Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to

Release of industrial effluents comprising dyes in water bodies is one of the foremost causes of water pollution. Therefore, the proper and proficient treatment of these dyes contaminated left-over material before their release is crucial. Herein, an eco-friendly biological macromolecule Gum-Acacia (GA) integrated Fe3O4 nanoparticles composite hydrogel was manufactured via co-precipitation technique for effective adsorption of Congo red (CR) dye existing in water bodies. The as-prepared magnetic GA/Fe3O4 composite hydrogel was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. These studies discovered the fruitful fabrication of biodegradable magnetic GA/Fe3O4 composite hydrogel possessing porous structure with large surface are

Introduction: Elite football performance hinges on rapid tactical decision-making under physical and cognitive strain. While peripheral fatigue’s effects on motor output are well documented, the neurophysiological markers of mental fatigue and their impact on in-game decision making remain underexplored. Objective: To determine how EEG-derived central fatigue indices—frontal theta power and the theta/alpha ratio—relate to tactical decision accuracy and speed in elite football players. Methodology: Twenty male national-level footballers (age 22.4 ± 2.1 years; ≥ 5 years’ experience) completed the Yo-Yo Intermittent Recovery Test Level 1 while wearing an 8-channel dry-electrode frontal EEG headset. Frontal theta

This study presents, for the first time, an innovative Jet Plasma-assisted technique for the green synthesis of TiO₂@Ag core–shell nanoparticles using chard leaf extract as a natural reducing and stabilizing agent. The Jet Plasma provides a highly energetic environment that accelerates nucleation and core–shell formation at low temperatures without toxic precursors. The synthesized nanoparticles exhibited uniform and stable structures, as confirmed by comprehensive characterization techniques including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) spectroscopy, transmission electron microscopy (TEM), and zeta potential analysis. XRD patterns confirmed the crystalline anatase

The melting duration in the photovoltaic/phase-change material (PV/PCM) system is a crucial parameter for thermal energy management such that its improvement can realize better energy management in respect to thermal storage capabilities, thermal conditions, and the lifespan of PV modules. An innovative and efficient technique for improving the melting duration is the inclusion of an exterior metal foam layer in the PV/PCM system. For detailed investigations of utilizing different metal foam configurations in terms of their convective heat transfer coefficients, the present paper proposes a newly developed mathematical model for the PV/PCM–metal foam assembly that can readily be implemented with a wide range of operating condition

The COVID-19 pandemic has deeply affected the respiratory health of people, leaving many sufferers with long term pulmonary problems. Artificial intelligence based physiological analysis of structured exercise program on lung function of recovered COVID 19 patient is studied. The research introduces an integrated data driven approach for assessing the improvement of respiratory through physical training. The approach is to integrate wearable sensor technology with machine learning algorithms. A controlled experimental study with three groups (recovered COVID-19 patients, smokers, healthy individuals) was used as a method. To that aim, each of the participants underwent an eight-week structured aerobic training program that included continuo