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Remote health solutions in far East Gippsland : a mixed-methods, co-designed evaluation of health service availability in isolated communities
Get PDFIntroduction: Australians living in isolated communities are more likely to experience poorer health outcomes as a result of rurality. This article provides a needs assessment of healthcare services in a geographically isolated region of Victoria, Australia. Methods: The research project employed a mixed-methods design. The study population consisted of members of the isolated communities in Victoria. The incorporation of qualitative data added depth to the quantitative data, ensuring that voices of community members were adequately represented in the needs assessment. Data analysis was undertaken using descriptive statistics and thematic analysis techniques. Results: Survey respondents from isolated regional locations highlighted the extended travel time and increasing wait times to see a medical practitioner, leading to a delay in seeking healthcare assistance. Respondents were less likely to have access to and use telehealth services, yet highlighted the service as beneficial to isolated regions. Survey findings were supported by in-depth interviews, with participants stating access to care was difficult, providing place-based suggestions of services to remove barriers to care such as a virtual care model and mobile services visiting the isolated regions. Conclusion: Access, use and facilitation of appropriate place-based health care within isolated Australia has the potential to increase wellbeing and enables residents to remain in regions that hold long historical and familial connections. By incorporating innovative technologies and models of care that have been evaluated across other isolated regions of Australia and globally, there is an opportunity to adapt existing models to conform to a post-COVID world. © (2025), (James Cook University). All rights reserved
The incidence and characteristics of heading in the 2019 FIFA Women’s World Cup™
Get PDFIntroduction: To quantify the incidence and characteristics of purposeful heading and other head impacts in professional women’s football at the 2019 FIFA Women’s World Cup™. Methods: This cross-sectional cohort study analysed purposeful headers (uncontested and contested) and their characteristics (e.g. playing position, match situation, field location, and distance ball travelled), and other head impact events using video analysis. Total headers and head impact events, and incidence rate (IR) per 1000 match-hours were calculated for countries, positions, and other characteristics, such as location on the pitch. Results: Purposeful headers accounted for 76% of all coded events (uncontested: 71%; contested: 29%), followed by attempted headers (21%), unintentional ball-head impacts (2%), and other head impacts (1%). Headers ranged from 0 to 22 per player, per match with a mean of 4.8 [±1.2]. Of all field positions, centrebacks had the highest heading rates and wingers the lowest. Strikers performed significantly more contested headers than any other position, and significantly less uncontested headers. Most headers occurred in the middle third (48%), from free game play (72%) and from long balls (>20 m) (68%). Conclusion: The findings of this study could assist the development of player heading risk profiles, sex-specific heading guidelines, and coaching practices. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Bond of FRP bars in fine-grained alkali-activated concrete
Get PDFAlkali-activated cement (AAC) is an alternative binder with a promising potential to replace ordinary Portland cement (OPC) and mitigate its environmental issues. The use of fiber-reinforced polymer (FRP) reinforcements with AAC concrete enables the development of corrosion-resistant, environmentally friendly reinforced concrete structures. Bond behavior is critical in reinforced concrete structures and must be thoroughly studied for such new alternative materials. This study employs pullout tests to investigate the bond behavior between FRP bars and fine-grained AAC concrete. Three fine-grained AAC concretes with low to high strength, glass and carbon FRP bars and wrapped, milled, and smooth surface treatments were examined. The effect of bar casting position was also investigated. The compressive strength showed a significant influence on the bond strength. An average bond strength of approximately 18 MPa was observed for both glass and carbon FRP bars when used with 65 MPa concrete. Both the glass and carbon FRP bars with wrapping showed a lower bond strength than their milled FRP bars counterparts. The carbon bars without surface preparation (smooth bars) resulted in a much lower bond strength, around 4 MPa. In terms of casting positions, the bars cast in the middle section of the concrete block showed a higher bond strength than those at the bottom and top. © 2024 The Author(s). Structural Concrete published by John Wiley & Sons Ltd on behalf of International Federation for Structural Concrete
Intelligent transportation system for automated medical services during pandemic
No full textInfectious viruses are spread during human-to-human contact and can cause worldwide pandemics. We have witnessed worldwide disasters during the COVID-19 pandemic because of infectious viruses, and these incidents often unfold in various phases and waves. During this pandemic, so many deaths have occurred worldwide that they cannot even be counted accurately. The biggest issue that comes to the forefront is that health workers going to treat patients suffering from COVID-19 also may get infected. Many health workers have lost their lives to COVID-19 and are still losing their lives. The situation can worsen further by coinciding with other natural disasters like cyclones, earthquakes, and tsunamis. In these situations, an intelligent automated model is needed to provide contactless medical services such as ambulance facilities and primary health tests. In this paper, we explore these types of services safely with the help of an intelligent automated transportation model using a vehicular delay-tolerant network. To solve the scenario, we propose an intelligent transportation system for automated medical services to prevent healthcare workers from becoming infected during testing and collecting health data by collaborating with a delay-tolerant network of vehicles in intelligent transport systems. The proposed model automatically categorizes and filters infected patients, providing medical facilities based on their illnesses. Our mathematical evaluation and simulation results affirm the effectiveness and feasibility of the proposed model, highlighting its strength compared to existing state-of-the-art protocols. © 2024 Elsevier B.V
