Search Results (1,317)

This paper interrogates two important but obscured admission policy developments at leading American universities in the post-World War II era. First, we critically examine the University of California’s “special admissions,” later formalized as the “Admission by Exception” policy adopted at two flagship campuses (Berkeley and UCLA) to open opportunities for veterans returning from the War under the GI Bill. The scale of this Admission by Exception policy was orders of magnitude larger than any comparable admissions policy in recent decades, including both the eras with and without legally permissible affirmative action. Second, we excavate archival evidence from the immediate aftermath of the 1954 Brown v. Board of Education decision, where leaders at the flagship University of Texas at Austin campus hastily adopted a new standardized exam requirement because their enrollment modeling indicated this was the most efficient way to not face further losses in federal court while excluding the largest number of African Americans (and thereby resisting Brown) and maintaining the same overall size of the freshmen class. These two post-war admission policy changes, one arising in de facto segregated California and the other in de jure segregated Texas, operated as racialized institutional mechanisms analogous to “redlining” racially restrictive housing policies that are a more familiar feature of the post-War era. We draw on historical data about earnings and wealth accumulation of the overwhelmingly white graduates of UC and UT in the 1950s–70s and connect these findings to the theoretical frameworks of Cheryl Harris’s “whiteness as property” and George Lipsitz’s racialized state investment. We show how these admission policies contributed to the intergenerational transfer of advantage. We then turn to the contemporary admissions landscape at highly selective American universities after the Supreme Court’s SFFA v. Harvard ruling. We link current trends at some elite institutions toward a return to standardized testing requirements, maintaining considerations of athletic ability mostly in “country club” sports as manifestations of bias in university admissions, which tend to favor white applicants (Jayakumar and Page 2021; Jayakumar et al. 2023b). The paper connects historical racialization of admissions to ongoing inequities in access and outcomes, showing how both historical and contemporary admissions policies reward inherited forms of cultural capital aligned with whiteness. Full article

Musculoskeletal disorders (MSDs) affect over 1.7 billion people globally and represent the leading cause of disability worldwide. Conventional rehabilitation strategies face challenges including limited accessibility, suboptimal adherence, and lack of personalization. Digital therapeutics (DTx)—evidence-based, software-driven interventions regulated as medical devices—have emerged as transformative solutions in chronic disease management. This review provides a narrative synthesis of representative studies in the field, drawing on a broad survey of literature from medical and engineering sources to capture current trends and clinically relevant developments. Seventy-five publications were examined, including clinical trials and validation studies, many of which reported outcomes comparable or superior to traditional rehabilitation approaches, with adherence gains of 15–40% and cost reductions of approximately 30–40%. We summarize the major technological foundations of musculoskeletal DTx and digital rehabilitation across orthopedic subspecialties, describing core-enabling technologies including artificial intelligence-driven motion analysis, wearable sensors, tele-rehabilitation platforms, and cloud-based ecosystems. Clinical applications spanning spine, upper and lower extremities, sports injuries, and trauma were analyzed alongside global regulatory frameworks, economic considerations, and implementation challenges. Early clinical evidence demonstrates improvements in functional outcomes, adherence, and cost-effectiveness. Future directions include digital twin-based precision rehabilitation, predictive analytics, and scalable integration into value-based orthopedic care. By establishing a comprehensive framework for musculoskeletal DTx implementation, this review highlights their potential to improve outcomes, reduce healthcare costs, and address global rehabilitation access gaps. However, evidence on long-term effectiveness, sustained cost benefits, and large-scale clinical integration remains limited and warrants further investigation. Full article

Smart textiles are emerging as transformative modern textiles in which sensing, actuation, and communication are directly embedded into textiles, extending their role far beyond passive wearables. This review presents a comprehensive analysis of the convergence between smart textiles, the Internet of Things (IoT), and human-centric design, with sustainability as a guiding principle. We examine recent advances in conductive fibers, textile-based sensors, and communication protocols, while emphasizing user comfort, unobtrusiveness, and ecological responsibility. Key breakthroughs, such as silk fibroin ionic touch screens (SFITS), illustrate the potential of biodegradable and high-performance interfaces that reduce electronic waste and enable seamless human–computer interaction. The paper highlights cross-sector applications ranging from healthcare and sports to defense, fashion, and robotics, where IoT-enabled textiles deliver real-time monitoring, predictive analytics, and adaptive feedback. The review also focuses on sustainability challenges, including energy-intensive manufacturing and e-waste generation, and reviews ongoing strategies such as biodegradable polymers, modular architectures, and design-for-disassembly approaches. Furthermore, to identify future research priorities in AI-integrated “textile brains,” self-healing materials, bio-integrated systems, and standardized safety and ethical frameworks are also visited. Taken together, this review emphasizes the pivotal role of smart textiles as a cornerstone of next-generation wearable technology, with the potential to enhance human well-being while advancing global sustainability goals. Full article

