ORCID Profile
0000-0002-8188-2601
Current Organisations
University of Sydney
,
Prince Sattam Bin Abdulaziz University
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Publisher: Springer Science and Business Media LLC
Date: 31-03-2021
DOI: 10.1186/S13643-021-01641-5
Abstract: Group A Streptococcus (Strep A) is an important cause of mortality and morbidity globally. This bacterium is responsible for a range of different infections and post-infectious sequelae. Summarising the current knowledge of Strep A transmission to humans will address gaps in the evidence and inform prevention and control strategies. The objective of this study is to evaluate the modes of transmission and attack rates of group A streptococcal infection in human populations. This systematic review protocol was prepared according to the Preferred Reporting Items for Systematic reviews and Meta-analysis Protocols (PRISMA-P) 2015 Statement. Using a comprehensive search strategy to identify any transmission studies that have been published in English since 1980, full-text articles will be identified and considered for inclusion against predefined criteria. We will include all studies reporting on Strep A transmission, who have identified a mode of transmission, and who reported attack rates. Risk of bias will be appraised using an appropriate tool. Our results will be described narratively and where feasible and appropriate, a meta-analysis utilizing the random-effects model will be used to aggregate the incidence proportions (attack rates) for each mode of transmission. In addition, we will also evaluate the emm genotype variants of the M protein causing Strep A infection and the association with transmission routes and attack rates, if any, by setting, socioeconomic background and geographical regions. We anticipate that this review will contribute to elucidating Strep A modes of transmission which in turn, will serve to inform evidence-based strategies including environmental health activities to reduce the transmission of Strep A in populations at risk of severe disease. Systematic review registration: PROSPERO ( CRD42019138472 ).
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.CHEMOSPHERE.2019.05.063
Abstract: Furan is a colorless toxic chemical produced in various food items during heat processing and in chemical industries. Both in vitro and in vivo studies have reported that it induces oxidative stress and endocrine disruption however, limited data are available regarding the effects of furan on the reproduction of mammals. In the present study, an in vitro experiment was designed to investigate the direct effects of furan exposure on oxidative stress and testosterone concentration in rat testicular tissue. Furan not only generated high oxidative stress but also decreased antioxidant enzyme activity in the testicular tissue. On the basis of in vitro study results, an in vivo sub-chronic exposure study was performed. Male rats were orally exposed to different concentrations of furan (0, 5, 10, 20, and 40 mg kg
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 09-2011
Publisher: IEEE
Date: 04-2014
Publisher: Elsevier BV
Date: 2017
Publisher: Asian Fisheries Society
Date: 31-03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-09-2020
DOI: 10.36227/TECHRXIV.12845717.V1
Abstract: The targeted advertising is based on preference profiles inferred via relationships among in iduals, their monitored responses to previous advertising and temporal activity over the Internet, which has raised critical privacy concerns. In this paper, we present a novel proposal for a Blockchain-based advertising platform that provides: a system for privacy preserving user profiling, privately requesting ads from the advertising system, the billing mechanisms for presented and clicked ads, the advertising system that uploads ads to the cloud according to profiling interests, various types of transactions to enable advertising operations in Blockchain-based network, and the method that allows a cloud system to privately compute the access policies for various resources (such as ads, mobile user profiles). Our main goal is to design a decentralized framework for targeted ads, which enables private delivery of ads to users whose behavioral profiles accurately match the presented ads, defined by the ad system. We implement a POC of our proposed framework i.e. a Bespoke Miner and experimentally evaluate various components of Blockchain-based in-app advertising system, implementing various critical components such as, evaluating user profiles, implementing access policies, encryption and decryption of users' profiles. We observe that the processing delay for traversing policies of various tree sizes, the encryption/decryption time of user profiling with various key-sizes and user profiles of various interests evaluates to an acceptable amount of processing time as that of the currently implemented ad systems. br br
Publisher: Springer Science and Business Media LLC
Date: 20-11-2021
DOI: 10.1186/S12879-021-06829-7
Abstract: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, 0.17605/OSF.IO/GEHFX ). In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92 1.02) with between-study heterogeneity not beyond chance (I 2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care.
Publisher: MDPI AG
Date: 10-06-2022
DOI: 10.3390/APP12125928
Abstract: The digital forensic tools used by law enforcement agencies for forensic investigations are mostly proprietary and commercially expensive although open-source tools are used, the investigations conducted with such tools are not verified by reputable organisations, and hence, users are reluctant to practice such tools. To address this issue, we experimentally evaluate three open-source forensic tools based on various requirements recommended by the National Institute of Standards and Technology (NIST) framework for forensic investigation. The experimental setup consists of a forensic workstation, write-blocker, and purchased USB hard drives investigated via digital forensic imaging tools, i.e., DC3DD, DCFLDD, and Guymager. We create various test cases, which distribute USB hard drives in different groups and investigate the functional and optional requirements of NIST along with recovering and analysing remnant data. We evaluate these forensic tools by analysing the log information, following, anonymously (to ensure that data were not disclosed or misused during or after the investigations) collecting, examining, and classifying the remnant data restored from the USB hard drives. We observe that the percentage of hardware resources usage and the processing time of each tool are remarkably different, e.g., Guymager was the fastest tool and met all the functional requirements in each test case, but it utilised more CPU and memory resources than DC3DD, DCFLDD. We note that 88.23% of the USB hard drives contained sensitive personal or business information (e.g., personal photos, bank transactions, and contracts). Subsequently, the remnant data analysis shows that consumers in New Zealand are unaware of personal data security and the associated vulnerabilities of data leakages.
