Bayesian random-effects NMAs estimated odds ratios (ORs) with 95% credible intervals (CrIs), complementary frequentist NMAs provided 95% confidence intervals and 95% prediction intervals. Results: ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

Objectives High-dose rifamycin (HDR) regimens have demonstrated significant potential in tuberculosis (TB) treatment. This study aims to evaluate the efficacy and safety profile of different HDR ...

1 Department of Computer Science, Rochester Institute of Technology, Rochester, USA. 2 Department of Computer Science, Rutgers University, New Brunswick, USA. Language identification is a fundamental ...

Cross-sectional network analysis was employed to explore the complex relationships between depression, anxiety, insomnia, somatic symptoms, childhood trauma, self-esteem, social support, and emotional ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial Patients with NSCLC completed ...

Abstract: Bayesian network is a graphical model based on probabilities to represent and inference in uncertain conditions. In the field of Bayesian network, structure learning from data is an ...

We searched PubMed, Embase, Web of Science, and the Cochrane Library for randomized controlled trials (RCTs) published up to April 2025 comparing latanoprost, bimatoprost, travoprost, and tafluprost ...

Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...