Background: Eye-tracking technology enables the objective quantification of oculomotor behavior, providing key insights into visuocognitive performance. This study presents a comparative analysis of visual attention patterns between rhythmic gymnasts and school-aged students using an optical eye-tracking system combined with machine learning algorithms. Methods: Eye movement data were recorded during controlled visual tasks using the DIVE system (sampling rate: 120 Hz). Spatiotemporal metrics—including fixation duration, saccadic amplitude, and gaze entropy—were extracted and used as input features for supervised models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (CART), Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (1D-CNN). Data were divided according to a hold-out scheme (70/30) and evaluated using accuracy, F1-macro score, and Receiver Operating Characteristic (ROC) curves. Results: XGBoost achieved the best performance (accuracy = 94.6%; F1-macro = 0.945), followed by Random Forest (accuracy = 94.0%; F1-macro = 0.937). The neural network showed intermediate performance (accuracy = 89.3%; F1-macro = 0.888), whereas SVM and k-NN exhibited lower values. Gymnasts demonstrated more stable and goal-directed gaze patterns than students, reflecting greater efficiency in visuomotor control. Conclusions: Integrating eye-tracking with artificial intelligence provides a robust framework for the quantitative assessment of visuocognitive performance. Ensemble algorithms demonstrated high discriminative power, while neural networks require further optimization. This approach shows promising applications in sports science, cognitive diagnostics, and the development of adaptive human–machine interfaces. Full article

This study examines the integration of artificial intelligence (AI)-based strategies within the framework of landscape sustainability science and urban ecology, focusing on urban sports and recreation areas in Turkey and Lithuania. In the era of sustainable urban transformation, AI technologies offer new opportunities for maintaining ecological integrity, enhancing green infrastructure connectivity, and supporting adaptive management of urban ecosystems. The research aims to comparatively analyze the role and effectiveness of AI applications—such as intelligent waste management, predictive maintenance, and spatial planning tools—in promoting clean, safe, and ecologically resilient environments. A qualitative design was employed, and data were collected through semi-structured interviews with 30 experts, including local administrators, facility managers, environmental professionals, AI specialists, and academics from both countries. Thematic analysis using NVivo revealed key themes linking AI functions to ecological outcomes, including improved resource efficiency, habitat connectivity, and data-informed governance. Results show that Lithuania’s institutionalized green infrastructure facilitates multi-scale AI adoption, while Turkey’s evolving policy framework presents significant potential for system integration. The study emphasizes the necessity of embedding AI-driven ecological indicators into landscape-scale planning and developing an interdisciplinary governance model to achieve sustainable, resilient, and inclusive urban ecosystems. Full article

To address the inherent limitations of easy oxidation and unstable electrical properties in two-dimensional MXene-based flexible sensors, this study developed a MXene/GO/rGO (reduced graphene oxide) textile-based flexible sensor using a lamination method and in situ steam reduction technology. The sensor was constructed on a high-elasticity knitted polyester fabric, with MXene as the primary conductive layer, graphene oxide (GO) as the adhesive layer, and reduced graphene oxide (rGO) as the protective encapsulation surface layer. The tensile strain-sensing and electrothermal properties of the resulting e-textile were systematically characterized. The MXene/GO/rGO textile demonstrated outstanding electrical and mechanical performance, achieving a conductivity of 39.7 S·m⁻¹, a gauge factors ranging from –3 to –1.6, and a controllable electrothermal heating range from 43 °C to 85 °C under currents of 0.02–0.05 A. Experimental results demonstrated that under applied currents of 0.02, 0.03, 0.04, and 0.05 A, the fabric reached temperatures of 43, 56, 73, and 85 °C, respectively, and remained constant over extended periods. In terms of strain sensing, the sensor exhibited a short response time (65 ms), high discriminability for different strain levels and stretching rates, and a consistent relative resistance change (ΔR/R0) under various stretching speeds (0.5, 1, 2, 4, and 6 mm/s). Compared with sensors based on a single conductive material, the MXene/GO/rGO polyester fabric sensor shows superior electrothermal and strain-sensing performance, indicating promising potential for applications in intelligent wearable textiles such as medical thermal therapy, sports monitoring, and health management. Full article

