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The effect of an oblique flow entry on the pressure losses in square channels
Get PDFFlows in square channels are common in applications, such as automotive after-treatment systems and heat exchangers. Flows with axial flow entry are well understood, but for oblique flow entry, there is no clarity on the additional pressure loss magnitude or the flow regime. Laminar flow is often assumed, even though flow separation at the channel entrance can cause a transition to turbulence. Here, the impact of oblique flow entry on the flow is investigated using LES (large eddy simulation) and RANS (Reynolds averaged Navier–Stokes) models, and their advantages and limitations are identified. The LES simulations show that the shear layer at the channel entrance produces continuous shedding of eddies that persist downstream even at moderate channel Reynolds numbers (≈ 2000). The LES predicted pressure losses mostly agree with experimental data, and the differences observed are attributed to the difficulty of accurately replicating the experimental geometry. It is shown that both LES and RANS results are sensitive to the rounding of the leading edge (present in experiments). Including edge rounding improves the pressure predictions. RANS simulations mostly agree with experimens, but unlike LES did not predict transitional flow phenomena for sharp leading edge. This study provides insight into the flow structure and sources of pressure losses in square channels, highlights the importance of understanding key flow and geometric features when using LES to predict complex flows involving flow separation and shear layers, and indicates the need to further investigate the complex instability arising from interactions between secondary flow and shear-layer roll-up.</p
Real-world evaluation of at-home cranial electrotherapy stimulation (CES) for the management of sleep, anxiety, depression, stress, quality of life, and self-efficacy
Get PDFBackground Direct-to-consumer neuromodulation technologies are transforming how sleep and mood disorders are self-managed outside clinical settings. Cranial electrotherapy stimulation (CES) is a low-intensity, portable intervention with growing accessibility but limited evidence in non-clinical populations and real-life contexts. This naturalistic cohort study investigated the effects of CES on sleep, anxiety, depression, stress, quality of life, and self-efficacy in a real-world, community-based setting. Methods Sixty adults agreed to use the Alpha-Stim AID CES device daily for 21 days (40–60 min/day). Validated self-report measures were completed at baseline, day 21 (end-of-treatment), and day 42 (follow-up). A subsample ( n = 27) wore actigraphy devices to monitor objective sleep changes. Results By day 21, sleep quality significantly improved, with 48 % achieving insomnia remission and 50 % a reduction in daytime sleepiness. Actigraphy data corroborated subjective sleep improvements. Anxiety and depression remission rates were 72.3 % and 71.2 %, with improvements maintained three weeks post-intervention. Stress levels decreased, while self-efficacy, wellbeing, and quality of life improved, with moderate to large effect sizes. CES was rated as safe, acceptable, and easy to use: 48 % of participants preferred it over psychotherapy or medication. Discussion CES is a safe, self-administered intervention that benefits sleep, mental health, and quality of life. This study presents the first actigraphy evidence of CES effects on sleep in a diverse, non-clinical population. Findings support a novel framework for accessible, non-pharmacological interventions for sleep and wellbeing with sustained impact at three-week follow-up. Results have significant implications for sleep quality and mental health, especially for populations underserved by traditional healthcare.</p
Abuse of Soccer Referees in Canada:Associations with Anxiety and Depression Symptoms and Demographic Factors
No full textIn today’s sports landscape, considerable emphasis is placed on ensuring athlete safety, yet the well-being of sports officials remains largely overlooked. We explored experiences of abuse in a sample of 1,183 soccer referees (66.8% White; 80.8% men, 17.6% women, 1.6% gender diverse) from Ontario, Saskatchewan, and British Columbia, Canada. Findings revealed widespread abuse: 34.9% of referees reported experiencing physical abuse at any rate, and 94.2% reported experiencing verbal abuse at any rate while officiating. Referees who were subjected to more frequent abuse reported higher symptoms of anxiety and depression. Contrary to hypotheses, neither women nor racialized referees experienced significantly higher rates of abuse compared to men and White referees; in fact, men reported experiencing more physical abuse than women. Although gender did not significantly moderate the relation between physical abuse and mental health outcomes, verbal abuse was positively associated with symptoms of anxiety and depression for both men and women, with a larger effect size for women. Race/ethnicity did not moderate the impact of abuse on mental health symptoms. These findings underscore an urgent need for programs and policies to address the high prevalence of abuse targeted at soccer referees and to improve their safety. Immediate action is necessary to safeguard the mental health and overall well-being of referees at all levels of sport
High resolution assessment of the impact of solar radiation modification on future Caribbean wind and solar energy sources