Secure electric vehicle charging infrastructure in smart cities : a blockchain-based smart contract approach
Get PDFHighlights: What are the main findings? Development of a blockchain-based smart contract system for secured operation of electric vehicle (EV) charging infrastructure within smart cities. The system prevents cyber-attacks on EV ecosystems through decentralized authentication, secure transaction validation, immutable record-keeping, and smart contract rules. Simulation results demonstrate the system’s efficacy in real-time operation with low computational cost and scalability for rapid expansion of EV charging networks. What are the implications of the main findings? Significantly enhances cyber resilience of EVs and their charging networks and increases public trust in their secured operation. Accelerates EV adoption, contributing to net-zero transition and smart city sustainability. Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. © 2025 by the authors
Prediction of HGI of South African coalfields : a comparative application of ANN, SVR and LSTM models
No full textHardgrove grindability index (HGI) is a significant index used to determine a mill’s capability and overall efficiency in the grinding of coal. There are several factors that affect HGI due to the complex nature of coal. The influence of proximate analysis, calorific value, total sulfur, and pyrite content on the HGI values of 292 coal samples from South African coalfields were examined. Predictive models of HGI are developed by using soft computing techniques such as Long Short-Term Memory (LSTM), support vector regression (SVR), and Artificial Neural Network (ANN). This study shows that ANN is the most effective predictive model for all the three coalfields, with SVR models being second and LSTM models being the least effective. The correlation between the predicted value and the input data is established by using the cosine amplitude method (CAM). It was found that HGI is most influenced by the fixed carbon content, calorific value, ash content and volatile matter content while pyrite has the least influence. © 2024 Taylor & Francis Group, LLC
In-paddock variability of plant available water
Get PDFSoil moisture is a major limiting factor in most dryland agricultural production systems around the globe. In dryland agriculture the amount of water available to grow a crop is determined primarily by the in-season rainfall and the amount of water stored in the soil profile prior to seeding of the crop. A bibliographic search of the measurement of soil moisture content at field-scale provided an overview of current practices to determine the spatial variability within a field, and their applicability to farm management practices. It concluded that current research is still some distance away from resolving reliable, trustworthy, timely, and accurate soil moisture mapping at field-scale. The areal extent of rainfall remains one of the most challenging meteorological variables to model accurately due to its high spatial and temporal variability. The merging of weather radar data with rain gauge data offers a practically usable and affordable technology to interpolate rainfall amounts at fine spatial resolution between sparsely located rain gauges, particularly in rain-fed agricultural regions. A case study was undertaken to compare rainfall measurements from a dense rain gauge network with the output from a weather radar installation of a major agricultural cropping and pasture region. Concluding that merging radar data with rain gauge data provides improved resolution of the spatial variability of rainfall, the result demonstrates a significantly improved data source for agricultural water management and hydrological modelling. The high levels of spatial heterogeneity of soil moisture both horizontally and vertically over small distances at field-scale are partly due to rainfall patterns in dryland agricultural regions. The use of a time series of single point measurements from multi-sensor soil moisture probes to display this heterogeneity presents a challenge in extending from point-scale to field-scale. Technologies to delineate soil-moisture management zones using a grid of soil moisture sensors, observed soil properties, electromagnetic surveys, and satellite-sensed normalized difference vegetative index (NDVI), were applied at a dryland agricultural research farm in south-eastern Australia. An assessment is made of the enhancements that could be made to the understanding of soil-moisture content through the adoption of surrogate technologies. The Agricultural Production Systems sIMulator (APSIM) SoilWat model for water balance simulation at the location of soil moisture probe, using infiltration data determined from Quantitative Precipitation Estimate (QPE) Merged Radar Rainfall data and local evapotranspiration data from the nearest official Bureau of Meteorology (BoM) site is applied. Comments are made about the technological limitations of soil moisture probes along with other available surrogate technologies with their high uncertainty’ of measurement (mostly epistemic, but also aleatory). With the widespread installation of single multi-sensor soil moisture probes within single fields in the broadacre dryland farming regions across Australia, a feasible narrative that reports on the soil moisture content status is investigated. Many pitfalls are likely to be encountered in establishing definitive soil moisture-based management zones at field- scale in the current cropping context. There is a need to balance complexity of analysis and the data required, with the desired functionality, to provide base input for decision support tools that guide management decisions for both cropping and grazing. The broad classification of the soil-moisture management zones is the knowledge the farmer ‘needs to know' to take the appropriate management action. This provides an applied solution, fit for purpose, that fills a gap in the need for much more precise estimation of soil moisture- based management zones (‘what there is to know'), thus facilitating better differential management of these zones, and giving guidance to decision making by the farmer, based on the current spatial distribution of soil water content in the field.Doctor of Philosoph
Use of digital health technologies for dementia care : bibliometric analysis and report
