ORCID Profile
0000-0003-4500-3443
Current Organisation
Swinburne University of Technology
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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.
Distributed Computing | Ubiquitous Computing | Distributed and Grid Systems | Manufacturing robotics | Distributed systems and algorithms | Cyberphysical systems and internet of things | Distributed computing and systems software
Computer Software and Services not elsewhere classified | Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Information and Communication Services not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences |
Publisher: Elsevier BV
Date: 09-2018
Publisher: arXiv
Date: 2021
Publisher: MDPI AG
Date: 13-01-2022
DOI: 10.3390/V14010141
Abstract: Rhinoviruses (RVs) have been reported as one of the main viral causes for severe respiratory illnesses that may require hospitalization, competing with the burden of other respiratory viruses such as influenza and RSV in terms of severity, economic cost, and resource utilization. With three species and 169 subtypes, RV presents the greatest ersity within the Enterovirus genus, and despite the efforts of the research community to identify clinically relevant subtypes to target therapeutic strategies, the role of species and subtype in the clinical outcomes of RV infection remains unclear. This review aims to collect and organize data relevant to RV illness in order to find patterns and links with species and/or subtype, with a specific focus on species and subtype ersity in clinical studies typing of respiratory s les.
Publisher: arXiv
Date: 2021
Publisher: MDPI AG
Date: 11-12-2019
DOI: 10.3390/S19245457
Abstract: As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.
Publisher: Elsevier BV
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-10-2023
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/MDM.2012.58
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/785434
Abstract: JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded.
Publisher: IEEE
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: University of Ottawa Library
Date: 15-03-2022
DOI: 10.18192/OLBIJ.V11I1.6179
Abstract: The New London Group’s 1996 manifesto was a clarion call to educational researchers to fundamentally redesign language and literacy education for the needs of global learners communicating in evolving digital media environments. In this conceptual overview, the “how”, “what” and “why” of multiliteracies are critically re examined from the perspective of mobile digital language learning in posthumanist media ecologies, with attention drawn to paradigm shifts in language, technology, multimodality and context. We argue that Web 3.0 environments, AI and rapidly emerging algorithmic cultures have outpaced earlier critical theorizations of multiliteracies and digitally mediated learning practices as well as meaningful implementation of multiliteracies pedagogies in schools. We then reconsider the affordances and constraints of Web 3.0 tools for multilingual lurilingual language learning, and sketch pathways for critical and productive engagements with mobile devices and multiliteracies pedagogies that reframe and advance the important critical work of the New London Group.
Publisher: Elsevier BV
Date: 05-2019
Publisher: IEEE
Date: 22-03-2021
Publisher: arXiv
Date: 2021
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 06-2018
Publisher: Public Library of Science (PLoS)
Date: 26-08-2016
Publisher: Wiley
Date: 02-05-2021
DOI: 10.1002/CPE.5323
Publisher: Springer International Publishing
Date: 2017
Publisher: CRC Press
Date: 02-12-2015
DOI: 10.1201/B19003
Publisher: JMIR Publications Inc.