Get PDFThis study employed the Weather Research and Forecasting (WRF) model to dynamically downscale outputs from the HadGEM2-ES global climate model for the Caribbean at resolutions of 40 km and 8 km. Simulations were conducted for a historical period (1980–1990) and two future periods corresponding to global warming limits of 1.5 °C (2024–2034) and 2 °C (2038–2048) derived from the representative concentration pathway (RCP) 4.5 projection. The future projections were from the RCP4.5 scenario and the solar radiation modification (SRM) G4 scenario from the Geoengineering Model Intercomparison Project, focusing on variables relevant to wind and solar energy assessments. Three bias-correction methods were independently applied to the downscaled outputs to evaluate their effectiveness in reducing bias while preserving projected climate change signals. The Quantile Delta Mapping and Delta method produced the best outputs and were subsequently used to assess the potential influence of SRM on solar and wind energy resources at the island scale. Results indicate that wind speeds under the G4 scenario generally decrease across much of the Caribbean, with parts of southern Jamaica and Hispaniola seeing the most notable increases. Changes in solar irradiance appear minimal; however, this finding remains inconclusive due to limitations in validating the more variable historical distribution of the WRF-derived outputs. These findings demonstrate the feasibility of conducting sub-regional and local-scale wind energy assessments in the Caribbean while underscoring the need for improved observational datasets to enhance solar resource validation
Transfer Learning-Driven Deep Neural Network for Pneumonia Detection from Chest X-Rays
No full textPneumonia is recognized as a serious medical condition with pronounced effects among children and older adults. Chest X-ray imaging is commonly employed as a primary clinical tool for identifying pneumonia cases. In this work, we systematically examine three learning-based approaches for pneumonia classification: a VGG16-based pretrained model, a custom convolutional neural network (CNN), and a baseline multi-layer perceptron (MLP). We propose LungXpert, a VGG16-based framework fine-tuned for chest X-ray classification. Experimental results show that the proposed model attains improved performance relative to previously reported approaches, reaching an accuracy of 97.22% with consistently strong precision, recall, and F1-score. The reported results demonstrate the practical value of pretrained representations in medical imaging tasks and indicate the potential of LungXpert as a dependable computer-aided diagnostic tool for pneumonia classification
Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features
Get PDFBike sharing is increasingly gaining popularity as an affordable and environmentally friendly mode of transportation in urban areas. However, the nature of bike sharing, where users can pick up and return bikes at different stations, often results in an uneven distribution of bikes across stations. Consequently, accurately predicting the future number of rented bikes at each station becomes crucial for bike-sharing operators to optimize the bike inventory at each location. This study introduces a multi-step-ahead forecasting model that employs machine learning methods to predict the hourly demand for rented bikes. We utilize information on rented bikes from the preceding day to forecast the forthcoming counts of rented bikes for the next 1, 3, 6, 12, and 24 h. Additional features extracted from timestamps are incorporated to enhance the accuracy of the model. We compare the proposed model, based on multilayer perceptron (MLP), with various machine learning prediction algorithms, including Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Decision Tree (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), and Linear Regression (LR). Applying the proposed MLP model to the Seoul bike-sharing dataset demonstrates a positive outcome, indicating a reduction in prediction error compared to other forecasting models. The proposed model achieves the highest R2 (coefficient of determination) values when compared to other models, with values of 0.973, 0.882, 0.82, 0.807, and 0.79 for prediction horizons of 1, 3, 6, 12, and 24 h, respectively. By obtaining future values for predicted rented bikes, the trained model is anticipated to assist in optimizing the number of available bikes for bike-sharing companies
The Collingridge Dilemma and Its Implications for Regulating Financial and Economic Crime (FEC) in the United Kingdom:Navigating the Tension Between Innovation and Control
Get PDFThe capacity of the United Kingdom (UK) to prosecute technology-enabled financial and economic crime (FEC) is increasingly shaped by the Collingridge dilemma. Even though the dilemma was broadly conceptualized in technology governance, its application to prosecutorial and enforcement practice, evidentiary standards, and criminal liability attribution represents uncharted scholarly territory. Through socio-legal mixed methods combining doctrinal analysis, case studies, and comparative analysis, the paper shows how the dilemma’s two horns or pillars (i.e., early epistemic uncertainty and late institutional inertia) manifest in criminal law and regulatory contexts. The paper finds that just like the European Union and United States, the UK criminal enforcement ecosystem exhibits both horns across cryptocurrency, algorithmic trading, artificial intelligence (AI), and fintech domains. By integrating supplementary theories such as responsive regulation, precautionary principles and technological momentum, the study advances a socio-legal framework that explains enforcement inertia and doctrinal gaps in liability attribution for emerging technologies. The paper demonstrates how epistemic uncertainty and institutional entrenchment shape enforcement outcomes and proposes adaptive strategies for anticipatory governance including technology-literate capacity building, anticipatory legal reform, and data-driven public-private coordination. These recommendations balance ex-ante legal clarity (reducing uncertainty) with ex-post enforcement agility (overcoming entrenchment) to provide a normative framework for navigating the Collingridge dilemma in FEC prosecution
