Search Results (3,587)

Background: Cluster sets (CSs) maintain velocity and power in compound movements by employing similar propulsion strategies or maintaining impulse through different mechanisms. This study aimed to explore the effect of four CS conditions on back squat (BS) propulsion and provide models for estimating changes in propulsion based on repetition and set number. Methods: Twenty male participants (age = 28.3 ± 3.1 years, stature = 1.74 ± 8.21 m, body mass = 84.80 ± 7.80 kg, BS 1RM = 140.90 ± 24.20 kg) completed four data collection sessions. Each session consisted of three sets of five repetitions at 80% 1RM BS with three minutes of unloaded inter-set rest, using varying intra-set rest intervals. Experimental conditions included 0 s (TRAD), 10 s (CS10), 20 s (CS20), and 30 s (CS30) inter-repetition rest, randomly assigned to sessions in a counterbalanced order. Ground reaction force data were collected on dual force platforms sampling at 1000 Hz, from which net propulsive impulse (J_PROP), mean force (MF), and propulsion time (t_PROP) were calculated. Conditions and sets were analysed using a 4 × 3 (CONDITION*SET) repeated-measures ANOVA to assess differences between conditions and sets, and linear mixed models (LMMs) were used to provide regression equations for each dependent variable in each condition. Results: The ANOVA revealed no significant interactions for any dependent variable. No main effects of CONDITION or SET were observed for J_PROP. The main effects of CONDITION showed that MF was significantly lower in TRAD than CS20 (g = 0.757) and CS30 (g = 0.749). t_PROP was significantly higher in TRAD than CS20 (g = 0.437) and CS30 (g = 0.569). The main effects of SET showed that MF was significantly lower in S2 (g = 0.691) and S3 (g = 1.087) compared to S1. t_PROP was significantly higher in S2 (g = 0.866) and S3 (g = 1.179) compared to S1. LMMs for CS20 and CS30 revealed no significant effect (p > 0.05) between repetition or set number and dependent variables. Conclusions: The results suggest that CS20 and CS30 maintain J_PROP by limiting MF and t_PROP attenuation. This is less rest than that suggested by the previous literature, which may influence programming decisions during strength and power mesocycles to maximise training time and training density. LMMs provide accurate estimates of BS propulsive force attenuation when separating repetitions by up to 30 s, which may help practitioners optimise training load for long-term adaptations. Full article

Background/Objectives: Wet age-related macular degeneration (AMD) is a progressive retinal disease characterized by macular neovascularization (MNV). Currently, the standard treatment for wet AMD is intravitreal anti-VEGF administration, which aims to control disease activity by suppressing neovascularization. In clinical practice, the decision to continue or discontinue treatment is largely based on the presence of fluid on optical coherence tomography (OCT) and changes in visual acuity. However, discrepancies between anatomic and functional responses can occur during these assessments. Methods: This article presents an artificial intelligence (AI)-based classification model developed to objectively assess the response to anti-VEGF treatment in patients with AMD at 3 months. This retrospective study included 120 patients (144 eyes) who received intravitreal bevacizumab treatment. After bevacizumab loading treatment, the presence of subretinal/intraretinal fluid (SRF/IRF) on OCT images and changes in visual acuity (logMAR) were evaluated. Patients were divided into three groups: Class 0, active disease (persistent SRF/IRF); Class 1, good response (no SRF/IRF and ≥0.1 logMAR improvement); and Class 2, limited response (no SRF/IRF but with <0.1 logMAR improvement). Pre-treatment and 3-month post-treatment OCT image pairs were used for training and testing the artificial intelligence model. Based on this grouping, classification was performed with a Siamese neural network (ResNet-18-based) model. Results: The model achieved 95.4% accuracy. The macro precision, macro recall, and macro F1 scores for the classes were 0.948, 0.949, and 0.948, respectively. Layer Class Activation Map (LayerCAM) heat maps and Shapley Additive Explanations (SHAP) overlays confirmed that the model focused on pathology-related regions. Conclusions: In conclusion, the model classifies post-loading response by predicting both anatomic disease activity and visual prognosis from OCT images. Full article