Publisher: BMJ
Date: 2022
DOI: 10.1136/BMJOPEN-2021-055217
Abstract: When the COVID-19 pandemic was declared, Governments responded with lockdown and isolation measures to combat viral spread, including the closure of many schools. More than a year later, widespread screening for SARS-CoV-2 is critical to allow schools and other institutions to remain open. Here, we describe the acceptability of a minimally invasive COVID-19 screening protocol trialled by the Western Australian Government to mitigate the risks of and boost public confidence in schools remaining open. To minimise discomfort, and optimise recruitment and tolerability in unaccompanied children, a combined throat and nasal (OP/Na) swab was chosen over the nasopharyngeal swab commonly used, despite slightly reduced test performance. Trialling of OP/Na swabbing took place as part of a prospective observational cohort surveillance study in 79 schools across Western Australia. Swabs were collected from 5903 asymptomatic students and 1036 asymptomatic staff in 40 schools monthly between June and September 2020. PCR testing was performed with a two-step diagnostic and independent confirmatory PCR for any diagnostic PCR positives. Concurrent surveys, collected online through the REDCap platform, evaluated participant experiences of in-school swabbing. 13 988 swabs were collected from students and staff. There were zero positive test results for SARS-CoV-2, including no false positives. Participants reported high acceptability: 71% of students reported no or minimal discomfort and most were willing to be reswabbed (4% refusal rate). OP/Na swabbing is acceptable and repeatable in schoolchildren as young as 4 years old and may combat noncompliance rates by significantly increasing the acceptability of testing. This kind of minimally-invasive testing will be key to the success of ongoing, voluntary mass screening as society adjusts to a new ‘normal’ in the face of COVID-19. Australian New Zealand Clinical Trials Registry—ACTRN12620000922976.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-11-2020
DOI: 10.36227/TECHRXIV.12952073.V2
Abstract: Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among in iduals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues. br
Publisher: Elsevier BV
Date: 09-2021
Publisher: The Royal Australian College of General Practitioners
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 27-04-2022
DOI: 10.36227/TECHRXIV.13198775
Abstract: Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.
Publisher: IEEE
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-09-2020
DOI: 10.36227/TECHRXIV.12952073.V1
Abstract: Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among in iduals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues. br
Publisher: MDPI AG
Date: 04-11-2022
DOI: 10.3390/S22218516
Abstract: Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks a malicious malware that encrypts the patients’ critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various s les of malware and 46 variants, with a duration of sixty minutes for each s le, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.
Publisher: AMPCo
Date: 11-2020
DOI: 10.5694/MJA2.50853
Publisher: AMPCo
Date: 18-06-2021
DOI: 10.5694/MJA2.51143
Publisher: MDPI AG
Date: 22-09-2022
DOI: 10.3390/ELECTRONICS11193015
Abstract: Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-09-2020
DOI: 10.36227/TECHRXIV.12845717
Abstract: The targeted advertising is based on preference profiles inferred via relationships among in iduals, their monitored responses to previous advertising and temporal activity over the Internet, which has raised critical privacy concerns. In this paper, we present a novel proposal for a Blockchain-based advertising platform that provides: a system for privacy preserving user profiling, privately requesting ads from the advertising system, the billing mechanisms for presented and clicked ads, the advertising system that uploads ads to the cloud according to profiling interests, various types of transactions to enable advertising operations in Blockchain-based network, and the method that allows a cloud system to privately compute the access policies for various resources (such as ads, mobile user profiles). Our main goal is to design a decentralized framework for targeted ads, which enables private delivery of ads to users whose behavioral profiles accurately match the presented ads, defined by the ad system. We implement a POC of our proposed framework i.e. a Bespoke Miner and experimentally evaluate various components of Blockchain-based in-app advertising system, implementing various critical components such as, evaluating user profiles, implementing access policies, encryption and decryption of users' profiles. We observe that the processing delay for traversing policies of various tree sizes, the encryption/decryption time of user profiling with various key-sizes and user profiles of various interests evaluates to an acceptable amount of processing time as that of the currently implemented ad systems. br br
Publisher: PeerJ
Date: 03-11-2020
DOI: 10.7717/PEERJ.10203
Abstract: Households are known to be high-risk locations for the transmission of communicable diseases. Numerous modelling studies have demonstrated the important role of households in sustaining both communicable diseases outbreaks and endemic transmission, and as the focus for control efforts. However, these studies typically assume that households are associated with a single dwelling and have static membership. This assumption does not appropriately reflect households in some populations, such as those in remote Australian Aboriginal and Torres Strait Islander communities, which can be distributed across more than one physical dwelling, leading to the occupancy of in idual dwellings changing rapidly over time. In this study, we developed an in idual-based model of an infectious disease outbreak in communities with demographic and household structure reflective of a remote Australian Aboriginal community. We used the model to compare the dynamics of unmitigated outbreaks, and outbreaks constrained by a household-focused prophylaxis intervention, in communities exhibiting fluid vs. stable dwelling occupancy. We found that fluid dwelling occupancy can lead to larger and faster outbreaks in modelled scenarios, and may interfere with the effectiveness of household-focused interventions. Our findings suggest that while short-term restrictions on movement between dwellings may be beneficial during outbreaks, in the longer-term, strategies focused on reducing household crowding may be a more effective way to reduce the risk of severe outbreaks occurring in populations with fluid dwelling occupancy.