Conscious Food Systems:Supporting Farmers’ Well-Being and Psychological Resilience
Get PDFAmid escalating ecological degradation, social fragmentation, and rising mental health challenges—especially in rural and agricultural communities—there is an urgent need to reimagine systems that support both planetary and human flourishing. This viewpoint examines an emerging paradigm in agriculture that emphasizes the role of farmers’ inner development in fostering practices that enhance ecological health, community well-being, and a resilient food system. A key goal is to draw more academic attention to growing communitycalls for more holistic, relational, and spiritually grounded approaches to food systems as an important focus for ongoing research. Drawing on diverse case studies from Japan, India, and Europe, we examine how small-scale and natural farming initiatives are integrating inner development, universal human values, and ecological consciousness. These case studies were developed and/or refined through a program led by the Conscious Food Systems Alliance (CoFSA), an initiative of the United Nations Development Programme (UNDP) that seeks to integrate inner transformation with sustainable food systems change. The initiatives are intended as illustrative examples of how agriculture can transcend its conventional, anthropocentric role as a food production system to become a site for cultivating deeper self-awareness, spiritual connection, and regenerative relationships with nature. Participants in these cases reported significant shifts in mindset—from materialistic and extractive worldviews to more relationaland value-driven orientations rooted in care, cooperation, and sustainability. Core practices such as mindfulness, experiential learning, and spiritual ecology helped reframe farming as a holistic process that nurtures both land and life. These exploratory case studies suggest that when farmers are supported in aligning with inner values and natural systems, they become empowered as agents of systemic change. By linking personal growth with planetarystewardship, these models offer pathways toward more integrated, life-affirming approaches to agriculture and future academic research
Exploring The Practical Application Of Leverage Points Framework For Transformative Interventions For Biodiversity:Case Study Leaders’ Perspectives On Integrating Diverse Viewpoints, Flexibility And Limitations
Get PDFThe leverage points framework identifies strategic points within complex systems where small shifts can lead to significant changes. This research paper explores the utilisation of the leverage points framework within the context of sustainability and transformative change research, specifically focusing on decision-making processes that aim to promote biodiversity-positive outcomes It seeks to provide insights into the practical application of the leverage points framework, examining the challenges in integrating diverse perspectives, the flexibility and limitations of this framework. This study primarily analyses survey data from case study leaders within the European Horizon project PLANET4B, capturing their experiences and reflections on applying the leverage points framework in their co-creative workshops with stakeholders. The findings emphasise the importance of translating academic concepts into accessible terms, creating inclusive environments for discussions, and adapting the framework to suit specific social-ecological contexts. This exploration enhances the applicability and relevance of the leverage points framework in real-world sustainability projects, thereby fostering changes in biodiversity and societal transformations.Keywords: biodiversity, transformative change, leverage points, systems thinking, diverse perspective<br/
Sedimentation fields as a method of saltmarsh restoration:continuity of human influence on natural processes
Get PDFSaltmarsh habitat provides important ecosystem services such as water quality regulation, carbon sequestration and flood defence, but is experiencing losses globally. Historically, this has been caused by land claim, and more recently by rising sea levels. Several methods have been implemented to compensate for saltmarsh habitat loss, including realigning defences, transplanting vegetation and building structures such as sedimentation fields to enclose areas of mudflat and encourage sediment deposition. It has been suggested that sedimentation fields may offer saltmarsh restoration without the limitations identified in other restoration approaches, such as poor drainage and anoxia caused by changes to the sediment structure due to prior human activity. In this article, we argue that restoration through sedimentation fields should be viewed as a continuation of human activity influencing natural processes,rather than as a method that overcomes the influence of prior human activity on saltmarsh ecosystem functioning. This opinion is evidenced by a critical review of the (pre-)historic human activity and saltmarsh restoration attempts at Rumney Great Wharf, Severn Estuary, Wales, where sedimentation fields were constructed between 1989 and 2005 and extended in 2024. We then evaluate the research requirements that need to be addressed to ensure the successful implementation of future schemes, including further understanding of the interactions between physical and biological processes, to enhance ecosystem functioning in sites restored using sedimentation fields