Get PDFBackground: Dementia is a syndrome that compromises neurocognitive functions of the individual and that is affecting 55 million individuals globally, as well as global health care systems, national economic systems, and family members. Objective: This study aimed to determine the status quo of scientific production on use of digital health technologies (DHTs) to support (older) people living with dementia, their families, and care partners. In addition, our study aimed to map the current landscape of global research initiatives on DHTs on the prevention, diagnosis, treatment, and support of people living with dementia and their caregivers. Methods: A bibliometric analysis was performed as part of a systematic review protocol using MEDLINE, Embase, Scopus, Epistemonikos, the Cochrane Database of Systematic Reviews, and Google Scholar for systematic and scoping reviews on DHTs and dementia up to February 21, 2024. Search terms included various forms of dementia and DHTs. Two independent reviewers conducted a 2-stage screening process with disagreements resolved by a third reviewer. Eligible reviews were then subjected to a bibliometric analysis using VOSviewer to evaluate document types, authorship, countries, institutions, journal sources, references, and keywords, creating social network maps to visualize emergent research trends. Results: A total of 704 records met the inclusion criteria for bibliometric analysis. Most reviews were systematic, with a substantial number covering mobile health, telehealth, and computer-based cognitive interventions. Bibliometric analysis revealed that the Journal of Medical Internet Research had the highest number of reviews and citations. Researchers from 66 countries contributed, with the United Kingdom and the United States as the most prolific. Overall, the number of publications covering the intersection of DHTs and dementia has increased steadily over time. However, the diversity of reviews conducted on a single topic has resulted in duplicated scientific efforts. Our assessment of contributions from countries, institutions, and key stakeholders reveals significant trends and knowledge gaps, particularly highlighting the dominance of high-income countries in this research domain. Furthermore, our findings emphasize the critical importance of interdisciplinary, collaborative teams and offer clear directions for future research, especially in underrepresented regions. Conclusions: Our study shows a steady increase in dementia- and DHT-related publications, particularly in areas such as mobile health, virtual reality, artificial intelligence, and sensor-based technologies interventions. This increase underscores the importance of systematic approaches and interdisciplinary collaborations, while identifying knowledge gaps, especially in lower-income regions. It is crucial that researchers worldwide adhere to evidence-based medicine principles to avoid duplication of efforts. This analysis offers a valuable foundation for policy makers and academics, emphasizing the need for an international collaborative task force to address knowledge gaps and advance dementia care globally. © 2025 JMIR Publications Inc.. All rights reserved
A novel approach for the thermodynamic modelling of two-phase power cycles
Get PDFWhen modelling thermodynamic power cycles, it is typical to determine state properties through the use of an equation of state to evaluate parameters of interest. This results in a black-box-like model of the cycle with heavy reliance upon equations of state, leading to compromises in algorithm speed and stability. This paper presents a new approach for cycles within which expansion is contained within the two-phase region (which can include the trilateral flash cycle and partial evaporation organic Rankine cycle), based upon fundamental thermodynamic relations. Thermodynamic properties need only be determined for saturated and liquid states, which along with system constants and independent variables, allows for the rapid evaluation of important cycle parameters. In this paper, a definition is provided for a two-phase power cycle, governing equations are presented, and the thermodynamic derivation of the model is provided. The different types of working fluid are discussed with respect to the considerations which must be made for each. Lastly, a comparison between three simple algorithms (proposed model, hybrid model, and conventional model) is presented to demonstrate the validity of the proposed model and the improvements possible over the conventional method. Results show that the algorithm running the proposed model shows substantial improvements when compared to the conventional approach, demonstrating an improvement in runtime by a factor of 44.77, a reduction in cyclomatic complexity of 72.73%, and a reduction in reliance upon equations of state of 99.72%, with no compromises in accuracy. © 2025 The Author
Peduncle detection of ripe strawberry to localize picking point using DF-Mask R-CNN and monocular depth
Get PDFAccurate localization of picking points and depth estimation is critical for implementing a robotic strawberry harvesting system. Due to the delicate nature of strawberries, harvesting must be performed without bruising or damage, typically by grasping and cutting the peduncle of the ripe strawberry. However, accurately detecting and localizing the thin peduncle in a cluttered environment is a significant challenge. This study proposed depth fused Mask R-CNN (DF-Mask R-CNN), which integrates depth information of the scene with the RGB image to enhance the detection, localization, and segmentation of strawberries and their peduncles in a greenhouse environment. To generate a dense depth map, a cutting-edge monocular depth estimator, ZoeDepth was used. The proposed DF-Mask R-CNN with ResNet101-FPN exhibited superior instance segmentation performance, with an overall mAP of 81.9%, with mAPsmall at 33.3%, mAPmedium at 78.79%, mAPlarge at 88.8 and APIOU=0.5 at 98.1%. In tests with 300 ripe strawberry samples, the method demonstrated a robust picking point detection, with a mean absolute error and root mean square error of 1.98 cm and 2.12 cm, respectively. These results highlight the effectiveness of the DF-Mask R-CNN model combined with the ZoeDepth estimator in enhancing the detection, localization, and segmentation of strawberries and their peduncles. This approach enables precise picking point localization and depth estimation for efficient vision systems for robotic strawberry harvesting. © 2013 IEEE