Search

Objective. Full immersive virtual reality (VR) technology shows potential for reducing the risks of falls in older adults. There is yet little evidence to support the usability and acceptance on using VR technology application in community aged care service. The study reports on research that aims to address that gap by evaluating the usefulness and acceptance of using an innovative VR application among Chinese older adults from Hong Kong. Methods. A single-arm exploratory study was conducted to evaluate how the participants experienced the use of a fully immersive cave automatic virtual environment (CAVE) VR program on fall prevention. Thirty-one participants were recruited by convenience sampling based on their fall concerns and potential risk of falls. The participants completed 16 training sessions over eight weeks using the VR CAVE application. They were asked to complete a VR usability questionnaire (HK-version) based on the Technology Acceptance Model and previous research, and they took fall risk assessments at the pretest, posttest, and follow-up. Results. The participants' group significantly showed improvements in reducing the risk factors of falls including balance, functional mobility, walking speed, and fear of falling after VR intervention. Perceived usefulness (PU), perceived enjoyment (PE), user experience (UE), and intention to use (IU) had an overall significant change at different time points. These are important factors to influence the participants' acceptance of the use of VR technology applications. Perceived ease of use (PEOU) and social norms (SNs) had an inconsistent result, and some items had low validity. The findings indicated a positive training effect on fall prevention and high acceptance of the adoption of the VR technology application. Conclusion. This study supports the growing evidence on the usefulness and acceptance of using full immersive VR training on fall prevention among Chinese older adults. They perceived that the VR CAVE application was useful and innovative as an effective fall prevention training. Technically, the application of VR CAVE technology faces many challenges and is not easily manageable under COVID-19 restrictions and the limitation on technological adaptation for older adults. However, investment in full immersive VR technology application is supported for future adoption in aged care and rehabilitation services. [ABSTRACT FROM AUTHOR]

Cloud computing is gaining increasing interest as an enabling technology for improved productivity, efficiency and cost reduction. It also has the potential to add value to reliability and scalability of organisation’s systems, which would allow organisations to better focus on their core business and strategies. This research paper aimed at exploring the perceived potential for value creation from cloud computing in Australian Regional Governments. Using the Technology-Organization-Environment (TOE) framework, the Diffusion of Innovation (DOI) theory and Desires Framework (DF), a conceptual research model is devised. The key identified factors that are likely to influence adoption of cloud computing are identified along with the implications to current policies, anticipated benefits and challenges of adoption of cloud computing. The research employed a qualitative approach involving 21 in-depth interviews with Australian local councils’ IT managers. The findings of this research are mapped to the research model factors, thereby presenting and validating a number of value-adding concepts that are expected to contribute to investment decisions on cloud computing adoption. [ABSTRACT FROM AUTHOR]

Background: Older people generally prefer to continue living in their own homes rather than move into residential age care institutions. Assistive technologies and sensors in the home environment and/or bodily worn systems that monitor people's movement might contribute to an increased sense of safety and security at home. However, their use can raise ethical anxieties as little is known about how older persons perceive assistive and monitoring technologies.Objectives: To review the main barriers to the adoption of assistive technologies (ATs) by older adults in order to uncover issues of concern from empirical studies and to arrange these issues from the most critical to the least critical.Method: A 4-step systematic review was conducted using empirical studies: locating and identifying relevant articles; screening of located articles; examination of full text articles for inclusion/exclusion; and detail examination of the 44 articles included.Results: Privacy is a top critical concern to older adults, registering a 34% of the total articles examined. Two other equally potent barriers to the adoption of ATs were trust and functionality/added value representing 27 and 25 per cent each respectively of the total studies examined. Also of serious concerns are cost of ATs and ease of use and suitability for daily use (23%) each respectively, perception of "no need" (20%), stigma (18%), and fear of dependence and lack of training (16%) each respectively. These underlying factors are generation/cohort effects and physical decline relating to aging, and negative attitudes toward technologies such as the so-called "gerontechnologies" specifically targeting older adults. However, more and more older adults adopt different kinds of ATs in order to fit in with the society.Conclusions: The identified underlying factors are generation/cohort effects and physical decline relating to aging, and negative attitudes toward technologies. The negative attitudes that are most frequently associated with technologies such as the so-called "gerontechnologies" specifically targeting older adults contain stigmatizing symbolism that might prevent them from adopting them. [ABSTRACT FROM AUTHOR]