Myocardial work (MW), derived from non-invasive pressure–strain loop (PSL) analysis, has recently emerged as a promising echocardiographic index for assessing left ventricular performance. It integrates speckle-tracking echocardiography with estimated left ventricular pressure, providing a load-adjusted measure of myocardial performance. This technique addresses the limitations of traditional parameters such as global longitudinal strain (GLS) and ejection fraction (EF), particularly in populations exposed to dynamic loading conditions, such as athletes. Athletic training induces a spectrum of cardiac adaptations, collectively referred to as the “athlete’s heart,” which may mimic or mask pathological conditions. In this context, MW represents a valuable tool to differentiate physiological remodeling from early myocardial dysfunction or underlying cardiovascular disease (e.g., cardiomyopathies, myocarditis). The aim of this review is to explore the physiological rationale for using MW in athletes, evaluate its relationship with performance metrics (e.g., VO₂max, lactate threshold), and discuss its potential, yet still emerging and not fully validated, role in informing training adaptation and detecting subclinical cardiac conditions. Additionally, we examine MW applications across different sport disciplines (strength, mixed-sport, and endurance), highlighting its role in individualized assessment and risk stratification. By synthesizing current evidence and outlining future research directions, this work emphasizes the potential of MW to become a standard component of cardiovascular evaluation in sports cardiology. Full article

Background/Objective: Kinesiology tape (KT) is commonly used in sports medicine and rehabilitation, but its impact on muscle strength over time remains unclear. Female futsal athletes experience high quadriceps demands and are at risk of anterior cruciate ligament injury; however, this population remains understudied. The purpose of this study was to investigate the longitudinal effect of repeated kinesiology taping applications on quadriceps muscle strength and lower limb function in female futsal players. Method: A longitudinal pilot randomized controlled trial was conducted during the Saudi Universities Sports Federation Futsal Championship. Twelve female athletes (aged 19–25 years) were randomly allocated to a KT (n = 6) or control group (n = 6). The KT protocol followed the standardized quadriceps facilitation guidelines and was applied repeatedly over 30 days. We measured isometric strength (hand-held dynamometer), eccentric/concentric torque and power (Biodex System), and functional performance (single-leg hop). Nonparametric tests (Wilcoxon signed-rank and Mann–Whitney U) and mixed ANOVA were used for analysis. Result: Post-intervention, the KT group demonstrated significant improvements in isometric strength (p = 0.03, r = 0.90), eccentric/concentric strength (p = 0.03, r = 0.90), and lower limb function (p = 0.03, r = 0.90). The between-group comparisons showed significant advantages for the KT group in isometric (p = 0.01, r = 0.83) and eccentric/concentric strength (p < 0.05, r = 0.67–0.74), but not in lower limb function (p = 0.20, r = 0.37). Conclusions: Repeated kinesiology taping over a 30-day period led to statistically greater longitudinal improvements in quadriceps muscle strength but did not affect functional performance. Kinesiology taping represents a non-invasive, low-cost treatment option for quadriceps strength measures in sports characterized by higher demands on the quadriceps, especially for female athletes with contributing injury risks. Further trials with more participants and a longer follow-up should be conducted. Full article

To investigate the characteristic pressure distribution patterns when gripping ski poles during skiing, this study addresses the challenges of measuring grip force on the complex curved surfaces of ski poles. A dataset of experimental samples was established, and grip force data were extracted using deep neural network (DNN) training. To reduce errors caused by dynamic force distribution and domain shifts due to varying hand postures, a hybrid method combining deep neural networks with the bio-inspired Gray Wolf Optimization (GWO) algorithm was proposed. This approach enables the fusion of hand-related feature data, facilitating the development of a high-precision grip force prediction model for skiing. A multi-point flexible array sensor was selected to detect force at key contact points. Through system calibration, grip force data were collected and used to construct a comprehensive database. A backpropagation (BP) neural network was then developed to process the sensor data at these characteristic points using deep learning techniques. The data fusion model was trained and further optimized through the GWO-BPNN (Gray Wolf Optimizer–backpropagation neural network) algorithm, which focuses on correcting and classifying force data based on dominant force-bearing units. Experimental results show that the optimized model achieves a relative error of less than 2% compared to calibration experiments, significantly improving the accuracy of flexible sensor applications. This model has been successfully applied to the development of intelligent skiing gloves, offering a scientific foundation for performance guidance and evaluation in skiing sports. Full article

