2026 April-2 - 163 articles
With the rapid development of the low-altitude economy, constructing an accurate unmanned aerial vehicle (UAV) air-to-ground channel model is crucial for ensuring communication quality. However, due to the significant fluctuations in UAV operation al...
This paper studies the reliability and availability of a k-out-of-(M+S) retrial machine repair system with two-way communication, consisting of M primary components and S warm standby components. The system incorporates the retrial behavior of failed...
Certain extensions of
In intensive care units, large-scale clinical time-series data are continuously accumulated through electronic medical records and bedside monitoring systems. However, direct utilization of such data for clinical decision-making remains challenging d...
We consider the transfer learning problem in the linear regression model, where the source domain and target domain have different features and the model exhibits heteroscedasticity. The existing homogeneous transfer learning methods cannot yet handl...
In this paper, we investigate a new class of three-component nonlinear rational difference equations of the second order characterized by structured periodic interactions. Through a carefully designed algebraic transformation and the introduction of...
Within the framework of Somigliana’s displacement and traction identities, we propose an extended equivalent elastic model that enables a BEM that is singularity-free in the primary solution stage for two-dimensional elastostatics. The central...
In the context of political and financial market turmoil, effectively forecasting financial market trends is crucial for investment decisions. Large language models (LLMs) have been applied in extant research to predict market trends, analyze investo...
Seismic traveltime tomography is essential for constructing subsurface velocity models that underpin high-resolution imaging and inversion. Traditional ray- and eikonal-based methods are sensitive to the starting model and lack a unified, physically...
Financial time series forecasting remains a persistent challenge due to the non-stationary nature, inherent noise, and multi-scale temporal dependencies present in market data. This paper presents MSTFNet, a multi-scale temporal fusion network that c...
Alzheimer’s disease (AD) develops as a progressive dementia condition through the step-by-step breakdown of nerve cells. Neurodegeneration in this context primarily results from metal ions, including copper, iron, zinc, and aluminum, building u...
To address the low accuracy of faulty feeder detection methods based on single-fault characteristics, we propose a faulty feeder detection method for resonant grounding systems that fuses multiple transient characteristics. First, we analyze the tran...
Traditional financial early warning models often rely on the independent and identically distributed (IID) assumption, failing to adequately capture cross-sectional spatial contagion effects and temporal dynamic mutations, and are susceptible to the...
This paper addresses the stability and stabilization problems of rolling mill main drive systems with time delay. The complete stability problem with respect to the delay parameter is investigated for linearized systems with and without control. The...
We study functions satisfying the composition law
In this article, we study scalar-on-function regression with functional covariates observed through replicated measurements subject to measurement error. Treating replicated curves as surrogates of an underlying latent process, the proposed framework...
Background: Topological Data Analysis (TDA) captures multi-scale geometric features of data as persistence diagrams, yet no principled information-theoretic framework quantifies how much information those features carry, how efficiently they compress...
This study proposes a novel transformation method for analyzing block minima using the generalized extreme value distribution (GEVD). The negative power transformation (NPT), which includes a tunable hyperparameter and reduces to the reciprocal trans...
Full fine-tuning of pre-trained models sometimes requires inserting trainable layers into the middle of a pre-trained backbone, but such middle-layer insertion can severely degrade downstream performance. We hypothesize that this degradation arises b...
Optimization Modulo Theory (OMT) is an important extension of Satisfiability Modulo Theory (SMT), aiming to find models that satisfy constraints while optimizing objective functions. OMT naturally arises in program analysis and formal methods, making...
Transportation problems constitute a fundamental class of optimization models; however, real-world applications involve uncertainty, hesitation, and expert disagreement that cannot be adequately captured by deterministic or classical fuzzy approaches...
There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules....
Weak ascent sequences are a generalization of ascent sequences that appear in many combinatorial problems. In this paper, we give a method to find generating functions that count how often a given statistic
With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positi...
To address the challenges of high miss detection rates and accuracy degradation in UAV-based insulator defect detection—primarily stemming from complex background interference and the loss of fine-grained features—this paper presents an o...
In urban traffic systems, traditional signal control can no longer meet the increasing traffic demand, and local congestion is severe during peak hours. Fixed detector data is characterized by high deployment density, full sample detection and restor...
