+MvawlhGidBsNLGdnZ2fW4D1LzG++/fbbS5cu4fM8PT0ZFchTXSe5BepR0GkTOAtkPSYmhpoLDw/HdxNUV2sgSEXSGVAQMDMza9++fWZmZkM6dGFGbhH8FN78H//4B5NTfMozzzyTkZGhrqsBDz30EIZODZTRsmVLPz8/XdVoMPSWR1JzLCws3nzzzZMnT2JWUOeFCxfq/oBZGQjo+vXrX3755fvvvz8kJKRNmzbl+kvt2L59O0b7p59+ool26NCBbMUVy2oRYv3uu+8yBmjBtmP2GWbs7OxYq+uvWWZgsNFcUMW36RZe96dUAce4a9cupuaLFi3C8mOGajLVkFRNPQo6ao40FxUV/frrr//3f/+3YMECGitVrlvNYpxHs5jlEUTZmenjHXQvKpEGd6P5VbUUsiVP2hOoKWoLk1ksML0oLCzs+PHjjDrqiluAQ5g/fz7z2TVr1oiYr776aoDODQblOvaIESPEsdzUJQ4MoL29vTBQgqNHj5LJ8uXL1XDlcHoZb3x8fNgjnUp3Og9Vl2fPnj0MV4xerOVY0Ah1hQZO4IwZM8iZI+3atSsqpq7QwNm+44472LBVq1a6M5iK93WUM7PTp0/v2bMnnZ+pHjn7+vpqc6YArVu3xjkOHTqU80yxkRuxCtauXcucj02ER67Flcpbh3P16quvuri4MOnUFSzUsKLGiRjdM79p06abkrkqstX+4BQdHc1II5arpl27dljy06dPc5IZBrQIoaeK+bthwwZtCf/66y8MmbGxMcsceE5OjvY2G/qXWCgHfV/UbzmHLhphFYj7eGqSsolTu0PQnCQ91KOgI7tULTN3xnN6KZ0W7V69erVug6MpUOuIoGgEYGpqSn/W7dKIOCPBU2V8//33TB5tNagpagXGnPLQ4FJTUxl4xLKuHNSaTp068VdcZ4RyNxg88sgjK1asEKsAaWNQefrpp9VwzUCIOb3MzdWwomBzkC0smBqunDfeeOPLL7984IEHUEOO+ueff1ZXaKiiPCkpKVOmTGHoZRNx80NkZKS6TlGuXLnCRJ6jfu2115YtWzZz5syNGzeq6zQ8//zz3bt3p/qYdjz66KPa+yjK3ddBqRhvxCotDF0oMqcOWf/000/Flevdu3fPmzevc+fOS5Ys6d+//yuvvCISCyjh1KlTvb29KS3jH7rDtENdVzNoFb9pYL7IsEf1sYwaqqurhKGL8jCiiNs5aMPDhw/X7Yc45fPnzzMInTp16uzZs9QmkYxVHTt2/Pzzz2mc7PSJJ5642WuPerMdOHAgYx71Ttej79xzzz0Vf0zTCx2TrWhatJaVK1fSDT/66CNONX6ftf7+/rQHquP9999ft27drFmzaOqUWWzLThldOPMcCFOEb7/9Vndk4uRQm/iPHTt2vPfeezt37hw2bJi6ToPo4FXAVIBpEH/VcHODk4PWGRkZmZmZqVE3A12A2lFPli6c5XqCPkC7xAvQMXCFdH46HoqMe1VTaKz35s2bv/vuOzoqQdofHYb2QUqRANiclvRkGbQMVIPMQU1x86SlpcXExBw6dAh7uFWHffv2qSluBjwp2qEGSkrEjcPiJoGKjBo1auLEiWqgjNdffx3XowZuhJzpQmpAh8mTJ2ORxDKnETVHhUWwCigb1um+++4TwYKCAiwY5RdBLXrLI24iZtKthm/kwQcfxAtzVtWwJnOxcPDgQTZE0EVQyAEyIYLlQAJcXV3VgAbxoxyioIbLYHQMDg6maYkgQwjJsJMs05CQGwYYsQrQI/o/YqeGawCje0UrxGAs1o4fPx6NZropgm+//Tbiog0iUrhODgS73atXL0YCEa+FKmOIQsHFPQKonog/d+7cyJEjOflUKKeUvkAZMjIyWMVpZBntEykBmWb41O4UKsv2zz//7NKlC+kxWOTJKe3Ro4fYsNpsEWvUmSLh8WlymDPGJ7GKBeajjJoM84wlP/zwg25hMBxUELlxRBgF3b0wf2UuyIasbdu2Le1NW48C0cGrgCOlgfFXDTcSK cRqSk5PxpmpsDaAncsg0VHFp+mZhX+XOmKAeb1tkr0xyDQwNbW1sGIhS09IOHTzImEzTp6GLNJQJVWU6FhAQQPfg8Ji2Y6ZQGTyOSEOd4eawjSIoOgljFMviby2gbHQSOg8FUKM0MOihAmqgxtB/6L10WhGkhuhOCLp4fANP9Oabbx4+fJjeRXOnIkNCQvbu3SsSC/BBOB3dSyhaMMJsKMRUF87SmDFjyJY+9scff+CVECCsrrq6ElA0ps+YViF/gF+jm2n9skBveWi19D0sJP4aOaCO1BUaxKwc3VTDOlDFpEcXxo4dK2Lo///5z3+QeJaZJbz77rscDsM/dU2NIBO0V+0NJEzJMeMVfzRDshEF3KgIoiZMXBB0Cok 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& quot alt=& quot & quot & & & & & & & & & & As a result, the misfit residuals show a pattern of under and over estimations for high/low& br& flows (Figure 3).& & & & & & & & & & img src=& quot data:image ng base64, 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 HHdzy7YofrRrIesRsSGVgRR4Tvk7pJyiVSq0HchbXu2KRWIbWq2Dy2mh9j7G//9m//9E//9Oc//3nDSZJeeumlW2+99ac//SlEv+F0StLf33/33Xd/9atfhbI3nHYzODh411133XLLLaZpNpyOE2otpRY6eKVNWGnTm+kh64RwSqiwDg6QGZBJfE+Y8zQq5yIOD2i6Ddvc4orKFYVTmRAkMireyQPHIY7TsOiPHFjlq1atqtfrExMTu3bt2mOwe54HaxeEm0cKigrE0Ng4MPBzjOO8EBwnCVN933i ouvfTSt771rbquN5yOF5xIjEocS8Nh2jItRply5lPLVvLjEt3d+2UyuAOMUbPmtXWwRAK5tOF+UKJRprjw6TvKlDOCazdNKiyG/aQozplo1osnJEXZe+9hjDKFkfuLX/xi+/btfX19c+fOffvb3/7qV78a7k888cSjjz4Kg/e8884bGhpKJBLnn38+VDKVSmEv3FEYrFmzBsly1qxZV1111bx585A7UCo89dRTOCesI8u8973vhdX/3HPPwabGnTzrrLOuvvpq3MmwzSebzY6NjSFOnOHatWsREBkNJ4wjXnLJJZqmjYyMIOy2bdsQJJ/PYxdiQOSIE+nh8ssvP+ecc/Bo7r// 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 i1MMaMR4VY2Nj0Gho/VVXXbVs2TKUOtgMi/YlS5Zg77p168rlMgq8lStXjoyMhKH20NTUNG/evOHhYRj+8B+q/OjoKIrnhQsXorCB+iMSFAwtLS2NMKcTYlwe7B6HRY0wJxykQlQYUR+cbGrV63WkMNgXzz//PEyVSqVyXAyxU5ygA4wTGOl7PwhouitGHuUd33R9JFOmygkraHa33ILtluAuppcWBQDuqOMzO6V3CFmPJhKYevC8VDkeU1LQEcZdH6Wz6HWKupsvRiFxLlNNE3Mpq/DbCHM8gGEOAxzP+4knnnj88cf7+/thYuM3XyicffbZ0OXNmzffe++9DzzwADR937oUwi5YsABVk2eeeQbe7r//ftj4vi+G2s+cOTOdTkPxW1tbIffw0whz2sG564Wm0vTlRMs6EhBMjJdeegkJCOlpjyOS5qOPPnrbbbf97Gc/+81vfrNjxw7UKMO9ZzZho0rQMetlcEM8ZpnuBERcpYYmxzU5GVdzlFBdjsEwr7uFqjXqNT5t6rh+3WO2LguVEVb+NCgRTzqQbE1J6WraZy4TUwVAHMV4AhSxlCi6nNCC1rAjLWXhH/La3t4+OaCiKFBkcH4AMs6XvvSlH/7wh5DguXPn9vb29vX2wsR+3eteh1Df/OY3v/rVry5evHjp0qWZTAYumqbBJ5TaMAy4f+xjH3vhhRf+8z//87e//S3c/9cHPwjbvC0A/q+55hoUHo0Dn5YwMVxg0nwb0xCowAmVAFQPoeDf/e53b7jhhquvvhoJDo5Ipj/+8Y+r1eq1117b2dn5ox/9qKOj49JLL50/f34Yal9giQAkd1Q59xQPJxJkEhhNKHvCKvDRAUWuORMVewDPQKF6wzWYkrfmjJWtwZiaht2h0lhMbYKIwH63vbLpFX2prihynLY6niVL0JekqiQNJa3ThK6kYUhSqjTimhri8TguPOyDcYLRAyilePTHcvP3AOGDYsryfmY/Rq1x69atjY29CbOOKIBF4RpM3gAdx51HSRyMJEDE+9d0iOmB3kkiEuQRpKtkMonNMAakcBhDWFFVFZeMdfxiF9I//GMvUiPOHzkCu/CLXchZcIcj7hVWECdcwjciiBze4BjGAK3HCrjrrrt+8IMffOpTn7rooovCo59erPxD+aVVRTc1Mu/KJy+a//ZcXLxjmJ6cUGt948aNkOzvfOc7sC/CZBqCTQgEjAXoODLYeeedV6vV+vr6GrvPXKACmhIXVXhYGMwVku2M1+yxqviaXSGclRcSD4+UqMEUMYm42pI1unKx7qTeDLMd8gGrELIuE83zrYozXLL7TXc8GGgX2exTipBC/CqoTokyNRNTs6JklRN4KIFOHpmdHoJQ0FnYy5NjgDpDuAFUGOoMzYVFDz9wQfmKFbjDM1Q7fBmLvYgE66FkYy/WsTeMP4wh9IbgKIhQSCAP9vT0QOuR+xBteNzTEhjq0SjTxt8TAtIfhPucc84JU1vDVZLy+TwSbjabDRNZd3c3LI49Q6LPYIRSB6IAFa6K8Uejlle0fTHTiOXit2JC3EW/l5hMNdwxSmSZ6rqc0uWkErTb6kpKk1PQ9LAZB0Iuus24RdstIXlHyj7V4KEICz3ovBguWIeLeLanDzt37vztb3+7ffv2Sy65BFq/305Bpw9B/7TgtdO05UQ3wgBY4p///OdvuOGG66+/PmyEeeyxx5CwoPivetWrsDk8PHzHHXe0tLS85z3vCUIIxsfHh4aGUJfEOuyO5uZmWPdIf/V6/bjUxI8UnDluHYqfY7mBLEh8xVrPeG1ryRpEXV6RNZkojl+vOwUx/khJZYwZmVi3oWb3tL+Lt6msbLE8YXpcbd2rcwXjPhK2QrVcfJ6ixORXdrA5XqBqj9t+Ut5/wPZEAsCjN03zeKVeGBz71bKDNsIcPQdphDkprFmz5v7778fNfO1rX3vBBRc0XE83Go0wyeGuS++/fPH7mhMLGjumH6eErD/44IMDAwOLFi264oorsAkF/9nPfgbhvvnmm4MQgqeffvqee+4Jp7lA/RFhb7zxRqyLkvlk2KShCuDQx3Z01BXZRGV7odZbsUcsr6oKq5x6vlV1Jmy3GtebU3pLJtYZ15r29Fx0fLNiD1ftkWxsVlJrhSEfuu8BlVAUDG3psxJa8757jwvh5Z+UAjUwj8XR/eP3TiVsxGhsTGKayPqZwR5Z77z0/iumt6zvx0I58RiGsDdh+YabjuPIshw2Be4hmUwiJ8wJmDVrVjabDfUUefukAEUDjY2jxfXcul0u1UcIUXOxuS3xBQmtPaY0wQZPaR0Q5bTW5niWaRctxwyPCDzPxoJ6JiUqfhuuk+AMEqXabs3z3caRjje4+ThQY+PEIi4woLF9PAjTWMQZAEQhaASb1pwS1jrqgM8++yxU+41vfCM2169f/8QTT3R1db35zW8OQggQqlgshrV+iH48HofQYwXuJyVb6rq+R1kaTkeOz9y6my/Ue7EOszromwj7F4+EO17FdMZ97sKcN+RUXGvRlca4cMvLO6wsKzShdEpc2bcVMehD7SV1lA0zFPnlDjbHkVjQEShsEzvBIM0AGNfV2ivGZB01MP7TqRTSUmN7EpG1fhoRWutOcmT2FX+4dMEfNSXmNXZMP06JYq2jowMSmc/nw1FIO3bsgGgi6Td2ByQSCQh9aK0jS2QymbBACoX1xIOjQ9dQw2hsHyWe69lQ8kDPRRd2IslEUqikqiShyzkqaZ7vOV7dgf57tuvVbaeCYCqNG2pGlhSEwmnsRRgzF/OyB6tTwKQDnWhw3PDRH7fDn4wpECKmCJGFgpbMxva05JS4eCj4jBkzLMt6/vnnV61a1dPT09bWNm3Mmf13maBUNZR0TM0pogc0LHJomRjHiGQb9KXLKVTl+wuLwkF08eIOReI+rfpjREQcD4g67afQOAkXj+pzLpdDLX7PSypK6WWXXdbe3v7II4/ccccdyWRyyZIlnZ2d4d4zGjFVupDsfWbTDV4MarqSNdScJidlqkCiZVmLac