Search Results (606)

Background: The purpose of this study was to evaluate Achilles tendon ruptures (ATR) in NHL players and the effects on return to play and player performance metrics. The incidence, mechanism of injury, management strategy, return to play (RTP), and post-injury were assessed from official online sports databases. Methods: A retrospective review of NHL players who sustained a partial or complete tear of the Achilles tendon from 2008 to 2024 was performed. Data were collected from NHL injury databases and media reports, and included player demographics, injury mechanism, treatment, and post-injury performance metrics. A Wilcoxon signed rank test was used to compare pre-injury and post-injury performance metrics, with significance set at p < 0.05. Results: Here, 15 NHL players with a mean age of 27.8 years were identified, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. Overall, 73.3% of ATRs were non-contact in nature, with 60.0% of ATRs occurring during off-season training. Fourteen players were managed with non-operative treatment, with no re-ruptures reported. The RTP rate was 93.3%, with players missing a mean number of 45.7 games. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury. Conclusions: This study found that Achilles tendon ruptures are an uncommon injury in NHL players, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. A high RTP rate of 93.3% was observed in this cohort. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury, highlighting the potential devastating sequelae of ATRs in elite NHL athletes. Full article

A high-quality set of typical scenarios is significant for power grid planning. Existing construction methods for typical scenarios do not account for the spatiotemporal correlations among renewable energy plant outputs, failing to adequately reflect the distribution characteristics of original scenarios. To address the issues mentioned above, this paper proposes a construction method for typical scenarios considering spatiotemporal correlations, providing high-quality typical scenarios for power grid planning. Firstly, a symmetric spatial correlation matrix and a temporal autocorrelation matrix are defined, achieving quantitative representation of spatiotemporal correlations. Then, distributional differences between typical and original scenarios are quantified from multiple dimensions, and a scenario reduction model considering spatiotemporal correlations is established. Finally, the genetic algorithm is improved by incorporating adaptive parameter adjustment and an elitism strategy, which can efficiently obtain high-quality typical scenarios. A case study involving five wind farms and fourteen photovoltaic plants in Belgium is presented. The rate of error between spatial correlation matrices of original and typical scenario sets is lower than 2.6%, and the rate of error between temporal autocorrelations is lower than 2.8%. Simulation results demonstrate that typical scenarios can capture the spatiotemporal correlations of original scenarios. Full article

Piano fingering estimation remains a complex problem due to the combinatorial nature of hand movements and no best solution for any situation. A recent model-free reinforcement learning framework for piano fingering modeled each monophonic piece as an environment and demonstrated that value-based methods outperform probability-based approaches. Building on their finding, this paper addresses the more complex polyphonic fingering problem by formulating it as an online model-free reinforcement learning task with a novel training strategy. Thus, we introduce a novel Elite Episode Replay (EER) method to improve learning efficiency by prioritizing high-quality episodes during training. This strategy accelerates early reward acquisition and improves convergence without sacrificing fingering quality. The proposed architecture produces multiple-action outputs for polyphonic settings and is trained using both elite-guided and uniform sampling. Experimental results show that the EER strategy reduces training time per step by 21% and speeds up convergence by 18% while preserving the difficulty level and result of the generated fingerings. An empirical study of elite memory size further highlights its impact on training performance in solving piano fingering estimation. Full article

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

Drones can improve the efficiency of point-to-point logistics and distribution and reduce labor costs; however, the complex three-dimensional airspace environment poses significant challenges for flight paths. To address this demand, this paper proposes a hybrid algorithm that integrates the Sparrow Search Algorithm (SSA) with the African Vulture Optimization Algorithm (AVOA). Firstly, the algorithm introduces Sobol sequences at the population initialization stage to optimize the initial population; then, we incorporate SSA’s discoverer and vigilant mechanisms to balance exploration and exploitation and enhance global exploration capabilities; finally, multi-guide differencing and dynamic rotation transformation strategies are introduced in the first exploitation phase to enhance the direction of local exploitation by fusing multiple pieces of information; the second exploitation phase achieved a dynamic balance between elite guidance and population diversity through adaptive weight adjustment and enhanced Lévy flight strategy. In this paper, a three-dimensional model is built under a variety of constraints, and SAVOA (Sparrow-Enhanced African Vulture Optimization Algorithm) is compared with a variety of popular algorithms in simulation experiments. SAVOA achieves the optimal path in all scenarios, verifying the efficiency and superiority of the algorithm in UAV logistics path planning. Full article