Technology plays an important role in helping organizations control quality and costs, and take advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to confirm the factors to be considered for cloud computing adoption in Australian regional municipal governments. The research involved data from interviews with IT managers from selected regional municipal governments, and survey data from 480 IT staff across 47 regional municipal governments. The major factors to be considered for the adoption of cloud computing in regional municipal governments were identified as Internet connectivity, Internet speed, availability, reliability, data storage location, security, data sovereignty, cost, integration, data backup, provider dependability, employees' knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when regional municipal governments planning to adopt cloud computing. [ABSTRACT FROM AUTHOR]

New developments in information technology (IT) provide opportunities for a better quality of life through benefits such as increased comfort and convenience. Compared to dedicated infrastructures such as cluster and grid computing, cloud computing can better cater to users' needs by increasing effectiveness, efficiency and functionality at a potentially lower cost. This research aims to provide insights into the challenges and issues faced by implementers and users of cloud computing by comparing the extant literature about this issue with current insights provided by IT managers. A systematic literature review and in-depth interviews with IT managers in local government councils were conducted for this research. The research indicated that the factors in the extant literature were supported; additional challenges and issues emerged which are related to effective network, data storage location, availability of different service providers, policy makers, a limited understanding of the cloud and business transformation. The findings of this research are expected to assist managers to evaluate possible adoption and increase their awareness about challenges and issues that influence the cloud adoption when planning to adopt it. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study is to identify the extent to which the Medicare item numbers and incentives, introduced in July 2011, have been effective in stimulating telehealth activity in Australia. Methods. A retrospective descriptive study utilising data on the uptake of telehealth item numbers and associated inperson services, from July 2011 to April 2014, were obtained from Medicare Australia. The main outcome measures were number of telehealth services over time, plus uptake proportionate to in-person services, by jurisdiction, by speciality, and by patient gender. Results. Specialist consultations delivered by video communication and rebated by Medicare rose to 6000 per month, which is 0.24% of the total number of specialist consultations. The highest proportional uptake was in geriatrics and psychiatry. In 52% per cent of video consultations the patient was supported by an on-site healthcare provider, most commonly a general practitioner. There were substantial jurisdictional differences. A significantly lower percentage of female patients were rebated for item 99, which is primarily used by surgeons. Conclusions. Medicare rebates and incentives, which are generous by world standards, have resulted in specialist video consultations being provided to underserved areas, although gaps still remain that need new models of care to be developed. [ABSTRACT FROM AUTHOR]

Purpose: The applications of artificial intelligence (AI), natural language processing and machine learning in e-commerce are growing. Recommender systems (RSs) are interaction-based technologies based on AI that can offer recommendations for products for use or of interest to a potential consumer. Curiosity, focused immersion and temporal dissociation are often treated as the dimensions of cognitive absorption, so exploring them separately can provide valuable insights into their dynamics. The paper aims to determine the effect of the cognitive absorption dimensions namely focused immersion, temporal dissociation and curiosity independently on RSs continuous use intention. Design/methodology/approach: A quantitative research design was used to explore the effect of dimensions of cognitive absorption on AI-driven RSs continuous use intention in e-commerce. Data were gathered from 452 active users of Amazon through an online cross-sectional survey and were analysed using partial least squares structural equation modelling. Findings: The findings indicated that curiosity and focused immersion directly affect RSs continuous use intention, but temporal dissociation does not affect RSs continuous use intention. Originality/value: The current research focused on Amazon's RSs that use AI and machine learning techniques. The research aimed to empirically explore the effects of the dimensions of cognitive absorption separately on AI-driven RSs continuous use intention in e-commerce. This research may be of interest to executives working in both public and private industries to better harness the potential of recommendations driven by AI to maximize RSs' reuse and to enhance customer loyalty. [ABSTRACT FROM AUTHOR]

This paper examines the future sustainability of the Fijian Ministry of Health's (MoH) information and communication technology (ITC) system for patient management (PATIS). PATIS was developed with AusAID funding and, as the owner of the system, AusAID has no commercial competence or interest in further development of the system. Thus, the question that arises is: should Fiji adopt a commercially available patient administration system or retain the existing PATIS? In-depth consultations with senior executives and line managers of units that were major users of PATIS were undertaken. Semi-structured interviews and focus group discussion approaches were utilised. The consensus or majority views of the users were that the existing PATIS performed more than adequately. The future sustainability of the system is threatened by the lack of investment in resources (e.g. hardware maintenance and human resources) required to keep the system operating at its optimum. It was found that PATIS provides Fiji with a satisfactory patient administration system. The identified problems with the system are not related to the application per se but rather to an under-investment in resources for its utilisation. [ABSTRACT FROM AUTHOR]