1pozuuNQX2vag0hKZrSLAFa9ZDAaDKCTI13WAiIk5ZYMpoFNoyrWX9JLSt44gwzJWAPcqOmrUXgHVZlrELWr9n776EnuHttB5lCl12/fpwZS2uU1P2GrENefYsrzRS3aQpibTeodIYDHaP20m9PZtoV3U6NLGV8pgYqbT7RuHeOl4Fap4V45VmwGY5yD08FqJRpsdC1LY+FYRt6156/NzrNy2ZeX3a6GjsmH6cHGsdmqiqL4sRQC7VNA1iAZBpkcemSI8ODkxdnzm2V627RcsrQ3OndM6gYOBoLK3PoFR1fbPhGiDmD3DHJ8xdCTWT1TtjahOsb000yzThDC23RKmSi830mFl3xy23AJe6m685o7JsQNPjWuvUaXpExClJaKGKcb9R23pEA4h4xR6cMLcV6z1le6Bk9eXrO0r1PturhM0dDX/HD8guJXJca4mrTVi13ZLrmZ5vOV7NdMbL1pBMVQi0rmYUqoeDGJFkKVFcZtXtoq5k0npnQm+Pqc0xNRdXW1L6DBQS2FTEW6PGUSIipgnhxHnIJtP8rVIk6wIY6R6za/ZIyRqo2MMwe2H8wgQW823ZQ1V72PGqfGqUHcAMT2jtCbUZNZZg6LmYpQvng7IEGm2oOXnSLGAgGGFELLeMRJw2upti87Kx2VljZjY2pzmxMKV3oAYgagLTO2VHTFfE+6rIWp/uQKwZ92r2aMHqgXbHtWZDzQbfnMvEhdmr12A42wPQ2d21vOOMmG5XSWZisztSy3LxORmjM6m3ZYzu5sS8hNaCE9jX9EDCVeQYqhcw+CHiCK6raV1JBEZ69EwjpjXIHdPcpokkAHDHrw1V1qo0BsN5rwRBIZtUD0z4QqDsUwWRqCIm4M0ltFb86nJcFZO97D91QrupLHMuvtjQcIqIiBAZBtb66T1X2bETyboEU93zraA/iUwhmK9UUiKgUHyP1RmfwpmtggMBKhbxXOhBFRtVDCHu4nwjTh54Ri6XTMbKvl/0ffzWGfOjsvZkwIOsgJ+gGXNa54tI1oVAQtkV8ZqFimGe+xDY0bofdAZvOE0xKGBwJkEpsn+BEGNLuUun/auhkwgeDOS7xtiI5457Xqjp+B3zvEIg7uyoxL1erz/yyCNf//rX16xZg/XQsVQqPfXUU/fff/90+AjBsRDc8eCVadS2fsbjed6eHLI/kBjEd20OopBBKhHeGttTDNKlKjppyft9TysMdYlTIqs0Ps2T70kEqm0yPuFD0L0qYxYT77gtxiuMwQUSH36r90hTDBLqY4899rWvfe3WW28dHR0NO+ZD1p955pkHHnigv78/9HYQxLui3fMr7Lt5hoPqrviGgR7Ud6cv0+LikVUmJiZc191/4hbtHjKqzgfOgmI8J0EdL6zmTT0K1USHRa056FjpIWPizHeDdYaEmzLadC0DcW+EiTiB4DFAwQu+V/F9FK1xSjRUncQvSVCKhFRlwnjHk4PXRpjDJhxpdffdd2/evLlWqzVcd2PbNhJz+DEjqDwUPxyO5zhOPp9HSTA2NgZ30zQnbyLxwwXrYcDBwUHEXK1Wh4eHLQtFEsPmyMgINsOjhGGxC/EPDAwgCCoKCI5j4dpDP6cawWnB3CGKrE9zc+ckjDI9LsAAB4c5yrRSqYyPjyO3zJgxQwvmjJwM457p5gdLLyS0tv3OTg5tdfxqWp+R0Np1pfGNx+MyyvTAiILE8WsVe9h0xkkwwoJK1Jc8l1manGhKdbZmZzoWMvMByqopZpqPMoW2jXruqOeng5dzQdPdy0U+nogbmADtipZAwbtPy95BRplCT7/73e/+5Cc/ueyyy9Lp9Hvf+95LLrkEWnz77bdDi9/1rnchAd9111333nsvjJW2trabbrrp5ptvbmlp2bRp0y9/+Uv4TCQSUOEPf/jDzz///K5du1KpFDa//OUv40LuueeeJ598EgHnzp0LD8hBv/71rz/60Y9ecMEFDz74IPZC+n/+85/jhvzLv/wLCoMPfOADTz/99G9+8xtIPBI8zuRzn/scjrXXpNmnCCseLq59saSm6q97l9aSmRF0852myF/4whcaq6cVyM8AeRvGyyF1zaxU8mOjiq6HNguKgXCG9z1AN33uYsH6XvavqMJy13JLmdhMTUkGfacESNk4Ls5halQVQoBKhKzKBkoa8U07Kov3+1QzlFRCa0nGW+NG2g1mU2+EOFoIY8RziesQ16W+6FojKrJix95iNJnw8qELje0TiJh0Iph24nAe/eGAqKBZuLmN7UmEJjBWVnv+Xa73Y9f9neeJxfXv89nvffYElx5mfN/lIV/8Ps7Yg54fBnnY817y/fmUJgkJvzgaHmIvoLkvvvgirOa//uu/vu++++Bt1qxZuM/r169HGdPV1YWVxx9//M///M/f8IY3IPmhbMOzWLRoEWxtaD3MF4jvW9/6VhQJUOqhoaHLL78cm4gHoo9rgVLfeOONcEfKQYQomHHhCP5UAGz2t7zlLYjzoYcewu+CBQu++c1vouSAI8ohmO24VzgfFOqN0z2V6NtZHxm2NY0tXB6LGyhPp/Yr7acyZ35VBWollwta307S31ccGS6XSqh7Isk2dotcLd6Ipo0uFO+wkT0xYQC0Uoi2zxzXF8M+U/qM4JOhk40UjmIAey1XfHcUv45v+uy4dZVB/kG61OQk6hBJvSOpdST0dvwm9RkxTUwkcOypVgi649BqRclPKKWiXC7JpZJSyNP8OHFs7G34i5CkAc6+7/lPu164PON56zy/l/FNvr/fZRtjWF7whM8wyCOud6vnlw+vHEomkxdddNGSJUs2btwIo3tP6YUqxfz581/3ute96c1vfvWrXw2phRDDQg/3opxDPeDqq6++6qqrUHJgM5vNhpuQ9XPPPRclwRtvvPH6669vbm6emJhAaQGzfc2aNYgE5QFKFFji27dv37JlC2Lr7OxE/DfccMOb3vQmHPHss8+GI0x+RBsc7ZSDEdEOI3NZjN6I2tbPSJARxMIkmh/zRzdUaisrw8/6Q7tYsVCvVEqlEpIyjJ3QMyGyoWSSWpumiFkPg+4lYoG0UqrqSqopPh+iHzbYIY9x7tterWaPVeyhmjNctocrznDVHjLdCdevBx6OjxWJI+K4hpqJa01ijgGtCSeDQuhgr3cPh/DuuA41a7RSprWaZNVh/YrfWlWGxJdKklD2kzCH2qmJg+cpbhp+dy8hk10OvRxuVyokO1Qg3va2t0Fqn3322T1N3jCTofXnn3/+imeffeyxxyDoMLdRpdizd/bs2cuWLQtrHtjs7u4ON2G8o5yYMWPGM08//cgjj0DTERCiP2/evA0bNoyNjeFA0HqY5+vWrYPQI+xZZ52F4K9//et37dr1xBNPoJaAIND0PbnmVIMR1DS5wqnM6bFlj9OeI5Z1PFQ8Wli7KN7Ddyz4xToqdKfOIxeiijzoS9yRvB0bRsYeeaH1ha2xZ3lhFR/a4o2PuGZtdGjIqtV82/Y9D4lApmpCb83FZmeMLkNJq9TQ5XhCbWmOzWtLLtVF84tonAnkkLm+VbYGC2ZPqd5Xc/KWV6o7E0WrN2/uqNkjDEcVmfikEZYr0JBgCRVlb4jvQ8GxiCeKOrVuSJom6To3YljkchFyL0EvRPAIKUGkRQTF/eQl2CFK//0tIWJ9t38xGuEV1b1Dcumlly5atAhZDELsB+8qkcWguT/4wQ/+6Z/+6Xvf+96mTZvcSV8Jh4UOGhvBJqz+cB2KjGLg29/+9he/+MUf/ehHiBOmOmR91qxZKBtQJ9B1ffHixTDPXwpAoYL1/v7+n/3sZwjyjW984/HHH0ckYWynJoxC1iWFycQ7dd/rnhiOWNYh3yjPb7vttn/913/99Kc//dd//def+cxn8ODvvPNOFPvVarXh76RCmMRt4pVIfVhZK/euTfWXVa83VVzZ+tyE84zcu5ps2qSgLNq6ZXDzptLQEDQuDKfSeEJry8Rm5eLzsvG5KaPTULN7ZUafORVrwLQLlCiGmlOD4aCqnIgpTSgbys4wTHh/KsejHgaQc89jNk5DjETdB5g1km3JZk3Y48o+jTmUsnhCLhWg7MJnhCRdqyjfjxsPp+Lh8mAqfmtM/7om/1BX97t8T1e+qyt3xfUHk7FGqGT8sXhs9hGOf3zTm97U2tp611135fN5lL/Iek899RQk/tZbb/3xT37yoQ99aP78+Q2vogTZU54Igtpew+XJJ598/vnnYYxDpiHrb33rW9vb21VVhRWPFVQIoIOw66HyyMU4Cqx1TdMefvhhKP4XvvCFn/zkJ//wD/9wwQUXhLGdmvhUfMRX9SmzTWl6VzQPN5GhbEcZ/t3vfvezn/3sl770pRUrVsBMaGpq6urqyuVyKMYfffRRuGMvTIkdO3acxAY45kluhdrjlDmUKowmszSeht3kU6miuVuyOzalVxTcF+W+ndbwkDc+bg0O5Ht7qG1TJt4WioYXIu9ZRBP3pNzCmGt75ao7JvwFg9kmgYAqleRivc/2qxDWRpgTCA5qeeVivWew/OJIdd1wZd1AedVIZb3lFkVfyT1mO8waWOKEQsGFITmZcBO/uHbGJf8kXMUpiEZIgpDM7iVHSDulHUhfnMM8ThEyeYlLEvy0y7RdVrKTQiVFA98r7/ZBQZJC/jr33HNbWloeeOABVJFhNjmOAy3u6OhQZHnz5s3bt28PDfk9QcKVvSiVSviFeY6o4BkmPyrZQT0tfvbZZ69atQrrsNbb2tr6+vrg2TAMGPuw4eABQfAL/wgFHQgjPAWBtc4orHXI+vH5yO3py2H1hMET/c1vfoOaIFZSqdTChQvPOeec5cuXo3hfsmQJNufMmYN0Fn5fFH5Q2tdqNSQ+eG5EcbxBKgR0354wkC6H+CZhLjQZ0iRRWcHTrtfztlTzCaurvqmYDil6XlGzqOwR4jHm+VA6JR4nMsIcLOPB/oVEWl4ppiUgiziHxo4AKDuO7/q1oBknhlzf2HFcQdZVFAWmFnL4np4wuAnQYGh63Rmve0WP1YP2IlRHRQ8fnzuUUJmIkbSiAOKcmiYJ5h/Yz/UGjqjBcFXjur4fc3769YTZC9wyMcmDRFzGRItbUGBiYcEoA0QYpyRF5XjwKZh90xMU8+A9Yfr7+9/znvcgKO6zruvITQ899NDs2bOR72BEQ3kHBgaQy4aGhgqFAuT+wgsvhOKvXr0awn3ZZZch4NjYGOLBdV1zzTXYRAwIhby5a9cu2OOoqY6OjuI2IvPiGh977LFLLrkk9HnPPffAUMM6sjZkfevWrTjEpk2bEBAH6unpOe+88+Bh347CJ50t/ebEkDvDU1vnjRrZZll5RW+3acVhyfrw8PB9992HPHz++eejVviGN7wBFgQSxNy5c5HU8Lto0SK4XHzxxdB3WPGw1iE3qNCh/tiI4nhzQFlnkleTmQu7SBijSKYxpUXzm70ajPc8IzaU3ZVZVbXLWkF3a9Tj1Jdlj1ZME7IOlUJcSO6N2PbBY7btlXzuaYqO3L2XrIdw7mtKUpXj++0Ff+zgonCGe8u6xIJZ2kctT3zmGyegUF2hRvBJAeKwmuc7waaY4lHIuoWiDye/P1kP8X2iKkw3JGU/DcLTXNaBGPYirG/q4mY2Ws7F3cS/OKFJWUaMB/oWzEFkHQ8UMgopv/rqq0MX2EbwPDExgdx35ZVXwqCGvO7cuRPnBkMbm3gKodyjSJg5c+ZZZ52FUKhAQ5Q7Ozthe2ETQgz/iHlwcBA59PLLLw/fo4YWOtyh4zDUcD8ROfIyygnkaxy6WCwi+yMq2G0QdBQM+G1pbY29sovwqcCWwdrEkN9pq5m5vfFch6JO337ryOCHzhhhoY00hNSGTWgZkogcgFSIBGSaJvI5EmtYhiOdwT+SC6pvQQTHHxwCIHsjFe6RNsBsyckLWZf1l6/LrJXGh3uKYzv7Y89MaBurtOxD/okkM2lWNdtdm5PzFtjx1nhbJ5J/oqkZIOZG4Fdie5WyPYjfmJGElOIc9rqBjPuuV0vpoj/inrFLxxdoBerIqBeHVXKcAGDcLZg7bb8CeaF0byHGWaGSkTXEl/BEbx9IdjGPIlGIzgFae0nd5ImEl2kSBvs+4OhIBiflHRoSIcBF4tHvt1g9UnA/oXpIzI3tSYQpv7GxP3DnUbbgGbiBwQ4V18S3NAkKzwOUloLoo3dTwW9Xjm5daV9ajs244akZZ11uJNoaO6Yfh9W2nkwmQ3Mg3ISarF27FnU6WAf4vf322z/96U9/5zvfQU0tzGbQRFgBTU1Nof8TCWeBwfTKLJVQ9M5YrllOdnvXzLHfMMNdrEoiD/tE6kkV1zSv25p4Klns9YYHWLWKIgr10zDg/hAN0lxCQXKg4hAy7yNTQ14bDlOP6JzDrGK9FyoTTF+3NzgZXUm73PFFLx2xLWxwPKwDy2LwGll8FbuxHXEAdEJSlOZk2iTTHKVJaHpjT8QJxaMSo0TxqefVT9isfKcmhyU90Kiw2ov17du3//jHP/785z//0ksvoUL3yCOP3Hrrraip3XvvvT//+c9ffPFFoWionMryfmu1U02o5/sqrkZoh5peEOuYoyyeza6c7VyWYWkINEqBmuL3J8eeb32qwF5kQxvIQL9rVoaG+oeGB2q1amCEvQzFlYnvjorO6Q2nVwJ3N5jmF1XwhtPUA2OdMbfR6nQAMxFnXnfGnaCJhhPKDUMIt+eKYmgv4OLYLJ7wY3EeyfpBCe63QDS1B0uDxv6IEwfycmjUOfubIG9accTKC5P80UcfNQyjra0NVu3GjRuhKO94xzvOPvvsbdu2rV69uuHvZIELwgPd55lSQhNEySqxDqWtmy6cKV3c7V/WxmbGmM4Ir6jeQLK0I7G+lz9dKqz0e7aO9W0bm9g5mu8plkeFiAupE5EGIz9TGk0w7geDUV8BE5P3ejE1p02aZuCEgNODpQI12b+eBEpDUd74wRs+4aRqLJGUUPbYluS6okNYaLxD6F1HkhU/mYL0713xiYg4VfEp92SRTT3kwkjWj4j+/v6RkZF3vetdy5cvHx4eLpfLF1988XsDsHfPOOaThsIJFvLK8XyoNyiK5HtwNojepKS79Y7FyuWz2JVtbGGap1VJ9igfTNZ2JHf1SCsmJp5yhzZ7EwOl8YHRib5iZWiPeU6pAslO6e0yVaDgPncYE1LuM9djEE1XplrWmKnJcdERZzcoADxmO75pe1UsTvAdaoRq7D4OQH2pMMP3LdACxC7OxPBU0ewuWqk4pSyTYckkFDx42bdnoXBhyRSLxdiBXx1HRJxqIAt7VLQqetP+o2FHLOue58Hwmz17NtZhnmMT+g6X5uZmuJz0cWiwPpU4pzoXQ0xZw8bmUC5NEyMuhYVNDEWZmYotbc51KQvm8tfMZleleZPMZXitaN6O9Pj67GpmrtdGRrRiqV4Y6x/cUTYHHdf0mQ/Zk4ma1NsTWjNqAIE6u4Hl7np+HfchpbUn9NY97atCTbnneFVTfBB1sGT1leu9FXug5oxD37FLSPEBtPjwEU1DRBOxBPodOu4Fipyk1qorGZxk6MJhsGebvPYZfjrLjDjTDWbEWDrjtbSyVFpSpvu3CCJOL1zKHBl5Hholi/Gm05gjzrepVEpV1SeffHLnzp3r16+3bXvJkiW+769bt85xnD2DlU8iROdyTIJVyhxY56ET8Y2Y29blWYpK65phiU4wkrQgk54db+riS+fYr5nhLtYl0YHEJ7yo2ys7NuwynnXG1xu9417e7unb1juws1oNJwgjUO2U0dkcX9AUn5vQWhNqLqm1NycWtiYWY3PyXeXcqzuF0erGvLnDcos+t13uWF6pUN85YW43nTxM+YbXY0LMMZ2Lic4VKGBCp8lwidl+WZb1vfpcMkKYqvBEEmrOMlnxm0hyFIG7pT8i4nTBlv264kPQPRc23LS2SI54Yl5o9+jo6G9+85snnnhiaGjo4osvvuSSSx544IHvf//72Wz2uuuuW7p0acPrVMIC6H4n5hVd/Ljotx60KDCL+hbqZlTOiK7eml+QvYr4TqjPFI 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 v/s2/mf/4QQNkO53O7bff/uEPf/iXf/mXweVrzCRUBwh25n7gygy4SmYAKDCU7xv3yBSGG3fs/SljnqnqHuFDKB5KV53ML3yyAqT4bZJDvyOaO449ZdkF6xe5i2UriCwV6rBWtzejPxHOrnUn8/wLk/EZSu/munCYkr+u1P99afUO0arOhO3vMb7RZYA4DuGhUm6cTB9fHZ/sjSHDY6cO7bae7g59PF3Sri9DbMd36vYh1mopV8Znj+5+6WzxzamHIKYTRF7sr5YeEO86svbT8f0PgYVml5dDb8YhFNxNFA3FZ9zXgt7/bx7ukldH5KNmPPW64+NlHy16eoj6Q/bDj0xnhuLkQv30xT/PzSDTC0VgX879N2o29LZFClzSo9PAJo65ii2VAisP1OFjw/IXRsWChbZu+tZJrTuQmlvnl5fcyqpPssZvDBo0Tqap7PZZmm5VxaNNH0sf5+WsXlz9rlJTIdG0koJSGPoCxO8LsU/Rgzi0k1pCHFJqn77Wt65e0tBgxbUDrPier7pZhjsCZwf+Zirq5tcrMNynuHbIqfnv18/6/T4qrfmPV2Wnzm184StPRKfT7vTQ/+vw6JkH3J/fe/vdKDAvT5I3pb0atX7x4sVnn3324Ycffk9j99xzD3L6/Pnzs+71OI4feeSRPM9XVlaWlq5p0OjOzs53vvOd48ePP//881/84hePHTsGRdDtdkHt+R4/ZLijYDdareMuQlUPFnSFhOiD+oYGTGhZV8ynoYpZQW/IBDf0bsv5mvEuPTPMDBeO844QK80lIBwUrAWt3p6mD3c7XwGhZlWR9085+6gzScTWe7q7xDczZ2srDG8m1ROx1f0y7lciq5mykpwHJJWQRY/5BZ+2J72k7rQt2gItNVjNysP96gjzk4rvWuFpilRgPFwo89Od86PCJVK0hUsEWgFWB6sAP0+PTGmAzKyiQTOC+izw1/DgOHAfPKoy6pABN6+8NV5WrTNf++nW9Bnnq0hlaKN8t/bDgFSUaNCcZnHBBGqsIpjg6qjpYbqoOqtW3VZVS458C1NUkKGzeEhNXl9wq5NnMPL55eNEpJlIkRXSmxq1iml84COFyzjekEgz8t0Iw9F58DHnfUlSHWtwlcgF3QyCpMBfre2pdVz7G1OtD8fTY+fObrnjq5NbJjJJO/yO1fiWOP5Jw 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 qbkeUXddF/PkJZhF9WvLtwtguVqleJiL0w6i8zYChrV6cX+8Vp1Nsjpgo1XKJZhy5+abx1aCKdrZici0rOKY/bmB8xr31ou/N+r1l9eON4Pjl5bx4t1LAZ6guyHDx+Gax4bG5udncUFhnkHzVevXg1zjUuS5s1fr+DBP/OZz2Ddd73rXX19ff/wD/8Ay3/xxRe/xpdNGdbPKawrGbei2bn2HpDdoi7TvSrhZrAN4nSI24yDSPe2ZEoRLLReyBmkaBAbGVcCDzpoW5U5w52UDPTF3bMQLrZodZ/R+7ettuGn7EAkxxxjEmEX7oIaHeeKxfxb54obmrkCnLgkYCrIHlM54YR39Sz8zcCCUW3qFSUxQnOknvuVwzolcGOmgQ6B1/oO19tLcP2SjszvztJI1+Y9Me3JBMge8fmGu2u2cOd87hEu+Ojs+wqtN7K4gl0gug498i+hoJEk2NeIS5I0OVCXbN5WisK86+oyTErPEHlKFeO6UqMu3FIOozkdGxPcjlmJGy53+lSu37TdLtYJU8zRbbXrbIx+Q6cbZUjTndOnDOssYnYnl6tVnUaZRxxmvF2cb1RmGpUFYYaCx/jpF9qJhIp80axFs41oti5rkfCpUDlaKFbX9ZY3hebAXb51e1390aoVt/T1rXFOTc7ruLAOgOIBxpMM8uKKgsK1Wq3T6SAnjmtcKBReYdKxTSyDucd5kbD8Qw899MILLyAWrAVaXXrppRdccMFrNBmWYf0cwXp3GrevBGcW68FkLDpBVJea2rCaps0rMe4H0Yn0dzVysb3PQaDuFA/7LZMFijErzyrvgDBKlMTRrGP1S3PF7jD6SrO9E1FIofmZSv9PAXwEw6BlYBpt55a58qam2x9YuZjlkx/oOOUELxTad/XU7+hfMKyk39GYG03n/bPl62bKq1uOm3ySCh2FIbaqKd+dSoQU5JhpcBMLJ1mSFO5GTFsd+2DNe6LmPubzhWJ7TblxldvZrrtG0t//iwTroS6f0YlLENG6rsPOF63YdXUJOXIPriEcLKyrsVPHYCYhLqNukvmJqO771eQesSpRrofZrukHYafJsGmcY71RX+8hKyiel8xV+CU7qgenXMsX68owQ8tuFt1mwfI9KnngNtvFxU5+0c81A7cd6Y839GmXSoQq7MS1plhsiXogmqA5D1ROWMXccL661iutcJyqaVUfbMtP19u4dndsXHdZLpc7uYpAR3W8FRy/973vLSwsYBzPM64rgNvb21upVLAnxxaHgeaHDh16/PHHd+/ejSWPsylHPBu5pKkAMBoP+9q1azdt2tTT05M+M68qMAVCMpNVcOyGnlmlh4+UtTutMae7/HJYyWIeIcwEh5iHX84sm5RRFXIVWIRE8aLNLM5sSrUvlUYsZMSwALVCWFVDBcrgvJjnxTJCYd71D3Y6KYrRoEp8Okb0rZGOYAXdGvtTZhybosVFyKQrYIB1G2GFmA8G9nBgKUV266+cksYGnPAZJ5RJm8CeYN3GBtJNJkq2e0Tp2EvTiV4+qXsjjnu9cCWXxZgvCj4f8zlw3BIVXQMRqYJuvlHAQ+kEg4QhW/StqbY1jnFCYkTN9XfmhjRCLKB3hCWeXXEFGLCWlKFuyZuFphlYTHJDhvUomFdwfgpbRa7Ap8nHqIYKqYgIQ4aF6ZfAP/4BOnHBeOG2Px03HszKCbyZO1an65Up7qXAclvFXL2S0w7dgStvVmZr/RP4waSHXlsw/SVBINstsViL5xbjyfloshnOi07d6sSlONdnrRgsbB7quaSv75JSZb1lVXeH6muN9kIc/3pP9T3Vapm/vuour6HjwvqBAwf++q//es+ePY1GA5cTaTVsGpiCEQwxDtsI6IP+O3fuvOeee37wgx+Mj4+DzsPDx9uIges4o6Oj27dvv/jii9etW4ccwGsfYUL1DOvnENYholuONi2e98yqa1U9qzdv9WIyjJvAN6O6WNmPFqgufE/Mrv7HBeBEVJ7ZPaYJfsU0T1mZJgu7lKzlfJ1pVhi4zxYQpG8KTT0NemST0nOvw5XhRAdd/wk38LnoiXjIdHccYCpXtDcwL67lqxJkV5ibwD1+sdj+FyccFrSsa8hQhk0R3bxuet91o9IUTkZeiihZIMkqdMOgZIQp2w1XVZtvUJQ03Bfa7nNcFjCfGvqLXKF7v8SUUCQKzNm2fSBii3rvDA6PToX+bBVHQWlMWaz0TiKTQwVtxaoh4iAODWK1mBOaSBsF78yJcAG7JqltMEcXdzEbKZeh30c3de0kOP7TVCCzjLCuLzBhEbc7Xq5RKc4NuM2y4HGjZ2pm5b6Z0f3tYi2yAt0LgQKG2q24vhBNzIZjs8HhRmdKdVqlEJaif61Yu97aPjT0xtLqy52+1cT2AqkOh+Hfzi/u8/2fLhR+f8XIkGXqlr9OkY63JgzA/fd///df//rXZ2ZmgN1t27aB2nDruMYA6/z8fNrH6eHDh/v7+z/0oQ9df/31r223T1IACpQVwpw7hTCvKiR8oWjNtnbCkoPvUsWtcM4P53HfMV3yoCWlbqLW5iXXrLTDuZyz2rIGCXVw69SknIhElPS+MSviF4PwYT+8F5c7aU3MCAKjW0FcvgQwQYzAWt/IDUTmNXOlzY1cKeKanYpyQQ7mOt8cmv/KqkndPkG6RmheMV3+9X1DQ75tJTVkugXuifR4UoZ/NCxdKT3liPNotEg/kqeyOx2wmfniPft7P1turq7WfpoHGyWJdf16WkMmoW0dbjovMhAyKnPlWtIypWuJAlW2yVvSDCRljHjc4USYOvWihi5Ed2esnlYhX/TEUHPKMXjIWYs6TOYLSGbTeNOTAXdpj8ZWSZKTKtJ4dS2jQhiFtJWXZgYLC32W7wZue2Z09/zghJ9rHr1LhBKw53Ph+Gw0Xo/mcfoLJD8YVUfrxf5Fz7XKdHiVXL1WDY3oeyX5gtoXco/v/9HUtPL9d/b1/uHoyOVJf0SnUMeFdSyDZ3gu0d69e5999lk4dxAc5h1sxZUolUorVqxYs2bNli1bNmzYUK1WEYKEN8P66dASwrpUIogb8+29CdY93EdChmB3IOpAvAlLqZ8dHADR3bHzIgx+wR61WB6QxFHNC7E3jHCETtIxaEvKfWG4LxazSWHy9+pNIwyRLHQpr+81+FUKrPe33Gkq3tBxdjS8NW03H5mVkBeSVoNrPN6bb/3+yimj1NIFMrh6MTdazq+P914xXxrtOIAvnllsLD2tGNEXOL2Rj57pdESXr3Sl73T96jX5T4guXGJN35qsO082nKdZNJBvXG0KpGRC0Sg0Z0I+Q6Wpi4uAZsXsuNcRHpMlDvPOG9RquBwJmxTMEUiPDBUixuKkdBuu7RXoAKsPmy6SRoHERFmWyuXS3cBpwMmIfeIMS7uKtdPTciq1LLCuDDOwc4vVwnw/j7mfa9T6Jhb6piKnE/NIURmrCDSfjybq8VxbNHFxcjTfL3r6alaxwVzp8EKVD64yBkZUoahsmySt2iEbd8D376g3v12rAakfG+i 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& quot alt=& quot & quot & & &
Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting
Publisher: Springer Science and Business Media LLC
Date: 11-05-2018
Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport
Publisher: Cold Spring Harbor Laboratory
Date: 29-03-2023
DOI: 10.1101/2023.03.28.534644
Abstract: While single-cell technologies have allowed scientists to characterize cell states that emerge during cancer progression through temporal s ling, connecting these s les over time and inferring gene-gene relationships that promote cancer plasticity remains a challenge. To address these challenges, we developed TrajectoryNet, a neural ordinary differential equation network that learns continuous dynamics via interpolation of population flows between s led timepoints. By running causality analysis on the output of TrajectoryNet, we compute rich and complex gene-gene networks that drive pathogenic trajectories forward. Applying this pipeline to scRNAseq data generated from in vitro models of breast cancer, we identify and validate a refined CD44 hi EPCAM + CAV1 + marker profile that improves the identification and isolation of cancer stem cells (CSCs) from bulk cell populations. Studying the cell plasticity trajectories emerging from this population, we identify comprehensive temporal regulatory networks that drive cell fate decisions between an epithelial-to-mesenchymal (EMT) trajectory, and a mesenchymal-to-epithelial (MET) trajectory. Through these studies, we identify and validate estrogen related receptor alpha as a critical mediator of CSC plasticity. We further apply TrajectoryNet to an in vivo xenograft model and demonstrate it’s ability to elucidate trajectories governing primary tumor metastasis to the lung, identifying a dominant EMT trajectory that includes elements of our newly-defined temporal EMT regulatory network. Demonstrated here in cancer, the TrajectoryNet pipeline is a transformative approach to uncovering temporal molecular programs operating in dynamic cell systems from static single-cell data.
Mobile Internet based construction supply chain management: A critical review
Publisher: Elsevier BV
Date: 12-2016
Impact of Migrant Workers on Total Factor Productivity in Chinese Construction Industry
Publisher: MDPI AG
Date: 12-02-2019
DOI: 10.3390/SU11030926
Abstract: Total factor productivity (TFP) is of critical importance to the sustainable development of construction industry. This paper presents an analysis on the impact of migrant workers on TFP in Chinese construction sector. Interestingly, Solow Residual Approach is applied to conduct the analysis through comparing two scenarios, namely the scenario without considering migrant workers (Scenario A) and the scenario with including migrant workers (Scenario B). The data are collected from the China Statistical Yearbook on Construction and Chinese Annual Report on Migrant Workers for the period of 2008–2015. The results indicate that migrant workers have a significant impact on TFP, during the surveyed period they improved TFP by 10.42% in total and promoted the annual average TFP growth by 0.96%. Hence, it can be seen that the impact of migrant workers on TFP is very significant, whilst the main reason for such impact is believed to be the improvement of migrant workers’ quality obtained mainly throughout learning by doing.
Does travel closer to TOD have lower CO2 emissions? Evidence from ride-hailing in Chengdu, China
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.JENVMAN.2022.114636
Abstract: Despite contributions to reducing private car dependency and carbon emissions, impacts of transit-oriented development (TOD) on ride-hailing usage are largely overlooked in existing studies. Using massive ride-hailing trips data in Chengdu, the influence of subway proximity on vehicle kilometers traveled (VKT) and corresponding CO
Ethyl 2-[(Z)-2-benzylhydrazin-1-ylidene]-2-bromoacetate
Publisher: International Union of Crystallography (IUCr)
Date: 18-08-2010
Investigation of the social and economic impacts of cross-regional mobility of construction and demolition waste in Australia
Publisher: Elsevier BV
Date: 03-2023
Sustainability Transition of the Chinese Construction Industry: Practices and Behaviors of the Leading Construction Firms
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2016
Utilizing the linkage between domestic demand and the ability to export to achieve sustainable growth of construction industry in developing countries
Publisher: Elsevier BV
Date: 04-2013
(1R,3S,4R,4aS,7R,7aS,10R,12aR)-3-Azido-4,7,10-trimethyl-1,10-epidioxyperhydropyrano[4,3-j][1,2]benzodioxepine
Publisher: International Union of Crystallography (IUCr)
Date: 26-06-2010
Enantioselective Petasis reaction among salicylaldehydes, amines, and organoboronic acids catalyzed by BINOL
Publisher: Elsevier BV
Date: 2013
Syntheses, crystal structures and anticancer activities of three novel transition metal complexes with Schiff base derived from 2-acetylpyridine and l-tryptophan
Publisher: Elsevier BV
Date: 08-2012
How Australian construction contractors responded to the economic downturn
Publisher: Vilnius Gediminas Technical University
Date: 09-10-2015
DOI: 10.3846/1648715X.2015.1052588
Abstract: The Global Financial Crisis (GFC) in 2008 had a significant impact on the world economy and the construction industry was no exception. This study investigates the major impacts of the 2008 GFC on the Australian construction industry and, in particular how the Australian construction contractors responded to the economic downturn. A total of 35 senior managers from the Top 100 Australian construction companies were interviewed. The findings indicate that construction companies, particularly the large ones were not affected in any significant way but are expecting some difficult financial times over the next few years and are taking actions to minimize the upcoming adverse impacts. The most common strategy adopted by Australian construction contractors is to concentrate on core business while avoiding aimless bidding. Similarly, great focus is placed on retaining human resources in order to maintain the skill set so that the company can respond quickly when market conditions improves. The research findings will provide construction contractors with insights on how to establish and sustain competitive advantages during economic slowdown and become more resilient in the future.
