Machine learning analysis of factors affecting college students' academic performance.

This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning motivation, participation in learning, environmental factors, time management, and mental health. By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. Experimental results indicate that the XGBoost model performs the best in terms of recall and accuracy, providing a robust prediction for academic performance.

Types of Septic Cardiomyopathy: Prognosis and Influencing Factors - A Clinical Study.

Objective: To explore the prognostic outcomes associated with different types of septic cardiomyopathy and analyze the factors that exert an influence on these outcomes.

Methods: The data collected within 24 hours of ICU admission included cardiac troponin I (cTnI), N-terminal pro-Brain Natriuretic Peptide (NT-proBNP); SOFA (sequential organ failure assessment) scores, and the proportion of vasopressor use. Based on echocardiographic outcomes, septic cardiomyopathy was categorized into left ventricular (LV) systolic dysfunction, LV diastolic dysfunction, and right ventricular (RV) systolic dysfunction.

Metal release from contaminated coastal sediments under changing pH conditions: Implications for metal mobilization in acidified oceans.

To investigate the impacts and processes of CO2-induced acidification on metal mobilization, laboratory-scale experiments were performed, simulating the scenarios where carbon dioxide was injected into sediment-seawater layers inside non-pressurized chambers. Coastal sediments were sampled from two sites with different contamination levels and subjected to pre-determined pH conditions. Sediment samples and overlying water were collected for metal analysis after 10-days.

Combined use of DGT and transplanted shrimp (Litopenaeus vannamei) to assess the bioavailable metals of complex contamination: implications for implementing bioavailability-based water quality criteria.

The diffusive gradients in thin films (DGT) were field deployed alongside the shrimp Litopenaeus vannamei at seven sites with different levels of contamination to assess the potentially bioavailable and toxic fraction of metal contaminants. After 7 days of exposure, several antioxidant biomarkers were quantified in hepatopancreas of exposed shrimps, and tissue levels as well as the total, dissolved, and DGT-labile concentrations of metal contaminants were determined in the pooled site samples. The results showed that the caged shrimps had high tissue contaminant concentrations and significantly inhibited antioxidant responses at the more contaminated sites.