Publisher: American Society for Microbiology
Date: 24-02-2021
Abstract: Staphylococcus aureus is an important human pathogen that causes a wide range of clinical infections. In the past 2 decades, an epidemic of community-associated skin and soft tissue infections has been driven by S. aureus strains with specific virulence factors and resistance to beta-lactam antibiotics.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 12-07-2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 19-07-2021
Publisher: IEEE
Date: 05-2011
Publisher: MDPI AG
Date: 20-06-2022
DOI: 10.3390/MATH10122141
Abstract: More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes. Machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. However, the ML-training procedure incorporates large data which is transferable to the central server since data are created continually by IoT devices at the edge. In other words, conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy. The Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. By exchanging updated weights with the centralized FL-server, the data are kept on local IoT devices while federating training cycles over GRUs (Gated Recurrent Units) models. The ensemble module of the technique assesses updates from several sources for improving the accuracy of the global ML technique. Experiments have shown that the proposed method surpasses the state-of-the-art techniques in protecting user data by registering enhanced performance measures of Statistical Analysis, Energy Efficiency, Memory Utilization, Attack Classification, and Client Accuracy Analysis for the identification of attacks.
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: BMJ
Date: 07-2021
DOI: 10.1136/BMJPO-2021-001130
Abstract: To characterise the epidemiology, clinical features and treatment of paediatric cellulitis. A retrospective study of children presenting to a paediatric tertiary hospital in Western Australia, Australia in 2018. All inpatient records from 1 January to 31 December 2018 and emergency department presentations from 1 July to 31 December 2018 were screened for inclusion. 302 episodes of cellulitis were included comprising 206 (68.2%) admitted children and 96 (31.8%) non-admitted children. The median age was 5 years (IQR 2–9), 40 (13.2%) were Aboriginal and 180 (59.6%) boys. The extremities were the most commonly affected body site among admitted and non-admitted patients. There was a greater proportion of facial cellulitis in admitted patients (27.2%) compared with non-admitted patients (5.2%, p .01). Wound swab was the most frequent microbiological investigation (133/302, 44.0%), yielding positive cultures in the majority of those tested (109/133, 82.0%). The most frequent organisms identified were Staphylococcus aureus (94/109, 86.2%) (methicillin-susceptible S. aureus (60/94, 63.8%), methicillin-resistant S. aureus ) and Streptococcus pyogenes (22/109, 20.2%) with 14 identifying both S. aureus and S. pyogenes . Intravenous flucloxacillin was the preferred antibiotic (154/199, 77.4%), with median intravenous duration 2 days (IQR 2–3), oral 6 days (IQR 5–7) and total 8 days (IQR 7–10). Cellulitis is a common reason for presentation to a tertiary paediatric hospital. We confirm a high prevalence of extremity cellulitis and demonstrate that children with facial cellulitis often require admission. Cellulitis disproportionately affected Aboriginal children and children below 5 years. Prevention of cellulitis involves early recognition and treatment of skin infections such as impetigo and scabies.
Publisher: MDPI AG
Date: 22-09-2021
DOI: 10.3390/ELECTRONICS10192320
Abstract: The Internet of Things (IoT) is aimed to provide efficient and seamless connectivity to a large number of low-power and low-cost embedded devices, consequently, the routing protocols play a fundamental role in achieving these goals. The IETF has recently standardized the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) for LLNs (i.e., Low-power and Lossy Networks) and is well-accepted among the Internet community. However, RPL was proposed for static IoT devices and suffers from many issues when IoT devices are mobile. In this paper, we first present various issues that are faced by the RPL when IoT devices are mobile. We then carry out a detailed survey of various solutions that are proposed in the current literature to mitigate the issues faced by RPL. We classify various solutions into five categories i.e., ‘Trickle-timer based solutions’, ‘ETX based solutions’, ‘RSSI based solutions’, ‘Position-based solutions’, and ‘Miscellaneous solutions’. For each category of these solutions, we illustrate their working principles, issues addressed and make a thorough assessment of their strengths and weaknesses. In addition, we found several flaws in the performance analysis done by the authors of each of the solutions, e.g., nodes mobility, time intervals, etc., and suggest further investigations for the performance evaluations of these solutions in order to assess their applicability in real-world environments. Moreover, we provide future research directions for RPL supporting various real-time applications, mobility support, energy-aware, and privacy-aware routing.
Publisher: Oxford University Press (OUP)
Date: 15-09-2020
DOI: 10.1093/JAC/DKAA382
Abstract: Itraconazole remains a first-line antifungal agent for certain fungal infections in children, including allergic bronchopulmonary aspergillosis (ABPA) and sporotrichosis, but poor attainment of therapeutic drug levels is frequently observed with available oral formulations. A formulation of ‘SUper BioAvailability itraconazole’ (SUBA-itraconazole Lozanoc®) has been developed, with adult studies demonstrating rapid and reliable attainment of therapeutic levels, yet paediatric data are lacking. To assess the safety, efficacy and attainment of therapeutic drug levels of the SUBA-itraconazole formulation in children. A single-centre retrospective cohort study was conducted, including all patients prescribed SUBA-itraconazole from May 2018 to February 2020. The recommended initial treatment dose was 5 mg/kg twice daily (to a maximum of 400 mg/day) rounded to the nearest capsule size and 2.5 mg/kg/day for prophylaxis. Nineteen patients received SUBA-itraconazole and the median age was 12 years. The median dose was 8.5 mg/kg/day and the median duration was 6 weeks. Indications included ABPA (16 patients), sporotrichosis (1), cutaneous fungal infection (1) and prophylaxis (1). Of patients with serum levels measured, almost 60% (10/17) achieved a therapeutic level, 3 with one dose adjustment and 7 following the initial dose. Adherence to dose-adjustment recommendations amongst the seven patients not achieving therapeutic levels was poor. Of patients with ABPA, 13/16 (81%) demonstrated a therapeutic response in IgE level. SUBA-itraconazole was well tolerated with no cessations related to adverse effects. SUBA-itraconazole is well tolerated in children, with rapid attainment of therapeutic levels in the majority of patients, and may represent a superior formulation for children in whom itraconazole is indicated for treatment or prevention of fungal infection.
