Sprint-freestyle performance prediction and interpretation require precise and actionable models for coaches and athletes. This study presents an interpretable machine learning model applied to lap-by-lap metrics from A-final 100-yard freestyle swims (n = 67). We construct a 12-dimensional feature vector from three technical metrics (mean stroke rate, cycle count, and breakout distance) across four laps, and construct both a regression task (smooth race time prediction) and a binary classification task (fast/slow, threshold at 41.4 s). Several algorithms were explored—Linear Regression, Random Forest, k-Nearest Neighbors (kNN), and Support Vector techniques—on multiple train/test splits and based on measures of R², MAPE, accuracy, and F1 score. Where regression R² values were low (best mean R² ≈ −0.042 for Random Forest), MAPE was nonetheless small (~0.011), with modest absolute error but little explained variance. Classification fared better: kNN recorded the best mean accuracy (≈0.727) and F1 (≈0.717). Most significantly, SHAP (Shapley Additive Explanations) identified Lap2_Stroke_Rate and Lap4_Breakout_Dist as two of the top features. Feature-selection tests showed that models that are trained on higher features perform with identical MAPE with significantly fewer inputs, towards useful, interpretable, and data-efficient ways for performance monitoring and coaching decisions.

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The high volatility and non-stationarity of financial markets pose significant challenges to the application of traditional machine learning models in portfolio optimization and asset pricing. Under distributional shifts and extreme market conditions, these models often suffer from performance degradation and failure in risk control. Although existing studies have made progress in factor modeling and portfolio optimization, most approaches rely on the assumption of independent and identically distributed data or weak robustness constraints, leaving the problem of generalization under non-stationary and adversarial environments insufficiently addressed. To tackle this issue, this paper proposes a statistical learning generalization guarantee framework tailored to adversarial distributional shifts. The framework incorporates adversarial regularization into a multifactor deep neural network and derives PAC-Bayes generalization error bounds, thereby achieving consistency between theoretical guarantees and empirical robustness. Empirical experiments are conducted using high-frequency factor and trading data from the Chinese A-share market and the U.S. NYSE/NASDAQ market between 2015 and 2023. Three experimental settings, baseline model comparisons, adversarial perturbation simulations, and cross-market transfer evaluations, are designed. Results show that the proposed method significantly outperforms OLS regression, LASSO regression, and standard deep neural networks in key metrics such as annualized return, Sharpe ratio, and maximum drawdown, while also demonstrating stronger risk control through improvements in the Robustness Index. Further cross-market and temporal transfer experiments confirm the generalizability of the proposed model, proving its applicability not only in stable markets but also under extreme shocks, where it maintains return consistency and robustness.

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This study aims to investigate the impact of cyanobacterial concentrations in water bodies on the prevalence of parasitic infections. We systematically reviewed nine peer-reviewed articles published between 2010 and 2023, focusing on empirical data linking cyanotoxins and parasitic infections in freshwater ecosystems. Studies involving marine systems and pathogen-related research unrelated to the study topic were excluded. Data were categorized into the following groups: 1) effects of cyanobacteria on hosts (e.g., toxin effects); 2) parasite transmission dynamics (e.g., host susceptibility). The study primarily addressed the following questions: Do algal blooms exacerbate parasite transmission within ecosystems? Is there an association between cyanobacterial concentration and the health status of other organisms in the water body? The findings support the hypothesis that cyanobacterial blooms intensify parasite infections. The study found that cyanobacterial blooms disrupt host physiology through toxin effects, increasing host susceptibility to parasites. Additionally, blooms impair hosts' antioxidant defense systems via oxidative stress, further compromising host health.

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The growing number of companion animals worldwide has raised public health concerns about zoonotic microorganisms, especially Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA). As an antibiotic-resistant bacterium, MRSA is a major concern across animal and human populations because it has high virulence, transmissibility, and multiantibiotic resistance. Close and frequent contact between humans and companion animals facilitates bidirectional transmission, enabling pets to act as potential reservoirs of community-acquired MRSA. This review after extensive compilation has revealed variations in the prevalence of MRSA with respect to geographic locations, ranging from North America to Europe and Asia, influenced to varying extents by variations in study protocols used, diagnostic methods, study animals, and current practices related to antimicrobial usage in different parts of the world. The appearance of new human clones such as CC398, ST1, and ST5 has clearly indicated the importance of zoonotic transmission. Notably, the diagnostic biases, with special emphasis on methicillin resistance in Staphylococcus pseudintermedius (MRSP), clearly has major implications in causing variability in the prevalence of MRSA in human and veterinary settings. Additionally, the review elaborates on key resistance mechanisms, including SCCmec-mediated β-lactam resistance and multidrug efflux systems, which complicate treatment protocols. Therefore, implementing standardized global surveillance under a One Health framework is imperative to control the spread of MRSA and mitigate its public health impact.

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