Introduction: Overuse injuries are a growing concern among adolescent soccer players, with the repetitive nature of the sport placing significant physical demands on young athletes. These injuries can have long-term implications for physical development, performance, and overall well-being. This narrative synthesis aimed to evaluate the existing literature on the epidemiology, risk factors, and management strategies for overuse injuries in adolescent soccer players. Methods: A comprehensive literature search was conducted using PubMed and Embase. A total of 123 articles were identified, 27 of which met the inclusion criteria after screening. Studies focusing on overuse injuries in adolescent soccer players aged 10–18 years were included, while those addressing acute injuries, non-soccer populations, or adult athletes were excluded. Relevant quantitative and qualitative data were extracted and evaluated. Due to heterogeneity in study designs and outcomes, findings were narratively synthesized rather than meta-analyzed. Results: The period around peak height velocity (PHV: 11.5 years in girls, 13.5 years in boys) was consistently identified as a high-risk window, with seven studies demonstrating a significantly increased incidence of overuse injuries. Additional risk factors included leg length asymmetry, truncal weakness, early sport specialization, high ratios of organized-to-free play, and increased body size. Injury burden was greatest for hamstring and groin injuries, often leading to prolonged time lost from play. Preventive interventions such as plyometric training, trunk stabilization, and structured load monitoring demonstrated reductions in injury incidence in several prospective studies, though protocols varied widely. Conclusion: This narrative synthesis highlights PHV as the most consistent risk factor for overuse injuries in adolescent soccer players, alongside modifiable contributors such as training load, sport specialization, and free play balance. Evidence supports neuromuscular training and structured monitoring as promising preventive strategies, but there remains a lack of standardized, evidence-based protocols. Future research should focus on optimizing and validating interventions, integrating growth and load monitoring, and leveraging emerging approaches such as machine learning-based risk prediction. Full article

This work proposes a design technique to facilitate the design and optimization of a highperformance power amplifier (PA) in an automated manner. The proposed optimizationoriented strategy consists of the implementation of four deep neural networks (DNNs), sequentially. Firstly, a bidirectional long short-term memory (BiLSTM)-based DNN is trained based on the X-parameters for which the hyperparameters are optimized through the multi-objective ant lion optimizer (MOALO) algorithm. This step is significant since it conforms to the hidden-layer construction of DNNs that will be trained in the following steps. Afterward, a generative adversarial network (GAN) is employed for forecasting the load–pull contours on the Smith chart, such as gate and drain impedances that are employed for the topology construction of the PA. In the third phase, the classification the BiLSTM-based DNN is trained for the employed high-electron-mobility transistor (HEMT), leading to the selection of the optimal configuration of the PA. Finally, a regression BiLSTMbased DNN is executed, leading to optimizing the PA in terms of power gain, efficiency, and output power by predicting the optimal design parameters. The proposed method is fully automated and leads to generating a valid PA configuration for the determined transistor model with much more precision in comparison with long short-term memory (LSTM)-based networks. To validate the effectiveness of the proposed method, it is employed for designing and optimizing a PA operating from 1.8 GHz up to 2.2 GHz at 40 dBm output power. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

Small targets in drone imagery are often difficult to accurately locate and identify due to scale imbalance and limitations, such as pixel representation and dynamic environmental interference, and the balance between detection accuracy and resource consumption of the model also poses challenges. Therefore, we propose an interpretable computer vision framework based on YOLOv12m, called LCW-YOLO. First, we adopt multi-scale heterogeneous convolutional kernels to improve the lightweight channel-level and spatial attention combined context (LA2C2f) structure, enhancing spatial perception capabilities while reducing model computational load. Second, to enhance feature fusion capabilities, we propose the Convolutional Attention Integration Module (CAIM), enabling the fusion of original features across channels, spatial dimensions, and layers, thereby strengthening contextual attention. Finally, the model incorporates Wise-IoU (WIoU) v3, which dynamically allocates loss weights for detected objects. This allows the model to adjust its focus on samples of average quality during training based on object difficulty, thereby improving the model’s generalization capabilities. According to experimental results, LCW-YOLO eliminates 0.4 M parameters and improves mAP@0.5 by 3.3% on the VisDrone2019 dataset when compared to YOLOv12m. And the model improves mAP@0.5 by 1.9% on the UAVVaste dataset. In the task of identifying small objects with drones, LCW-YOLO, as an explainable AI (XAI) model, provides visual detection results and effectively balances accuracy, lightweight design, and generalization capabilities. Full article

