Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

Phase 1 was conducted between October 25 and October 27, 2025, consisting of two north–south oriented channels totalling 94.5 metres across exposed pegmatite outcrop. The best summarized interval ...

Abstract: In this communication, a new sampling method called the knowledge-based sampling method (KBSM) is proposed for deep learning (DL)-assisted planar antenna array design. This approach uses ...

As global manufacturing and infrastructure sectors adapt to post-pandemic economic realities and evolving environmental standards, the industrial plate steel market is undergoing a significant ...

A marriage of formal methods and LLMs seeks to harness the strengths of both.

Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely ...

Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...

Purpose: Different respiratory sampling methods exist to identify lower airway pathogens in patients with cystic fibrosis (CF), of which bronchoalveolar lavage (BAL), and expectorated sputum are ...

Abstract: While ultra-wideband (UWB) technology provides additional connectivity and location awareness to the Internet of Things (IoT), its complexity requirements ...

Sampling (or sample) bias is a widespread concern in scientific research, across several disciplines. The concept of sampling bias originated in statistical studies. The consequence of a biased sample ...