Date: 27-04-2022
DOI: 10.2196/34833
Abstract: Adherence to anticancer medicines is critical for the success of cancer treatments however, nonadherence remains challenging, and there is limited evidence of interventions to improve adherence to medicines in patients with cancer. This overview of reviews aimed to identify and summarize available reviews of interventions to improve adherence to oral anticancer medicines in adult cancer survivors. A comprehensive search of 7 electronic databases was conducted by 2 reviewers who independently conducted the study selection, quality assessment using the A Measurement Tool to Assess Systematic Reviews 2, and data extraction. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist was adapted to report the results. A total of 29 reviews were included in the narrative synthesis. The overall quality of the systematic reviews was low. The 4 main strategies to promote adherence were focused on education, reminders, behavior and monitoring, and multicomponent approaches. Digital technology–based interventions were reported in most reviews (27/29, 93%). A few interventions applied theories (10/29, 34%), design frameworks (2/29, 7%), or engaged stakeholders (1/29, 3%) in the development processes. The effectiveness of interventions was inconsistent between and within reviews. However, interventions using multiple strategies to promote adherence were more likely to be effective than single-strategy interventions (12/29, 41% reviews). Unidirectional communication (7/29, 24% reviews) and technology alone (11/29, 38% reviews) were not sufficient to demonstrate improvement in adherence outcomes. Nurses and pharmacists played a critical role in promoting patient adherence to oral cancer therapies, especially with the support of digital technologies (7/29, 24% reviews). Multicomponent interventions are potentially effective in promoting patient adherence to oral anticancer medicines. The seamless integration of digital solutions with direct clinical contacts is likely to be effective in promoting adherence. Future research for developing comprehensive digital adherence interventions should be evidence-based, theory-based, and rigorously evaluated.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
DOI: 10.1109/MITP.2016.90
Publisher: American Physiological Society
Date: 11-2021
Publisher: Springer Science and Business Media LLC
Date: 03-11-2014
Publisher: Informa UK Limited
Date: 23-03-2017
Publisher: American Physiological Society
Date: 05-2023
DOI: 10.1152/AJPLUNG.00192.2022
Abstract: Club cells are found in human small airways where they play an important role in immune defense, xenobiotic metabolism, and repair after injury. Over the past few years, data from single-cell RNA sequencing (scRNA-seq) studies has generated new insights into club cell heterogeneity and function. In this review, we integrate findings from scRNA-seq experiments with earlier in vitro, in vivo, and microscopy studies and highlight the many ways club cells contribute to airway homeostasis. We then discuss evidence for loss of club cells or club cell products in the airways of patients with chronic obstructive pulmonary disease (COPD) and discuss potential mechanisms through which this might occur.
Publisher: IEEE
Date: 04-2013
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-5225-6067-8.CH007
Abstract: Type II diabetes is a rapidly growing non-communicable chronic disease that is causing significant concern to healthcare systems around the world. As there is no foreseeable cure, the most effective solution is to focus on strategies to control blood glucose levels by regular monitoring of diet, exercise, and when necessary, medication management. In today's environment, to do so effectively necessitates the need for a personalised self-management technology solution which can help patients take control of their diabetes. This chapter presents initial data from a research in progress study focused on designing a personalised diabetic application. The authors proffer a design science research methodology (DSRM) approach to design, develop, and ultimately, evaluate a patient-centric diabetes platform. This application is not only a smart patient empowering solution which serves to guide patients and assist their care team with regard to diet, exercise, medication and their respective impacts on blood sugar levels but is also designed to be culturally sensitive.
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/CIC.2015.34
Publisher: IEEE
Date: 12-2020
Publisher: Association for Computing Machinery (ACM)
Date: 16-12-2019
DOI: 10.1145/3363563
Abstract: Social isolation has been identified as a major risk in elderly people living alone because of their association with cognitive decline, depression, and other mental health-related issues. Ambient Assisted Living (AAL) is identified as a key technology to facilitate independent living and maintain social connnectedness between elderly, their families, and caregivers. AAL combines Internet of Things, Smart Homes, and machine learning to produce a smart solution that encourages independent, safe, and socially active life for elderly people within their own home. In this article, we propose, develop, implement, and validate a novel Internet-of-Things-based solution that uses passive (i.e., non-obstructive methods) sensing for real-time monitoring of elderly in their homes. The significance of the proposed solution is in the use of machine learning and statistical models to automatically build a personalised model by learning the normal behavioural pattern for the person from deployed sensors in the house. It then uses this model to detect significant changes in the behavioural pattern, should they occur, that could be a consequence of possible health deterioration. We evaluate the performance of the proposed solution via real-world in-home trials installed in six elderly people’s home for a period from 1.5 to 4 months. A discussion and analysis of the in-home trial outcomes and feedback from elderly who participated in the trials conclude the article.
Publisher: IEEE
Date: 12-2016
DOI: 10.1109/INIS.2015.64
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 05-2020
Publisher: IEEE
Date: 07-2023
Publisher: ICST
Date: 2008
Publisher: American Physiological Society
Date: 08-2023
DOI: 10.1152/AJPLUNG.00074.2022
Abstract: Bronchoconstriction is the main physiological event in asthma, which leads to worsened clinical symptoms and generates mechanical stress within the airways. Virus infection is the primary cause of exacerbations in people with asthma, however, the impact of bronchoconstriction on host antiviral responses and viral replication is unknown. We developed two disease models, in vitro, and found suppressed IFN response from cells following the application of compression and RV-A1 infection. This explains why people with asthma have deficient IFN response.