Publisher: BMJ
Date: 03-2022
DOI: 10.1136/BMJOPEN-2021-056239
Abstract: To establish the priorities of primary care providers to improve assessment and treatment of skin sores and sore throats among Aboriginal and Torres Strait Islander people at risk of acute rheumatic fever (ARF) and rheumatic heart disease (RHD). Modified eDelphi survey, informed by an expert focus group and literature review. Primary care services in any one of the five Australian states or territories with a high burden of ARF. People working in any primary care role within the last 5 years in jurisdiction with a high burden of ARF. Nine people participated in the scoping expert focus group which informed identification of an access framework for subsequent literature review. Fifteen broad concepts, comprising 29 strategies and 63 different actions, were identified on this review. These concepts were presented to participants in a two-round eDelphi survey. Twenty-six participants from five jurisdictions participated, 16/26 (62%) completed both survey rounds. Seven strategies were endorsed as high priorities. Most were demand-side strategies with a focus on engaging communities and in iduals in accessible, comprehensive, culturally appropriate primary healthcare. Eight strategies were not endorsed as high priority, all of which were supply-side approaches. Qualitative responses highlighted the importance of a comprehensive primary healthcare approach as standard of care rather than disease-specific strategies related to management of skin sores and sore throat. Primary care staff priorities should inform Australia’s commitments to reduce the burden of RHD. In particular, strategies to support comprehensive Aboriginal and Torres Strait Islander primary care services rather than an exclusive focus on discrete, disease-specific initiatives are needed.
Publisher: Elsevier BV
Date: 02-2021
Publisher: MDPI AG
Date: 11-05-2023
DOI: 10.3390/MATH11102262
Abstract: Internet of Things (IoT) technology has uncovered a wide range of possibilities in several industrial sectors where smart devices are capable of exchanging real-time data. Machine-to-machine (M2M) data exchange provides a new method for connecting and exchanging data among machine-oriented communication entities (MOCE). Conspicuously, network services will be severely affected if the underneath IoT infrastructure is disrupted. Moreover, it is difficult for MOCEs to re-establish connectivity automatically. Conspicuously, in the current paper, an analysis is performed regarding potential technologies including unmanned aerial vehicles, blockchain, and mobile edge computing (MEC) that can enable the secure establishment of M2M communications networks that have been compromised to maintain the secure transmissible data. Furthermore, a Markov decision process-based joint optimization approach is proposed for blockchain systems that aims to elevate computational power and performance. Additionally, the dueling deep Q-network (DDQ) is incorporated to address the dynamic and complex optimization issue so that UAV selection is ensured to maximize performance. The results of experimental simulation with several statistical attributes suggest that the proposed framework can increase throughput optimally in comparison to state-of-the-art techniques. Additionally, a performance measure of reliability and stability depicts significant enhancement for the proposed framework.
Publisher: MDPI AG
Date: 28-12-2022
DOI: 10.3390/MATH11010156
Abstract: IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computing nodes has been a major challenge. State-of-the-art task allocation techniques such as Min–Max, Minimum Completion time, and Round Robin perform task allocation, butv several limitations persist including large energy consumption, delay, and error rate. Henceforth, the current work provides a Quantum Computing-inspired optimization technique for efficient task allocation in an Edge Computing environment for real-time IoT applications. Furthermore, the QC-Neural Network Model is employed for predicting optimal computing nodes for delivering real-time services. To acquire the performance enhancement, simulations were performed by employing 6, 10, 14, and 20 Edge nodes at different times to schedule more than 600 heterogeneous tasks. Empirical results show that an average improvement of 5.02% was registered for prediction efficiency. Similarly, the error reduction of 2.03% was acquired in comparison to state-of-the-art techniques.
Publisher: Public Library of Science (PLoS)
Date: 18-11-2020
DOI: 10.1371/JOURNAL.PONE.0242107
Abstract: Group A streptococcal (GAS) pharyngitis has traditionally been considered the sole precursor of acute rheumatic fever (ARF). Evidence from Australia, however, suggests that GAS skin infections may contribute to the pathogenesis of ARF. A missing piece of evidence is the incidence of sore throat and GAS pharyngitis in this setting. We conducted a systematic review and meta-analysis of the incidence of sore throat and GAS pharyngitis in all children at risk of developing ARF. Databases were systematically searched for studies reporting on the incidence of pharyngitis among children from low to upper-middle income countries, and Indigenous children living in high-income countries. Studies were subjected to data extraction by two independent reviewers. Following an assessment of the methodological quality of the studies, we extracted incidence rates (IRs) and conducted a meta-analysis. This systematic review is registered on PROSPERO (CRD42019113019). From 607 titles identified by the search, 11 articles met the predetermined inclusion criteria ten studies reported IRs while for the remaining study, the incidence was calculated. The pooled incidence estimated for sore throat was 82.5 per 100 child-years (95% confidence interval [CI], 6.5 to 1044.4 per 100 child-years, I 2 = 100%) and GAS pharyngitis was 10.8 per 100 child-years (95% CI, 2.3 to 50.0 per 100 child-years, I 2 = 99.9%). The pooled IRs for sore throat in children at risk of developing ARF were higher than rates reported in developed nations (32.70–40 per 100 child-years) and similar for GAS pharyngitis (12.8–14 per 100 years). The limited Australian data lend support to the need for further studies to inform the role of GAS pharyngitis in the development of ARF in Australian Indigenous children, so as to inform local primary prevention strategies for ARF and Rheumatic Heart Disease (RHD).
