Symmetry and asymmetry significantly influence running biomechanics, performance, and injury risk. Given the practical, ethical, and methodological constraints inherent in human-subject studies, computational modeling emerges as a valuable alternative for exploring biomechanical asymmetries in detail. This study systematically evaluated the mechanical effects of lower limb imbalance during running using a simulation-based musculoskeletal framework in OpenSim. A total of 130 simulations were performed, incorporating controlled asymmetries in limb strength, stride length, and ground reaction forces (±5% and ±10%), to quantify alterations in joint moments, ground reaction forces (GRF), and muscular activation patterns. Results demonstrated clear biomechanical deviations under asymmetric conditions. Vertical ground reaction forces (GRF) decreased on the weaker limb and increased on the stronger limb, with peak knee joint moments rising by up to 20% under pronounced asymmetry. Muscle activation in major lower limb muscles, including the gastrocnemius and quadriceps, increased substantially on the stronger side, reflecting compensatory mechanical loading. These findings highlight the negative consequences of uneven limb loading and support the use of computational modeling to guide personalized training, rehabilitation, and injury prevention strategies.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H–Q imbalance using biomechanical features derived entirely from synthetic running trials and conceptually mappable to inertial-sensor domains. Key biomechanical predictors included the dynamic hamstrings-to-quadriceps ratio (H:Qdyn), the knee moment limb symmetry index (LSI), and the early-stance co-contraction index (CCI), all standard indicators of muscular balance and coordination in sports medicine. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943 (95% CI 0.924–0.962), PR-AUC 0.918 (95% CI 0.892–0.943), F1 0.940 (95% CI 0.919–0.958), and Brier 0.056 (95% CI 0.041–0.072), outperforming the logistic baseline. Dynamic H:Q ratio and knee moment symmetry were the dominant predictors, while co-contraction provided complementary biomechanical nuance. These results demonstrate that simulation-derived frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for transparent assessment of muscular balance in sports medicine.

Player exploitation in esports refers to the practice of exploiting players for financial or other gain without providing fair compensation or benefits. This can include issues such as low wages lack of job security and poor working conditions. Examples of player exploitation in esports include signing players with long-term contracts with low wages and no benefits and the use of non-disclosure agreements. Agreement (NDA) prevents players from discussing their pay or working conditions. This situation makes it difficult for players to negotiate better contracts or speak out against abuse leaving teams if they are unhappy with their situation. Another example is the lack of proper training support and benefits for players such as healthcare and mental health. Many esports teams do not provide enough resources for players to improve their skills and maintain good physical condition. This can lead to fatigue and injuries which can seriously affect a player’s career. Many esports players are young and the lack of access to these resources makes them vulnerable to exploitation. The esports industry is still relatively new and unregulated which can make it easy for companies and teams to exploit players. However, there are many organizations and advocacy groups working to improve working conditions and ensure fair treatment for sports athletes. Athlete exploitation in esports in general is a serious issue that needs to be addressed to ensure that athletes are treated fairly and have the resources they need to succeed in their careers.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico study introduces a simulation-derived machine-learning framework designed to estimate H–Q imbalance using biomechanical features conceptually mappable to inertial-sensor domains. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943, PR-AUC 0.918, F1 0.940, and Brier 0.056, outperforming a calibrated logistic baseline. Dynamic H:Q ratio and knee-moment symmetry were the dominant predictors, while co-contraction contributed complementary nuance. These results indicate that simulation-derived digital frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for objective assessment of muscular balance in sports medicine.

Tom Brady, Cristiano Ronaldo, Lionel Messi, and others are household names, while Lee Faker Sang-Hyeok is less well-known. Although Kim Doinb Tae-sang and Luka Perkz Perkovi are still well known, they are also rising to the status of world-renowned athletes. The most popular computer video game in the world, League of Legends, has professional players like Faker Perks and Toinb. The League is one of the many ambitious and well-liked sporting events that make up the fast-growing esports category. Administrators operate in what is referred to as a global gray area. Esports is a lucrative corporate sponsorship option for athletes because of its enormous popularity (some events regularly draw tens of thousands of viewers), but it is also fluid and lacks defined standards. This article describes the global phenomenon of esports; provides an overview of the role and practice of esports; highlights why esports is considered an integral part of the evolving internet culture and introduces the reader to the structure of gaming more broadly.

We live in a world where the media is ubiquitous, through the multiple and diverse ways of expression, a world in which the press has become an “industry”, with a coverage and influence that is difficult to anticipate not too long ago, having the ability to capture the imagination and exert its influence on all walks of life. Conquering and then broadening the area of the public are really the raison of a media. This conquest and expansion are, however, based on three guiding principles: to do the job seriously (credibility), to produce pleasure (the show) and to be empathetic (empathy). Throughout this “industrial platform”, the sports press finds itself as a very important segment, with a major influence in society. Sport can also be regarded as a large global industry, with fans and spectators acting as consumers who demand. The research question is about the influence of media communication in the sports industry.