This research aims to create and evaluate a model for a culturally appropriate, interactive, multimedia and informative health program for Aboriginal and Torres Strait Islander health workers that aims to improve the capacity to independently control their learning within an attractive learning environment. The research also aims to provide recommendations for policy development and further research. This study involved four phases: program needs assessment, identification of the key factors that should be considered in developing the CD-RO M, model development, and formative evaluation for the model. A general needs assessment as a first step in program development highlighted the presence of continuing education gaps and indicated the need for a new means to deliver a sustainable, efficient and culturally acceptable program. Exploring the key factors for development of an interactive multimedia health informatics program leads to factors which could be categorised under four main categories: cultural factors, information technology availability and literacy, learning aspects, and interactive multimedia factors. The results and recommendations from stage one and two in the study were used as a guide in the third stage of the study, the development of an interactive multimedia health informatics CDRO M. This paper discusses in details the fourth stage in the study, the formative evaluation for the developed CD-RO M. [ABSTRACT FROM AUTHOR]

Aboriginal and Torres Strait Islander health workers are key providers of primary health services to Aboriginal communities especially in remote and rural areas. They are often overloaded with competing demands. There has been limited attention given to the maintenance and ongoing enhancement of their skills and knowledge following the completion of formal training. A culturally appropriate interactive multimedia self-paced health program as a mechanism to improve the accessibility and the use of scientific data and information for health purposes is proposed as a basic method for better supporting Aboriginal and Torres Strait Islander primary health care workers in their practice locations. This paper explores different approaches for the development of a culturally appropriate interactive multimedia educational health program for Aboriginal and Torres Strait Islander health workers and it also explores cultural matters concerning program development in the light of existing literature. [ABSTRACT FROM AUTHOR]

One of the challenges facing health and welfare policymakers as well as researchers in most developed countries is the increasing demand for aging services and aged care. Low birth rates and rapid increases in the percentages of elderly people make aging and aged care one of the top-priority issues among the national agenda of many countries. The responses of governments have included initiatives to extend productive working lives and promote self-funded retirement; to promote healthy, active aging; and to encourage more care to be delivered in home and community settings. Technology will be a major enabler of these strategies. People requiring health services are increasingly being offered more care in their own homes and community settings as an alternative to hospital admission and to delay or avoid moving into institutional care. Research is providing intelligent technology to enable care in the home as well as to monitor safety, security, and quality. Innovation will provide greater independence and better access to care in their own homes for the elderly, sufferers of chronic illness, and persons with disability and reduce the incidence of hospital admissions and the length of stay when admissions do occur. Technologies will support families and professional caregivers and are expected to reduce costs. This paper reports on developments in technology to support care for the aged in home and community settings. [ABSTRACT FROM AUTHOR]

The rising popularity of veganism and vegetarianism has attracted significant market attention, yet limited understanding exists regarding consumer perceptions and adoption of these ethical and sustainable lifestyle choices. To address this gap, we conducted a netnographic analysis using Google Trends and HappyCow. Google Trends data found that food and restaurant-related searches predominated, with lower volumes in France, indicating a distinct food culture and potentially less interest in veganism. Australians exhibited higher search frequencies for “vegan,” reflecting a keen interest in vegan food establishments and products. HappyCow data indicated a relatively more significant number of vegan outlets in Australia. This study provides cross-cultural insights into consumer information-seeking behavior regarding veganism, highlighting the availability of vegan choices in diverse food settings and influencing factors for plant-based alternatives. [ABSTRACT FROM AUTHOR]

