Search Results (51)

Brucellosis remains an important zoonotic disease in southern Saudi Arabia; however, community-level knowledge, risk-related practices, and information pathways in Najran City are insufficiently characterized. This study assessed brucellosis-related knowledge, attitudes, practices, and information pathways among adults in Najran City to inform locally relevant One Health interventions. In this cross-sectional survey, adults were recruited using stratified time–location (venue-based) sampling across community and exposure-relevant sites in Najran City. A total of 608 adults completed a structured interviewer-administered questionnaire. Composite scores were calculated for knowledge (0–21), attitude (0–22), practice (0–64), and information-source breadth (0–6). Descriptive statistics, group comparisons, correlation analyses, and multivariable linear regressions were performed. The findings suggest that participants more commonly relied on interpersonal social networks, especially family and friends, for information related to brucellosis (53.9%), whereas formal sources were less commonly reported, including health professionals (7.9%), media (4.6%), internet sources (3.3%), educational institutions (2.0%), and agricultural or veterinary organizations (1.3%). Mean knowledge scores were moderate (10.7/21), attitudes were generally favorable (19.5/22), and practice scores were moderate (36.6/64). Exposure-related behaviors remained common, particularly the consumption of unpasteurized milk or dairy products (56.6%). The breadth of information sources showed a moderate positive correlation with knowledge (rho = 0.561), whereas attitude showed only small positive correlations with knowledge and practice. Finally, knowledge was weakly and inversely correlated with practice. Among adults recruited in this venue-based sample, favorable attitudes did not consistently correspond to safer practices. These findings support practical One Health interventions, including coordinated veterinary–public health messaging on animal abortion events, safe-dairy guidance at points of sale and community venues, workplace-based training for livestock-contact groups, and referral pathways linking suspected animal cases with veterinary services and human care-seeking. Because recruitment was venue-based and non-probability, the results should be interpreted as descriptive and hypothesis-generating rather than population-representative; however, they still identify practical communication and service-delivery priorities for future intervention studies in Najran. Full article

Background: In March 2026, a meningococcal cluster centred on the University of Kent, England, caused two deaths and resulted in over 20 reported cases within the first week, including confirmed and suspected invasive cases. Subsequent UKHSA updates in early April 2026 reported 21 laboratory-confirmed MenB cases (18 linked to the outbreak strain) and two deaths, with the outbreak subsequently spreading to a second Canterbury university, Canterbury Christ Church University, and confirmed as Neisseria meningitidis serogroup B (MenB). Sub-Saharan Africa (SSA) bears a disproportionate global burden of meningococcal disease, yet university settings remain a critically understudied outbreak amplifier. This narrative review extracts epidemiological and policy lessons from the Kent event and applies them to the SSA context. Methods: We conducted a narrative review following the SANRA criteria, searching PubMed, Embase, Scopus, Google Scholar, and African Journals Online (2000–2026), with supplementary grey literature retrieved from World Health Organisation (WHO), Africa Centre for Disease Control, and United Kingdom Health Security Agency (UKHSA). Outbreak data were drawn from official UKHSA public-health statements (grey literature, archived), the University of Kent communications, and peer-reviewed expert commentary. Results: The Canterbury outbreak exposed six reproducible vulnerabilities: unprotected serogroup circulation (confirmed MenB, not covered for the current university-age cohort), nightlife-linked transmission amplification, delayed serogroup identification, poor student symptom-recognition, inadequate institutional response capacity, and, critically, multi-institutional spread via shared nightlife venues (confirmed extension to Canterbury Christ Church University within five days). Each vulnerability is demonstrably more severe in SSA universities, which face a broader multi-serogroup threat environment (NmA, B, C, W, X), virtually no university-entry vaccination requirement, and critical evidence gap of campus-specific meningococcal evidence in the published literature. Conclusions: This review proposes a five-pillar preparedness framework for SSA tertiary institutions, derived from a synthesis of the Kent outbreak and broader epidemiological evidence, intended to inform policy discussion and future research. Moreover, these should be embedded within a broader age-linked prevention strategy that begins before university entry, particularly during the transition into secondary school in high-risk settings. Priority measures include meningococcal vaccination at key educational transition points, prophylactic antibiotic pre-positioning, serogroup-capable surveillance, symptom-recognition training, and pan-continental alert A predominantly reactive response may carry substantial risk in SSA settings. Full article

