Search Results (1,312)

Background: The purpose of this study was to compare the effects of two months of exergaming, conventional resistance exercise training, and no exercise on body composition and cardiometabolic risk factors in physically inactive older adults. Methods: For the preliminary study, twenty-four physically inactive adults aged 60–74 yrs. were allocated to an active video game training group (AVG n = 8), a conventional exercise group (CEG n = 7), or a non-exercising control group (CON n = 9). The AVG and CEG completed 24 supervised exercise training sessions over two months (three sessions per week) at self-selected, predominantly moderate-to-vigorous intensity, while the CON maintained usual daily activities. Body weight, skeletal muscle mass, body fat percentage, phase angle, and fasting blood biomarkers (glucose, total cholesterol, LDL, HDL, VLDL, and triglycerides) were assessed before and after the intervention. Results: No significant interactions were observed for body composition variables. Body weight decreased significantly following exercise training in both the AVG and CEG (p < 0.05). Significant interactions were found for total cholesterol (p = 0.001) and LDL cholesterol (p = 0.009). The AVG demonstrated significant reductions in fasting glucose, total cholesterol, and LDL cholesterol (p < 0.05), whereas the CEG showed a significant reduction only in total cholesterol. In contrast, the CON exhibited a significant increase in total cholesterol over the same period (p < 0.05). Conclusions: Two months of exergaming-based exercise training may lead to greater improvements in lipid-related cardiometabolic risk factors compared with conventional resistance exercise training in physically inactive older adults. These findings suggest that exergaming could be a promising exercise modality for supporting cardiometabolic health in aging populations. Full article

Anterior cruciate ligament (ACL) injury risk is elevated in female youth football, yet knee joint loading has mainly been studied under controlled laboratory conditions. This limits understanding of how injury risk emerges during realistic match situations. This study provided a field-based kinetic characterization of football-specific movements by estimating knee abduction moments (KAMs) using wearable sensors and machine learning. Fifty-two highly talented female youth players performed agility tasks during training, including structured exercises (F-EX) and game-based play (F-GAME). Full-body kinematics were collected with inertial measurement units, and a validated support vector machine model, trained on synchronized motion capture and force plate data, classified trials as high or low KAM. Across 662 change-in-direction trials, 9–12% were classified as high KAM in both conditions, indicating that potentially high-risk loading regularly occurs during routine actions. High KAM trials showed reduced knee and pelvis flexion, increased hip flexion, and greater pelvis rotation toward the cutting direction, reflecting upright, stiff movement strategies. Performance analyses revealed smaller cut angles in exercises and greater approach acceleration in game play, without differences in peak velocity. These findings demonstrate the feasibility of field-based kinetic screening and support a complex-systems perspective on ACL injury risk. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article

This study examined whether different body fat percentages (BF%) may influence performance, physiological responses, and fatigue in professional soccer players during a simulated soccer game protocol on a treadmill. Twenty professional male soccer players were categorized in higher (HBF%) and lower (LBF%) body fat percentage groups [HBF% > 11.5%; n = 11, BF% = 14.2 ± 2, LBM = 65.3 ± 8 kg, age = 22.7 ± 4 years, height = 177 ± 7 cm, weight = 76 ± 9 kg, $\dot{V}$O₂max = 60.1 ± 4.5]; [LBF% < 11.5%, n = 9; BF% = 8.1 ± 1, LBM = 65.9 ± 5 kg, age = 20.1 ± 3 years, height = 179 ± 4 cm, weight = 72 ± 5 kg, $\dot{V}$O₂max = 61.6 ± 4). Players underwent a simulated soccer game protocol on a treadmill. Cardiometabolic and hormonal responses, and fuel oxidation and performance, were evaluated. At baseline, apart from the BF% variable (p < 0.0001), the groups did not differ in any other physiological or physical characteristic (p > 0.05). There were no differences between the groups in any performance or biological parameters evaluated (p > 0.05), except for plasma glucose, which was higher in the HBF% group at rest and during the soccer game protocol (p < 0.05). In conclusion, the theory of a uniform ideal (~10 ± 2%) of BF% in elite soccer is not supported by the present study. This study suggests that when muscle mass and fitness levels of the soccer players are maintained at high levels during the competitive period, BF% represents a highly individualized characteristic rather than a uniform target across players. However, a higher BF% increased resting and exercising blood glucose concentrations, even in highly trained professional soccer players, without concomitant effects on metabolism or fuel oxidation during match play. Full article

