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Abstract Physical sciences are essential to the predictive modeling of complex environmental systems, particularly environmental monitoring and sustainability applications, as they provide the deterministic foundation re...

Abstract Medical image segmentation is fundamental for delineating lesion and organ boundaries in clinical workflows. While UNet-based models remain widely used, CNN-dominant designs are limited in modeling long-range co...

Abstract An accurate modeling of materials is essential to obtain reliable results in fields calculation. The Jiles-Atherton approach is widely used for modeling the magnetic hysteresis and depends on its set of five par...

The analysis and modeling of educational data are of great significance to the evaluation of teaching quality and personalized learning guidance. However, the acquisition of academic data is often limited by the costs of...

This paper proposes an innovative automated multiphysics Microelectromechanical systems (MEMS) co-optimization platform integrating the embedded reduced-order modeling offered by Coventor MEMS+ with MATLAB’s numerical ...

In recent years， quantitative microbial risk assessment （QMRA） for food safety has developed rapidly both domestically and internationally. QMRA integrates multidisciplinary theoretical knowledge and technical meth...

The purpose of this study is to justify the design parameters of spiral-vortex equipment ensuring energy-efficient desulfurization of flue gases from autonomous medium-capacity gas-piston power plants (GPPPs). The study ...

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especi...

Abstract Facial Expression Recognition (FER) demonstrates significant value in practical scenarios such as intelligent human–computer interaction. However, conventional FER methods often struggle to balance performance...

The free surface resolution in current load calculations increases costs significantly when using computational fluid dynamics (CFD), even though the small Froude number of a dynamic positioning (DP) vessel suggests mini...

Although vaccination remains one of the most impactful interventions in contemporary public health, with consistent and widely documented reductions in morbidity and mortality, the traditional empirical development of va...

Video snapshot compressive imaging is a computational imaging technique based on compressed sensing that acquires high-speed frames through a 2D compressed measurement, followed by an algorithmic reconstruction. Current ...

Abstract Preterm birth (PTB) affects an estimated 15 million infants worldwide each year, raising an enormous challenge in the field of maternal-fetal medicine. This work explores machine learning approaches for disting...

INTRODUCTION: Accurate power quality disturbances (PQDs) classification is critical for maintaining grid stability and reliability in modern power systems. However, existing deep learning methods predominantly rely on si...

Autonomous underwater vehicles (AUVs) operating in harsh, communication-limited underwater environments require robust fault diagnosis systems to ensure mission success and safety. Existing model-based approaches suffer ...

Introduction and aims: Oral and maxillofacial surgery (OMFS) produces complex clinical data, while artificial intelligence (AI) applications in this field remain limited despite great potential. This study aimed to map t...

Drug-induced cardiac arrhythmias are central to physiology and pharmacology education, yet students often struggle to connect electrophysiological mechanisms with dynamic electrocardiographic (ECG) changes. We developed ...

Abstract This work introduces a metaheuristic (MH) optimization method, which is inspired by the red-tailed hawks’ predatory behavior is Improved Red-tailed Hawk (IRTHA) Algorithm. The algorithm uses a dynamic adjustme...

This paper presents an innovative parallel computational methodology employing adaptive fractional differential operators to address Monkeypox epidemiological modeling challenges. The complex dynamics of such models ofte...

Recently, in the image super-resolution (SR) domain, Transformers have outperformed Convolution Neural Networks (CNNs) with reduced computational complexity and parameters by modeling long-range dependencies input-depend...

Abstract As electro-hydrostatic actuator (EHA) technology advances towards lightweight and integration, the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies. However, owing to the constrai...

Using historical data from blasting operations of open-pit mines to predict the average rock fragment size is crucial for optimizing blasting parameters. However, existing methods face 3 major challenges: interference fr...

Recent advances in protein structure prediction, such as AlphaFold, have demonstrated the power of deep neural architectures like the Evoformer for capturing complex spatial and evolutionary constraints on protein confor...

Abstract Argument annotation in medical drug reviews is a challenging task due to noisy user-generated content, domain-specific terminology, and subjective expressions of medication efficacy and adverse effects. While ar...