Computational modeling and machine learning have impacted several different areas of science and accelerated advancements in multiple venues. Yet traditional machine learning models have many well-known drawbacks: besides demanding a significant amount of data, they may fail to generalize beyond training data, are often treated as “black boxes”, and may predict physically inconsistent results. In response to these limitations, Physics-Informed Machine Learning (PIML) has emerged as a new area that integrates domain knowledge, such as energy or mass conservation, directly into data-driven algorithms. This review paper examines the foundations and main strategies of PIML, organizing the approaches into three categories: automated discovery and system identification, continuous-time modeling, and operator learning. In addition, Physics-Informed Neural Networks are analyzed in a dedicated section that covers architecture fundamentals, forward and inverse problem formulations, loss function design and implementation challenges. The paper also presents a survey of interdisciplinary applications of PIML in materials science, biomedical engineering, and fractional calculus. In this context, the review also analyzes open challenges and outlines future directions in the field. Full article

Cyber aggression and abuse (CAA) has become a major interdisciplinary research area spanning psychology, communication, public health, and computer science. Existing reviews have largely focused on detection methods and model performance, offering limited insight into how CAA research themes have evolved over time at the field level. This study addresses this gap by, to the best of our knowledge, applying Latent Dirichlet Allocation (LDA) to 2309 Web of Science–indexed publications with English-language abstracts published between 2000 and 2024, providing a large-scale, longitudinal, and multi-level analysis of the literature. The model identifies 29 latent topics, which are organized using the User–Activity–Content (UAC) framework to link psychosocial research, platform-mediated behaviors, and computational detection approaches. Temporal analysis reveals a clear methodological transition: early dominance of survey-based and psychosocial themes gradually declines in relative prominence, while computational topics related to machine learning, deep learning, and pre-trained language models exhibit sustained growth, particularly after 2010. A Hot–Cold topic classification further distinguishes emerging, stable, and declining research directions. Journal-level, disciplinary, and geographic analyses reveal systematic differentiation across venues and regions, with complementary emphases on psychosocial and computational approaches. These findings provide a structured, field-level perspective on the evolution of CAA research and offer practical value for researchers, funding agencies, journal editors, and publishers by identifying dominant, emerging, and declining themes that can inform research prioritization, editorial planning, and strategic investment. Full article

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

Recommendation systems are popular information systems that help consumers manage information overload. Whilst personality has been recognised as an important factor influencing consumers’ choice, it has not yet been fully exploited in recommendation systems. This study proposes a restaurant recommendation approach that integrates customer personality traits, opinions and preferences, extracted either directly from online review platforms or derived from electronic word of mouth (eWOM) text using information extraction techniques. The proposed method leverages the concept of venue personality grounded in personality–brand congruence theory, which posits that customers are more satisfied with brands whose personalities align with their own. A novel model is introduced that combines fine-tuned BERT embeddings with linguistic features to infer users’ personality traits from the text of their reviews. Customers’ preferences are identified using a custom named-entity recogniser, while their opinions are extracted through structural topic modelling. The overall framework integrates neural collaborative filtering (NCF) features with both directly observed and derived information from eWOM to train an extreme gradient boosting (XGBoost) regression model. The proposed approach is compared to baseline collaborative filtering methods and state-of-the-art neural network techniques commonly used in industry. Results across multiple performance metrics demonstrate that incorporating personality, preferences and opinions significantly improves recommendation performance. Full article

Accurate potential energy surfaces (PESs) are the prerequisite for precise studies of molecular dynamics and spectroscopy. The permutationally invariant polynomial neural network (PIP-NN) method has proven highly successful in constructing full-dimensional PESs for gas-phase molecular systems. Building upon over a decade of development, we present CQPES v1.0 (ChongQing Potential Energy Surface), an open-source software package designed to automate and accelerate PES construction. CQPES integrates data preparation, PIP basis generation, and model training into a modernized Python-based workflow, while retaining high-efficiency Fortran kernels for processing dynamics interfaces. Key features include GPU-accelerated training via TensorFlow, the robust Levenberg–Marquardt optimizer for high-precision fitting, real time monitoring via Jupyter and Tensorboard, and an active learning module that is built on top of these. We demonstrate the capabilities of CQPES through four representative case studies: CH₄ to benchmark high-symmetry handling, CH₃CN for a typical unimolecular isomerization reaction, OH + CH₃OH to test GPU training acceleration on a large system, and Ar + H₂O to validate the active learning module. Furthermore, CQPES provides direct interfaces with established dynamics software such as Gaussian 16, Polyrate 2017-C, VENUS96C, RPMDRate v2.0, and Caracal v1.1, enabling immediate application in chemical kinetics and dynamics simulations. Full article

