Understanding the processes controlling benzene adsorption in soils is critical for predicting their environmental fate and associated risks. However, the adsorption behavior of benzene across different soil components and under varying environmental... read more
In the context of globalized food production, traceability is a key requirement in the fisheries sector, particularly for prepared and packaged fish products, where processing may hinder visual inspection and increase the risk of mislabeling. Reliabl... read more
BACKGROUND: Coronary computed tomography angiography (CTA) with analysis by artificial intelligence (AI) can personalize coronary artery disease risk stratification. CASE SUMMARY: A 61-year-old man with coronary artery disease risk factors sought ris... read more
Machine learning struggles with imbalanced data. Although several mitigation approaches exist, their application depends on the extent of imbalance. To determine the latter, a protocol was developed. Across 428 synthetic and 70 real datasets, 8 imbal... read more
OBJECTIVES: This study aimed to construct and verify machine learning (ML) models to predict long-term abdominal obesity (AO) risk in children and adolescents. METHODS: We trained and externally validated ML models to predict pediatric AO 4-5 years l... read more
In adjuvant research, which has long relied on experience and trial and error, advancements in technologies in broad areas, including artificial intelligence (AI)/machine learning, enabling data-driven research, have supported the introduction of mor... read more
BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet their clinical impact remains uncertain, as most studies focus on model development rather than prospec... read more
To address the challenges of achieving organic compliance in kitchen wastewater treatment and the high cost of chemical dosing, this study established a two-stage reactor incorporating both biological and chemical processes and proposed an optimal re... read more
The analysis of Electrocardiogram (ECG) signals is critical for clinical applications, but current machine learning methods often face limitations when dealing with smaller datasets or intricate signal patterns. We introduce HeartBERT, a novel model ... read more
Dilated cardiomyopathy (DCM), a leading cause of heart failure, is characterized by progressive cardiomyocyte (CM) loss and mitochondrial dysfunction; yet, the molecular drivers of mitochondrial oxidative stress (MitOS) remain unclear. By integrating... read more
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