Recent advancements in wearable technology have allowed researchers to collect high-resolution data on athletes’ workloads and performance, paving the way for more accurate mathematical models in sports science. In this paper, inspired by the modeling of heart rate during exercise, we introduce a novel linear, time-varying, two-state discrete-time dynamical model for predicting athletic performance in elite-level soccer players. Model parameters are estimated via the Differential Evolution optimization algorithm, and GPS-derived metrics such as metabolic power and equivalent distance index are incorporated. The model originally accounts for complex interactions between a performance-related state variable and a second lumped variable, whose dynamics are intertwined. This model was compared to the most effective deterministic (though uncertain) one in the literature, namely the (nonlinear) Busso model. Results, concerning two professional soccer players over a half-season period, show that the proposed model outperforms the traditional approach in estimation and predictive accuracy, with significantly higher correlation coefficients and lower estimation and prediction errors across all players. These findings suggest that integrating two-state dynamics and fine-grained GPS metrics provides a more biologically realistic framework for load monitoring in team sports. The proposed model thus represents a powerful tool for training optimization and athlete readiness assessment, with potential applications in real-time decision support systems for coaching staff. By predicting the effects of training load on future performance, it might also contribute to injury risk reduction and the prevention of maladaptive responses to excessive workload. Full article

Chronic venous insufficiency (CVI) is a common peripheral vascular condition characterised by the retrograde blood flow in the lower extremities and its consequences such as oedema and other complications. Clinical severity of CVI is assessed according to the CEAP (Clinical, Etiological, Anatomic, and Physiopathologic) classification, which recognises seven grades of increasing clinical severity (C₀–C₆). Compression therapy aims to accelerate vein, lymph, and microcirculation flow and therefore reduce chronic nonbacterial inflammation and oedema of the extremities. In accordance with the elasticity and stiffness, compression bandages and garments are divided into short-stretch and long-stretch compression materials. Compression therapy is applicable in all stages of CVI. Moreover, compression therapy in conjunction with physical therapy and lifestyle modifications is more effective in reducing oedema, preventing venous distension, and reducing venous wall tension, all while improving calf muscle pump function. Physical therapy in CVI treatment combines everyday lifestyle modifications, physical activity, medical exercise, sports activity, hydrotherapy, and electrotherapy. Therefore, physical therapy is used either for prevention or either for therapeutic purposes in CVI. For grades CEAP C₀–C_2, preventive measures consist of education and counselling, medical exercise and general fitness, and sports and physical activities. However, for therapy in grades CEAP C₃–C₆, medical exercise and a specific rehabilitation programme, manual lymphatic drainage and massage, balneotherapy, and electrotherapy are recommended. Full article

Background: Adolescence represents a critical period for growth and athletic development, yet young athletes frequently demonstrate significant gaps in nutritional knowledge that can impair performance and long-term health outcomes. Limited research exists on comprehensive nutrition education interventions for adolescent soccer players in North African populations. Objective: To evaluate both general and sports-specific nutritional knowledge among adolescent soccer players from an elite Tunisian club and assess the feasibility of a digital nutrition intervention using mobile application technology. Methods: A cross-sectional survey was conducted between June and August 2024 among 50 male soccer players aged 11–18 years from Étoile du Sahel club in Sousse, Tunisia. Data were collected via a structured questionnaire comprising sections on basic nutrition knowledge, influences on food choices, sports nutrition knowledge and practices, and demographic information. A pilot digital intervention using the FatSecret app was implemented with 8 participants over 4 weeks, involving meal photo uploads and nutritionist feedback. Results: Participants had a mean age of 15.16 ± 1.55 years, with 92% reporting no formal nutrition education. While 90% correctly identified carbohydrates as the primary energy source, only 2% recognized that fat provides the highest energy density. Significant misconceptions existed regarding sports nutrition: 74% incorrectly believed that consuming protein 2–4 h before an event enhances performance, and only 17% knew the recommended pre-event carbohydrate intake. Food choices were primarily influenced by cravings (80%) and sensory appeal rather than health considerations (20%). The digital intervention demonstrated extremely low engagement, with minimal participation in meal photo uploads. Conclusions: This study reveals critical gaps in both general and sports-specific nutritional knowledge among adolescent soccer players in Tunisia, providing important descriptive information about knowledge distribution in this population. While knowledge deficits are substantial, it is important to acknowledge that this cross-sectional assessment documents only knowledge patterns, without measures of actual dietary intake or athletic performance. The persistent misconceptions and the low feasibility of the digital intervention provide important lessons regarding technology-based approaches to nutrition education in this age group, highlighting challenges in sustained engagement that must be addressed in future intervention design. Full article