Background: Global positioning system (GPS) technology is widely used to quantify external training load (ETL) in youth soccer. Despite its extensive application in training and match contexts, considerable heterogeneity is present in the selection, definition, and interpretation of GPS-derived variables, limiting comparability between studies and practical implementation by coaches. Objective: This narrative review aimed to summarize and critically evaluate the current literature on GPS-based ETL monitoring in youth soccer players, with a focus on commonly used variables, methodological considerations, and practical applications in training and match contexts. Methods: A narrative literature search was conducted using PubMed, SPORTDiscus, and Scopus databases. Peer-reviewed studies published in English between the years of 2012 and 2025 were included. Data were extracted on participant characteristics, GPS technology, monitored ETL variables, and contextual settings. Results: The 34 reviewed studies primarily reported total distance (TD; m), high-speed running distance (HSR; m), sprint distance (SD; m), distance per minute (m·min⁻¹), peak speed (km·h⁻¹), and acceleration- and deceleration-based (ACC, DEC; count) ETL variables. Substantial variability was observed in speed thresholds, acceleration definitions, and data processing methods. Positional roles, training formats (e.g., small-sided games), and seasonal phase influenced ETL demands, although methodological inconsistencies limited cross-study comparisons. Conclusion: GPS technology provides valuable insights into the ETL demands of youth soccer. The lack of standardized variable definitions and thresholds remains a major limitation. Greater methodological consistency and clearer reporting standards are required to enhance the practical usefulness of GPS monitoring for coaches in youth soccer. Full article

Open-pit mining operations rely heavily on visual inspection to identify indicators of slope instability such as surface cracks. Early identification of these geotechnical hazards enables timely safety interventions to protect both workers and assets in the event of slope failures or landslides. While computer vision (CV) approaches offer a promising avenue for autonomous crack detection, their effectiveness remains constrained by the scarcity of labelled geotechnical datasets. Deep learning (DL)-based models, in particular, require large amounts of representative training data to generalize to unseen conditions; however, collecting such data from operational mine sites is limited by safety, cost, and data confidentiality constraints. To address this challenge, this study proposes a novel hybrid game engine–generative artificial intelligence (AI) framework for large-scale dataset generation without requiring real-world training data. Leveraging a parameterized virtual environment developed in Unreal Engine 5 (UE5), the framework generates realistic images of open-pit surface cracks and enhances their fidelity and diversity using StyleGAN2-ADA. The synthesized datasets were used to train the YOLOv11 real-time object detection model and evaluated on a held-out real-world dataset of open-pit slope imagery to assess the effectiveness of the proposed framework in improving model generalizability under extreme data scarcity. Experimental results demonstrated that models trained using the proposed framework consistently outperformed the UE5 baseline, with average precision (AP) at intersection over union (IoU) thresholds of 0.5 and [0.5:0.95] increasing from 0.792 to 0.922 (+16.4%) and 0.536 to 0.722 (+34.7%), respectively, across the best-performing configurations. These findings demonstrate the effectiveness of hybrid generative AI frameworks in mitigating data scarcity in CV applications and supporting the development of scalable automated slope monitoring systems for improved worker safety and operational efficiency in open-pit mining. Full article

