2026 March - 78 articles
We study the design of efficient approximation algorithms for the ℓ-center clustering and minimum-diameter ℓ-clustering problems in high-dimensional Euclidean and Hamming spaces. Our main tool is randomized dimension reduction. First, we...
Forecasting crop pest outbreaks under conditions of increasing agroclimatic variability is a critical task for intelligent decision support systems in agriculture. Traditional statistical and empirical models typically have limited transferability an...
Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation...
Fire detection is a critical task for early warning systems, particularly in environments where visual sensing is unreliable. While most existing approaches rely on image-based or smoke-based detection, acoustic signals provide complementary informat...
Vision–language models (VLMs) have demonstrated remarkable performance on a wide range of multimodal reasoning tasks, yet their visual grounding mechanisms remain poorly understood and are often unreliable for fine-grained visual concepts. Exis...
Accurate identification of acute and subacute subdural hematoma (acute/subacute SDH) is critical for improved patient outcomes. However, large-scale research is hindered by unreliable identification methods in electronic health records (EHRs). Curren...
The analysis of brain data through electroencephalography (EEG) has become essential in neuroscience, affective computing, and brain–computer interfaces. Recent work associates EEG features with artificial neurotransmitter models, simulating em...
This paper presents a channel segmentation proofreading network for crack counting with imbalanced samples. The network is built by stacking basic blocks called channel segmentation proofreading blocks, which are composed of the Approximate Overlappi...
Emotion identification via computer vision has made continuous progress over the last few years. Although images have been the gold standard for the past two decades, video is increasingly common. Video is particularly suitable for the study of emoti...
Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances....
Flight Schedule Problem optimization is a typical NP-hard combinatorial optimization problem that is challenging to solve using traditional algorithms, so metaheuristic algorithms are commonly adopted for such problems. This paper proposes a Discrete...
The construction of partial minimum spanning trees is an NP-hard problem, leading to the development of various heuristic algorithms. Existing heuristics, including Kruskal’s algorithm, frequently employ shortest paths to connect tree component...
Chinese font generation is important for digital typography, cultural preservation, and personalized user interfaces. However, existing methods often face challenges in maintaining structural consistency, supporting diverse stylistic variations, and...
This study proposes Advisory Board Reinforcement Learning (AdvB-RL), a cooperative reinforcement-learning framework that integrates multiple advisory neural networks to guide policy optimization. Unlike conventional single-agent architectures, AdvB-R...
In recent years, algorithms employing similarity functions beyond equality to unveil hidden knowledge have surged in popularity. Nonetheless, a notable challenge accompanying these algorithms is the proliferation of numerous frequent similar patterns...
Traditional domain adaptation methods often assume balanced data distributions. However, this assumption is frequently violated in real-world industrial scenarios, where normal samples predominate while fault samples are inherently scarce. Under seve...
The paper discusses a greedy algorithm that can be used to solve non-smooth optimization problems in which its objective function can be represented as a minimum of a compactly parameterized family of uniformly smooth functions. The algorithm guarant...
Engineering drawings are fundamental to industries such as oil and gas, construction, and manufacturing. However, current practices relying on manual design or rigid parametric templates often suffer from inefficiency and layout inconsistencies. To a...
To address the grey target group decision-making method (GDMM) concerning the aggregation of decision-maker information and the approach to ranking alternatives via bidirectional projection (BP), this paper develops a novel methodological framework t...
Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization f...
Accurately predicting and controlling the attitude of a shield tunneling machine is critical for quality assurance in shield tunneling projects. Existing prediction methods utilize historical data to construct a machine learning framework to predict...
Radiological departments face challenges in efficiency and diagnostic consistency. The interpretation of radiographs remains highly variable between practitioners, which creates potential disparities in patient care. This study explores how artificia...
The rapid proliferation of mobile Internet of Things (IoT) devices has introduced significant resource scheduling challenges in multi-edge computing networks, where device mobility leads to dynamic network connectivity and load imbalance, complicatin...