Background: Stepped-wedge cluster randomized trials (SW-CRTs) are increasingly used to evaluate population-level interventions, but trial validity depends on timely cluster transitions. Rollout feasibility is often assumed rather than modelled. In the context of a planned national trial of CPR training kiosks, we developed a Monte Carlo simulation algorithm to quantify logistical feasibility under uncertainty. Methods: A stochastic Monte Carlo algorithm was implemented to simulate deploying 100 CPR kiosks across eight Canadian cities under four team structures. Inputs included productivity (0.8–1.2 kiosks/day), disruption probabilities (weather, venue access, technical failure, staff illness, transport delays), and cost parameters (salaries, per diems, travel). Each scenario was simulated across 3000 iterations. Outputs included per-city feasibility (p ≤ 60 days), total project duration, and risk–cost trade-offs. Results: Single-team strategies required 9–10 months for full rollout, with winter-exposed cities such as Halifax and Charlottetown having up to 30% probability of exceeding 60 days. Two-team strategies halved rollout time (4–5 months) and achieved >95% on-time rollout across cities. Adding a third onsite staff member reduced risk by 5–15% with modest additional cost (~CAD 1500–2000 per city). Risk–cost analysis identified two teams with three staff as the most reliable strategy. Conclusions: Monte Carlo simulation provides a practical framework for assessing rollout feasibility in SW-CRTs. Applied to CPR kiosk deployment, it highlights the importance of staffing, seasonality, and city-level context. The approach is generalizable to other national interventions requiring phased rollout under uncertainty. Full article

Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments. Full article

Objectives: SailGP is an international professional mixed-sex sailing competition, which uses F50 foiling catamarans capable of reaching speeds up to ~100 km/h. This seminal study assesses injuries and illnesses observed by male and female sailors during trainings and competitions in SailGP’s third season. This study aims to assess injury and illness incidence, comparing results with other professional sailing events and high-performance sports. In addition, injury and illness risk factors (sex and position) will be explored with the goal to reduce morbidity for future seasons. Materials and Methods: This retrospective cohort design analysed medical records of male and female sailors during SailGP’s third season (April 2022 to May 2023). Risk factors assessed included sailor sex, sailor position (helm, strategist, grinder, flight controller and wing trimmer), sailing venue, wind speed and mechanism of injury/nature of illness. International Olympic Committee reporting guidelines on injuries and illnesses were followed, including the STROBE-SIIS checklist. Confidence intervals were set at 95%, statistical tests were two-sided and p-values < 0.05 were considered statistically significant. Results: A total of 40 on-water injuries were reported in 32 athletes. Injury incidence was greater during competitions than trainings, with strategists and then grinders being the most frequently injured positions. Competition injury incidence was 32.6 per 1000 h and 6.42 injuries per 365 days. Training injury incidence was 2.62 injuries per 1000 h and 3.82 injuries per 365 days. Knee, ankle, hand and head injuries were most prevalent, with three concussions observed during trainings and competitions (two female and one male). Direct impacts and falls during manoeuvres caused most injuries. Overall injury incidence (IRR = 2.69 [95% CI 1.41–5.16]), risk of training injuries (RR = 3.75 [95% CI 1.59–8.83], p = 0.001), risk of competition injuries (RR = 1.79 [95% CI 0.65–4.90], p = 0.25) and overall concussion risk (RR = 10.04 [95% CI 0.91–110.46], p = 0.02) were greater in females. Ten sailors accounted for 17 illnesses. Females had a 3.33 increase in training and competition illnesses (IRR = 3.33 [95% CI 0.94–11.81]). Conclusions: Competition injury incidence was higher than previous reported sailing studies. Knee injuries were most prevalent and direct impacts caused most injuries. Female sailors reported a higher injury and illness incidence. These results may guide injury prevention efforts and the development of an IOC-equivalent consensus statement. Future studies should examine time loss. Full article

We propose a hybrid CNN-LSTM architecture for energy-efficient spatiotemporal modeling in sports venue analytics, addressing the dual challenges of computational efficiency and prediction accuracy in dynamic environments. The proposed method integrates layered mixed-precision training with gradient accumulation, dynamically allocating bitwidths across the spatial (CNN) and temporal (LSTM) layers while maintaining robustness through a computational memory unit. The CNN feature extractor employs higher precision for early layers to preserve spatial details, whereas the LSTM reduces the precision for temporal sequences, optimizing energy consumption under a hardware-aware constraint. Furthermore, the gradient accumulation over micro-batches simulates large-batch training without memory overhead, and the computational memory unit mitigates precision loss by storing the intermediate gradients in high-precision buffers before quantization. The system is realized as a ResNet-18 variant with mixed-precision convolutions and a two-layer bidirectional LSTM, deployed on edge devices for real-time processing with sub 5 ms latency. Our theoretical analysis predicts a 35–45% energy reduction versus fixed-precision models while maintaining <2% accuracy degradation, crucial for large-scale deployment. The experimental results demonstrate a 40% reduction in energy consumption compared to fixed-precision models while achieving over 95% prediction accuracy in tasks such as occupancy forecasting and HVAC control. This work bridges the gap between energy efficiency and model performance, offering a scalable solution for large-scale venue analytics. Full article