Commons theory is one of the influential economic theories that study the governance of shared resources, including knowledge. This paper provides a comprehensive view of the application of the concept of the commons towards supporting innovation in the Knowledge Management (KM) literature. A systematic literature review identified forty-four (44) relevant research papers discussed the commons published in twenty-three (23) high-impact KM journals. The research found that the application of commons in KM literature covers diverse areas, including Intellectual Property, Knowledge Cities, and Industrial Commons, that are related to innovation. The study found that extant literature does not adequately address innovation-centric knowledge. To address the gap, a conceptual model is presented to apply the Institutional Analysis and Development (IAD) framework to Open Innovation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic reshaped every aspect of life, including food safety. Understanding food safety behaviour at home is necessary for developing effective strategies to mitigate foodborne disease during and after this pandemic. This study administered a cross-sectional survey among 503 domestic food handlers to examine the food handlers' food safety concerns during the pandemic and pandemic-related knowledge and hygiene behaviour (PRKHB) in Bangladesh. The results found that only 35.8% of respondents in this study were more concerned about food safety because of the COVID-19 pandemic. Although the results found a good PRKHB among 95.8% of urban food handlers, overall, 62% showed a poor level of PRKHB. Only 38.8% reported washing their hands after returning home or preparing meals every time. The regression model found that food safety concerns positively related to the PRKHB, and participants who lived in rural areas had a negative association with the PRKHB. The study also explored sociodemographic variations and significant differences observed between urban and rural areas. Meticulous educational campaigns and targeted messages to the food handlers on food safety risks, food handling practices and hand hygiene are necessary to minimise the foodborne disease burden in this region. • 38.8% of participants reported washing their hands after returning home or before preparing meals. • Only 35.8% of domestic food handlers were more aware of food safety due to the COVID-19 pandemic. • 62% of respondents showed a poor level of pandemic-related knowledge and hygiene behaviour (PRKHB). • Males, older adults, higher income, higher education, and fewer family members had the higher PRKHB. • PRKHB level highly significantly differed between the urban and rural food handlers. [ABSTRACT FROM AUTHOR]

• Investigated role of automated detection of airflow obstructive disease to reduce cost/improve diagnostics. • Applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA to search IEEE/Google Scholar/pubmed databases. • We identified 65 papers published between 2012 and 2022, covering 67 studies. • For automated obstructive airway disease detection medical imaging adopted in 14 studies, Genetic/physiological signals used in 13, medical records in seven airflow in seven studies and 7 research studies. • Artificial Intelligence can improve decision quality and efficiency of health professionals in Chronic Obstructive Pulmonary Disease (COPD)/asthma diagnosis. Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article about telecommunication in medicine.

Background: The evolving, adoption and high failure nature of health information technology (HIT)/IS/T systems requires effective readiness assessment to avert increasing failures while increasing system benefits. However, literature on HIT readiness assessment is myriad and fragmented. This review bares the contours of the available literature concluding in a set of manageable and usable recommendations for policymakers, researchers, individuals and organizations intending to assess readiness for any HIT implementation.Objectives: Identify studies, analyze readiness factors and offer recommendations.Method: Published articles 1995-2016 were searched using Medline/PubMed, Cinahl, Web of Science, PsychInfo, ProQuest. Studies were included if they were assessing IS/T/mHealth readiness in the context of HIT. Articles not written in English were excluded. Themes that emerged in the process of the data synthesis were thematically analysed and interpreted.Results: Analyzed themes were found across 63 articles. In accordance with their prevalence of use, they included but not limited to "Technological readiness", 30 (46%); "Core/Need/Motivational readiness", 23 (37%); "Acceptance and use readiness", 19 (29%); "Organizational readiness", 20 (21%); "IT skills/Training/Learning readiness" (18%), "Engagement readiness", 16 (24%) and "Societal readiness" (14%). Despite their prevalence in use, "Technological readiness", "Motivational readiness" and "Engagement readiness" all had myriad and unreliable measuring tools. Core readiness had relatively reliable measuring tools, which repeatedly been used in various readiness assessment studies CONCLUSION: Thus, there is the need for reliable measuring tools for even the most commonly used readiness assessment factors/constructs: Core readiness, Engagement and buy-ins readiness, Technological readiness and IT Skills readiness as this could serve as an immediate step in conducting effective/reliable e-Health readiness assessment, which could lead to reduced HIT implementation failures. [ABSTRACT FROM AUTHOR]