In this study, we investigate the mechanical energy absorption performance of reusable 3D-printed honeycomb structures fabricated using fused deposition modeling with three thermoplastic polyurethane variants: TPU 70A, TPU 85A, and TPU 95A. Prior to manufacturing, the mechanical properties of the TPU filaments were analyzed as a function of printing temperature to optimize tensile strength and layer adhesion. Four honeycomb configurations, including hexagonal and circular cell geometries, both with and without a 30° twist, were subjected to out-of-plane compression testing to evaluate energy absorption efficiency, specific energy absorption, and crushing load efficiency. The highest energy absorption efficiency, 47%, was achieved by the hexagonal honeycomb structure fabricated from TPU 95A, surpassing the expected values for expanded polystyrene and approaching the performance reported for high-cost advanced lattice structures. Additionally, twisted honeycomb configurations exhibited improved crushing load efficiency values (up to 73.5%), indicating better stress distribution and enhanced reusability. Despite variations in absorbed energy, TPU 95A demonstrated the best balance of elasticity, structural integrity, and reusability across multiple compression cycles. These findings suggest that TPU-based honeycomb structures could provide a viable, cost-effective alternative for energy-absorbing applications in impact protection systems, automotive safety, and sports equipment. Full article

Background: Professional football requires more attention in planning work regimens that balance players’ sports performance optimization and reduce their injury probability. Machine learning applied to sports science has focused on predicting these events and identifying their risk factors. Our study aims to (i) analyze the differences between injury incidence during training and matches and (ii) build and classify different predictive models of risk based on players’ internal and external loads across four sports seasons. Methods: This investigation involved 96 male football players (26.2 ± 4.2 years; 181.1 ± 6.1 cm; 74.5 ± 7.1 kg) representing a single professional football club across four analyzed seasons. The research was designed according to three methodological sets of assessments: (i) average season performance, (ii) two weeks’ performance before the event, and (iii) four weeks’ performance before the event. We applied machine learning classification methods to build and classify different predictive injury risk models for each dataset. The dependent variable is categorical, representing the occurrence of a time-loss muscle injury (N = 97). The independent variables include players’ information and external (GPS-derived) and internal (RPE) workload variables. Results: The Kstar classifier with the four-week window dataset achieved the best predictive performance, presenting an Area Under the Precision–Recall Curve (AUC-PR) of 83% and a balanced accuracy of 72%. Conclusions: In practical terms, this methodology provides technical staff with more reliable data to inform modifications to playing and training regimens. Future research should focus on understanding the technical staff’s qualitative vision of predictive models’ in-field applicability. Full article

Background: Gait event detection from inertial sensors offers scalable insights into locomotor health, with applications in clinical monitoring and mobile health. However, supervised methods are limited by scarce annotations, device variability, and sensor placement shifts. This in silico study evaluates self-supervised learning (SSL) as a resource-efficient strategy to improve robustness and generalizability. Methods: Six public smartphone and wearable inertial measurements unit (IMU) datasets (WISDM, PAMAP2, KU-HAR, mHealth, OPPORTUNITY, RWHAR) were harmonized within a unified deep learning pipeline. Models were pretrained on unlabeled windows using contrastive SSL with sensor-aware augmentations, then fine-tuned with varying label fractions. Experiments systematically assessed included (1) pretraining scale, (2) label efficiency, (3) augmentation contributions, (4) device/placement shifts, (5) sampling-rate sensitivity, and (6) backbone comparisons (CNN, TCN, BiLSTM, Transformer). Results: SSL consistently outperformed supervised baselines. Pretraining yielded accuracy gains of ΔF1 +0.08–0.15 and reduced stride-time error by −8 to −12 ms. SSL cut label needs by up to 95%, achieving competitive performance with only 5–10% labeled data. Sensor-aware augmentations, particularly axis-swap and drift, drove the strongest transfer gains. Robustness was maintained across sampling rates (25–100 Hz) and device/placement shifts. CNNs and TCNs offered the best efficiency–accuracy trade-offs, while Transformers delivered the highest accuracy at greater cost. Conclusions: This computational analysis across six datasets shows SSL enhances gait event detection with improved accuracy, efficiency, and robustness under minimal supervision, establishing a scalable framework for human performance and sports medicine in clinical and mobile health applications. Full article