Despite widespread industry recommendations, a new ETH Zurich paper concludes that AGENTS.md files may often hinder AI coding agents. The researchers recommend omitting LLM-generated context files ...
Conservation has long wrestled with a deceptively simple question: not whether to act, but where action will matter most. Forest restoration, protected areas, wildlife corridors, and enforcement ...
Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
Abstract: Decision tree-based random forest algorithms can efficiently process multi-source heterogeneous data, accurately predict complex hydrological processes, and optimize power plant operation ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Having a shutdown bullpen is one of the most important keys to success in baseball. Sure, it’s not necessarily the flashiest position group in the entire world, but it’s still one of, if not the most ...
Accurate classification of tobacco leaf diseases is critical for objective disease assessment and management. However, traditional manual observation methods are inherently subjective, and ...
There was good news on the injury front to emerge today for Manchester City. Pep Guardiola has revealed that Rodri is a chance to return for City’s important Premier League fixture against Nottingham ...
Background: The prognosis of hypertrophic cardiomyopathy (HCM) varies by several factors, including imaging characteristics and genotype, reflecting underlying pathology. Traditional machine learning ...
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