Metaheuristic algorithms have become effective tools for solving complex engineering optimization problems. The Parrot Optimization (PO) algorithm is a recently proposed method with promising performance; however, it still suffers from premature conv...
Stain variability in digital pathology affects both cross-center diagnostic consistency and the robustness of downstream computational analysis. In this work, we formulate stain normalization as a variational inverse problem and derive a Screened Poi...
In this paper, we study the weighted approximation properties of a family of Szász–Mirakyan-type operators preserving two exponential functions on the unbounded interval
The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimizatio...
Event detection is crucial for disaster response, public safety, and trend analysis, enabling real-time identification of critical events. Social media platforms provide a vast content source, offering timely and diverse event coverage compared to tr...
Adversarial examples reveal critical vulnerabilities in deep neural networks, posing significant risks in real-world deployment. In black-box settings, transferable targeted attacks rely on surrogate models but often suffer from low success rates. We...
Recent automated search methods have improved lower bounds for several Ramsey numbers, but the strongest gains often depend on structured seeding and cell-specific heuristic discovery. This leaves open a more fundamental question: Can a useful search...
Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. Thes...
This study investigates a single-machine throughput maximization problem (TMP), in which each job is characterized by a processing time, ready date, due date, and weight, and must be completed within its time window to yield profit. The objective is...
Multi-objective optimization problems (MOPs) are ubiquitous in scientific research and engineering applications, where multiple conflicting objectives must be optimized simultaneously. Unlike traditional multi-objective evolutionary algorithms, which...
This paper introduces a q-deformed extension of the Lindley distribution. This extension is obtained by replacing the classical exponential with the q-exponential function from Tsallis non-extensive statistical techniques. This transformation offers...
Multivariate time series forecasting (TSF) is a fundamental task in intelligent systems, yet accurate and efficient modeling remains challenging under high dimensionality, non-stationarity, and complex cross-variate dependencies. The Mamba architectu...
Two-phase displacement between cement slurry and drilling fluid in wellbore systems is inherently nonlinear, interface-dominated, and strongly affected by geometric confinement, posing substantial challenges to efficient and stable numerical simulati...
Macroeconomic conditions are widely believed to influence the direction of equity markets, yet most forecasting models either ignore macroeconomic information or incorporate it through a small set of ad hoc predictors. We propose XAttnFusion, a macro...
Time-reversible synchronization (TRS) of nonlinear oscillators is a recently proposed technique that ensures super-exponential convergence of dynamics between master and slave systems, which is beneficial in many real-time applications. Nevertheless,...
The three main IBE schemes, i.e., Boneh–Franklin, Cocks and Boneh–Gentry–Hamburg present both strengths and weaknesses. In this paper, we introduce a more efficient variant of BGH (while preserving receiver’s anonymity) based...
Active learning (AL) aims to maximize model performance while minimizing annotation costs. With the rapid adoption of deep learning, AL approaches have evolved to meet contemporary demands. We systematically examine the literature published from 2018...
Federated learning enables privacy-preserving distributed intelligence but faces challenges in balancing computation, communication, and privacy in heterogeneous networks. To address these issues, this paper proposes a privacy-preserving U-shaped spl...
Equitable prioritization of public investments is increasingly critical as municipalities face constrained budgets, heterogeneous neighborhood needs, and demands for transparent decisions. This paper proposes a fairness-aware group multi-criteria dec...
Financial fraud is still one of the most important threats to the financial industry, causing enormous economic losses and mounting difficulties for conventional fraud detection systems. The systems tend to face challenges in dealing with the rising...
In this paper, based on the Caputo-like delta fractional difference operator, we will present a fractional discrete model of a 4D Power System. We present an extension of the popular integer-order single-machine infinite-bus formulation to two fracti...
To improve the decoding performance of multi-edge type low-density parity-check (MET-LDPC) codes, this paper proposes an edge-type differentiated parameter coupling offset min-sum (EDPC-OMS) decoding algorithm. The contributions are threefold. First,...
Detecting changepoints in time series is a fundamental task in statistical modeling and data-driven decision-making. We introduce a novel set partitioning-based model for changepoint detection that leverages combinatorial optimization to identify an...
The increasing penetration of photovoltaic (PV) systems in distribution networks has introduced new challenges in voltage regulation and energy loss mitigation, particularly under time-varying loading conditions. This paper presents a constrained mul...
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