Publisher: Elsevier BV
Date: 02-2023
Publisher: Frontiers Media SA
Date: 22-02-2021
DOI: 10.3389/FPUBH.2021.636921
Abstract: Introduction: Amidst the evolving COVID-19 pandemic, understanding the transmission dynamics of the SARS-CoV-2 virus is key to providing peace of mind for the community and informing policy-making decisions. While available data suggest that school-aged children are not significant spreaders of SARS-CoV-2, the possibility of transmission in schools remains an ongoing concern, especially among an aging teaching workforce. Even in low-prevalence settings, communities must balance the potential risk of transmission with the need for students' ongoing education. Through the roll out of high-throughput school-based SARS-CoV-2 testing, enhanced follow-up for in iduals exposed to COVID-19 and wellbeing surveys, this study investigates the dynamics of SARS-CoV-2 transmission and the current psychosocial wellbeing impacts of the pandemic in school communities. Methods: The DETECT Schools Study is a prospective observational cohort surveillance study in 79 schools across Western Australia (WA), Australia. To investigate the incidence, transmission and impact of SARS-CoV-2 in schools, the study comprises three “modules”: Module 1) Spot-testing in schools to screen for asymptomatic SARS-CoV-2 Module 2) Enhanced surveillance of close contacts following the identification of any COVID-19 case to determine the secondary attack rate of SARS-CoV-2 in a school setting and Module 3) Survey monitoring of school staff, students and their parents to assess psycho-social wellbeing following the first wave of the COVID-19 pandemic in WA. Clinical Trial Registration: Trial registration number: ACTRN12620000922976
Publisher: Springer Science and Business Media LLC
Date: 21-06-2021
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.FCT.2019.05.020
Abstract: Furan is a colorless toxic organic compound that is produced during thermal degradation of natural food constituents, and is present in various processed foods such as coffee and processed baby foods. The present study investigated the endocrine disrupting potential of furan in Sprague Dawley male pups. On postnatal day 0 (PND 0), pups were ided into five groups. The control group received subcutaneous injections of corn oil (50 μL), while the treated groups were injected with one of four concentrations of furan (1, 5, 10 and 20 mg kg
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/LCN.2017.63
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-11-2020
DOI: 10.36227/TECHRXIV.12952073
Abstract: Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among in iduals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues. br
Publisher: Wiley
Date: 02-03-2021
DOI: 10.1111/JPC.15385
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 24-12-2022
DOI: 10.1007/S10207-022-00655-X
Abstract: Targeted advertising has transformed the marketing landscape for a wide variety of businesses, by creating new opportunities for advertisers to reach prospective customers by delivering personalised ads, using an infrastructure of a number of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process, and trade a vast amount of users’ personal data, which has prompted serious privacy concerns among both in iduals and organisations. This article presents a comprehensive survey of the privacy risks and proposed solutions for targeted advertising in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, the measurement analysis of targeted advertising based on user’s interests and profiling context, and the ads delivery process, for both in-app and in-browser targeted ads we also include an overview of data sharing and tracking technologies. We discuss challenges in preserving the mobile user’s privacy that include threats related to private information extraction and exchange among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks. Subsequently, we present various techniques for preserving user privacy and a comprehensive analysis of the proposals based on such techniques we compare the proposals based on the underlying architectures, privacy mechanisms, and deployment scenarios. Finally, we discuss the potential research challenges and open research issues.
Publisher: Wiley
Date: 29-10-2020
DOI: 10.1111/AJO.13270
Publisher: American Society of Tropical Medicine and Hygiene
Date: 02-12-2020
Publisher: MDPI AG
Date: 14-04-2022
DOI: 10.3390/MATH10081298
Abstract: The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. Software and communication technology have advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a new era of intelligent devices. However, the rapid expansion has raised security concerns. Another challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric strategy results from network congestion, data bottlenecks, and longer response times to security threats. Fog computing addresses these difficulties by bringing computation to the network edge. The current research provides a comprehensive review of the IoT evolution, Fog computation, and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog computing security concerns. Additionally, it covers future research objectives in the crucial field of IoT security.
Publisher: IEEE
Date: 12-2014
Publisher: Oxford University Press (OUP)
Date: 06-12-2020
DOI: 10.1093/CID/CIAA1825
Abstract: The role of children in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains highly controversial. To address this issue, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n = 213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a pediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR], 0.17 95% confidence interval [CI], 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate in pediatric household contacts was assessed. The secondary attack rate in pediatric household contacts was lower than in adult household contacts (RR, 0.62 95% CI, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic, including potential vaccine prioritization strategies.