Background: Small-sided games (SSG) and running-based high-intensity interval training (HIIT) are commonly used in soccer conditioning to improve aerobic fitness and performance. Although both modalities induce high cardiovascular stress, their acute neuromuscular, perceptual, and cognitive responses remain incompletely understood when examined within the same cohort. This study compared the acute physical, psychophysiological, and cognitive responses to SSG and Tabata-type HIIT in amateur male soccer players. Methods: Thirty-two male amateur players (n = 32; age: 20.53 ± 1.65 years) completed a counterbalanced within-subject crossover design. Participants performed a 4v4 SSG protocol and a running-based Tabata-HIIT protocol (8 × 20 s, 10 s recovery) on separate days (48 h apart). Countermovement jump (CMJ), squat jump (SJ), 20-m sprint, agility t-test, heart rate, perceived exertion (Borg CR-10), mental effort, and cognitive performance (d2 test) were assessed pre- and post-exercise. Parametric variables were analyzed using 2 × 2 repeated-measures ANOVA (time × protocol; η²p), and non-parametric data were analyzed using Friedman and Wilcoxon tests (r) (p < 0.05). Results: Both protocols elicited similar cardiovascular responses (~90% HR_max). A significant protocol × time interaction was observed for CMJ (p < 0.001), showing a decline after Tabata-HIIT, whereas performance was maintained after SSG. No inter-protocol differences were found for SJ, sprint, or agility. Perceived exertion and mental effort during recovery were higher following Tabata-HIIT (p < 0.05). Cognitive performance improved after both protocols (p < 0.001), with no between-protocol differences. Conclusions: Despite comparable cardiovascular load, Tabata-HIIT was associated with greater acute neuromuscular and perceptual strain, whereas SSG preserved neuromuscular performance. Perceptual and mental responses may therefore differ despite similar physiological intensity, which may inform soccer training prescription. Full article

Mental fatigue (MF) has been hypothesized to contribute to injury risk in athletes, but observational studies have not directly investigated this relationship. Therefore, the current study evaluates potential relationships between mental fatigue and subsequent injury occurrence in basketball. Using an observational design, we monitored fourteen male semi-professional basketball players (age: 22 ± 4 years; stature: 192.6 ± 8.8 cm; body mass: 85.5 ± 9.1 kg; Tier 3) from a single team for 21 weeks throughout the competitive season. Each week, the players participated in 5 team-based training sessions, 2–4 individual training sessions, and 1–2 official games. Subjective MF ratings were collected using 100 mm visual analogue scales twice a week (the day before and after the official game) and then averaged. Time-loss injuries were registered, noting the body location, mechanism, and context (training and games). Generalized logistic mixed models were employed to evaluate whether MF levels were associated with injury occurrence in the subsequent 1, 3, and 5 days and 1, 2, 3, and 4 weeks of basketball activity. A total of 11 injuries were registered during the study (7.40 per 1000 h of basketball activity), with an average time loss of 12 ± 19 days. There were no associations between MF and injury occurrence in the following 1, 3, 5 days nor 1, 2, 3, 4 weeks (all p > 0.05, odds ratios: 1.00–1.28). In male semi-professional basketball settings, preliminary evidence indicates that MF might not be associated with injury occurrence. However, due to the dearth of injury events, the statistical power of this study is insufficient to detect potential small–medium effects. Therefore, the current results should be considered exploratory as opposed to a definitive rejection of the hypothesis. Future studies should evaluate the relationship between MF and injury risk in larger samples and among professional athletes. Full article

Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study focuses on object detection in handball and targets key entities, such as players, referees, goalkeepers, and the ball. A comprehensive dataset was created through a collaborative annotation process, consisting of annotated images extracted from real handball games. The YOLOv8 model was then trained and evaluated on this dataset to assess its effectiveness in entity recognition. The proposed approach achieved an object detection accuracy of 86.8% on a relatively small held-out test set, providing an indicative first benchmark for the application of state-of-the-art machine learning models to handball. To the best of our knowledge, the dataset generated in this study is the first comprehensive collection of annotated handball images, providing a valuable resource for further research. By bridging sports analytics and computer vision, this study contributes to the advancement of performance assessment in handball. These exploratory results suggest potential directions for future real-time systems and practical applications, such as improved understanding of player performance, team dynamics, and strategic decision-making. Full article