Publisher: IEEE
Date: 02-2018
Publisher: ACM
Date: 30-11-2020
Publisher: ACM
Date: 30-11-2020
Publisher: IEEE
Date: 07-2023
Publisher: Springer Science and Business Media LLC
Date: 12-2016
Publisher: MDPI AG
Date: 20-12-2022
DOI: 10.3390/S23010007
Abstract: With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world ex le from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 24-02-2021
DOI: 10.1007/S43926-021-00006-7
Abstract: The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decision-making (actuation) process. To ensure that such IoT applications (we term them autonomic) function as expected, it is necessary to measure and evaluate their quality, which is challenging in the absence of any human involvement or feedback. Existing Quality of Experience (QoE) literature and most QoE definitions focuses on evaluating application quality from the lens of human receiving application services. However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses. In this paper, we present a vision, survey and future directions for QoE research in IoT. We review existing QoE definitions followed by a survey of techniques and approaches in the literature used to evaluate QoE in IoT. We identify and review the role of data from the perspective of IoT architectures, which is a critical factor when evaluating the QoE of IoT applications. We conclude the paper by identifying and presenting our vision for future research in evaluating the QoE of autonomic IoT applications.
Publisher: Springer Science and Business Media LLC
Date: 16-04-2014
Publisher: Elsevier BV
Date: 02-2023
DOI: 10.1016/J.JACI.2022.09.026
Abstract: Microbiota are recognized to play a major role in regulation of immunity through release of immunomodulatory metabolites such as short-chain fatty acids (SCFAs). Rhinoviruses (RVs) induce upper respiratory tract illnesses and precipitate exacerbations of asthma and chronic obstructive pulmonary disease through poorly understood mechanisms. Local interactions between SCFAs and antiviral immune responses in the respiratory tract have not been previously investigated. We sought to investigate whether pulmonary metabolite manipulation through lung-delivered administration of SCFAs can modulate antiviral immunity to RV infection. We studied the effects of intranasal administration of the SCFAs acetate, butyrate, and propionate on basal expression of antiviral signatures, and of acetate in a mouse model of RV infection and in RV-infected lung epithelial cell lines. We additionally assessed the effects of acetate, butyrate, and propionate on RV infection in differentiated human primary bronchial epithelial cells. Intranasal acetate administration induced basal upregulation of IFN-β, an effect not observed with other SCFAs. Butyrate induced RIG-I expression. Intranasal acetate treatment of mice increased interferon-stimulated gene and IFN-λ expression during RV infection and reduced lung virus loads at 8 hours postinfection. Acetate ameliorated virus-induced proinflammatory responses with attenuated pulmonary mucin and IL-6 expression observed at day 4 and 6 postinfection. This interferon-enhancing effect of acetate was confirmed in human bronchial and alveolar epithelial cell lines. In differentiated primary bronchial epithelial cells, butyrate treatment better modulated IFN-β and IFN-λ gene expression during RV infection. SCFAs augment antiviral immunity and reduce virus load and proinflammatory responses during RV infection.
Publisher: Wiley
Date: 16-08-2016
DOI: 10.1002/SPE.2432
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 07-2022
Publisher: American Physiological Society
Date: 12-2022
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
DOI: 10.1109/MCC.2016.91
Publisher: IEEE
Date: 12-2022
DOI: 10.1109/SMARTWORLD-UIC-ATC-SCALCOM-DIGITALTWIN-PRICOMP-METAVERSE56740.2022.00212
Publisher: MDPI AG
Date: 31-03-2023
DOI: 10.3390/S23073670
Abstract: Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.
Publisher: Association for Computing Machinery (ACM)
Date: 22-06-2023
DOI: 10.1145/3589662
Publisher: Elsevier BV
Date: 05-2022
Publisher: MDPI AG
Date: 10-01-2017
DOI: 10.3390/JSAN6010001
Publisher: Wiley
Date: 21-11-2022
DOI: 10.1002/WIDM.1480
Abstract: Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and in idualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from ersified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective. This article is categorized under: Application Areas Health Care
Publisher: ACM
Date: 29-11-2021
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 28-11-2015
Publisher: IEEE
Date: 07-2007
Publisher: MDPI AG
Date: 27-10-2023
DOI: 10.3390/S23218779
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 2020
Publisher: ACM
Date: 29-01-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/MDM.2014.74
Publisher: IEEE
Date: 06-2017
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-1832-7.CH019
Abstract: Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from erse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors' main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.