This research aims to explore the multi-focus group method as an effective tool for systematically eliciting business requirements for business information system (BIS) projects. During the COVID-19 crisis, many businesses plan to transform their businesses into digital businesses. Business managers face a critical challenge: they do not know much about detailed system requirements and what they want for digital transformation requirements. Among many approaches used for understanding business requirements, the focus group method has been used to help elicit BIS needs over the past 30 years. However, most focus group studies about research practices mainly focus on a particular disciplinary field, such as social, biomedical, and health research. Limited research reported using the multi-focus group method to elicit business system requirements. There is a need to fill this research gap. A case study is conducted to verify that the multi-focus group method might effectively explore detailed system requirements to cover the Case Study business's needs from transforming the existing systems into a visual warning system. The research outcomes verify that the multi-focus group method might effectively explore the detailed system requirements to cover the business's needs. This research identifies that the multi-focus group method is especially suitable for investigating less well-studied, no previous evidence, or unstudied research topics. As a result, an innovative visual warning system was successfully deployed based on the multi-focus studies for user acceptance testing in the Case Study mine in Feb 2022. The main contribution is that this research verifies the multi-focus group method might be an effective tool for systematically eliciting business requirements. Another contribution is to develop a flowchart for adding to Systems Analysis & Design course in information system education, which may guide BIS students step by step on using the multi-focus group method to explore business system requirements in practice. [ABSTRACT FROM AUTHOR]

Wind is an important source of renewable energy, often used to provide clean electricity to remote areas. For optimal extraction of this energy source, there is a need for an accurate and robust wind speed forecasting. The intermittent nature of wind makes this goal quite challenging. This research proposes a novel hybrid bidirectional LSTM (BiLSTM) model for near real-time wind speed forecasting. The hybrid model is developed using wind speed and selected climate indices from a group of neighbouring reference stations as predictors to forecast wind speed of a target station. A 3-stage feature selection is applied on the predictors to robustly extract highly significant input features. Stage 1 employs partial auto-correlation and cross-correlation, stage 2 uses the RReliefF filter algorithm, and Boruta-RF wrapper method is implemented in the final stage to improve the BiLSTM model with an efficient Bayesian optimization used for hyperparameter tuning. The proposed model has been benchmarked with comparative models including standalone and hybrid LSTM, RNN, MLP and RF. The proposed hybrid BiLSTM algorithm is found to be superior in wind speed prediction for all tested sites with ≈ 76. 6 − 84. 8 % of errors being ≤ | 0. 5 | ms − 1 . The hybrid BiLSTM model also registered the lowest Relative Root Mean Square Error (9. 6 − 23. 8 %) and Mean Absolute Percentage Error (8. 8 − 21. 5 %) among all the tested algorithms. This research ascertains that the proposed model can accurately predict wind speed and capacitate wind energy availability to be regularly monitored at a near real-time level. [Display omitted] • A novel deep learning model is proposed for near real-time wind forecasting. • BiLSTM integrated with 3-stage feature selection and Bayesian optimization is used. • Wind speed at six target sites is predicted using reference stations data. • Proposed hybrid model outperforms all comparative models. • The study has prospective implications for intermittent wind energy forecast. [ABSTRACT FROM AUTHOR]

This paper examines the future sustainability of the Fijian Ministry of Health's (MoH) information and communication technology (ITC) system for patient management (PATIS). PATIS was developed with AusAID funding and, as the owner of the system, AusAID has no commercial competence or interest in further development of the system. Thus, the question that arises is: should Fiji adopt a commercially available patient administration system or retain the existing PATIS? In-depth consultations with senior executives and line managers of units that were major users of PATIS were undertaken. Semi-structured interviews and focus group discussion approaches were utilised. The consensus or majority views of the users were that the existing PATIS performed more than adequately. The future sustainability of the system is threatened by the lack of investment in resources (e.g. hardware maintenance and human resources) required to keep the system operating at its optimum. It was found that PATIS provides Fiji with a satisfactory patient administration system. The identified problems with the system are not related to the application per se but rather to an under-investment in resources for its utilisation. [ABSTRACT FROM AUTHOR]

E-learning systems are increasingly essential in universities, schools, government departments and other organizations that provide an education or training service. The objective for adopting e-learning systems is to provide students with educational services via electronic channels. The focus of this study is on the impact of IT infrastructure services and IT quality on perceptions of usefulness of e-learning systems. A model is proposed which includes five constructs: IT infrastructure services, system quality, information quality, service delivery quality, and perceived usefulness. A quantitative study was conducted at an Australian university with 720 survey responses from students who were enrolled in online courses. The results suggest that IT infrastructure services play a critical role in generating information with high quality, enhancing the aspects of e-learning system quality, and improving service delivery quality. The impact of IT infrastructure services, system quality, and information quality on perceived usefulness is fully mediated by service delivery quality. Universities need to be aware of the critical impact of IT infrastructure services and consider how investment in these services could improve system and information quality, service delivery quality, and the usefulness and success of e-learning systems. [ABSTRACT FROM AUTHOR]