Novel Control of DFIG with ESD to Improve LVRT Capability and to Perform Voltage Support during Grid Faults
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICEI.2017.31
How to achieve low/no-fossil carbon transformations: With a special focus upon mechanisms, technologies and policies
Publisher: Elsevier BV
Date: 10-2017
SUSTAINABLE RETIREMENT VILLAGE FOR OLDER PEOPLE: A CASE STUDY IN BRISBANE, AUSTRALIA
Publisher: Vilnius Gediminas Technical University
Date: 19-06-2015
DOI: 10.3846/1648715X.2015.1029564
Abstract: Retirement villages are regarded as a viable accommodation option for the ever increasing ageing population in Australia. This paper aims to identify sustainability features and practices adopted in retirement villages and associated benefits to improve the life quality of older people. A case study of an existing retirement village 10 kms from Brisbane CBD was conducted involving a series of interviews with the village managers and residents together with documents relating to the village's operations and activities. The environmentally friendly features that were incorporated into the development mainly include green design for the site and floor plan and waste management in daily operation. More importantly, a variety of facilities are provided to strengthen the social engagement and interactions among the residents. Additionally, different daily services are provided to assist independent living and improve the health conditions of residents. Also, the relatively low vacancy rate in this village indicates that these sustainability features offer good value of money for the residents. The paper provides a first look at sustainable retirement villages in terms of triple bottom line sustainability with emphasis on social aspects, reveals the importance in maintaining an appropriate balance, and provides ex les of how this can be achieved in practice.
Green Universities in China – what matters?
Publisher: Elsevier BV
Date: 12-2013
The energy-saving effects of ground-coupled heat pump system integrated with borehole free cooling: A study in China
Publisher: Elsevier BV
Date: 11-2016
Are migrant workers satisfied with public rental housing? A study in Chongqing, China
Publisher: Elsevier BV
Date: 08-2016
Carbon transfer within China: Insights from production fragmentation
Publisher: Elsevier BV
Date: 02-2020
Phase change materials for solar thermal energy storage in residential buildings in cold climate
Publisher: Elsevier BV
Date: 08-2015
The status quo of innovations within the construction industry: a conceptual model
Publisher: Inderscience Publishers
Date: 2016
Readiness for sustainable community: A case study of Green Star Communities
Publisher: Elsevier BV
Date: 02-2018
Low temperature synthesis and photoluminescent properties of CaMoO4:Eu3+ red phosphor with uniform micro-assemblies
Publisher: Elsevier BV
Date: 09-2011
Control Dust Pollution on Construction Sites: What Governments Do in China?
Publisher: MDPI AG
Date: 20-08-2018
DOI: 10.3390/SU10082945
Abstract: Dust pollution is a key issue that contractors ought to address in the sphere of sustainable construction. Governments on behalf of the public assume part of the responsibilities for minimizing dust emissions on construction sites. However, the measures that are useful for governments to fulfill such a responsibility have not been explored explicitly in previous studies. The aim of this research is to map out China’s practices in this area with the intention of filling the knowledge gap. Using a combination of research methods, five categories of governmental measures are proposed: technological, economic, supervisory, organizational, and assessment-based. Data from 37 major cities in China are collected for analysis. While the proposed categories of measures are demonstrated in China, the data analysis results show that governments prefer technological and organizational measures, and institutional guarantees and technological innovation are a prerequisite for dust-free construction. This research provides a comprehensive examination of construction dust control from the perspective of governments, and it can assist governments in improving the performance of dust management in the construction context.
Foreign architectural and engineering design firms’ competitiveness and strategies in China: A diamond model study
Publisher: Elsevier BV
Date: 07-2012
A critical review of factors affecting the wind power generation industry in China
Publisher: Elsevier BV
Date: 03-2013
Organocatalytic Asymmetric Michael/Friedel–Crafts Cascade Reaction of 3-Pyrrolyl-oxindoles and α,β-Unsaturated Aldehydes for the Construction of Chiral Spiro[5,6-dihydropyrido[1,2-a]pyrr
Publisher: American Chemical Society (ACS)
Date: 26-05-2015
Abstract: An efficient and unprecedented organocatalytic asymmetric reaction of 3-pyrrolyl-oxindoles with α,β-unsaturated aldehydes to generate spirocyclic oxindole compounds was developed. The reactions were catalyzed by diphenylprolinol silyl ether and 2-fluorobenzoic acid via an asymmetric Michael/Friedel-Crafts cascade process, followed by dehydration with p-toluenesulfonic acid to afford a wide variety of structurally erse spiro[5,6-dihydropyrido[1,2-a]pyrrole-3,3'-oxindole] derivatives in high yields (up to 93%) and with high to excellent diastereo- and enantioselectivities (up to >99:1 dr and 97% ee).
The Development of Building Energy Conservation in China: A Review and Critical Assessment from the Perspective of Policy and Institutional System
Publisher: MDPI AG
Date: 18-09-2017
DOI: 10.3390/SU9091654
Identification of pigments from the Shrine of Kaiping Diaolou by micro‐Raman spectroscopy
Publisher: Wiley
Date: 23-02-2011
DOI: 10.1002/JRS.2879
Synthesis of furoxan derivatives: DABCO-mediated cascade sulfonylation/cyclization reaction of α-nitro-ketoximes
Publisher: Elsevier BV
Date: 03-2015
Comprehensive assessment of flexibility of the wind power industry chain
Publisher: Elsevier BV
Date: 02-2015
Sustainability attitude and performance of construction enterprises: A China study
Publisher: Elsevier BV
Date: 2018
Structure transformation and remarkable site-distribution modulation of Eu3+ions in CaMoO4 : Eu3+nanocrystals under high pressure
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C5CE01580D
Constraints to achieve infrastructure sustainability for mountainous townships in China
Publisher: Elsevier BV
Date: 03-2018
Can virtual water trade save water resources?
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.WATRES.2019.07.015
Abstract: At times, certain areas of China suffering from water shortages. While China's government is spurring innovation and infrastructure to help head off such problems, it may be that some water conservation could help as well. It is well-known that water is embodied in traded goods-so called "virtual water trade" (VWT). In China, it seems that many water-poor areas are perversely engaged in VWT. Further, China is engaging in the global trend of fragmentation in production, even as an interregional phenomenon. Perhaps something could be learned about conserving or reducing VWT, if we knew where and how it is practiced. Given some proximate causes, perhaps viable policies could be formulated. To this end, we employ China's multiregional input-output tables straddling two periods to trace the trade of a given region's three types of goods: local final goods, local intermediate goods, and goods that shipped to other regions and countries. We find that goods traded interregionally in China in 2012 embodied 30.4% of all water used nationwide. Nationwide, water use increased substantially over 2007-2012 due to greater shipment volumes of water-intensive products. In fact, as suspected, the rise in value chain-related trade became a major contributing factor. Coastal areas tended to be net receivers of VWT from interior provinces, although reasons differed, e.g. Shanghai received more to fulfill final demand (67.8% of net inflow) and Zhejiang for value-chain related trade (40.2% of net inflow). In sum, the variety of our findings reveals an urgent need to consider trade types and water scarcity when developing water resource allocation and conservation policies.
Environmental and economic impacts assessment of concrete pavement brick and permeable brick production process - A case study in China
Publisher: Elsevier BV
Date: 2018
Critical factors for project efficiency in a defence environment
Publisher: Elsevier BV
Date: 07-2014
Editorial note for Virtual Special Issue on energy performance to indoor environmental quality
Publisher: Elsevier BV
Date: 05-2018
Opportunities of Building Information Modelling in Australian Offsite Manufacturing
Publisher: Unpublished
Date: 2014
CONTROL OF SUPERCONDUCTING POWER SUBSTATION TO SMOOTH WIND GENERATION AND IMPROVE SYSTEM FAULT DYNAMICS
Publisher: ACTA Press
Date: 2016
Assessing and predicting the illegal dumping risks in relation to road characteristics
Publisher: Elsevier BV
Date: 09-2023
Evolution analysis of environmental standards: Effectiveness on air pollutant emissions reduction
Publisher: Elsevier BV
Date: 04-2017
Virtuality of hybrid design teams within the construction context: a conceptual model
Publisher: Inderscience Publishers
Date: 2017
Research on modeling method describing wind power fluctuation probability density based on nonparametric kernel density estimation
Publisher: IEEE
Date: 11-2016
Key factors for the BIM adoption by architects: a China study
Publisher: Emerald
Date: 16-11-2015
DOI: 10.1108/ECAM-04-2015-0053
Abstract: – There has been a growing attention to building information modeling (BIM) globally due to its benefits to various stages of a building’s life cycle. To facilitate the implementation of BIM in the construction industry effectively, the purpose of this paper is to gain a better understanding of the mechanism for BIM adoption by practitioners such as architects. – A questionnaire survey of architects is conducted in Shenzhen, China. A structural equation model is built with survey data to identify the key factors affecting architects’ BIM adoption in the design firms. – It is found that motivation, technical defects of BIM and BIM capability are the statistically significant factors affecting architects’ BIM adoption whereas management support and knowledge structure are not. – Only one architectural, engineering and construction (AEC) profession, i.e. architects were selected as research participants. In future, other professions such as construction engineers, project managers, etc. should be investigated with respect to their BIM adoption issues. – BIM technology developers should improve the technology along the objectives of economic benefits, effectiveness and efficiency of BIM adoption. The compatibility and integration between BIM and other widely available software in the industry should also be improved. Moreover, AEC company and project managers should provide architects with opportunities of BIM training so that architects are more likely to apply BIM in future projects. – A quantitative theoretical model, i.e. structural equation model is built to identify key factors affecting architects’ BIM adoption, which takes one step further to reveal the BIM adoption mechanism in contrast to previous descriptive-oriented studies.
Organocatalytic asymmetric double Michael reaction of Nazarov reagents with alkylidene azlactones for the construction of spiro-fused cyclohexanone/5-oxazolone system
Publisher: Elsevier BV
Date: 09-2014
Dynamics of Sewage Charge Policies, Environmental Protection Industry and Polluting Enterprises—A Case Study in China
Publisher: MDPI AG
Date: 31-07-2014
DOI: 10.3390/SU6084858
41-Azido-41-deoxyrapamycin
Publisher: International Union of Crystallography (IUCr)
Date: 28-03-2012
DOI: 10.1107/S1600536812012548
Abstract: The title compound, C 51 H 78 N 4 O 12 , is a derivative of rapamycin, a triene macrolide antibiotic molecule isolated from Streptomyces hygroscopicus . The macrocyclic ring structure has 15 chiral centres, with one of the substituent hydroxy groups giving an intramolecular hydrogen bond to a ketone O-atom acceptor. The molecules also form intermolecular hydroxy–ketone O—H...O hydrogen-bonding associations, giving one-dimensional chains extending along (010). The crystal has 108 Å 3 solvent-accessible voids.