Sliding mode control (SMC) provides robustness and disturbance rejection in permanent magnet synchronous motor (PMSM) control but faces the challenge of speed degradation during sudden load disturbance changes without a load observer. This paper proposes a backpropagation neural network-adjusted improved SMC (BPNN-ISMC). A simplified PMSM model is established by ignoring the disturbance term. An improved arrival law is developed by optimizing the constant-speed approach term of the traditional exponential arrival law and embedding an adaptive term. A BPNN is designed with performance metrics including speed error, its derivative, and maximum error to improve training efficiency. Speed/position estimation combines a sliding mode observer with an extended Kalman filter to suppress jitter. Simulation results demonstrate the significant advantages of the BPNN-ISMC method: in set-point control, overshoot suppression is evident, and the relative error during the stable phase after sudden load disturbance increases is reduced by 93.62% compared to the ISMC method and by 99.80% compared to the SMC method. Compared to the ADRC method, although the steady-state errors are the same, the BPNN-ISMC method exhibits smaller speed fluctuations during sudden changes. In servo control, the root mean square error of speed tracking is reduced by 18.83% compared to the ISMC method, by 89.70% compared to the SMC method, and by 37.14% compared to the ADRC method. This confirms the dynamic performance improvement achieved through adaptive adjustment of neural network parameters. Full article

To address thermal management challenges in CR400BF high-speed EMU electrical cabinets—stemming from heterogeneous component integration, multi-condition dynamic thermal loads, and topological configuration variations—a dual-metric-driven finite element model calibration method is proposed using ANSYS Workbench. A multi-objective optimization function, constructed via the coefficient of determination ($R^2$) and root mean square error ($RMSE$), integrates gradient descent to inversely solve key parameters, achieving precise global–local model matching. This establishes an equivalent model library of 52 components, enabling rapid development of multi-physical-field coupling models for electrical cabinets via parameterization and modularization. The framework supports temperature field analysis, thermal fault prediction, and optimization design for multi-topology cabinets under diverse operating conditions. Validation via simulations and real-vehicle tests demonstrates an average temperature prediction error $\le 10\%$, verifying reliability. A thermal management optimization scheme is further developed, constructing a full-process technical framework spanning model calibration to control for electrical cabinet thermal design. This advances precision thermal management in rail transit systems, enhancing equipment safety and energy efficiency while providing a scalable engineering solution for high-speed train thermal design. Full article

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article

Accurately predicting building heating and cooling loads is crucial for optimizing HVAC systems and enhancing energy efficiency. However, data-driven models often face overfitting issues due to scarce training data, a common challenge for new constructions or under data privacy constraints. Transfer learning (TL) offers a solution, but its effectiveness heavily depends on selecting an appropriate source domain through effective similarity measurement. This study systematically evaluates the performance of 20 prevalent similarity metrics in TL for building heating load forecasting to identify the most robust metrics for mitigating data scarcity. Experiments were conducted on data from 500 buildings, with seven distinct low-data target scenarios established for a single target building. The Relative Error Gap (REG) was employed to assess the efficacy of transfer learning facilitated by each metric. The results demonstrate that distance-based metrics, particularly Euclidean, normalized Euclidean, and Manhattan distances, consistently yielded lower REG values and higher stability across scenarios. In contrast, probabilistic measures such as the Bhattacharyya coefficient and Bray–Curtis similarity exhibited poorer and less stable performance. This research provides a validated guideline for selecting similarity metrics in TL applications for building energy forecasting. Full article