Food contamination and food adulteration are widely known public health issues in Bangladesh. This research explores consumers' perceptions of food hazards and safe food purchasing strategies. The study uses data from a survey of 450 primary household food purchasers in an urban supermarket and two traditional bazaars in urban and regional areas. This study explored the differences between consumers in urban and regional areas. The urban supermarket consumers showed more significant concerns about food hazards and safe food purchasing strategies than consumers at the urban and regional bazaars. Urban consumers indicated a higher food safety perception, whereas regional consumers were concerned about food safety deterioration. This study finds that urban supermarket consumers considered biological food hazards more during food purchasing than the regional bazaar. Most of the consumers in this study were concerned about chemical food hazards such as formalin and pesticide residues, and these consumers considered food purchasing factors such as tastes and expiry dates more while purchasing food. On the other hand, consumers concerned about biological hazards emphasised food purchasing place and product origin. Urban and regional bazaar consumers considered prices more than urban supermarket consumers. The study also explores sociodemographic variations, such as women having more concern about chemical food hazards than men and people with less formal education levels showing less concern about food hazards. Therefore, dissemination of safe food knowledge, adequate infrastructure, monitoring, and policymaking are some immediate necessary steps to improve food safety situations and safe food accessibility in Bangladesh. [ABSTRACT FROM AUTHOR]

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe's coefficient ENS = 0.85, and Legate and McCabe's Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems. [ABSTRACT FROM AUTHOR]

More timely and accurate air quality forecasting could contribute to better public health protection and air pollution prevention. Particulates are a significant indicator for measuring the degree of air pollution. This paper reports on research to model an early warning tool for coarse particulates when assessing the impact of the 12 satellite-derived and ground-based meteorological pollutants out of 30 pollutants considered using hourly Australian data from January 2018–December 2020. A one-dimensional convolutional neural network (CNN) was integrated with a one-directional fully gated recurrent unit (GRU) to forecast consecutive hours' air quality. The CNN model acts as a spatial feature extractor, whereas the new generation GRU makes it computationally efficient. The resultant hybrid ' CNN-GRU' is then comprehensively benchmarked outperforming an ensemble of six other deep learning models. The proposed model's efficacy is indicated at the four most air polluted Australian postcodes in the testing phase. A detailed error analysis with visual and statistical metrics for air quality forecasting ascertains the proposed model's countermeasure to reduce harm and loss. The practical tool is immensely beneficial and can be widely deployed to the regions of public health concern where air pollution is a significant hazard. [Display omitted] • A novel early warning hybrid PM10 forecasting framework designed for Australia. • 30 satellite and ground-based meteorological pollutants were considered with 7 modeling approaches. • CNN integrated with GRU outperformed the other models. • Early warning AI model show potential in the health, environment sector, and decision-making. [ABSTRACT FROM AUTHOR]

Evaluation of e-learning systems success is an essential process in managing and developing these types of systems. There are still arguments about the factors can be used to evaluate the success of e-learning systems. Inadequate attention has been paid to investigate the role of IT infrastructure services as a foundation to create the success of e-learning systems. The focus of this study is the role of IT infrastructure services in terms of the success of e-learning systems. A model is proposed which includes five constructs: IT infrastructure services; perceived usefulness; user satisfaction; customer value; and organizational value. A quantitative study was conducted at an Australian University, with survey responses from 110 Academic staff members and 720 students who use the e-learning system. The empirical study confirmed that an IT infrastructure services is a valid and reliable construct to measure e-learning systems success. In addition, this study provides evidence of the critical role of IT infrastructure services in the success of e-learning systems via its significant effect on perceived usefulness, user satisfaction, customer value, and organizational value. [Copyright &y& Elsevier]

Background: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments. [ABSTRACT FROM AUTHOR]