Adaptive Zone-Division AVC Method Based on Reactive Power Margin Evaluation for Wind Farm
Publisher: IEEE
Date: 05-2018
Reprint of: Challenges of developing sustainable neighborhoods in China
Publisher: Elsevier BV
Date: 10-2017
Determinants of contractor’s construction and demolition waste recycling intention in China: Integrating theory of planned behavior and norm activation model
Publisher: Elsevier BV
Date: 04-2023
A review of performance assessment methods for construction and demolition waste management
Publisher: Elsevier BV
Date: 11-2019
Understanding of physicochemical properties and formation mechanisms of fine particular matter generated from Canadian coal combustion
Publisher: Elsevier BV
Date: 02-2016
Controlled hydrothermal synthesis and luminescent properties of Y2WO6:Eu3+ nanophosphors for light-emitting diodes
Publisher: Springer Science and Business Media LLC
Date: 28-11-2016
Indoor environment quality of green buildings: Case study of an LEED platinum certified factory in a warm humid tropical climate
Publisher: Elsevier BV
Date: 2015
How the public reacts to social impacts in construction projects? A structural equation modeling study
Publisher: Elsevier BV
Date: 11-2016
The development of new energy vehicles for a sustainable future: A review
Publisher: Elsevier BV
Date: 02-2015
Transnational recycling of Australian export waste: An exploratory study
Publisher: Elsevier BV
Date: 09-2023
Research on probability density modeling method of wind power fluctuation based on nonparametric kernel density estimation
Publisher: IEEE
Date: 04-2018
Towards a sustainable society: the status and future of energy performance contracting in China
Publisher: Elsevier BV
Date: 2016
Feasibility of ground coupled heat pumps in office buildings: A China study
Publisher: Elsevier BV
Date: 2016
Chilling Prospect: Climate Change Effects of Mismanaged Refrigerants in China
Publisher: American Chemical Society (ACS)
Date: 07-05-2018
Abstract: The global community has responded to the dual threats of ozone depletion and climate change from refrigerant emissions (e.g., chlorofluorocarbons, CFCs, and hydrofluorocarbons, HFCs) in refrigerators and air conditioners (RACs) by agreeing to phase out the production of the most damaging chemicals and replacing them with substitutes. Since these refrigerants are "banked" in products during their service life, they will continue to impact our environment for decades to come if they are released due to mismanagement at the end of life. Addressing such long-term impacts of refrigerants requires a dynamic understanding of the RACs' life cycle, which was largely overlooked in previous studies. Based on field surveys and a dynamic model, we reveal the lingering ozone depletion potential (ODP) and significant global warming potential (GWP) of scrap refrigerants in China, the world's largest producer (62%) and consumer (46%) of RACs in 2015, which comes almost entirely from air conditioners rather than refrigerators. If the use and waste management of RACs continue with the current trend, the total GWP of scrap refrigerants in China will peak by 2025 at a level of 135.2 ± 18.9 Mt CO
Do environmental taxes reduce air pollution? Evidence from fossil-fuel power plants in China
Publisher: Elsevier BV
Date: 10-2021
4-[(E)-(2-Methoxyphenyl)iminomethyl]-N,N-dimethylaniline
Publisher: International Union of Crystallography (IUCr)
Date: 15-05-2010
Public Perception towards Waste-to-Energy as a Waste Management Strategy: A Case from Shandong, China
Publisher: MDPI AG
Date: 20-08-2019
Abstract: Municipal solid waste (MSW) is posing great challenge for most countries in the world, which can cause severe negative impacts to the environment and human health. Waste-to-energy has great potential in China because of its technological maturity and policy support at the national level. However, there are significant conflicts between the huge market demand and strong public opposition. It is imperative to examine the public perception of waste-to-energy, especially for developing countries where a large number of projects are under construction or have been approved. The public perception of waste-to-energy was carried out by a questionnaire survey in this research. A total of 650 questionnaires were distributed and 629 questionnaires were returned, with a response rate of 96.8%. The results show that the public showed general concern in regard to environmental issues. Respondents had an overall positive attitude towards waste-to-energy, but it varied according to the demographic details of residents, such as age, education, and income. Recognition level of the benefits was higher than the concern of associated risks. Multiple linear regression shows that awareness of environmental issues had no impact on public attitude towards waste-to-energy, while public awareness and perceived benefits had notable positive impacts. Perceived risks had a positive correlation with public attitude. In order to promote the development of MSW incinerators, the government should make more publicity efforts. Rural residents, people over 50 years old, and people with low education and low income are the major groups which should be focused on to enhance the public perception. The findings provide a theoretical and practical reference for enhancing the social acceptance of waste-to-energy development.
A conceptual framework for member selection in global virtual teams (GVTs)
Publisher: Unpublished
Date: 2013
Coordinated development of energy, economy and environment subsystems—A case study
Publisher: Elsevier BV
Date: 11-2014
Novel Polypyridyl chelators deplete cellular zinc and destabilize the X‐linked inhibitor of apoptosis protein (XIAP) prior to induction of apoptosis in human prostate and breast cancer cells
Publisher: Wiley
Date: 15-06-2012
DOI: 10.1002/JCB.24132
Determining Optimal Proportion of Design in Design-Build Request for Proposals
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2013
Advances and challenges in commercializing radiative cooling
Publisher: Elsevier BV
Date: 12-2019
2-Amino-5-methylpyridinium nitrate
Publisher: International Union of Crystallography (IUCr)
Date: 13-06-2012
Stakeholders’ willingness to pay for enhanced construction waste management: A Hong Kong study
Publisher: Elsevier BV
Date: 07-2015
Exploring the determinants of migrant workers' housing tenure choice towards public rental housing: A case study in Chongqing, China
Publisher: Elsevier BV
Date: 11-2016
Exploring the Adequacy of Massive Constructed Public Housing in China
Publisher: MDPI AG
Date: 02-04-2019
DOI: 10.3390/SU11071949
Abstract: Public housing programs are an effective strategy to provide adequate housing, not only in developed countries, but also developing countries. This study holistically investigates the sitting occupants’ perception of adequate housing towards their public housing units using Chongqing, a typical city in western China, as a case study. Results showed that generally, the public rental housing (PRH) programs were perceived to be adequate by their residents in the estates s led. The components of neighborhood environment, housing unit, and affordability were the top three factors affecting the overall housing adequacy. The importance of physical aspects as well as the nonphysical aspects of adequate housing is likely to change according to their residential purpose. Therefore, the residential purpose of residents should be taken into consideration when planning the physical and nonphysical elements of public housing programs. Meanwhile, socioeconomic characteristics of age, family income, family members, residence length, and housing types have significant effects on overall housing adequacy and its components. These findings shed some useful insights on the sustainable development of public housing in China and provide a useful reference for future public housing developments in developing countries. The provision of adequate housing also helps to attract and retain talent, which consequently improves the competitiveness of the city.
Discovering the Transition Pathways toward Sustainability for Construction Enterprises: Importance-Performance Analysis
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2017
Robustness of eco-industrial symbiosis network: a case study of China
Publisher: Springer Science and Business Media LLC
Date: 20-07-2018
DOI: 10.1007/S11356-018-2764-X
Abstract: As a complex network, eco-industrial symbiosis network is featured with complexity, openness, and non-linearity. A methodology is proposed to analyze and optimize the eco-industrial symbiosis network from the perspective of complex network theory. Structural robustness index and performance robustness index are established as the analysis model. Consequently, a robust method is developed to optimize the eco-industrial symbiosis network system based on the percolation theory. A conceptual framework is put forward to improve the robustness of eco-industrial symbiosis network system by introducing the "spare core" enterprise which is validated by quantitative analysis. The empirical results show that the robustness of eco-industrial symbiosis network varies under both random failure and intentional disturbance scenarios. However, eco-industrial symbiosis network system has strong self-regulation capability as long as the core enterprise is still in operation. It is recommended that supplementary chain could be added to those enterprises with lower network node connectivity to form "spare core" enterprise. This can not only effectively reduce the dependence of other enterprises on core enterprises, but also further improve the robustness of eco-industrial symbiosis network. This methodology is practically validated by a case analysis of eco-industrial park in China. The findings provide useful inputs to the design and operation of eco-industrial parks.
Achieving transparency in carbon labelling for construction materials - Lessons from current assessment standards and carbon labels
Publisher: Elsevier BV
Date: 12-2014
From green buildings to living buildings? Rating schemes and waste management practices in Australian educational buildings
Publisher: Emerald
Date: 19-08-2020
DOI: 10.1108/ECAM-03-2019-0177
Abstract: There is an increasing level of recognition of the pressing issues associated with climate change and resource depletion. As a result, it is well recognised that higher education institutions bear responsibilities to promote “sustainable development”. Many universities have adopted green building practices in the construction of their building infrastructure. A variety of Green Building Rating Tools (GBRTs) have been designed to facilitate green building developments. Thus, the aim of this research is to identify mechanisms to improve current GBRTs in terms of waste management (WM) practices by using green star accredited educational buildings in Australia. A qualitative approach was adopted in this study to achieve the research aim by conducting three case studies of educational buildings in South Australia. Thirty three interviews were carried out in a face-to-face, semi-structured manner and project documentations were reviewed. The participants were asked to provide their expert opinions on the GS initiative and its ability to minimise waste generation, the impact of the GS initiative on solid WM practices and problems associated with the implementation process of the GS initiative. Data was analysed using code-based content analysis using the NVivo software package. Tables and figures were used as the visualization technique to present an expedient understanding in a holistic manner. Findings showed that the Green Star (GS) initiative drives change in the way current practices are performed in the Australian construction industry. However, this study revealed that WM targets outlined in the GS initiative are not challenging enough. Thus, suggestions are provided in this research to improve the WM aspects of GS initiatives by looking beyond a focus on “sustainability” and “waste minimisation” towards a focus on regenerative environments. These findings are valuable for practitioners and policymakers seeking to improve WM practices and to address issues associated with climate change and resource depletion.
How affordable housing becomes more sustainable? A stakeholder study
Publisher: Elsevier BV
Date: 09-2017
Effects of team diversity on project performance in construction projects
Publisher: Emerald
Date: 28-02-2019
DOI: 10.1108/ECAM-05-2018-0220
Abstract: The purpose of this paper is fourfold: first, to investigate the effect of team ersity on different types of conflicts second, to determine if team ersity is significantly correlated with project performance third, to investigate the mediating effect of project conflicts on the relationship between team ersity and project performance and fourth, to examine the relationship between different types of conflicts and project performance in construction projects. A theoretical model was developed and a questionnaire survey was conducted with 246 professionals. The structural equation modeling technique was applied to analyze the data. The results showed that: team ersity was positively associated with project performance the introduction of conflicts significantly weakened the effect of ersity on performance conflicts have both constructive and destructive effects on project performance and team ersity affected project performance through the mediating effects of task conflict and relationship conflict, thus adding both positive and negative effects on performance. There are other factors which may affect conflicts and project performance such as communication, trust and contract. Future research could be conducted to determine the role of these variables in determining the effects of team ersity on performance. It is necessary to reduce the relationship conflict whereas maintaining a “healthy” level of task conflict. In light of this, the conclusions of this study highlight practical implications as follows: project teams should attach importance to erse partner selection and select cooperative partners whose value orientations are similar when implementing a construction project, the structure of erse project teams should keep relatively stable, avoiding too many teams entering or exiting the project in a short period project teams should make greater efforts to deal with destructive conflicts via relational governance such as trust and communication. This study contributes to the literature in three areas. First, this study investigated the dynamic mechanism between team ersity, conflicts and performance in construction projects. Second, this study contributes to the body of knowledge on validating the mediating effects of conflicts on the relationship between team ersity and performance. Third, this study validated the positive and negative effects of team ersity on performance with different types of conflicts as mediation variables in construction projects.
The nexus of water-energy-food in China's tourism industry
Publisher: Elsevier BV
Date: 2021
Cost prediction of building projects using the novel hybrid RA-ANN model
Publisher: Emerald
Date: 24-01-2023
DOI: 10.1108/ECAM-07-2022-0666
Abstract: Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available. The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China. The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors. (1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.
