Search Results (6,001)

Background/Objectives: Institutionalized aging is associated with severe physical deconditioning, a high risk of falls, and a pervasive fear of falling. Physical exercise mitigates these factors, but the comparative efficacy of different training methodologies in this specific population remains unclear. The objective of this study was to compare the impact of a multicomponent exercise program versus a dual-task (cognitive-motor) training program on reducing fall risk, decreasing the fear of falling, and improving physical performance in institutionalized older adults. Methods: A randomized, parallel group controlled trial involving 21 older adults residing in a nursing home (Mean age = 83.67 ± 6.17 years). Participants were allocated to either a Multicomponent Group (n = 11) or a Dual-Task Group (n = 10) for a 12-week intervention (2 sessions/week). Fall risk, fear of falling, and global physical performance were assessed at baseline and post-intervention. Results: No significant improvements were observed in fall risk assessment execution time for either group. The Multicomponent Group showed a significant reduction in the fear of falling (−29.1%; 95% CI [−17.27, −1.27], p = 0.025) and a clinically significant improvement in physical performance (+40.9%; 95% CI [1.11, 3.43], p < 0.001), supported by large time effects (FES-I: F(1, 19) = 4.52, η²p = 0.192; SPPB: F(1, 19) = 13.68, η²p = 0.419). The Dual-Task Group achieved no significant changes in these dimensions. Furthermore, a marginally significant time-by-group interaction was observed for physical performance, favoring the multicomponent approach (F(1, 19) = 3.83, p = 0.065, η²p = 0.168 [large effect]). Conclusions: Multicomponent training proved superior in improving physical performance and reducing the fear of falling. In a frail, institutionalized population, the attentional cost demanded by dual-task training appears to limit the physical and psychological benefits of exercise. Full article

School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article

Background/Objectives: Dental Practice-Based Research Networks (DPBRNs) bridge the gap between academic research and private dental practice, addressing questions relevant to everyday medical care. Despite their growing scientific output, little research has explored the experiences of practitioners engaged in these networks. Our study therefore aims to investigate these practitioners’ perspectives in order to identify strategies for improving investigator recruitment, training and data quality in future DPBRN studies. Methods: The qualitative methodology was chosen, and our study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines. Semi-structured interviews were conducted with dentists who had participated in a DPBRNs study and transcribed before being thematically analysed using Braun and Clarke’s framework. MaxQDA 2022 software was used to facilitate coding of the verbatim quotes. Results: Three major themes emerged: (1) obstacles to participation, including time constraints, difficulties in patient recruitment, and a perceived disconnect between academia and private practice; (2) facilitators of engagement, such as strong leadership, logistical support, and a collaborative research environment; and (3) personal benefits, such as skill development, breaking professional routines, and counteracting stereotypes about private practitioners’ involvement in research. Conclusions: The findings align with existing literature on medical Practice-Based Research Networks (PBRNs), highlighting logistical and motivational barriers while also emphasizing the importance of social and professional benefits. Notably, although financial compensation or credits for continuing professional development are frequently cited as motivators for research participation, these were not significant concerns for our participants. This study sheds light on the experiences of health practitioners in PBRNs, offering recommendations to overcome challenges through strategies such as accessible training, practical incentives and collaboration opportunities. Full article

This paper introduces a new hybrid forecasting architecture that combines Nonlinear Autoregressive (NAR) models, the proxy Standardized Precipitation-Evapotranspiration Index (SPEI), and a Nonlinear Autoregressive with Exogenous Inputs (NARX) framework for Soil Moisture Index (SMI) prediction. The suggested methodology solves the crucial difficulty of combining future climatic knowledge into soil moisture forecasting by using a cascaded approach. Stage 1 uses univariate NAR models to create multi-step-ahead predictions of precipitation and temperature. Stage 2 converts these forecasts into proxy SPEI values using a physically based water balance computation, and Stage 3 employs a NARX model that uses observed historical SMI and forecast-derived proxy SPEI as exogenous inputs. The framework is assessed using high-frequency observations from 2014 to 2020, with training data through 2019 and validation covering the whole 2020 horizon. The study combining forecast-driven climatic indicators with autoregressive soil moisture dynamics resulted in prediction accuracy (R² = 0.9888, RMSE = 0.0827, MAE = 0.0567). This study presents a new NAR–SPEI–NARX model for SMI prediction forecasting, based on three-stage modeling, where NAR models forecast precipitation and temperature and then turn them into SPEI-proxy as an exogenous input for NARX. Full article