Aims and objectives. To support policy planning for health, the barriers to the use of health information and computer technology (ICT) by nurses in Australia were determined. Background. Australia, in line with many countries, aims to achieve a better quality of care and health outcomes through effective and innovative use of health information. Nurses form the largest component of the health workforce. Successful adoption of ICT by nurses will be a requirement for success. No national study has been undertaken to determine the barriers to adoption. Design. A self-administered postal survey was conducted. Method. A questionnaire was distributed to 10,000 members of the Australian Nursing Federation. Twenty possible barriers to the use of health ICT uptake were offered and responses were given on a five point Likert scale. Results. Work demands, access to computers and lack of support were the principal barriers faced by nurses to their adoption of the technology in the workplace. Factors that were considered to present few barriers included age and lack of interest. While age was not considered by the respondents to be a barrier, their age was positively correlated with several barriers, including knowledge and confidence in the use of computers. Conclusions. Results indicate that to use the information and computer technologies being brought into health care fully, barriers that prevent the principal users from embracing those technologies must be addressed. Factors such as the age of the nurse and their level of job must be considered when developing strategies to overcome barriers. Relevance to clinical practice. The findings of the present study provide essential information not only for national government and state health departments but also for local administrators and managers to enable clinical nurses to meet present and future job requirements. [ABSTRACT FROM AUTHOR]

Australia has long been observed to experience the ENSO (El Niño Southern Oscillation) of periodic alternation of flooding and drought. Australia has experienced consecutive years of the La Niña cycle of heavy rain and flooding. At some stage, this will revert to one or more years of drought, often severe. In previous years of drought, consideration was given to alternative water supply, including recycled wastewater. This paper reflects on the case of the previous period of El Niño drought in South East Queensland (SEQ), aiming to contribute to drought mitigation planning through reviving consideration of recycled water. • In depth case study on Queensland response to drought conditions. • Includes analysis of attempts to introduce other water sources, particularly recycled water for potable and non potable uses. • Examines reasons for failure and non-acceptance and uses comparisons with successful schemes to suggest policy changes for a way forward. [ABSTRACT FROM AUTHOR]

• Three MRI datasets were collected to detect PD symptoms. • Nested patch division was presented. • A hand-modeled classification architecture was proposed. • Our architecture yielded over 98.50% classification accuracies for all cases. • Our framework outperformed. Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification. [ABSTRACT FROM AUTHOR]

Many health-care providers in Australia are exploring the use of wireless technology to improve service delivery. It appears that the solutions so far have been dictated by the hardware vendors and that the business case is yet to drive the implementations. A focus group discussion was facilitated with eight senior management staff involved in health care in Western Australia. This resulted in a set of challenges, which were used to invite opinions from nursing staff in Queensland. A total of 31 interviews were conducted. The analysis returned a set of 63 themes, which were grouped. These groupings reflected the challenges as lack of user-friendly applications, unreliable technology, substandard testing, shortage of staff, concerns for security, reliance on technology, existing problems, work schedule, training, outdated health policy, coverage of wireless links, confidentiality and lack of awareness. The interviews clearly indicated the need for training and awareness procedures. The present study provides some of the information necessary to realize an enterprise-wide implementation of wireless technology. [ABSTRACT FROM AUTHOR]

To investigate the parameters of sucrose dust explosion, the minimum ignition energy (MIE) and minimum ignition temperature (MIT) were evaluated. The experiments tested the MIE of sucrose dust under different conditions of dust quantity, ignition delay time (IDT), and powder injection pressure (PIP). The experiments tested the MIT of different particle sizes. The results demonstrate that the MIE of sucrose powder under three conditions was an open-up quadratic polynomial. When the dust quantity, the IDT, and PIP were 0.5 g (417 g/m3), 90 ms, and 150 kPa, respectively, the MIE was 58.9 mJ, 62.6 mJ, and 52.4 mJ. The MIT was positively correlated with the particle size of sucrose dust, and the MIT was 340 °C. At the molecular level, the "O–H" bonds of the sucrose molecule hydroxyl groups were broken by the discharge of electrodes or high temperature to generate H2. The combustion of H2 caused the explosion to spread to the surrounding sucrose dust and made the deposited dust rise, forming an interlocking explosion. The explosion would not stop until the dust concentration dropped below the lowest explosion limit. The results of this study can provide guidance for sucrose enterprises to prevent dust explosion accidents. [ABSTRACT FROM AUTHOR]

Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally. [ABSTRACT FROM AUTHOR]