Combining life cycle assessment and Building Information Modelling to account for carbon emission of building demolition waste: A case study
Publisher: Elsevier BV
Date: 2018
Offsite manufacturing in developing Countries: Current situation and opportunities
Publisher: Unpublished
Date: 2014
(Dis)Trust, Control, and Project Success: From a Chinese Project Owner’s Perspective
Publisher: MDPI AG
Date: 31-10-2017
DOI: 10.3390/SU9111936
Considering life-cycle environmental impacts and society's willingness for optimizing construction and demolition waste management fee: An empirical study of China
Publisher: Elsevier BV
Date: 2019
Pressure effect on optical properties and structure stability of LaPO4:Eu3+ hollow spheres
Publisher: Elsevier BV
Date: 12-2010
Cellular and computational studies of proteasome inhibition and apoptosis induction in human cancer cells by amino acid Schiff base–copper complexes
Publisher: Elsevier BV
Date: 2013
Sustainability in hydropower development—A case study
Publisher: Elsevier BV
Date: 03-2013
Critical factors for implementing sustainable construction practice in HOPSCA projects: A case study in China
Publisher: Elsevier BV
Date: 02-2018
Cross-regional mobility of construction and demolition waste in Australia: An exploratory study
Publisher: Elsevier BV
Date: 05-2020
A critical analysis of the photovoltaic power industry in China – From diamond model to gear model
Publisher: Elsevier BV
Date: 12-2011
Indoor PM2.5 concentrations in residential buildings during a severely polluted winter: A case study in Tianjin, China
Publisher: Elsevier BV
Date: 10-2016
A discourse on virtuality of teams for construction project managers
Publisher: Unpublished
Date: 2014
Economic Development, Energy Consumption, and Air Pollution: A Critical Assessment in China
Publisher: Informa UK Limited
Date: 25-09-2014
Barriers and challenges of collaborative procurements: An exploratory study
Publisher: Informa UK Limited
Date: 06-06-2014
Industrial CO2 emissions in China based on the hypothetical extraction method: Linkage analysis
Publisher: Elsevier BV
Date: 11-2013
Analysis of selection criteria for design-builders through the analysis of Request for Proposals (RFPs)
Publisher: American Society of Civil Engineers (ASCE)
Date: 2013
Organocatalyzed Enantioselective Decarboxylative Stereoablation Reaction for the Construction of 3,3′-Disubstituted Oxindoles Using β-Ketoacids and 3-Halooxindoles
Publisher: American Chemical Society (ACS)
Date: 07-12-2012
DOI: 10.1021/JO302048V
Abstract: An unprecedented method for the construction of optically active 3,3'-disubstituted oxindoles via an organocatalyzed decarboxylative stereoablation reaction has been developed. We describe the first asymmetric reaction between β-ketoacids and 3-halooxindoles utilizing an organocatalyst. This method allows for the formation of a variety of 3,3'-disubstituted oxindoles bearing a keto-carbonyl group, which are not easily accessible using other methodologies, in moderate to good yields with high enantioselectivities.
Assessing the carbon footprint of the transport sector in mega cities via streamlined life cycle assessment: a case study of Shenzhen, South China
Publisher: Springer Science and Business Media LLC
Date: 08-09-2016
International cooperation on renewable energy development in China – A critical analysis
Publisher: Elsevier BV
Date: 03-2011
Performance and Strategy of Chinese Contractors in the International Market
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2009
Developing the International Construction Contracting Market: Enterprise Niche Approach
Publisher: American Society of Civil Engineers (ASCE)
Date: 2017
The sustainable future of hydropower: A critical analysis of cooling units via the Theory of Inventive Problem Solving and Life Cycle Assessment methods
Publisher: Elsevier BV
Date: 2017
Reducing wasteful household behaviours: Contributions from psychology and implications for intervention design
Publisher: Routledge
Date: 03-07-2013
PACAP causes PAC1/VPAC2 receptor mediated hypertension and sympathoexcitation in normal and hypertensive rats
Publisher: American Physiological Society
Date: 10-2012
DOI: 10.1152/AJPHEART.00464.2012
Abstract: Pituitary adenylate cyclase-activating polypeptide (PACAP) is an excitatory neuropeptide that plays an important role in hypertension and stress responses. PACAP acts at three G protein-coupled receptors [PACAP type 1 receptor (PAC 1 ) and vasoactive intestinal peptide receptor types 1 and 2 (VPAC 1 and VPAC 2 )] and is localized to sites involved in cardiovascular control, most significantly the rostral ventrolateral medulla (RVLM). The RVLM is crucial for the tonic and reflex control of efferent sympathetic activity. Increases in sympathetic activity are observed in most types of hypertension and heart failure. PACAP delivered intrathecally also causes massive sympathoexcitation. We aimed to determine the presence and abundance of the three PACAP receptors in the RVLM, the role, in vivo, of PACAP in the RVLM on tonic and reflex cardiovascular control, and the contribution of PACAP to hypertension in the spontaneously hypertensive rat (SHR). Data were obtained using quantitative PCR and microinjection of PACAP and its antagonist, PACAP(6–38), into the RVLM of anesthetized artificially ventilated normotensive rats or SHRs. All three receptors were present in the RVLM. PACAP microinjection into the RVLM caused sustained sympathoexcitation and tachycardia with a transient hypertension but did not affect homeostatic reflexes. The responses were partially mediated through PAC 1 /VPAC 2 receptors since the effect of PACAP was attenuated (∼50%) by PACAP(6–38). PACAP was not tonically active in the RVLM in this preparation because PACAP(6–38) on its own had no inhibitory effect. PACAP has long-lasting cardiovascular effects, but altered PACAP signaling within the RVLM is not a cause of hypertension in the SHR.
Quantification of carbon footprint of urban roads via life cycle assessment: Case study of a megacity-Shenzhen, China
Publisher: Elsevier BV
Date: 11-2017
Application of Innovative Critical Chain Method for Project Planning and Control under Resource Constraints and Uncertainty
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2010
Construction and demolition waste research: a bibliometric analysis
Publisher: Informa UK Limited
Date: 08-01-2019
Future of lignite resources: a life cycle analysis
Publisher: Springer Science and Business Media LLC
Date: 23-09-2016
DOI: 10.1007/S11356-016-7642-9
Abstract: Lignite is a low-quality energy source which accounts for 13 % of China's coal reserves. It is imperative to improve the quality of lignite for large-scale utilization. To further explore and analyze the influence of various key processes on the environment and economic costs, a lignite drying and compression technology is evaluated using an integrated approach of life cycle assessment and life cycle costs. Results showed that lignite mining, direct air emissions, and electricity consumption have most significant impacts on the environment. An integrated evaluation of life cycle assessment and life cycle costs showed that the most significant contributor to the environmental impacts and economic costs was the lignite mining process. The impact of transportation and wastewater treatment process on the environment and economic costs was small enough to be ignored. Critical factors were identified for reducing the environmental and economic impacts of lignite drying and compression technology. These findings provide useful inputs for both industrial practice and policy making for exploitation, processing, and utilization of lignite resources.
Status quo and future directions of construction and demolition waste research: A critical review
Publisher: Elsevier BV
Date: 12-2019
Coordinated voltage control of synchronous condensers using wide-area measurements
Publisher: IEEE
Date: 10-2016
Comparison of sustainable community rating tools in Australia
Publisher: Elsevier BV
Date: 12-2015
Effectiveness of waste-to-energy approaches in China: from the perspective of greenhouse gas emission reduction
Publisher: Elsevier BV
Date: 10-2017
Retirement villages in Australia: a literature review
Publisher: Informa UK Limited
Date: 02-01-2017
Exploring the management of sustainable construction at the programme level: a Chinese case study
Publisher: Informa UK Limited
Date: 06-2012
A comparative life-cycle assessment of hydro-, nuclear and wind power: A China study
Publisher: Elsevier BV
Date: 09-2019
Post Occupancy Evaluation Study in Hospital Buildings – a Pilot Study
Publisher: Trans Tech Publications, Ltd.
Date: 09-2011
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.94-96.2248
Abstract: Building stock plays a critical role in the sustainability agenda worldwide. It consumes a large proportion of energy and is one of the biggest carbon emitters. Hospital buildings are no exception. Therefore, the performance of hospital buildings is critical to achieve the overall carbon emission goal. Post occupancy evaluation has been a popular tool and technique to evaluate the building performance which provides feedback to the building design and construction. This paper investigates the post occupancy evaluation of hospital buildings. A typical hospital c us in China is selected for this research purpose. The results showed that, even though not being implemented widely in China, the POE is an effective and useful tool for building performance evaluation. In addition, the relative importance of POE study criteria is highlighted.
Stakeholders’ willingness to pay for the new construction and demolition waste landfill charge scheme in Shenzhen: A contingent valuation approach
Publisher: Elsevier BV
Date: 2020
Sustainability Literacy of Older People in Retirement Villages
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/919054
Abstract: With many developed countries experiencing the aging of the population, older people play a large role in contributing to environmental problems but also to environmental solutions. The purpose of this research is to understand the awareness and behavior of current older people living in retirement villages towards sustainability development. To achieve this, a sustainability literacy survey was conducted with 65 older residents of a private retirement village located 10 Km outside the Brisbane, Australia’s central business district (CBD). Most of residents recognized the importance of environment protection and would like to lead a more environmentally friendly lifestyle. In addition, the majority were willing to pay higher prices for a living environment with sustainable features. The importance of positive social communications was emphasized with most residents having established good relationships with others in the village. The findings provide an important insight into consumer perspectives regarding the sustainable features that should and can be incorporated into the village planning and development.
Effects of fairness perceptions on conflicts and project performance in Chinese megaprojects
Publisher: Informa UK Limited
Date: 16-08-2019
Review of community facilities in A ustralian retirement villages: A content analysis
Publisher: Wiley
Date: 23-04-2014
DOI: 10.1111/AJAG.12153
Abstract: Facilities in retirement villages form a supportive environment for older residents. The purpose of this paper is to investigate the provision of these facilities in retirement villages, which are regarded as a viable accommodation option for the increasing ageing population in Australia. A content analysis of facilities in 124 retirement villages operated by 22 developers in Queensland and South Australia was conducted. The most widely provided facilities are community centres, libraries, barbeque facilities, hairdressers/salons and billiards/snooker ool tables. Commercial operators provide more facilities than not-for-profit organisations, and larger retirement villages normally have more facilities due to the economies of scale involved. The results of the study provide a useful reference for providing facilities within retirement villages that may support the quality lifestyles of older residents.
From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology
Publisher: Elsevier BV
Date: 03-2015
Sources of atmospheric pollution: a bibliometric analysis
Publisher: Springer Science and Business Media LLC
Date: 29-05-2017
Leveraging digital technologies for circular economy in construction industry: a way forward
Publisher: Emerald
Date: 27-07-2023
DOI: 10.1108/SASBE-05-2023-0111
Abstract: This study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry. A comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE. A total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C& D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction. This research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.
System Dynamics Model and Simulation of Employee Work-Family Conflict in the Construction Industry
Publisher: MDPI AG
Date: 28-10-2016
Structural Evolution of Household Energy Consumption: A China Study
Publisher: MDPI AG
Date: 02-04-2015
DOI: 10.3390/SU7043919
A longitudinal ethnographic study of hospital staff attitudes and experiences of change in nutrition care
Publisher: Wiley
Date: 27-01-2020
DOI: 10.1111/JHN.12734
Abstract: Change promotes quality in healthcare, yet adopting change can be challenging. Understanding how change in nutrition care is adopted may support better design and implementation of interventions that aim to address inadequate food intake in hospital. The present study followed the process of change in a healthcare organisation, exploring staff attitudes, beliefs and experiences of the implementation of a mealtime intervention. In total, 103 h of fieldwork were conducted in this longitudinal ethnographic study over a 4-month period. Over 170 staff participated, with data captured using observation, interviews and focus groups. Data were analysed using an inductive, thematic approach, informed by implementation theory. Attitudes and experiences of change in nutrition care are described by three themes: (i) staff recognised the inevitability of change (ii) staff cooperated with the intervention, recognising potential value in the intervention to support patient care, where increased awareness of their mealtime behaviours supported adopting practice changes and (iii) some staff were able to reflect on their practice after implementing the intervention, whereas others could not. A model illustrating the interconnectedness of factors influencing implementation emerged from the research, guiding future nutrition care intervention design and supporting change. The requirement to address the underlying perceptions of staff about the need to change should not be underestimated. Increased efforts to market the change message to specific staff groups and physical behavioural reinforcement strategies are needed. Nutrition care in the future should focus on helping staff feel positive about making practice changes.