Publisher: Wiley
Date: 08-06-2021
DOI: 10.1111/JPC.15588
Abstract: In 2020, school and early childhood educational centre (ECEC) closures affected over 1.5 billion school‐aged children globally as part of the COVID‐19 pandemic response. Attendance at school and access to ECEC is critical to a child's learning, well‐being and health. School closures increase inequities by disproportionately affecting vulnerable children. Here, we summarise the role of children and adolescents in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) transmission and that of schools and ECECs in community transmission and describe the Australian experience. In Australia, most SARS‐CoV‐2 cases in schools were solitary (77% in NSW and 67% in Victoria) of those that did progress to an outbreak, % involved fewer than 10 cases. Australian and global experience has demonstrated that SARS‐CoV‐2 is predominantly introduced into schools and ECECs during periods of heightened community transmission. Implementation of public health mitigation strategies, including effective testing, tracing and isolation of contacts, means schools and ECECs can be safe, not drivers of transmission. Schools and ECEC are essential services and so they should be prioritised to stay open for face‐to‐face learning. This is particularly critical as we continue to manage the next phase of the COVID‐19 pandemic.
Publisher: MDPI AG
Date: 31-01-2022
DOI: 10.3390/S22031094
Abstract: Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.
Publisher: AMPCo
Date: 14-06-2021
DOI: 10.5694/MJA2.51126
Publisher: IEEE
Date: 12-2012
Publisher: Public Library of Science (PLoS)
Date: 24-02-2021
DOI: 10.1371/JOURNAL.PNTD.0009149
Abstract: The suboptimal sensitivity and specificity of available diagnostic methods for scabies h ers clinical management, trials of new therapies and epidemiologic studies. Additionally, parasitologic diagnosis by microscopic examination of skin scrapings requires s le collection with a sharp scalpel blade, causing discomfort to patients and difficulty in children. Polymerase chain reaction (PCR)-based diagnostic assays, combined with non-invasive s ling methods, represent an attractive approach. In this study, we aimed to develop a real-time probe-based PCR test for scabies, test a non-invasive s ling method and evaluate its diagnostic performance in two clinical settings. High copy-number repetitive DNA elements were identified in draft Sarcoptes scabiei genome sequences and used as assay targets for diagnostic PCR. Two suitable repetitive DNA sequences, a 375 base pair microsatellite (SSR5) and a 606 base pair long tandem repeat (SSR6), were identified. Diagnostic sensitivity and specificity were tested using relevant positive and negative control materials and compared to a published assay targeting the mitochondrial cox1 gene. Both assays were positive at a 1:100 dilution of DNA from a single mite no lification was observed in DNA from s les from 19 patients with other skin conditions nor from house dust, sheep or dog mites, head and body lice or from six common skin bacterial and fungal species. Moderate sensitivity of the assays was achieved in a pilot study, detecting 5/7 (71.4% [95% CI: 29.0% - 96.3%]) of clinically diagnosed untreated scabies patients). Greater sensitivity was observed in s les collected by FLOQ swabs compared to skin scrapings. This newly developed qPCR assay, combined with the use of an alternative non-invasive swab s ling technique offers the possibility of enhanced diagnosis of scabies. Further studies will be required to better define the diagnostic performance of these tests.
Publisher: MDPI AG
Date: 21-08-2020
DOI: 10.3390/ELECTRONICS9091361
Abstract: The proliferation of IoT devices has led to the development of smart appliances, gadgets, and instruments to realize a significant vision of a smart home. Conspicuously, this paper presents an intelligent framework of a foot-mat-based intruder-monitoring and detection system for a home-based security system. The presented approach incorporates fog computing technology for analysis of foot pressure, size, and movement in real time to detect personnel identity. The task of prediction is realized by the predictive learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) through which the proposed model can estimate the possibility of an intruder. In addition to this, the presented approach is designed to generate a warning and emergency alert signals for real-time indications. The presented framework is validated in a smart home scenario database, obtained from an online repository comprising 49,695 datasets. Enhanced performance was registered for the proposed framework in comparison to different state-of-the-art prediction models. In particular, the presented model outperformed other models by obtaining efficient values of temporal delay, statistical performance, reliability, and stability.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 08-12-2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 27-01-2021
Publisher: IEEE
Date: 02-2014
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/MATH11061348
Abstract: Unmanned aerial vehicles, drones, and internet of things (IoT) based devices have acquired significant traction due to their enhanced usefulness. The primary use is aerial surveying of restricted or inaccessible locations. Based on the aforementioned aspects, the current study provides a method based on blockchain technology for ensuring the safety and confidentiality of data collected by virtual circuit-based devices. To test the efficacy of the suggested technique, an IoT-based application is integrated with a simulated vehicle monitoring system. Pentatope-based elliptic curve encryption and secure hash algorithm (SHA) are employed to provide anonymity in data storage. The cloud platform stores technical information, authentication, integrity, and vehicular responses. Additionally, the Ethbalance MetaMask wallet is used for BCN-based transactions. Conspicuously, the suggested technique aids in the prevention of several attacks, including plaintext attacks and ciphertext attacks, on sensitive information. When compared to the state-of-the-art techniques, the outcomes demonstrate the effectiveness and safety of the suggested method in terms of operational cost (2.95 units), scalability (14.98 units), reliability (96.07%), and stability (0.82).
