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People learn from experience, but with considerable individual differences in the degree and type of behavioral adjustments resulting from a given experience. Error driven learning rules provide an elegant framework for ...

Abstract In this work, a new method employs Bioinspired Computational (BIC) optimization from the genetic algorithm, bat algorithm, and flower pollination algorithm. Robust and accurate modeling of the input parameters a...

The rapid expansion of whole-genome sequencing (WGS) has highlighted the important contribution of noncoding variants to human disease, yet their pathogenic mechanisms remain difficult to resolve. Traditional statistical...

Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific...

Prediction invariance of causal models under heterogeneous settings has been exploited by a number of recent methods for causal discovery, typically focussing on recovering the causal parents of a target variable of inte...

The growing demand for intensive artificial intelligence applications, such as deep neural networks and Large Language Models (LLMs), has driven the creation of architecture extensions and accelerators for matrix multipl...

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in...

Underwater radiated noise (URN) from submarine propulsors requires reliable flow prediction as a prerequisite for cavitation and acoustic analyses. This study validates a computational fluid dynamics (CFD) workflow to pr...

With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progr...

Abstract Early and reliable smoke detection is critical for preventing fire-related disasters, safeguarding human lives, and ensuring infrastructure safety. Conventional sensor-based systems, however, are often constrain...

Novel innovations in large language models (LLMs) have demonstrated their ability to generate and analyze literary texts. As a result of intricate semantic layers, metaphors, polyphony, and nonlinear narrative structures...

Knowledge of standard air temperature, pressure, density, speed of sound, and viscosity as a function of altitude is essential information in aircraft design, performance testing, pressure altimeter calibration, and seve...

Reconstructing the early spatiotemporal dynamics of emerging infectious diseases (EIDs) is essential for effective public health response but remains difficult due to reporting delays, heterogeneous surveillance systems,...

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...

Abstract In this paper, it is presented a general optimization methodology for improving empirical models predicting Okumura-Hata, Cost-231, ECC-33, and Egli using Genetic and Differential Evolution Algorithms. The metho...

In classical vernacular Chinese fiction, inferring affective stance in the presence of rhetorical devices often depends on implicit semantics and contextual cues, which poses substantial challenges for joint pragmatic mo...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

Plant disease diagnosis in field settings is challenged by subtle symptomology, high inter-class visual similarity, and class imbalance, making automated detection particularly difficult. While deep learning models achie...

Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The pur...

Abstract Background N6-methyladenosine ( $$m^{6}A$$ ) plays a crucial role in enriching RNA functional and genetic information. While deep learning has advanced $$m^{6}A$$ site identification, current state-of-the-art me...

Pain after surgery is of major perioperative concern because it is associated with substantial complications, including chronic post-surgical pain (CPSP) development. The CPSP incidence is high, and its accurate predicti...

Abstract Ventricular shunt catheters are the standard of care for managing hydrocephalus, yet 40% of implanted catheters fail within two years of implantation, mainly due to proximal catheter obstruction. Prior literatur...

Abstract Alkaline water electrolysis represents a promising pathway for green hydrogen production, yet comprehensive multi-physics simulation remains computationally prohibitive for practical design optimization. This st...

Short-term wind speed forecasting remains challenging due to the inherent nonstationarity and multiscale variability of atmospheric processes. This study proposes a hybrid multiscale forecasting framework combining four-...