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

Quinoa (Chenopodium quinoa Willd.) has emerged as a climate-resilient, nutrient-dense crop with increasing global popularity because of its adaptability under current environmental variations. To address the limited understanding of quinoa’s genotypic performance under local agro-environmental conditions, this study hypothesized that elite genotypes would exhibit significant variation in agronomic, physiological, and biochemical traits. This study aimed to elucidate genotypic variability among 23 elite quinoa lines under field conditions in Faisalabad, Pakistan, using a multidimensional framework that integrated phenological, physiological, biochemical, root developmental, and yield-related attributes. The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. Moreover, Q9 and Q52 showed enhanced accumulation of antioxidant compounds such as phenolics and anthocyanins, suggesting potential for functional food applications and breeding program for improving these traits in high-yielding varieties. Furthermore, root trait analysis revealed Q15, Q24, and Q82 with well-developed root systems, suggesting efficient resource acquisition and sufficient support for above-ground plant parts. Moreover, principal component analysis further clarified genotype clustering based on trait synergistic effects. These findings support the use of multidimensional phenotyping to identify ideotypes with high yield potential, physiological efficiency and nutritional value. The study provides a foundational basis for quinoa improvement programs targeting climate adaptability and quality enhancement. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article

Background/Objectives: The role of body composition in sports performance has been widely studied, particularly in soccer. Understanding how anthropometric characteristics impact movement efficiency and neuromuscular performance is crucial for optimizing player performance. This study examined the relationship between body composition and locomotor performance in elite soccer players. Methods: Thirty-six male soccer players from the Mexican National Team participated in the study, where body composition was assessed using surface anthropometry. Players underwent tests to measure countermovement jump (CMJ) performance, sprinting speed, maximum acceleration, and distance covered during two games of the CONCACAF Nations League quarterfinals. Correlation matrices were created to identify the most significant associations, followed by generalized linear models (GLMs) to associate body composition variables with performance metrics. Results: Anthropometric profile tables were created by playing position. Higher body fat percentage (%BF) was associated with lower performance. Specifically, higher %BF was associated with slower sprint speed (B = −0.74 m/s, p < 0.001) and shorter distance covered (B = −4.86 m/min, p < 0.001). Conversely, greater muscularity, reflected by corrected girth values for the thigh and calf, was associated with improved CMJ performance. Thigh corrected girth was positively associated with concentric mean force (B = 48.85 N, p < 0.001), and calf corrected girth was positively associated with peak power (B = 240.50 W, p < 0.001). These findings underscore the importance of low body fat and high lean mass for efficient movement. Conclusions: The results highlight the critical role of body composition in enhancing soccer performance, particularly for explosive movements like jumps, sprints, and accelerations. This study suggests that monitoring and optimizing body composition should be a central focus of nutrition, training, and conditioning strategies, adapted to the specific positional demands of professional soccer. Full article

Maintaining body symmetry in sports characterized by high lateralization is crucial for optimizing long-term athletic performance and mitigating injury risk. This study aimed to evaluate the extent of morphological asymmetry in anthropometric features among elite professional fencers. Additionally, the presence of functional asymmetry and its associations with morphological asymmetry were assessed. Thirty-two Polish adult female fencers, aged 18–33 yrs, were examined. Data collection involved a questionnaire survey, anthropometric measurements, calculation of anthropological indices, and assessment of functional asymmetry. For the 24 bilateral anthropometric features, small differences were found in seven characteristics: foot length, subscapular skinfold thickness, upper arm circumference, minimum and maximum forearm circumference, upper limb length, and arm circumference in tension. Morphological asymmetry index did not exceed 5%. Left-sided lateralization of either the upper or lower limbs was associated with significantly high asymmetry, specifically indicating larger minimum forearm circumferences in the right limb. Continuous, individualized monitoring of morphological asymmetry and its direction in athletes is essential, demanding concurrent consideration of functional lateralization. This ongoing assessment establishes a critical baseline for evaluating training adaptations, reducing injury susceptibility, and optimizing rehabilitation strategies. Deeper investigation of symmetry within non-dominant limbs is warranted to enhance our understanding. Full article

Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. Interviews with current and former EKO Cobra members reveal key risk factors, including overconfidence, insufficient training, inadequate equipment, and the challenges of high-stakes scenarios. Using a structured yet thematically flexible interview analysis approach, the study adopts group dynamics theory as its framework and applies a semi-inductive, semi-deductive qualitative methodology. It examines risk categorisation in ad hoc operations, as well as the interplay between risk perception and training, proposing actionable strategies to enhance safety and preparedness through tailored training programmes. The findings underscore the transformative impact of intensive scenario-based and high-stress training, which enhances situational awareness and reinforces team-based responses through cohesion and effective communication. Group dynamics, including cohesion and effective communication, play a pivotal role in mitigating risks and ensuring operational success. Importantly, this research advocates for continuous, adaptive, and specialised training to address evolving challenges. By linking theoretical frameworks with practical and actionable insights, this study proposes a holistic training approach that promotes both resilience and long-term sustainability in police operations. These findings offer valuable guidance to elite units like EKO Cobra for broader policy frameworks by providing insights that make police operations safer and more effective and resilient. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

This study examines the political and elite motives behind Colombo’s ‘world-class city’ initiative and its impact on public housing in underserved communities. Informed by interviews with high-ranking government officials, including urban planning experts and military officers, this study examines how President Rajapaksa’s elite-driven postwar Sri Lankan government leveraged military capacities within the neoliberal developmental framework to transform Colombo’s urban space for political and economic goals, often at the expense of marginalized communities. Applying a contextual discourse analysis model, which views discourse as a constellation of arguments within a specific context, we critically analyzed interview discussions to clarify the rationale behind the militarized approach to public housing while highlighting its contradictions, including the displacement of underserved communities and the ethical concerns associated with compulsory relocation. The findings suggest that Colombo’s postwar public housing program was utilized to consolidate authoritarian control and promote speculative urban transformation, treating public housing as a secondary aspect of broader political and economic agendas. Anchored in militarized urban governance, these elite-driven strategies failed to achieve their anticipated economic objectives and deepened socio-spatial inequalities, raising serious concerns about exclusionary and undemocratic planning practices. The paper recommends that future urban planning strike a balance between economic objectives and principles of spatial justice, inclusion, and participatory governance, promoting democratic and socially equitable urban development. Full article

Mental health disorders, particularly depression and anxiety, have become increasingly prevalent among elite athletes, exacerbated by factors such as competitive pressure and the Coronavirus Disease 19 (COVID-19) pandemic. This study analyzes trends in the use of antidepressants, anxiolytics, and cannabinoids (delta-9-tetrahydrocannabinol (THC)/cannabidiol (CBD)) among Italian athletes from 2011 to the first half of 2023 (FH2023), referring to anti-doping reports published by the Italian Ministry of Health. Data from 13,079 athletes were examined, with a focus on non-prohibited medications, banned substances, and regulatory impacts, including threshold adjustments for THC since 2013 and the legalization of CBD. The results show fluctuating use of antidepressants/anxiolytics, with peaks in 2021 and the FH2023, coinciding with post-pandemic awareness. Positive THC cases rose following regulatory changes, reflecting socio-cultural trends. Gender disparities emerged, with THC use predominantly among males (e.g., nine males vs. one female in 2013), though female athletes were underrepresented in testing. This study highlights the need for personalized, evidence-based strategies that balance therapeutic efficacy and anti-doping compliance. Clinicians should carefully consider prescribing selective serotonin reuptake inhibitors (SSRIs) and benzodiazepines to address depression and anxiety and should monitor the risks of CBD contamination. Future research should adopt longitudinal, gender-sensitive approaches to refining guidelines and combating stigma in professional sports. Full article