The evolving digital landscape, including artificial intelligence (AI) and its generative forms, is changing how younger generations learn. As students utilize generative AI systems, they cultivate trust in such technology to support their current and long-term learning. The objective of this study was to investigate the relationship between generative AI use among students in health professions and their trust in this technology to support their lifelong learning as future health professionals. This study employed a survey methodology using a cross-sectional study design. The survey included sociodemographic variables and questions regarding students’ generative AI use and their trust in this technology to support their lifelong learning. Descriptive and inferential statistical procedures were used to analyze the data. A total of 558 students representing various health professions responded to the survey. In the regression analysis, after controlling for student’s sex and location variables, greater generative AI use was associated with students’ increased trust in this technology to support their lifelong learning (beta = 0.58, p < 0.001), explaining close to 40% of the total variance. Given the rapidly evolving digital landscape, this finding warrants further study, with implications for training of the future health workforce. Full article

Road traffic accidents at intersections pose a persistent challenge in Riyadh, Saudi Arabia, contributing significantly to public health burdens and economic losses. Traditional statistical approaches often fail to capture the complex, non-linear interactions among geometric design, traffic parameters, and accident severity. This study develops a multi-methodological machine learning framework to predict intersection accident severity using the Equivalent Property Damage Only (EPDO) metric. Historical data (2017–2023) from Riyadh Municipality for 150 high-risk intersections were analyzed, incorporating predictors such as service road distance (SRD), U-turn distance (UTD), median width (MW), peak hour volume (PHV), heavy vehicle percentage (HV%), and injury/frequency counts. Six algorithms, i.e., Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Linear Regression, and Artificial Neural Network, were compared using a 70/30 train–test split and k-fold cross-validation in this study. The Gradient Boosting model achieved superior performance (R² = 0.89 with MSE = 63.43 and RMSE = 7.96) and was selected for final deployment. SHAP feature importance analysis revealed minor injuries (MIs), serious injuries (SRIs), and fatalities (FAs) as the most important dominant predictors, with geometric factors (UTD, MW) and traffic composition (HV%) providing actionable infrastructure insights. The model ranked intersections and identified the “Jeddah Road with Taif Road” (predicted EPDO = 137.22) as the highest-risk location. Evidence-based recommendations include enforcing the minimum 300 m U-turn buffers with staggering service road exits ≥150 m and restricting heavy vehicles during peak hours. The scalable framework developed in this study supports the data-driven prioritization of safety interventions and aligns with sustainable urban mobility goals and offers transferability to other metropolitan contexts worldwide. Full article

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

Exercise-derived reactive oxygen species (ROS) are required for mitochondrial and hypertrophic adaptations, creating a practical trade-off: antioxidant strategies may support short-term performance and recovery yet blunt training signals when mis-timed or over-dosed. We performed a structured narrative review informed by transparent database searches of MEDLINE, Scopus, and SPORTDiscus (2000–2025), prioritizing human intervention studies and using mechanistic evidence to interpret plausibility. Evidence was mapped by antioxidant class, dose, timing, training modality, and context. Across trials, chronic high-dose vitamins C/E taken close to key sessions are most consistently associated with attenuation of redox-sensitive signaling, whereas food-first polyphenols and selected bioactives (e.g., tart cherry/anthocyanins, pomegranate, and curcumin) more often support recovery when positioned away from adaptation-critical workouts, without clear evidence of impaired training gains. N-acetylcysteine can acutely improve tolerance to repeated high-intensity exercise, but effects during prolonged training remain uncertain and appear context-dependent. We propose Redox-Adaptive Periodization, aligning antioxidant class, dose, and timing with the primary objective (adaptation vs. immediate readiness) and environmental constraints, and we outline methodological priorities to advance precision redox management. Full article

Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: This scoping review critically synthesizes current applications of artificial intelligence (AI) and wearable technologies (WT) in ST, with emphasis on methodological approaches, data characteristics, explainability, and practical readiness. Methods: Searches of PubMed and Scopus identified 13 peer-reviewed studies (2015–2025). Evidence was charted and synthesized to compare AI models, wearable sensor configurations, validation strategies, and translational potential. Results: Studies employed classical machine learning, deep learning, and hybrid approaches alongside inertial, force, strain, and physiological sensors to support exercise classification, load estimation, fatigue detection, and performance monitoring. Deep learning models dominated movement recognition tasks, whereas simpler models often aligned better with small datasets and interpretability requirements. However, most studies relied on limited, homogeneous samples and internal validation, restricting generalizability and real-world applicability. Explainability was inconsistently addressed, particularly in higher-risk applications such as injury prediction. Conclusions: AI-enhanced wearables provide objective and individualized ST monitoring, but current evidence remains largely experimental. To ensure a practical application is implemented, standardized datasets, robust external validation, and greater integration of explainable AI are required to support and deliver trustworthy decision-making. Full article

Background/Objectives: Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Methods: Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, tooth number extraction, consistency checking, and iterative regeneration. The framework functioned as an external validation mechanism for GPT outputs. Performance was compared against the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth displacement, relationships with other structures, and tooth number. Results: The SLSO framework improved output accuracy for multiple items compared to the CoT method, with the most notable improvements observed in tooth number identification, tooth displacement detection, and root resorption assessment. In successful cases, consistently structured outputs were achieved after up to five regenerations. The framework enforced explicit negative finding descriptions and suppressed hallucinations, although accurate identification of extensive lesions spanning multiple teeth remained limited. Conclusions: This investigation established the feasibility of the proposed integrated processing methodology and provided a foundation for future validation studies with larger, more diverse datasets. Full article

The surface quality of machined gears is closely related to operational energy efficiency and service durability, which affect the achievement of dual carbon goals in sustainable manufacturing. This study proposes a radial pre-stressed grinding method for gear manufacturing. Firstly, an analytical model for the radial pre-stress exerted on the gear inner hole was established by virtue of thick-walled cylinder theory. Secondly, a simulation and experiment were conducted under the same pre-stress conditions to obtain the radial stress. The theoretical, simulated, and experimental results were compared and discussed. Then, gear grinding simulations were performed at different pre-stress levels, grinding depths and grinding speeds. Finally, the grinding parameters were optimized by means of response surface methodology (RSM). This study recommends incorporating gears manufactured with radial pre-stressing into relevant industrial standards for green and low-carbon development. The results indicate that applying radial pre-stress to the gear inner hole significantly influences surface roughness and residual compressive stress after grinding, whereas it exhibits a minimal effect on grinding force. After optimization, compared with the initial simulation results, surface roughness is reduced by 12.5%, the absolute value of residual compressive stress is increased by 52.6%, and grinding force is decreased by 2.1%. The implementation of radial pre-stressed grinding in gear manufacturing requires institutional support, including its integration into green standard institutions, the development of technical specifications, and the establishment of promotion mechanisms. Such integration can be facilitated through national ‘Green Factory’ initiatives, comprehensive intellectual property protection, and targeted personnel training. Full article