Publisher: Elsevier BV
Date: 11-2016
Publisher: American Thoracic Society
Date: 21-08-2023
Publisher: MDPI AG
Date: 10-04-2023
DOI: 10.3390/SOC13040100
Abstract: According to KPMG, Internet of Things (IoT) technology was among the top 10 technologies of 2019. It has been growing at a significant pace, influencing and disrupting several application domains. It is expected that by 2025, 75.44 billion devices will be connected to the Internet. These devices generate massive amounts of data which, when harnessed using the power of data science (DS) techniques and approaches such as artificial intelligence (AI) and machine learning (ML), can provide significant benefits to economy, society, and people. Ex les of areas that are being disrupted are digital marketing and retail commerce services in smart cities. This paper presents a vision for Marketing 4.0 that is underpinned by disruptive digital technologies such as IoT and DS. We present an analysis of the current state of the art in IoT and DS via the three pillars of marketing: namely, people, products, and places. We propose a blueprint architecture for developing a Marketing 4.0 solution that is underpinned by IoT and DS. We conclude the paper by highlighting the open challenges that need to be addressed in order to realise the Marketing 4.0 blueprint architecture, including supporting the integration of IoT data concerning people, products, and places and using DS to make efficient and effective recommendations.
Publisher: ACM
Date: 08-04-2019
Publisher: Elsevier BV
Date: 11-2017
Publisher: BMJ
Date: 07-2023
DOI: 10.1136/BMJOPEN-2022-071492
Abstract: In iduals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These in iduals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination. However, ongoing systematic clinical follow-up programmes are uncommon. Digital health solutions offer efficient and sustainable ways to deliver affordable and equitable care. This paper outlines the codesign and development of a digital health platform to facilitate long-term clinical and psychological care, and foster self-efficacy in in iduals with a genetic disease predisposition. We adopt a mixed-methods approach for data gathering and analysis. Data collection is in two phases. In phase 1, 300 in iduals with a high-risk genetic predisposition to adult disease will undertake an online survey to assess their use of digital health applications (apps). In phase 2, we will conduct focus groups with 40 in iduals with a genetic predisposition to cardiac or cancer syndromes, and 30 clinicians from erse specialities involved in their care. These focus groups will inform the platform’s content, functionality and user interface design, as well as identify the barriers and enablers to the adoption and retention of the platform by all endusers. The focus groups will be audiorecorded and transcribed, and thematic and content data analysis will be undertaken by adopting the Unified Theory of Acceptance and Use of Technology. Descriptive statistics will be calculated from the survey data. Phase 3 will identify the core skillsets for a novel digital health coordinator role. Outcomes from phases 1 and 2 will inform development of the digital platform, which will be user-tested and optimised in phase 4. This study was approved by the Peter MacCallum Human Research Ethics Committee (HREC/88892/PMCC). Results will be disseminated in academic forums, peer-reviewed publications and used to optimise clinical care.