Publisher: Oxford University Press (OUP)
Date: 29-03-2020
DOI: 10.1111/BJD.18943
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: National Library of Serbia
Date: 2016
Abstract: The integration of heterogeneous wireless access technologies has numerous issues for multimedia applications, such as to smoothly continue over new connections without any service disruption during vertical handovers. We propose State-Aware Feedback extension to Datagram Congestion Control Protocol (DCCP) that meets QoS requirements of multimedia applications throughout the handover process. We consider movement of mobile subscribers among heterogeneous access technologies from highly unstable to more stable environment and model their mobility patterns as Uniform, Pareto, and Exponentially distributed. We present detailed analysis on the performance of proposed mechanism in terms of resuming transmissions during the handover process. In particular, we propose Markov model of TFRC capturing TCP timeouts, loss rates, and no-feedback timer expirations. We validate the proposed analytical model through simulations and show that it accurately predicts various congestion events that may happen during the handover process. We then evaluate the performance of S-TFRC via both simulations and analytical model and observe the sender rates and throughputs achieved. We also consider transmission delays and transmission rates as performance metrics and compare performance of S-TFRC with the standard TFRC. Our results show that S-TFRC is capable of providing better QoS to multimedia applications than standard TFRC by significantly reducing transmission delays and provides better throughput to multimedia applications.
Publisher: MDPI AG
Date: 19-06-2020
DOI: 10.3390/S20123467
Abstract: Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.
Publisher: BMJ
Date: 22-04-2021
Publisher: BMJ
Date: 25-08-2022
DOI: 10.1136/ARCHDISCHILD-2021-322507
Abstract: Following a relative absence in winter 2020, a large resurgence of respiratory syncytial virus (RSV) detections occurred during the 2020/2021 summer in Western Australia. This seasonal shift was linked to SARS-CoV-2 public health measures. We examine the epidemiology and RSV testing of respiratory-coded admissions, and compare clinical phenotype of RSV-positive admissions between 2019 and 2020. At a single tertiary paediatric centre, International Classification of Diseases, 10th edition Australian Modification-coded respiratory admissions longer than 12 hours were combined with laboratory data from 1 January 2019 to 31 December 2020. Data were grouped into bronchiolitis, other acute lower respiratory infection (OALRI) and wheeze, to assess RSV testing practices. For RSV-positive admissions, demographics and clinical features were compared between 2019 and 2020. RSV-positive admissions peaked in early summer 2020, following an absent winter season. Testing was higher in 2020: bronchiolitis, 94.8% vs 89.2% (p=0.01) OALRI, 88.6% vs 82.6% (p=0.02) and wheeze, 62.8% vs 25.5% (p .001). The 2020 peak month, December, contributed almost 75% of RSV-positive admissions, 2.5 times the 2019 peak. The median age in 2020 was twice that observed in 2019 (16.4 vs 8.1 months, p .001). The proportion of RSV-positive OALRI admissions was greater in 2020 (32.6% vs 24.9%, p=0.01). There were no clinically meaningful differences in length of stay or disease severity. The 2020 RSV season was in summer, with a larger than expected peak. There was an increase in RSV-positive non-bronchiolitis admissions, consistent with infection in older RSV-naïve children. This resurgence raises concern for regions experiencing longer and more stringent SARS-CoV-2 public health measures.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-11-2020
DOI: 10.36227/TECHRXIV.13198775.V1
Abstract: Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process and trade rich amount of consumer's personal data and carries out interests-based ads targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address user's privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns and interest-based ads targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve equivalent problem using Lyapunov optimisation and to overcome difficulty of solving nonlinear programming by decomposing it into various cases and achieve trade-off between user privacy, cost and targeted ads. We carry out extensive experimentations and demonstrate proposed framework's applicability by implementing its critical components using POC `System App'. We compare proposed framework with other privacy protecting approaches and investigate that it achieves better privacy and functionality for various performance parameters. br
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 15-07-2017
DOI: 10.1007/S10695-017-0402-Z
Abstract: In the present review, the ongoing researches about selenium research in fish nutrition have been comprehensively discussed. Selenium research is getting popularity in fish nutrition as it is required for the normal growth and proper physiological and biochemical functions in fish. Its deficiency or surplus amounts create severe problems in fish. It is available as inorganic form, organic form, and nano form. In fish, most of the previous research is about the selenium requirements for fish by using only one selenium source mainly the inorganic one. Selenium shows maximum biological activity and bioavailability when it is supplied in proper form. However, to differentiate the more bioavailable and less toxic form of selenium, sufficient information is needed about the comparative bioavailability of different selenium forms in different fish species. In fish, important data about the new forms of selenoproteins is still scarce. Therefore, it is necessary to focus on the determination and elucidation of the new selenoproteins in fish through the utilization of recent approaches of molecular biology and proteomics. The adaptation of these new approaches will replace the old fashioned methodologies regarding the selenium research in fish nutrition. Moreover, the use of molecular biology and proteomics-based new approaches in combination with selenium research will help in optimizing the area of fish nutrition and will improve the feed intake, growth performance, and more importantly the flesh quality which has a promising importance in the consumer market.