Background: Liberia modernized vaccination data systems in 2023–2025 by piloting a District Health Information System (DHIS2)-based Digital Vaccination Registry (Electronic Immunization Registry, EIR) to address the limitations of paper-based workflows and of a proprietary COVID-19 electronic platform (offline gaps, lack of unique identifiers, performance issues and cost). Objective: To assess a pilot platform by evaluating training, registry use and device management, utility for routine immunization, vaccine logistics and Adverse Events Following Immunization (AEFI) data, and routine immunization data quality in the DHIS2 mobile application compared with paper registers. Methods: Using the Public Health Informatics Institute’s Collaborative Requirements Development Methodology, stakeholders defined requirements, trained users and implemented a pilot. Mixed methods were used; a mini data audit was performed, and qualitative data were collected across 19 facilities in Montserrado, Gbarpolu and Grand Bassa. Seventy-eight health workers were trained to use the DHIS2 mobile application. Results: The future state design replaces paper aggregation steps with real-time mobile entry to a national registry and dashboard. Dual entry persisted during high-volume periods. The mini data audit found discrepancies between facility paper registers and DHIS2-EIR entries for child enrollment data and, Bacillus Calmette Guérin and Diphtheria–Pertussis–Tetanus dose administration records Participants attributed these discrepancies to internet and device problems and challenges navigating the system. Participants requested a training manual, improved connectivity at point of service, integration with supportive supervision, additional staff and system features (field to record hospital number, automated next visit date, and vaccination status prompts). Conclusions: Lessons from the pilot will inform country-wide implementation, including planned linkage with electronic birth and death registration to enable a unique child identifier and reduce manual errors and delays. Full article

This study presents a practical methodology for executing Man-in-the-Middle (MitM) attacks on industrial control systems that utilize PROFINET I/O—a communication layer that remains largely underexplored in ICS cybersecurity research. A hybrid digital-twin-based testbed is developed by integrating Siemens S7-1500 and S7-1200 PLCs with a process replica implemented in PCSimu, together with a malicious application that modifies specific process data before it is delivered through the PROFINET I/O channel, enabling controlled falsification of process information in real time. The attacker operates through a Modbus TCP control channel while injecting the manipulated values into the 40-byte Real-Time Class 1 (RTC1) cyclic process-data payload while preserving frame integrity and protocol-level validity indicators. Experimental results show that SDU-level modifications on the 2-ms RTC1 cycle produced deterministic and fully reproducible effects on PLC-level behavior, including forced actuator confirmations and falsified process states, demonstrating the feasibility of both DI- and DO-level manipulation scenarios. Network captures and MSSQL-based event logs provide bit-level correlation between the injected SDU modifications and their impact on the automation sequence, confirming the reliability of the proposed manipulation mechanism. The testbed also supports the systematic generation of labeled datasets for training and evaluating machine-learning-based intrusion and anomaly-detection methods, and offers direct applicability to research, education, and operator-training activities in industrial cybersecurity. Overall, the proposed platform offers a secure, reproducible, and practically applicable environment for vulnerability assessment, attack simulation, and the development of detection techniques in industrial PROFINET networks. Full article

As safety and reliability requirements continue to rise in energy storage systems and related applications, fault prognosis has become a key enabler of stable operation and proactive safety management for lithium-ion batteries. Unlike conventional fault detection and diagnosis, fault prognosis focuses on predicting the occurrence time, evolution trend, and severity of potential faults, thereby strengthening risk awareness and decision-making proactivity in battery management systems (BMSs). However, existing prognosis methods still face substantial challenges under complex operating conditions, heterogeneous data sources, and highly nonlinear degradation dynamics, resulting in limited cross-scenario generalization, unstable long-horizon prediction, and insufficient uncertainty characterization. These limitations are becoming increasingly critical as lithium-ion batteries are widely deployed in electric vehicles and large-scale energy storage systems, creating an urgent need for prognosis approaches that are more accurate, robust, and scalable. Against this backdrop, this review provides a structured and forward-looking overview of lithium-ion battery fault prognosis with three objectives: systematically summarizing representative methodological routes, clarifying key technical challenges, and identifying research priorities for intelligent prognosis enabled by pre-trained models (PTMs). Specifically, we examine recent developments across three major technical routes—model-based, signal processing-based, and artificial intelligence (AI)-based methods. Building on this synthesis, we further discuss the opportunities introduced by PTMs for battery health management and analyze the key challenges of integrating PTMs into fault prognosis. Finally, in line with the evolution of intelligent BMSs, we outline future directions for enabling efficient, reliable, and trustworthy PTM-driven applications in lithium-ion battery fault prognosis, offering forward-looking insights for next-generation intelligent battery health management. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article