Publisher: Springer International Publishing
Date: 2016
Publisher: ICST
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: IEEE
Date: 12-2021
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS53846.2021.00006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: IEEE
Date: 14-06-2021
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 06-2020
Publisher: Elsevier BV
Date: 2017
Publisher: IEEE
Date: 04-2015
Publisher: Elsevier BV
Date: 06-0006
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/SCC.2014.45
Publisher: ACM
Date: 29-11-2021
Publisher: IEEE
Date: 27-10-2020
Publisher: IEEE
Date: 07-2019
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.IJPHARM.2022.121586
Abstract: Nanoparticle-based delivery is a strategy for increasing the therapeutic window of inhaled immunomodulatory drugs that have inflammatory activity. TLR7 agonists are a class of immunomodulators that have been considered for the treatment of virus-induced respiratory diseases. However, due to high immune-stimulatory activity, TLR7 agonists, delivered via direct exposure, generally have a narrow therapeutic window. To address this, we have developed lipid olymer hybrid nanoparticles (NPs) conjugated with anti-EpCAM monoclonal antibody for targeted delivery of TLR7 agonist (CL264) to airway epithelial cells (AECs)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: American Astronomical Society
Date: 11-2022
Abstract: The contemporaneous detection of gravitational waves and gamma rays from GW170817/GRB 170817A, followed by kilonova emission a day after, confirmed compact binary neutron star mergers as progenitors of short-duration gamma-ray bursts (GRBs) and cosmic sources of heavy r -process nuclei. However, the nature (and life span) of the merger remnant and the energy reservoir powering these bright gamma-ray flashes remains debated, while the first minutes after the merger are unexplored at optical wavelengths. Here, we report the earliest discovery of bright thermal optical emission associated with short GRB 180618A with extended gamma-ray emission—with ultraviolet and optical multicolor observations starting as soon as 1.4 minutes post-burst. The spectrum is consistent with a fast-fading afterglow and emerging thermal optical emission 15 minutes post-burst, which fades abruptly and chromatically (flux density F ν ∝ t − α , α = 4.6 ± 0.3) just 35 minutes after the GRB. Our observations from gamma rays to optical wavelengths are consistent with a hot nebula expanding at relativistic speeds, powered by the plasma winds from a newborn, rapidly spinning and highly magnetized neutron star (i.e., a millisecond magnetar), whose rotational energy is released at a rate L th ∝ t −(2.22±0.14) to reheat the unbound merger-remnant material. These results suggest that such neutron stars can survive the collapse to a black hole on timescales much larger than a few hundred milliseconds after the merger and power the GRB itself through accretion. Bright thermal optical counterparts to binary merger gravitational wave sources may be common in future wide-field fast-cadence sky surveys.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Wiley
Date: 25-12-2019
DOI: 10.1002/WIDM.1350
Abstract: Healthcare 4.0 is a term that has emerged recently and derived from Industry 4.0. Today, the health care sector is more digital than in past decades for ex le, spreading from x‐rays and magnetic resonance imaging to computed tomography and ultrasound scans to electric medical records. With the wide spectrum of digital technologies underpinning Healthcare 4.0 to deliver more effective and efficient health care services, in this article, we use the wisdom pyramid methodology to conduct a systematic review of current digital frontiers in Healthcare 4.0. This article is categorized under: Technologies Computer Architectures for Data Mining Application Areas Health Care Application Areas Data Mining Software Tools Fundamental Concepts of Data and Knowledge Knowledge Representation
Publisher: IGI Global
Date: 2016
Abstract: Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from erse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors' main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.
Publisher: Springer International Publishing
Date: 2017
Publisher: MDPI AG
Date: 19-06-2022
DOI: 10.3390/S22124620
Abstract: Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in in iduals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.
Publisher: Springer Science and Business Media LLC
Date: 30-05-2022
DOI: 10.1038/S41564-022-01135-7
Abstract: Genetically distinct variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged since the start of the COVID-19 pandemic. Over this period, we developed a rapid platform (R-20) for viral isolation and characterization using primary remnant diagnostic swabs. This, combined with quarantine testing and genomics surveillance, enabled the rapid isolation and characterization of all major SARS-CoV-2 variants circulating in Australia in 2021. Our platform facilitated viral variant isolation, rapid resolution of variant fitness using nasopharyngeal swabs and ranking of evasion of neutralizing antibodies. In late 2021, variant of concern Omicron (B1.1.529) emerged. Using our platform, we detected and characterized SARS-CoV-2 VOC Omicron. We show that Omicron effectively evades neutralization antibodies and has a different entry route that is TMPRSS2-independent. Our low-cost platform is available to all and can detect all variants of SARS-CoV-2 studied so far, with the main limitation being that our platform still requires appropriate biocontainment.