Publisher: MDPI AG
Date: 29-06-2022
DOI: 10.3390/SU14137950
Abstract: In differentiated learning, the teacher needs to be aware of the learning styles of students in the classroom to accommodate specific learning preferences, e.g., the Felder–Silverman learning style model. The corresponding instrument, i.e., the Felder–Silverman Index of Learning Style (ILS), was designed to assess learning styles, specifically for engineering students. The ILS has been tested at the middle school level to identify the learning styles however, validity/reliability had not been established in earlier studies with large s les. The focus of this study was to identify the validity and reliability of an ILS instrument for middle school students (N=450) by investigating the factor structure through factor analysis. This includes internal consistency reliability and constructing validity report of the ILS. An exploratory and confirmatory factor analysis was undertaken to investigate the factor structure to establish validity. As a result of the study, the reliability of the instrument was established. Five-factors emerged through exploratory factor analysis (EFA), which were subjected to confirmatory factor analysis (CFA). The outcome provided five-factors (i.e., Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Goodness of Fit (GFI)), out of which four factors were related to the four dimensions of the Felder–Silverman model, and the fifth factor was related to the association of sensing/intuitive and sequential/global dimensions of the model, which is in agreement with the theoretical construct of ILS. As a result of CFA, ILS entailing 24 items indicates a good fit with five-factor structure. CFI=0.922 TLI=0.927 RMSEA=0.026 SRMR=0.585 GFI=0.911 X2=277 df=42 =0.60. This study suggests that the ILS for the secondary-grade students needs to be revised with fewer items to improve the reliability, as supported by empirical evidence through the EFA and CFA.
Publisher: CSIRO Publishing
Date: 10-10-2022
DOI: 10.1071/MA22033
Abstract: Achieving healthy skin requires the prevention of infectious diseases that affect the skin. Prevention activities range from environmental health improvements to address inequities in living situations, through to community-wide treatment programs to reduce transmission and improve skin health. In this paper we discuss the pathogens that cause and conditions that arise when skin is infected, the burden of disease in northern Australia, and some of the current research underway to address this high burden, which predominantly affects remote-living Aboriginal and Torres Strait Islander children and families.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 27-04-2022
DOI: 10.36227/TECHRXIV.13198775.V2
Abstract: Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.
Publisher: Oxford University Press (OUP)
Date: 05-06-2022
DOI: 10.1093/CID/CIAB510
Abstract: Staphylococcus aureus is a common cause of bacteremia, yet the epidemiology and predictors of poor outcome remain inadequately defined in childhood. ISAIAH (Invasive Staphylococcus aureus Infections and Hospitalizations in children) is a prospective, cross-sectional study of S. aureus bacteremia (SAB) in children hospitalized in Australia and New Zealand over 24 months (2017–2018). Overall, 552 SABs were identified (incidence 4.4/100 000/year). Indigenous children, those from lower socioeconomic areas and neonates were overrepresented. Although 90-day mortality was infrequent, one-third experienced the composite of: length of stay & days (26%), intensive care unit admission (20%), relapse (4%), or death (3%). Predictors of mortality included prematurity (adjusted odds ratio [aOR],16.8 95% confidence interval [CI], 1.6–296.9), multifocal infection (aOR, 22.6 CI, 1.4–498.5), necrotizing pneumonia (aOR, 38.9 CI, 1.7–1754.6), multiorgan dysfunction (aOR, 26.5 CI, 4.1–268.8), and empiric vancomycin (aOR, 15.7 CI, 1.6–434.4) while infectious diseases (ID) consultation (aOR, 0.07 CI .004–.9) was protective. Neither MRSA nor vancomycin trough targets impacted survival however, empiric vancomycin was associated with nephrotoxicity (OR, 3.1 95% CI 1.3–8.1). High SAB incidence was demonstrated and for the first time in a pediatric setting, necrotizing pneumonia and multifocal infection were predictors of mortality, while ID consultation was protective. The need to reevaluate pediatric vancomycin trough targets and limit unnecessary empiric vancomycin exposure to reduce poor outcomes and nephrotoxicity is highlighted. One in 3 children experienced considerable SAB morbidity therefore, pediatric inclusion in future SAB comparator trials is paramount to improve outcomes.
Publisher: IEEE
Date: 12-2011
Publisher: Elsevier BV
Date: 02-2017
Publisher: Japanese Society of Toxicology
Date: 2014
DOI: 10.2131/JTS.39.829
Abstract: The purpose of this study was to evaluate behavioral responses and biochemical changes induced by the extensively used pesticide cypermethrin (CYP) in liver, gills, brain and muscle tissues of mahseer (Tor putitora) fry. Behavioral changes in fish after exposure to an acute concentration of CYP involved jumping, abrupt swimming, loss of balance and equilibrium, increased surface activity and air gulping. These changes were more prominent with the passage of time. After a longer period of exposure, the fish became sluggish and before dying occasionally became motionless and sometimes showed a vertical position. Internal hemorrhage was also obvious. CYP exposure resulted in a significant decrease in total protein content in different tissues while antioxidant enzymes, catalase (CAT), peroxidase (POD) and glutathione reductase (GR) showed a time-dependent increasing trend in their activities in liver, brain, gills and muscle tissues. Similarly, lipid peroxidation (LPO) level also increased with time in different tissues of CYP-exposed fish. The results of the present study revealed that CYP is toxic to the mahseer Tor putitora. Therefore, its indiscriminate use can contribute in decreasing the population of mahseer in natural water bodies.
Publisher: MDPI AG
Date: 14-04-2023
DOI: 10.3390/S23084003
Abstract: Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, st s, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.
Publisher: AMPCo
Date: 28-02-2021
DOI: 10.5694/MJA2.50933
Publisher: Elsevier BV
Date: 3
Publisher: MDPI AG
Date: 29-03-2022
DOI: 10.3390/S22072630
Abstract: Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).
Publisher: Friends Science Publishers
Date: 07-2015
Publisher: ACM
Date: 26-02-2014
Start Date: 2019
End Date: 2020
Funder: Prince Sattam bin Abdulaziz University
View Funded Activity