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.