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/MDM.2016.61
Publisher: IEEE
Date: 11-2008
Publisher: ACM
Date: 30-01-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 03-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 06-2018
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 10-2015
Publisher: Hawaii International Conference on System Sciences
Date: 2022
Publisher: Hawaii International Conference on System Sciences
Date: 2019
Publisher: ACM
Date: 17-10-2022
Publisher: MDPI AG
Date: 10-02-2022
DOI: 10.3390/S22041344
Abstract: The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI’s novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI’s architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 09-2019
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: ACM
Date: 17-10-2022
Publisher: ACM
Date: 13-07-2009
Publisher: TESL Canada Federation
Date: 14-01-2023
DOI: 10.18806/TESL.V39.I1/1375
Abstract: This article discusses a 2021 survey of French as a second language (FSL) teacher candidates (TCs) in Faculties of Education in Ontario whose practice teaching experiences were affected by the COVID-19 pandemic, pivoting them into remote FSL teaching and learning. The survey, which formed a component of a larger mixed method SSHRC-funded research project1, was designed to capture the varied practice teaching experiences of FSL teacher candidates in order to ascertain symmetries and asymmetries in their preferred digital practices, devices and tools for social communication, and for French language teaching and learning. Survey respondents (N=17) from different teacher education programs in universities across Ontario provided a picture of scattered and fragmented approaches to FSL digital pedagogies and hinted at a persistent reliance on traditional FSL pedagogies in the classroom. Digital preferences for teaching and learning were, interestingly, not parallel, and were anchored in common educational tools and platforms that reaffirmed teacher-centred approaches to FSL rather than more innovative, learner-centred, and agentive language teaching and learning. The survey results raise an important question: Has FSL teacher education adequately moved with the communicative changes wrought by socio-technical change and related pedagogical innovations?
Publisher: IEEE
Date: 03-2014
Publisher: MDPI AG
Date: 16-10-2021
DOI: 10.3390/RS13204148
Abstract: The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.
Publisher: IEEE
Date: 04-2008
DOI: 10.1109/MDMW.2008.6
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 2010
DOI: 10.1109/MDM.2010.59
Publisher: American Association for the Advancement of Science (AAAS)
Date: 16-11-2022
DOI: 10.1126/SCITRANSLMED.ABO5795
Abstract: Interstitial lung disease and associated fibrosis occur in a proportion of in iduals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied in iduals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. In iduals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these in iduals at 12 months after recovery indicated that a subset of the in iduals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: American Society for Clinical Investigation
Date: 15-04-2022
DOI: 10.1172/JCI120901
Publisher: IEEE
Date: 12-2021
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS53846.2021.00023
Publisher: JMIR Publications Inc.
Date: 09-11-2021
Abstract: dherence to anticancer medicines is critical for the success of cancer treatments however, nonadherence remains challenging, and there is limited evidence of interventions to improve adherence to medicines in patients with cancer. his overview of reviews aimed to identify and summarize available reviews of interventions to improve adherence to oral anticancer medicines in adult cancer survivors. comprehensive search of 7 electronic databases was conducted by 2 reviewers who independently conducted the study selection, quality assessment using the A Measurement Tool to Assess Systematic Reviews 2, and data extraction. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist was adapted to report the results. total of 29 reviews were included in the narrative synthesis. The overall quality of the systematic reviews was low. The 4 main strategies to promote adherence were focused on education, reminders, behavior and monitoring, and multicomponent approaches. Digital technology–based interventions were reported in most reviews (27/29, 93%). A few interventions applied theories (10/29, 34%), design frameworks (2/29, 7%), or engaged stakeholders (1/29, 3%) in the development processes. The effectiveness of interventions was inconsistent between and within reviews. However, interventions using multiple strategies to promote adherence were more likely to be effective than single-strategy interventions (12/29, 41% reviews). Unidirectional communication (7/29, 24% reviews) and technology alone (11/29, 38% reviews) were not sufficient to demonstrate improvement in adherence outcomes. Nurses and pharmacists played a critical role in promoting patient adherence to oral cancer therapies, especially with the support of digital technologies (7/29, 24% reviews). ulticomponent interventions are potentially effective in promoting patient adherence to oral anticancer medicines. The seamless integration of digital solutions with direct clinical contacts is likely to be effective in promoting adherence. Future research for developing comprehensive digital adherence interventions should be evidence-based, theory-based, and rigorously evaluated.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 2018
Publisher: IEEE
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Wiley
Date: 17-01-2017
DOI: 10.1002/SPE.2475
Publisher: IEEE
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 08-01-2022
Publisher: European Respiratory Society (ERS)
Date: 04-05-2022
DOI: 10.1183/16000617.0274-2021
Abstract: Respiratory virus infections initiate in the upper respiratory tract (URT). Innate immunity is critical for initial control of infection at this site, particularly in the absence of mucosal virus-neutralising antibodies. If the innate immune response is inadequate, infection can spread to the lower respiratory tract (LRT) causing community-acquired pneumonia (as exemplified by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/coronavirus disease 2019). Vaccines for respiratory viruses (influenza and SARS-CoV-2) leverage systemic adaptive immunity to protect from severe lung disease. However, the URT remains vulnerable to infection, enabling viral transmission and posing an ongoing risk of severe disease in populations that lack effective adaptive immunity. Innate immunity is triggered by host cell recognition of viral pathogen-associated molecular patterns via molecular sensors such as Toll-like receptors (TLRs). Here we review the role of TLRs in respiratory viral infections and the potential of TLR-targeted treatments to enhance airway antiviral immunity to limit progression to severe LRT disease and reduce person-to-person viral transmission. By considering cellular localisation and antiviral mechanisms of action and treatment route/timing, we propose that cell surface TLR agonist therapies are a viable strategy for preventing respiratory viral diseases by providing immediate, durable pan-viral protection within the URT.