Plasma amino acid (PAA) concentration in horses vary according to the exercise type. This study evaluated the changes in PAA levels and the associated metabolites, urea and ammonia, following short-duration, high-intensity cross-country exercise in eventing horses. Twenty eventing horses participated in 55 rides at 14 international competitions (2* to 4* levels) across five venues in Germany and Poland. Blood samples were collected at four timepoints: before exercise (TP0), at 10 min (TP1), and at 30 min (TP2) post-exercise, as well as in the morning on the day after the competition (TP3). A total of 23 different PAAs and two metabolites (ammonia and urea) were analyzed. PAA concentration difference over time was assessed by a mixed ANOVA. Significant fluctuations were observed in 18/25 parameters. For 21/23 PAAs, levels increased at TP1 and/or TP2, while cysteine concentrations decreased. Concentrations returned to pre-competition levels for 21/23 PAAs by TP3. Proline levels remained elevated (p = 0.002), while those of glycine significantly decreased (p = 0.027) at TP3. Plasma ammonia and urea levels increased at TP1, TP2 and TP3. This study provides foundations for supplementation strategies and can inform future works exploring PAAs’ role in performance and training adaptation in eventing horses and their potential as performance-related biomarkers. Full article

With the growing awareness of public health, urban parks have increasingly become popular venues for physical activities due to their accessibility and pleasant landscapes, among which walking, jogging, and running dominate. This study innovatively integrates exercise trajectory data from the Strava platform and semantic segmentation technology to analyze the interaction mechanisms among park view elements, physical activities, and physiological responses, based on empirical data from three representative parks in Shanghai. This study includes the following: (1) acquiring hotspot exercise paths and physiological data (heart rate and speed) of walking, jogging, and running users through the open Strava platform; (2) conducting semantic segmentation on real-word photos of three case parks to extract 17 types of park elements; (3) applying Spearman’s correlation analysis to reveal the differential impacts of park elements on physiological responses under walking, jogging, and running behaviors, demonstrating that combinations of elements such as trees, water bodies, fences, and sky influence exercise performance; and (4) constructing scene modules for site attraction, training improvement, and restorative relaxation for walking, jogging, and running, and proposing phased landscape scene sequence strategies to provide quantitative guidance for health-oriented park planning and design. This study breaks through the limitations of traditional subjective evaluations by coupling objective physiological data with spatial elements, offering novel insights for optimizing the exercise functionality of urban green spaces. Full article

Background/Objectives: Artificial intelligence-based software as a medical device (AI-SaMD) refers to AI-powered software used for medical purposes without being embedded in physical devices. Despite increasing approvals over the past decade, research in this domain—spanning technology, healthcare, and national security—remains limited. This research aims to bridge the existing research gap in AI-SaMD by systematically reviewing the literature from the past decade, with the aim of classifying key findings, identifying critical challenges, and synthesizing insights related to technological, clinical, and regulatory aspects of AI-SaMD. Methods: A systematic literature review based on the PRISMA framework was performed to select the relevant AI-SaMD studies published between 2015 and 2024 in order to uncover key themes such as publication venues, geographical trends, key challenges, and research gaps. Results: Most studies focus on specialized clinical settings like radiology and ophthalmology rather than general clinical practice. Key challenges to implement AI-SaMD include regulatory issues (e.g., regulatory frameworks), AI malpractice (e.g., explainability and expert oversight), and data governance (e.g., privacy and data sharing). Existing research emphasizes the importance of (1) addressing the regulatory problems through the specific duties of regulatory authorities, (2) interdisciplinary collaboration, (3) clinician training, (4) the seamless integration of AI-SaMD with healthcare software systems (e.g., electronic health records), and (5) the rigorous validation of AI-SaMD models to ensure effective implementation. Conclusions: This study offers valuable insights for diverse stakeholders, emphasizing the need to move beyond theoretical analyses and prioritize practical, experimental research to advance the real-world application of AI-SaMDs. This study concludes by outlining future research directions and emphasizing the limitations of the predominantly theoretical approaches currently available. Full article