• A zeolitic imidazolate frameworks (ZIF-8)/ionic liquid composites modified electrode was developed. • The modified electrode exhibits good electrocatalytic activity for the oxidation of 1-hydroxypyrene (1-OHP). • The fabricated electrode was successfully applied to the determination of 1-OHP in urine samples. 1-hydroxypyrene (1-OHP) is an important biomarker for evaluating human exposure to polycyclic aromatic hydrocarbons (PAHs). In this work, a simple sensor based on nano-carbon paste electrode modified with zeolitic imidazolate frameworks (ZIF-8)/ionic liquid composites was developed for convenient and fast electrochemical detection of 1-OHP. The structure of ZIF-8 and the morphologies of different electrodes were investigated by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The electrochemical behavior of l-OHP on the constructed modified electrode was studied using differential pulse voltammetry (DPV). Under the optimized conditions, the fabricated sensor gave a linear calibration curve of the concentration ranging from 0.05 to 20 μM with a detection limit of 0.01 μM for 1-OHP. The proposed method was successfully applied to the determination of 1-OHP in urine samples with the recoveries of 94.2%–103.2%. [ABSTRACT FROM AUTHOR]

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately. [ABSTRACT FROM AUTHOR]

• Automated detection of pain from facial expressions is a challenge in medical care. • A new deep neural network algorithm designed to detection pain intensity effectively. • A new feature extraction algorithm developed to speed up the algorithm. • The new enhanced deep learning algorithm can detect pain in multi level effectively. Automated detection of pain intensity from facial expressions, especially from face images that show a patient's health, remains a significant challenge in the medical diagnostics and health informatics area. Expert systems that prudently analyse facial expression images, utilising an automated machine learning algorithm, can be a promising approach for pain intensity analysis in health domain. Deep neural networks and emerging machine learning techniques have made significant progress in both the feature identification, mapping and the modelling of pain intensity from facial images, with great potential to aid health practitioners in the diagnosis of certain medical conditions. Consequently, there has been significant research within the pain recognition and management area that aim to adopt facial expression datasets into deep learning algorithms to detect the pain intensity in binary classes, and also to identify pain and non-pain faces. However, the volume of research in identifying pain intensity levels in multi-classes remains rather limited. This paper reports on a new enhanced deep neural network framework designed for the effective detection of pain intensity, in four-level thresholds using a facial expression image. To explore the robustness of the proposed algorithms, the UNBC-McMaster Shoulder Pain Archive Database, comprised of human facial images, was first balanced, then used for the training and testing of the classification model, coupled with the fine-tuned VGG-Face pre-trainer as a feature extraction tool. To reduce the dimensionality of the classification model input data and extract most relevant features, Principal Component Analysis was applied, improving its computational efficiency. The pre-screened features, used as model inputs, are then transferred to produce a new enhanced joint hybrid CNN-BiLSTM (EJH-CNN-BiLSTM) deep learning algorithm comprised of convolutional neural networks, that were then linked to the joint bidirectional LSTM, for multi-classification of pain. The resulting EJH-CNN-BiLSTM classification model, tested to estimate four different levels of pain, revealed a good degree of accuracy in terms of different performance evaluation techniques. The results indicated that the enhanced EJH-CNN-BiLSTM classification algorithm was explored as a potential tool for the detection of pain intensity in multi-classes from facial expression images, and therefore, can be adopted as an artificial intelligence tool in the medical diagnostics for automatic pain detection and subsequent pain management of patients. [ABSTRACT FROM AUTHOR]

• A nitrogen-doped graphene-ionic liquid-glassy carbon microspheres paste electrode (N-GE/GCILE) was constructed in a simple procedure for the determination of quercetin (Qu). • The combination of N-GE, IL and glassy carbon microspheres would enlarge electrochemical effective surface area, facilitate the electron transfer, and improve the sensitivity of the composite electrode significantly. • The method provided wide linear ranges, low detection limit and good stability. • The fabricated electrode was successfully applied for Qu detection in blueberry juice with satisfactory recoveries. The analysis of quercetin (Qu) is of great significance owing to its multiple biomedical effects. In this work, a nitrogen-doped graphene-ionic liquid-glassy carbon microsphere paste electrode (N-GE/GCILE) was constructed for the determination of Qu. Cyclic voltammetry (CV) and square wave voltammetry (SWV) were employed to investigate the electrochemical behavior of Qu. In comparison with unmodified glassy carbon microsphere paste electrode, the modified electrode exhibited better electrocatalytic activity towards Qu. The influencing conditions on sensitivity such as the amount of modifier, accumulation potential and time, and electrolyte pH value were respectively discussed. Under the optimized conditions, two linear ranges of 0.002- 0.1 μM and 0.1–10 μM were obtained, with a detection limit of 1 nM (S/N = 3). The method was applied in Qu determination in blueberry juice with the recoveries of 102.5–105.0%. [ABSTRACT FROM AUTHOR]