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Fine-tuned ResNet34 for efficient brain tumor classification

Constructing Dynamic Rh ^δ+ –O _v –Ti Interfacial Sites for Highly Efficient and Stable Photothermal Catalytic Methane Dry Reforming

A novel machine learning approach to analysis of electroosmotic effects and heat transfer on Multi-phase wavy flow

Abstract In this contribution, a novel hybrid approach involving artificial neural networks (ANNs) and heuristic algorithms is employed for Hall currents and electromagnetic effects analysis for a multi-phase wavy flow. The governing partial differential equations (PDEs) for flow dynamics are reduced into a corresponding system of ordinary differential equations (ODEs) with a pertinent transformation technique. The novelty of this work is combination of Morlet wavelet and hyperbolic tangent (Tanh) functions is employed as an activation function in artificial neural networks (ANNs) to effectively capture nonlinear behavior for flow dynamics. The novel effective Morlet wavele Tanh neural networks (MTNNs) based fitness function is formulated for solution estimation of the model. The weights and biases of MTNNs are optimized with a global searching technique by particle swarm optimization (PSO). Numerical solution of ODEs is also obtained through Python physics informed neural networks (PINNs) with Adam optimizer for validation of the proposed solutions. Statistical analysis involving histogram visualizations, probability plots, and boxplots is performed for accuracy, robustness, convergence, and stability evaluation of the proposed solution with respect to crucial error measures such as cost function, absolute error, and mean squared error (MSE). The MSE values for velocity and temperature range from $$\:{10}^{-07}$$ to $$\:{10}^{-09}$$ and $$\:{10}^{-06}$$ to $$\:{10}^{-09}$$ , respectively. Graphical analysis reveals that flow velocity and thermal distributions are influenced directly by electroosmotic factor but are affected inversely with the applied magnetic field. The proposed MTNNs yield results that closely align with those obtained using PINNs.

CO Reduction to Ethylene and Cyclopropane via a Trappable Ruthenium Methylidene

Enhanced security of affine ciphers using digraph transformation and a modified three pass protocol

Machine learning predictions of climate change effects on nearly threatened bird species (Crithagra xantholaema) habitat in Ethiopia for conservation strategies

The surface and micellar properties of ethanolamine based surface active ionic liquids in the presence of drug aspirin

Abstract Bio-based surface-active ionic liquids (SAILs) offer promising advantages for pharmaceutical applications, particularly in enhancing drug solubility and bioavailability. Aspirin, classified under the Biopharmaceutics Classification System (BCS) as poorly soluble in the gastrointestinal tract, require effective solubilization strategies for improved therapeutic efficacy. This study investigates the micellization behavior and thermophysical properties of three natural based SAILs (2-hydroxyethyl)ammonium oleate ([2-HEA][Ole]), bis(2-hydroxyethyl)ammonium oleate ([BHEA][Ole]), and tris(2-hydroxyethyl)ammonium oleate ([THEA][Ole]) in aqueous solutions of aspirin at 298 K. Micellization characteristics, including critical micelle concentration (CMC), were determined using electrical conductivity and surface tension measurements for SAILs in the presence of the aspirin aqueous solutions systems at 298 K. Key interfacial parameters such as interface surface pressure ( $$\Pi$$ ), minimum surface area occupied per molecule ( $${A_{\hbox{min} }}$$ ), Gibbs maximum excess surface concentration ( $${\Gamma _{\hbox{max} }}$$ ) were also calculated. Additionally, the Conductor-like Screening Model (COSMO) was employed to elucidate molecular interactions between SAILs and the studied drug. The results indicate that CMC values decrease in the presence of aspirin. Among the studied systems, [THEA][Ole] exhibited the lowest CMC, as determined by electrical conductivity and surface tension measurements, particularly in the presence of higher concentrations of aspirin in aqueous media. Furthermore, COSMO analysis revealed that [THEA][Ole], possessing the highest surface cavity volume ( V ), demonstrated the most favorable interactions with aspirin, highlighting its potential as an effective solubilizing agent. Finally, interactions between SAILs and aspirin were investigated through limiting molar conductivity  $$\wedge_0$$ , and association constant  $$K_A$$ , determination.

Efficient progressive training with granularity cross for image super-resolution

Copper-Catalyzed Asymmetric C–H Sulfilimination of Arenes via HAT-Primed C–S Radical–Radical Coupling

Single center experience with covered stent closure of sinus venosus atrial septal defect

Enhanced power conversion efficiency in high power single spatial mode ridge waveguide diode lasers using extreme triple asymmetric epitaxial structure

Metal Electronics Mediate Steric and Coordination Number Effects on Palladium(II) C–X Reductive Elimination

Natal and neonatal teeth in newborns and infants: a case-control study

Failure risk analysis of dangerous earth‒rock dams on the basis of element failure probabilities

Using AI and big data analytics to support entrepreneurial decisions in the digital economy

Serum adropin and miR-21 expression as predictors of endothelial dysfunction in type 2 diabetes mellitus and vascular complications

Robust load frequency control in renewable integrated Multi Area grids using hybrid SA and QIO tuned PIDF controller

Multimodal neural feedback collaborative training system for executive function and tactical cognition enhancement in football athletes

Abstract Contemporary football demands exceptional cognitive abilities alongside physical prowess, yet current training methodologies lack precision for optimizing cognitive performance through objective neural monitoring. This computational study develops and validates a theoretical multimodal neural feedback collaborative training system that simultaneously enhances executive function and tactical cognition in football contexts. The proposed system integrates electroencephalography (EEG), eye-tracking, and physiological monitoring to provide real-time feedback during cognitive training protocols. Through computational validation utilizing synthetic neural signal datasets and algorithmic performance modeling, we evaluated the theoretical system’s efficacy across executive function components (working memory, inhibitory control, cognitive flexibility) and tactical cognition domains (pattern recognition, strategic planning, decision-making). Computational results demonstrated significant theoretical improvements in executive function capabilities averaging 23.7% and tactical cognition enhancements reaching 27.8% compared to baseline algorithmic performance. The collaborative training approach consistently outperformed isolated training modalities in simulations, with large effect sizes (Cohen’s d = 0.96 to d = 1.24, representing substantial theoretical effects) across cognitive domains. Neurophysiological simulations revealed enhanced theta-gamma coupling, increased alpha synchronization, and strengthened fronto-parietal connectivity patterns supporting improved cognitive performance. The mathematical frameworks and algorithmic validation establish theoretical foundations for understanding executive function-tactical cognition interactions while demonstrating the computational potential for neurotechnology-enhanced cognitive training. Future empirical studies with actual athletes are needed to validate these theoretical findings in practical settings.

Charge-Transfer States Enable Spin-Selective Formation of Quartet State Qudits in Luminescent Radical-Chromophore Dyads

Fenofibrate differentially activates PPARα-mediated lipid metabolism in rat kidney and liver