Serious video games are digital games designed for purposes beyond entertainment, typically to support education, training, health interventions, or behaviour change. They combine game mechanics with psychological and pedagogical principles, such as feedback, repetition, goal-setting, and scaffolding, to create interactive environments that facilitate learning, skill development, and sustained engagement. In many cases, they are built to simulate realistic tasks or decision contexts, allowing users to practise skills, test strategies, and learn from consequences in a low-risk setting. Within cyberpsychology, serious video games are particularly valuable because they provide structured digital contexts for examining how technology shapes cognition, emotion, motivation, and behaviour. They enable researchers and practitioners to observe how users respond to digital rewards, challenges, social features, and immersive environments, as well as how these features influence outcomes such as self-efficacy, persistence, attention, and emotion regulation. As a result, serious video games operate at the intersection of psychological theory, human–technology interaction, and applied digital intervention design. This entry provides an overview of their development, theoretical foundations, applications, effectiveness, and associated ethical considerations. Full article

Curling is a sport in which two teams take turns delivering stones on ice and compete for total scores. It is a highly strategic sport, often referred to as “Chess on Ice”. In recent years, research on curling AI and statistical analysis aimed at tactical evaluation has been active. Decision-making in curling highly depends on the current stone position state, so obtaining stone positions is essential for tactical analysis. This study proposes an object detection model capable of acquiring stone coordinates with high accuracy and generality from stone position images of actual games. The proposed model was realized with a small amount of manually annotated data and pseudo-labeled images. Using the active testing method, the image-level accuracy of data—a strict criterion requiring perfect detection of all stones in a single image—for approximately 100,000 items was estimated to be 99.37%. Furthermore, we measured the positional error of the detected stones and found an average result of 0.472 px. We determined that this model had sufficient accuracy for practical use, so we decided to store the acquired coordinates in a database and use them as training data for the curling AI and statistical analysis. Full article

The high penetration of distributed energy resources (DERs) poses numerous challenges to community energy management, including intense source-load stochasticity, synchronized load surges triggered by multi-agent gaming, and potential privacy breaches. To tackle these issues, this paper proposes a coordinated energy trading framework driven by an intermediate market-rate pricing mechanism. Within this framework, a novel Multi-Agent Transformer Proximal Policy Optimization (MATPPO) algorithm is developed, adopting an LSTM–Transformer hybrid architecture and the centralized training with decentralized execution (CTDE) paradigm. During centralized training, an LSTM network extracts temporal evolution features from source-load data to handle environmental uncertainty, while a Transformer-based self-attention mechanism reconstructs the dynamic agent topology to capture spatial correlations. In the decentralized execution phase, prosumers make independent decisions using only local observations. This eliminates the need to upload internal device states, significantly enhancing the privacy of sensitive local information during the online execution phase. Additionally, a parameter-sharing mechanism enables agents to share policy networks, significantly enhancing algorithmic scalability. Simulation results demonstrate that MATPPO effectively mitigates power peaks and reduces the transformer capacity pressure at the main grid interface. Furthermore, it significantly lowers total community electricity costs while maintaining high computational efficiency in large-scale scenarios. Full article

Soccer is an intermittent sport that requires complex and well-adjusted physiological responses from athletes. The training load allows athletes to optimize physical adaptations and reduces the risk of musculoskeletal injuries. In women’s soccer, the implementation of load control and individualization strategies has shown promise for enhancing anaerobic performance and injury prevention. This study aimed to compare the performance levels of professional women’s soccer players before and after the implementation of relative external load (RELC) for training prescription. Twenty-seven female professional soccer athletes (mean age 29.4 ± 6.2 years) were evaluated. Metrics such as total distance, sprint distance, number of sprints, accelerations, and decelerations were collected using the GPS-based device Catapult One (Catapult). Athletes were assessed in two games, with 6 months’ difference between matches: Game 1, without RELC implementation, and Game 2, with RELC. Significant differences were found between both periods. Sprint distance increased from 391 m to 450 m (+15%, d = 0.49, p ≤ 0.05), and sprint count increased from 14 to 17 (+21%, d = 0.35, p ≤ 0.05), showing improved performance related to increased physical output in the second half of the season. These findings suggest potential performance improvements associated with individualized load control over the course of the season. Full article