Assessment of green building policies – A fuzzy impact matrix approach
Publisher: Elsevier BV
Date: 08-2014
Characterizing the generation and environmental impacts of subway-related excavated soil and rock in China
Publisher: Elsevier BV
Date: 03-2020
Quinine-catalyzed asymmetric domino Mannich-cyclization reactions of 3-isothiocyanato oxindoles with imines for the synthesis of spirocyclic oxindoles
Publisher: Elsevier BV
Date: 02-2015
Tandem Michael Addition–Ring Transformation Reactions of 3-Hydroxyoxindoles/3-Aminooxindoles with Olefinic Azlactones: Direct Access to Structurally Diverse Spirocyclic Oxindoles
Publisher: American Chemical Society (ACS)
Date: 09-05-2014
DOI: 10.1021/JO500432C
Abstract: An efficient method for the direct construction of two classes of spirocyclic oxindoles by the reactions of 3-hydroxyoxindoles/3-aminooxindoles and (Z)-olefinic azlactones through a tandem Michael addition-ring transformation process has been developed. With DBU as the catalyst, a range of spiro-butyrolactoneoxindoles and spiro-butyrolactamoxindoles, containing an oxygen or a nitrogen heteroatom, respectively, in the spiro stereocenter, were smoothly obtained with good to excellent diastereoselectivities in high yields.
Achieving carbon neutrality in commercial building developments – Perceptions of the construction industry
Publisher: Elsevier BV
Date: 04-2012
Corporate social responsibility on global construction supply chains
Publisher: Springer Berlin Heidelberg
Date: 18-06-2014
How would social acceptance affect nuclear power development? A study from China
Publisher: Elsevier BV
Date: 10-2017
Documentation Quality in Construction Projects: A Qualitative Inquiry
Publisher: Springer Berlin Heidelberg
Date: 18-06-2013
(2,2′-Bipyridine-κ2
N,N′){N-[(2-oxidonaphthalen-1-yl-κO)methylidene]-L -valinato-κO}copper(II) trihydrate
Publisher: International Union of Crystallography (IUCr)
Date: 26-05-2012
DOI: 10.1107/S1600536812022702
Abstract: In the title complex, [Cu(C 16 H 15 NO 3 )(C 10 H 8 N 2 )]·3H 2 O, the Cu II atom is five coordinated by O , N , O ′-donor atoms of the Schiff base ligand and by two N atoms of the 2,2′-bipyridine ligand in a distorted square-pyramidal geometry. In the crystal, molecules are linked into a two-dimensional network parallel to (011) by O—H...O hydrogen bonds.
Are construction enterprises ready for industrialized residential building policy? A case study in Shenzhen
Publisher: Elsevier BV
Date: 08-2018
A Competitiveness Analysis on Chinese Construction Companies in the Global Market Using Diamond Model
Publisher: IEEE
Date: 08-2011
Nutritional Compounds as Chemopreventive Agents by Proteasome Inhibition
Publisher: Springer Netherlands
Date: 2012
Chinese Construction Industry: Governance, Procurement and Culture
Publisher: Springer Netherlands
Date: 30-12-2010
Supporting and impeding factors for partnering in construction - a China study
Publisher: Emerald
Date: 16-08-2013
Abstract: Partnering has drawn attention from both academics and practitioners in the construction industry in the context of construction and facilities management. The past decades have seen a number of articles reporting the application of partnering in construction. The Chinese construction industry is one of the largest industries in the world however, to the authors' best knowledge, no project in mainland China has adopted this procurement approach in a formal and systematic manner as yet. This paper presents a timely study that aims to investigate the feasibility of implementing the partnering concept into Chinese industry and to understand the current barriers to this concept in China. The study employed a qualitative approach to investigate the factors that support or impede the implementation of partnering in mainland China. The methodology encompassed a critical review of relevant laws, regulations, and policy documents and semi‐structured interviews. The findings indicate that the partnering practice is feasible in the construction industry of China due to the large demand brought about by China's strong economic growth and government support. However, the implementation of partnering in the Chinese construction industry is being impeded by the restrictions of the current Chinese regulatory framework and tender evaluation framework, the incompatible features of Chinese culture and the general lack of trust. Six strategies that help to facilitate the implementation of partnering in China have been developed based on the supporting and impeding factors identified in this study. It is worth noting that not all aspects of Chinese culture are compatible with partnering principles. This study offers a useful reference to implement collaborative contracting models such as partnering in developing countries with a consideration of new factors such as political environment and emerging economies.
Optimization of Ecological Industrial Chain design based on reliability theory – a case study
Publisher: Elsevier BV
Date: 06-2016
(E)-3-(2-Hydroxy-5-methylphenylimino)indolin-2-one
Publisher: International Union of Crystallography (IUCr)
Date: 27-08-2011
Characteristics of public concern on haze in China and its relationship with air quality in urban areas
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.SCITOTENV.2018.04.382
Abstract: Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China (2) according to daily data analysis, the search index of haze has increased progressively with increased PM
Synthesis, Crystal Structure, and Theoretical Calculation of the Cd(II) Complex with 2-Aminobenzothiazole
Publisher: Informa UK Limited
Date: 21-11-2013
GREEN BUILDING RATING FOR OFFICE BUILDINGS – LESSONS LEARNED
Publisher: College Publishing
Date: 03-2016
Abstract: Office buildings constitute a significant proportion of the non-residential building stock. In recent years, various rating tools have been developed to foster green office building development. The Green Building Council of Australia (GBCA) has developed the Green Star - Office rating tools for this purpose. There are an increasing number of stakeholders adopting these tools to showcase their efforts in sustainable development. This research focuses on the challenges and barriers in obtaining GBCA ratings in Australian Office buildings. To accomplish this, the scoring sheets from the rating of 264 certified office buildings were collected and critically analysed. The findings indicated that credits related to the attributes of innovation, ecology and energy are comparatively difficult to achieve. It was also found in this study that a large number of projects did not apply for the specific credits of refrigerant global warming potential, re-use of façade, topsoil and fill removal from site, and in idual comfort control. This study provides a useful reference to both the property developer and the Green Building Council of Australia for green building developments in the future. In particular, the findings provide useful inputs to the development of the next generation of green building rating tools.
Comparative assessment of performance of foreign and local wind turbine manufacturers in China
Publisher: Elsevier BV
Date: 03-2012
Environmental performance of off-site constructed facilities: A critical review
Publisher: Elsevier BV
Date: 2020
Integrated Sustainability Assessment of Public Rental Housing Community Based on a Hybrid Method of AHP-Entropy Weight and Cloud Model
Publisher: MDPI AG
Date: 13-04-2017
DOI: 10.3390/SU9040603
ENERGY CONSUMPTION, ENVIRONMENTAL IMPACTS AND EFFECTIVE MEASURES OF GREEN OFFICE BUILDINGS: A LIFE CYCLE APPROACH
Publisher: College Publishing
Date: 11-2015
DOI: 10.3992/JGB.10.4.161
Abstract: The last few decades have witnessed a rapid development of green buildings in China especially the office sector. The life cycle assessment (LCA) approach has potential to weigh the benefits and costs associated with green building developments. Essentially, the LCA method evaluates the costs and benefits across a building's life cycle with a system approach. In this study, a green office building in Beijing, China, was analyzed by life cycle assessment to quantify its energy use and evaluate the environmental impacts in each life cycle stage. The environmental impacts can be reduced by 7.3%, 1.6% and 0.8% by using 30% gas-fired electricity generation, increasing the summer indoor temperature by 1°C, and switching off office equipment and lighting during lunchtime, respectively. Similarly, by reusing 80% of the selected materials when the building is finally demolished, the three major adverse environmental impacts on human health, ecosystem quality, and resource depletion can be reduced by 11.3% 12.7%, and 7.1% respectively. Sensitivity analysis shows that electricity conservation is more effective than materials efficiency in terms of a reduction in environmental impacts. These findings are useful to inform decision makers in different stages of the green building life cycle.
Provincial air pollution responsibility and environmental tax of China based on interregional linkage indicators
Publisher: Elsevier BV
Date: 10-2019
What leads to low-carbon buildings? A China study
Publisher: Elsevier BV
Date: 10-2015
Evaluating virtuality in teams: a conceptual model
Publisher: Informa UK Limited
Date: 22-01-2015
Delivering sustainable communities: a case study in China
Publisher: Emerald
Date: 04-07-2016
DOI: 10.1108/BEPAM-07-2014-0032
Abstract: – China’s accelerated urbanisation leads directly to pressure on the urban environment. The purpose of this paper is to identify best practices involved in a real sustainable community projects for the experience to be replicated in future. – To explore the practical development experiences and technological applications, a case study was conducted, involving both document analysis and semi-structured interviews. – The findings identify the green technologies and strategies used in the project planning and design process. The social considerations of the project development are also recognised in providing comfort, convenience and safety for their residents. Furthermore, the research highlights the fact that sustainable communities can incur less operational costs compared with traditional ones and therefore provide a feasible option for clients with a greater capability for upfront investment. – The results of the research provide valuable references for developers in the development of sustainable communities in both China and other countries.
Designers’ attitude and behaviour towards construction waste minimization by design: A study in Shenzhen, China
Publisher: Elsevier BV
Date: 12-2015
Selection of project procurement methods in the consumer electronics industry - a Hong Kong study
Publisher: Inderscience Publishers
Date: 2012
Perceived obstacles to multi-storey timber-frame construction: an Australian study
Publisher: Informa UK Limited
Date: 16-05-2014
Addressing the efficiency of the core ecological industrial chain: A DEA approach
Publisher: Elsevier BV
Date: 07-2017
Pricing and affordability of renewable energy in China – A case study of Shandong Province
Publisher: Elsevier BV
Date: 03-2011
Carbon-Neutral Commercial Building Development
Publisher: American Society of Civil Engineers (ASCE)
Date: 2013
Adaptive reuse of inner-city buildings: Methods for minimising waste and stimulating the economy
Publisher: Springer Singapore
Date: 19-12-2018
Recent Advances of α‐Isothiocyanato Compounds in the Catalytic Asymmetric Reaction
Publisher: Wiley
Date: 25-09-2015
Abstract: α‐Isothiocyanato amides, esters and phosphonates have emerged as new types of versatile reagents for various organo‐ and metal‐catalyzed asymmetric cascade reactions, which have attracted increasing interest of many chemists. In this review, recent advances of α‐isothiocyanato compounds for the asymmetric cascade aldol/cyclization, Mannich/cyclization, Michael/cyclization, erse [3+2] cyclization, and self‐cyclization/addition reactions are summarized and classified according to the types of acceptors. magnified image
Studies of wall painting fragments from Kaiping Diaolou by SEM/EDX, micro Raman and FT-IR spectroscopy
Publisher: Elsevier BV
Date: 11-2010
Key Performance Indicators for Regional Construction Supervision Systems in China
Publisher: Informa UK Limited
Date: 03-07-2017
Spatial production fragmentation and PM2.5 related emissions transfer through three different trade patterns within China
Publisher: Elsevier BV
Date: 09-2018
Organic cadmium complexes as proteasome inhibitors and apoptosis inducers in human breast cancer cells
Publisher: Elsevier BV
Date: 06-2013
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