Most Recent

In this paper, a well-balanced and positivity-preserving scheme for the nonconservative two-layer shallow water equations is developed in the framework of the finite volume method. To address the challenges posed by wet–dry fronts, the focus of...

This manuscript proves specific theorems for transforming Boolean expressions of logical formulas when moving from one basis to another, simplifying the solution of complex equations, especially for cryptographic applications. The paper develops meth...

To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, inc...

The spectral property $\left(\omega \pi \right)$ generalizes the a-Weyl theorem and characterizes the relationships between the spectrum and the upper semi-Fredholm spectrum. This paper proves that any nonzero scalar multiple of an operator verifying the property...

I address the empirical properties of the popular robust LM tests of Anselin et al. (1996) for the specification of spatial models when employed in a scenario characterized by unobserved common factors with idiosyncratic loadings. I describe the smal...

Fractional order controllers are more frequently encountered in industrial applications due to their robustness and the improved performance they offer to the system. A large majority of research papers focus on methods for tuning controllers that ar...

This paper introduces polynomial F-contractions, a novel category of contractive mappings within metric spaces. This concept synthesizes two powerful generalizations of the Banach contraction principle: the F-contractions originally developed by Ward...

This paper investigates the distributed event-triggered fixed-time time-varying formation control problem for a class of nonlinear multi-agent systems subject to model uncertainties and unknown time-varying disturbances. To address issues in traditio...

The Capacitated Vehicle Routing Problem (CVRP) has a wide range of applications in logistics and transportation. Current metaheuristics typically rely on manually added constraints. A hyper-heuristic framework can reduce the dependency on domain-spec...

In response to the widespread adoption of hybrid work models, organizations must strategically address the challenges of knowledge transfer and organizational learning in distributed environments. We extend March’s computational model of organi...

Polygenic adaptation in response to natural selection on a quantitative trait has become an important topic in population genetics and evolution. We modeled a scenario in which a population was assumed to be in equilibrium between mutation, selection...

This paper proposes a transient response enhancement of a personal mobility device (PMD) battery charger using a rule-based fuzzy logic control (RBFLC) method. PMD encompasses various applications, including electric kickboards, electric scooters, an...

Let G be a graph with n vertices and m edges. A vertex magic total labeling of G is a bijection $f:V\left(G\right)\cup E\left(G\right)\to \{1,2,\dots ,n+m\}$ such that, for each vertex $u\in V\left(G\right)$, the sum of the label of u and the labels of all edges incident to u is equ...

This paper introduces a collocation algorithm for numerically solving the third-order Gilson–Pickering equation (GPE) and the classical Rosenau–Hyman equation (RHE). We employ newly developed shifted Pell polynomials as basis functions. N...

The lateral abutment stress and damage range of the coal seam are prerequisites for the layout of gob-side entries and surrounding rock control. They are influenced by the structure and mechanical properties of the coal seam and the overlying strata....

The rapid development of digital manufacturing and robotic systems places increased demands on the accuracy and reliability of industrial manipulators. Traditional time-based reliability metrics do not reflect the robot’s ability to consistentl...

In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accu...

A critical decision in pre-disaster humanitarian logistics planning is determining the amount of aid to preposition to ensure timely and effective emergency response. To support managers in this process, we propose four mathematical formulations desi...

Traditional scheduling theory optimizes initial task assignments under static assumptions, yet operational systems face repeated disruptions requiring both immediate rescheduling and long-term structural adaptation. Existing approaches treat each dis...

Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they stil...

This paper addresses nonlinear time-varying cascade systems governed by differential equations with finite delay. Several sufficient conditions for asymptotic stability are derived, based on differing assumptions regarding the isolated subsystems and...

Accurate segmentation of pellet microstructure images is crucial for evaluating their metallurgical performance and optimizing production processes. To address the challenges posed by complex structures, blurred boundaries, and fine-grained textures...

In recent years, the mortality due to diabetes has increased around the world. In particular, diabetes is the second leading cause of mortality in Mexico, with a heterogeneous distribution of mortality rates at the municipality level. The objective o...

This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordin...

Low contrast is a frequent challenge in image analysis, especially within medical imaging and highly saturated scenes. To address this issue, we present a nonlinear transformation for local contrast enhancement in digital images. Our method adapts th...

Aggregation operators are mathematical tools that, under certain constraints in the form of properties imposed on those mathematical functions, provide a representative value for the whole set of input values or structures. The question of when and w...

