سید محمد حسینی

The recent surge of artificial-intelligence (AI) activity has sparked concern that large-scale model training, cloud inference, and data-centre expansion could accelerate national energy demand. We marshal a 21-year annual panel for the United States (2004–2024) that couples multiple AI proxies—technology-stock valuations and a ChatGPT-era dummy—with four aggregate energy series (fossil fuels, nuclear, renewables, total primary energy). A five-stage empirical protocol implemented in Python combines Engle–Granger cointegration testing, higher-order ADF stationarity checks, linear and nonlinear dependence diagnostics (Pearson, Dynamic Time Warping, mutual information), multicollinearity screening (variance-inflation factors), and out-of-sample forecasting with linear regression, decision trees, random forests, and support-vector machines augmented by SHAP explainability. Across all tests we find no evidence that AI developments imprint on national energy use: AI variables cointegrate only with one another, their short-run correlations with energy vanish once trends are removed, their mutual-information scores remain near zero, and their inclusion never improves predictive accuracy beyond a parsimonious macro model driven by GDP, inflation, and population. SHAP rankings confirm that AI features carry negligible explanatory weight relative to conventional fundamentals. We conclude that, to date, AI’s macro-level energy footprint is statistically invisible—any electricity it consumes is either too small or offset by efficiency gains within the wider economy. Policymakers should therefore continue to anchor long-range energy scenarios to established economic drivers while monitoring localised data-centre hotspots that national aggregates obscure.

Background : Chronic non-specific low back pain is extremely common in the general population. Athletes are often at special risk for more serious  associated factors of low back pain, which are often sport-specific in their etiology. Aim: The purpose of this study was to investigate the epidemiology and  associated factors of chronic non-specific low back pain in male athletes of track and field in the national league.     Materials and Methods : A total of 63 individuals were randomly selected to enter the study. The mean age of the subjects with the standard deviation was 25.2±4.5. To investigate the disability due to chronic non-specific low back pain, Oswestry questionnaire was used. For the evaluation of the pain perception, the Visual Analog Scale (VAS) was employed. Statistical analysis was performed using chi-square (χ²), spearman correlation and multiple linear regression at a significance level of 0.05. All statistical calculations were done using SPSS-21 software.  Results: Out of 63 subjects, 32 individuals (50.8%) had chronic non-specific low back pain. Of these, 29 subjects(46%)  had low disability and 3 subjects (4.8%) had moderate disability due to low back pain. None of the participants had  severeor very  severedisability. Pain perception measured using the VAS, had a mean of 35.4%. The most frequent cause of Chronic non-specific low back pain in track and field athletes was high-intensity training (26.7%, n=24), followed by training and activity location (21.1%, n=19) (x 2 = 64.88, p=0.000). The regression analysis showed a significant relationship between high-intensity training and Chronic non-specific low back pain among the mentioned athletes (p≤0.05). Conclusion: The results of this study shows a high epidemiology of Chronic non-specific low back pain in male track and field athletes, which requires serious attention. Additionally, high-intensity training and the location of training and activities were identified as key  associated factors in Chronic non-specific low back pain.

Background: The FIFA 11+ program has been shown to be effective in improving physical fitness factors and preventing injuries in soccer players.Aim: The purpose of this study was to review the effect of the FIFA 11+ injury prevention program on youth soccer players’ performance.Materials and Methods: Relevant articles were searched in Science Direct, PubMed, Scopus, PEDro, and Google Scholar using the keywords "FIFA 11+", "performance", and "youth". The search yielded 32 articles, of which 14 were shortlisted, and ultimately, four were selected for review.Results: The reviewed articles have indicated that the FIFA 11+ program results in a significant improvement in various performance factors (P < 0.05). These studies demonstrated that the FIFA 11+ program enhances stability, knee flexor muscle strength, sprint performance, agility, Slalom Dribbling, and flexibility. However, no significant improvements were observed in long jump, vertical jump, VO2max, balance, and lower extremity range of motion.Conclusion: Studies suggest that the FIFA 11+ warm-up program is more effective than conventional warm-ups in enhancing performance. Therefore, based on the literature reviewed, the FIFA 11+ program is recommended to enhance muscle strength, flexibility, and agility, thereby reducing injury risk in youth football players.