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/MDM.2016.40
Publisher: Springer Science and Business Media LLC
Date: 08-08-2016
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 15-08-2017
DOI: 10.3390/JSAN6030017
Publisher: MDPI AG
Date: 26-03-2019
DOI: 10.3390/S19061478
Abstract: As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between ‘things’, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.
Publisher: ACM
Date: 07-12-2020
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2022
DOI: 10.1145/3510411
Abstract: This article surveys existing techniques for meeting the time-bound requirements of time-sensitive applications in the Internet of Things (IoT). To provide the foundation for identifying and classifying relevant techniques, we present three s le time-sensitive IoT applications and their time-bound requirements, describe the main computation and network resources in IoT that can be used to process such applications, and identify the main challenges in meeting their time-bound requirements. Based on these, the article presents a comprehensive literature review of existing techniques and tools that can help meet application-specific time-bound requirements in IoT. The article also includes a gap analysis in existing research outcomes and proposes research directions for bridging the remaining research gaps in supporting time-sensitive IoT applications.
Publisher: American Physiological Society
Date: 10-2022
DOI: 10.1152/AJPLUNG.00230.2022
Abstract: Primary bronchial epithelial cells (pBECs) obtained from donors have limited proliferation capacity. Recently, conditional reprogramming (CR) technique has overcome this and has provided the potential for extended passaging and subsequent differentiation of cells at air-liquid interface (ALI). However, there has been no donor-specific comparison of cell morphology, baseline gene expression, barrier function, and antiviral responses compared with their “parent” pBECs, especially cells obtained from donors with asthma. We, therefore, collected and differentiated pBECs at ALI from mild donors with asthma ( n = 6) for the parent group. The same cells were conditionally reprogrammed and later differentiated at ALI. Barrier function was measured during the differentiation phase. Morphology and baseline gene expression were compared at terminal differentiation. Viral replication kinetics and antiviral responses were assessed following rhinovirus (RV) infection over 96 h. Barrier function during the differentiation phase and cell structural morphology at terminal differentiation appear similar in both parent and CR groups, however, there were elongated cell structures superficial to basal cells and significantly lower FOXJ1 expression in CR group. IFN gene expression was also significantly lower in CR group compared with parent asthma group following RV infection. The CR technique is a beneficial tool to proliferate pBECs over extended passages. Considering lower FOXJ1 expression, viral replication kinetics and antiviral responses, a cautious approach should be taken while choosing CR cells for experiments. In addition, as lab-to-lab cell culture techniques vary, the most appropriate technique must be utilized to best match in idual cell functions and morphologies to address specific research questions and experimental reproducibility across the labs.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: American Physiological Society
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Elsevier BV
Date: 02-2019
Publisher: MDPI AG
Date: 09-11-2016
DOI: 10.3390/S16111884
Publisher: IEEE
Date: 07-2019
Publisher: Springer International Publishing
Date: 2017
Publisher: TESL Canada Federation
Date: 14-01-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 06-06-2014
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 2014
Publisher: MDPI AG
Date: 14-10-2021
DOI: 10.3390/S21206829
Abstract: Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.
Publisher: IEEE
Date: 07-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Start Date: 06-2020
End Date: 12-2024
Amount: $390,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2022
End Date: 04-2025
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2024
End Date: 06-2029
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded Activity