Dysfunctions and disorders of the craniomandibular system are accompanied by pathophysiological changes of muscle groups in the throat/neck and facial area, e.g., pain in the jaw and muscles of mastication and disturbance of occlusion, leading to teeth injury (loss of dental hard tissue, fractures/sensibility disorders, etc.). For muscular dysfunctions, even in the context of psychosomatic disorders and chronic stress, hippotherapy is particularly suitable, since it helps actively to relieve muscle tensions. In the current project we combined hippotherapy with progressive muscle relaxation (PMR) to achieve a synergistic effect. The horses used for therapy (two mares and five geldings between seven and twenty-one years old) were especially suitable because of their calm temperament. In two cases, trained therapy horses were used; in five other cases, the patients used their own horses, which were not specially trained. Right from the beginning, the project was accompanied by veterinary support. Conditions of horse keeping (active stable, same-sex groups, no boxes) were assessed as well as the horses themselves prior to, during, and after each therapy unit. In patients, cortisol, as a quantifiable parameter for stress, was measured before and after each therapy unit. From before the start until the end of each therapy unit of 15 min, the heart rate variability (HRV) of both patients and horses was registered continuously and synchronously. In addition, the behavior of the horses was monitored and recorded on video by an experienced coach and a veterinarian. The stress load during the tension phases in the therapy units was low, perceivable in the horses lifting their heads and a slightly shortened stride length. Likewise, the horses reflected the patients’ relaxation phases, so that at the end of the units the horses were physically and psychically relaxed, too, noticeable by lowering their necks, free ear movement, and a decreasing heart frequency (HF). Altogether, the horses benefited from the treatment, too. Obvious stress signs like unrest, head tossing, tail swishing, or tense facial expressions were not noticed at any time. Twenty jumpers served as a control group in different situations (training, tournament, and leisure riding). Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Clinical manifestations of salmonellosis in humans typically include acute gastroenteritis, abdominal pain, diarrhea, nausea, and fever. Diarrhea and anorexia may persist for several days. In some cases, the organisms may invade the intestinal mucosa and cause septicemia, even in the absence of significant gastrointestinal symptoms. Most clinical signs are attributed to hematogenous dissemination of the pathogen. As with other microbial infections, disease severity is influenced by the serotype of the organism, bacterial load, and host susceptibility. Serotyping analysis of Salmonella spp. using the White–Kauffmann–Le Minor scheme remains the gold standard for strain typing. However, this method is expensive, time-consuming, and requires significant expertise and visual interpretation by trained personnel, which is why it is typically restricted to regional or national reference laboratories. In this study, we evaluated a spectroscopic technique coupled with chemometrics and multivariate machine learning algorithms for its ability to discriminate the main Salmonella spp. serogroups in a clinical routine setting. We analyzed 95 isolates of Salmonella that were randomly selected, including four strains of S. Typhi. The I-dOne Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) system (Alifax S.r.l., Polverara, Italy) also shows promising potential for distinguishing Salmonella Typhi within the D serogroup. The I-dOne system enables simultaneous identification of both species and subspecies using the same workflow and instrumentation, thus streamlining the diagnostic process. Full article

This study explored the use of Borassus fruit fiber as reinforcement for earthen matrices (BFRC). The experimental results of the testing carried out on the structural properties were used to generate a primary dataset for training and testing machine learning (ML) models. Linear regression (LR), Decision tree regressor (DTR), and gradient boosting regression (GBR) were used to build an ensemble learning (EL) model during the prediction of the hygroscopic properties, Young’s modulus, and compressive strength of the BFRC. Fiber content, activation concentration, curing days, dry weight, saturated weight, mass, flexural vibration, longitudinal vibration, correction factor, maximum load, and cross-sectional area were the various inputs considered in the structural properties prediction. The performance of both EL and single models (SMs) was appraised via three performance metrics—mean square error (MSE), root mean square (RMSE), and the coefficient of determination (R²)—to comparatively ascertain the model’s efficiency. Results showed that all models exhibited high accuracy in predicting Young’s modulus and compressive strength. Ensemble learning outperformed single models in predicting these properties, with MSE, RMSE, and R² of 0.01 MPa, 0.1 MPa, and 99% and 3,923,262.5 MPa, 1980.7 Pa, and 99% for compressive strength and Young’s modulus, respectively. However, for hygroscopic behavior, linear regression (LR) demonstrated superior performance compared to other models, with MSE, RMSE, and R² values of 0.13%, 0.36%, and 99%. Full article

This study explores the application of federated learning (FL) in security camera surveillance systems to overcome the structural limitations inherent in traditional centralized artificial intelligence (AI) training approaches, while simultaneously enhancing operational efficiency and data security. Conventional centralized AI models require the transmission of raw surveillance data from individual security camera units to a central server for model training, which poses significant challenges, including network congestion, a heightened risk of personal data leakage, and inadequate adaptation to localized environmental characteristics. These limitations are particularly critical in high-security environments such as military bases and government facilities, where reliability and real-time processing are paramount. In contrast, FL enables decentralized training by retaining data on local devices and sharing only model parameters with a central aggregator, thereby improving privacy preservation, reducing communication overhead, and facilitating adaptive, context-aware learning. This paper does not present a new federated learning algorithm or original experiment. Instead, it synthesizes existing research findings and applies the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical factors for deploying FL in surveillance systems. By combining literature-based evidence with structured expert judgment, this study provides practical guidelines for real-world application. This paper identifies four key performance metrics—detection accuracy, false alarm rate, response time, and network load—and conducts a comparative analysis of FL and centralized AI-based approaches in the recent literature. In addition, the AHP is employed to evaluate expert survey data, quantitatively prioritizing eight critical factors for effective FL implementation. The results highlight detection accuracy and data security as the most significant concerns, indicating that FL presents a promising solution for future smart surveillance infrastructures. This research contributes to the advancement of AI-powered surveillance systems that are both high-performing and resilient under stringent privacy and operational constraints. Full article