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The arithmetic of Hilbert modular forms has been extensively studied under the assumption that the forms concerned are “paritious”—all the components of the weight are congruent modulo 2. In contrast, non-paritious...

Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health ques...

Mathematics and computer science play a pivotal role in modern biology. Dynamical models based on differential equations are particularly adapted to describe dynamic behaviors of many biological processes. However, tradi...

The study of optimality conditions and constraint qualification is a key  topic in nonlinear optimization. In this work, we present a reformulation of the well-known second-order constraint qualification described by ...

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 Gray-Scott (GS) model is a nonlinear reaction diffusion system widely used in applied sciences. This paper delves into the investigation of a spectral method for the GS model with homogeneous Neumann boundary conditi...

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...

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...

Inverse rising factorial moments [Formula: see text] are fundamental quantities for positive integer-valued random variables, with broad applications in probability and combinatorics. Despite their importance, there is a...

Modeling data that falls within the [0,1] domain is a huge task in most scientific disciplines. This is because most classical distributions do not address the issues of complex data shapes as well as managing the dynami...