This paper develops simultaneous confidence intervals (SCIs) for pairwise differences of means with zero-inflated Rayleigh (ZIR) distributions, a flexible framework for modeling positively skewed data with excess zeros. Closed-form expressions for th...

To provide information support for relay protection and emergency automation algorithms, electromagnetic measuring voltage and current transformers are most often used. As practice shows, the magnetic core of the current transformers can be saturated...

X-ray imaging is the most widely used modality for fracture diagnosis in clinical practice due to its efficiency and accessibility. However, automated X-ray fracture detection faces two major challenges. First, fracture regions often contain subtle a...

To address the challenge of model selection in large-scale traffic flow prediction tasks, this paper proposes a dynamic multi-model selection framework based on Deep Contextual Bandits (DCB). Centered on the optimal combination of sub-models, the fra...

Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint...

This article presents a model for pricing an exchange option considering stochastic volatility and liquidity risk. The impact of liquidity risk on an asset price is considered by utilizing a liquidity discount process that is influenced by both marke...

In this paper, a complete analysis is presented to study a reaction–diffusion system of general gene expression with two time delays and with Neumann boundary conditions. The global existence of a unique strong solution and the existence of an...

This paper studies the conformal geometry of complete gradient Schouten solitons (GSSs) admitting closed conformal vector fields (CVFs). We establish rigidity and characterization results for nonparallel, homothetic closed CVFs under the assumption t...

This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence,...

We propose a generic approach to stochastic model improvement by first introducing an archetypal algorithm based on error minimisation and establishing two results on the weak convergence of the probability laws associated with the models under impro...

Let $\mathcal{A}$ be a unital associative *-algebra over the complex field $\mathbb{C}$ containing a nontrivial projection and $\xi$ be a nonzero scalar. In this paper, we give a characterization of $\xi$-bi-skew Lie triple derivation on $\mathcal{A}$. As applications, we apply the abov...

Airport digital transformation is commonly approached through technological integration and data-driven optimization, yet such perspectives provide limited insight into system-level reasoning and governance. This paper introduces the cognitive airpor...

Research on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact o...

Meta-analysis of proportions requires a rigorous transformation model due to the inherent mathematical constraints of proportional data (boundedness and non-constant variance). This study compared four proportions (Untransformed, Freeman–Tukey,...

This study develops a generalized Newton method to address the nonlinear Kolmogorov forward equation (KFE) under the local stochastic volatility (LSV) framework. Analytical convergence conditions are derived via the geometric series theorem, and empi...

Short-term electricity load forecasting has become increasingly challenging due to growing demand volatility, nonlinear load patterns, and the dynamic penetration of renewable energy sources. Conventional forecasting models often suffer from sensitiv...

Backpropagation Neural Networks (BPNNs) are widely used in fault diagnosis and parameter prediction due to their simple structure and strong universal approximation capabilities. However, BPNNs suffer from slow convergence and susceptibility to poor...

Malicious Tor traffic data contains deep domain-specific knowledge, which makes labeling challenging, and the lack of labeled data degrades the accuracy of learning-based detectors. Real-world deployments also exhibit severe class imbalance, where ma...

This study develops a mathematical optimization framework to determine optimal inspection policies for imperfect production systems subject to stochastic deterioration. System degradation is modeled using a Weibull power law process, which captures t...

When a physical theory is in its early stages of development, it presents many concepts and constructs that may not be necessary for its interpretation, or may simply be equivocal. As the theory is developed by the physics community, it hopefully pas...

We present a one-parameter family of smooth generalized pinball loss functions to overcome the challenges of non-differentiability, noise sensitivity, and resampling instability inherent in traditional loss functions such as hinge loss. These functio...

Photovoltaic (PV) systems play a significant role in renewable energy production. Due to the nonlinear and multi-modal nature of PV models, using accurate model parameters is crucial. In recent years, metaheuristic algorithms have been utilized to es...

Millimeter-wave technology helps achieve antenna miniaturization and high gain, but it is limited by factors such as short wavelength, high transmission loss, and high signal-to-noise ratio, which put higher requirements on the accuracy and computing...

In this work, a robust optimization approach for energy-aware federated learning (FL) in industrial IoT networks is proposed that addresses uncertainties in harvested energy, device failures, and dynamic topologies. The proposed neurosymbolic reasoni...