Aiming at solving the problem that existing automatic route-planning algorithms fail to consider the navigation rules in traffic separation scheme (TSS) zones, this paper proposes a ship automatic route-planning algorithm that fully considers TSS-zon...
Accurate crack segmentation is critical for automated infrastructure inspection but remains challenging due to the inherent conflict between preserving fine-grained geometric details and modeling global semantic context. Existing deep learning approa...
Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is pres...
In this paper, we first introduce the adjustable half-hyperbolic (adj HH) tangent function as an activation function. We then establish both quantitative and qualitative convergence results for HH-activated convolution-type positive linear operators...
Traditional hardware-based approaches for depth-of-field extension (DOF-E), such as optimized lens design or focus-stacking via layer scanning, are often plagued by bulkiness and prohibitive costs. Meanwhile, conventional multi-focus image fusion alg...
Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text ex...
Large language models (LLMs) hold promise for automated extraction of structured biological information from scientific literature, yet their reliability in some domain-specific tasks, such as DNA probe parsing remains underexplored. We developed a v...
Urban air quality assessment increasingly relies on spatial interpolation to complement fixed monitoring networks; however, the reliability of geostatistical methods depends strongly on temporal conditions and pollutant characteristics. Despite exten...
Accurate prediction of submitochondrial localization is fundamental to understanding mitochondrial biogenesis and cellular metabolic pathways. While deep representations from pre-trained protein language models (pLMs) have significantly advanced the...
The integrity protection of digital signals is an important task in modern applications. We propose DIPS (Data Integrity Protection of Signals), a fragile watermarking algorithm aiming to protect the integrity of sampled signals like images composed...
This paper investigates M-PSK symbol detection in Orthogonal Frequency Division Multiplexing (OFDM) systems for wideband Vehicle-to-Vehicle (V2V) communications using lightweight convolutional neural networks. In doubly dispersive channels, Inter-Car...
To mitigate color crosstalk in projected images induced by non-uniform projection surfaces and dynamic ambient lighting, we propose MAPCNet, an adaptive photometric compensation network that enables robust color reproduction across diverse environmen...
Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abru...
The Particle swarm optimization (PSO) algorithm has strong universality and fast convergence speed, but when solving complex multimodal optimization problems, it is prone to fall into local optimum due to insufficient population diversity. To address...
In recent years, the refinement of bounding box representations has emerged as a major research focus in remote sensing. Nevertheless, mainstream detection algorithms typically ignore the disruptive impacts induced by the diverse morphologies and arb...
The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM–IRC, an epidemiology-aware hyper-heuristic framework d...
Wind power generation exhibits pronounced volatility and intermittency, and direct grid connection may cause instability in grid frequency. To address this issue, this paper proposes an optimisation strategy for hybrid energy storage systems to mitig...
The state of health (SOH) estimation of lithium-ion batteries faces significant challenges under complex operating conditions due to transient disturbances and distribution shifts. This paper proposes a deep learning framework named Conformer-KAN, wh...
An improved Sinh Cosh optimizer (ISCHO) is proposed to resolve load frequency control (LFC) tasks. The original Sinh Cosh optimizer (SCHO) employs a fixed iteration-based switching function to balance exploration and exploitation, which lacks awarene...
This study investigates the impact of a Virtual Reality (VR)-based intervention on the enhancement of executive functions—cognitive flexibility, inhibitory control, and working memory—in children diagnosed with Autism Spectrum Disorder (A...
The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pi...
Machine translation for low-resource languages such as Kazakh remains a complex task due to the scarcity of training data, intricate morphological structures, and culturally specific linguistic characteristics. This study presents the first extensive...
Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzz...
Background: Smart mining is rapidly evolving through the integration of automation, advanced sensing technologies, and Machine Learning (ML). Methods: This Systematic Literature Review (SLR), based on 99 peer-reviewed studies published between 2021 a...
Unlike traditional power systems, the heterogeneous energy support of electric–gas regional distribution networks brings new challenges to resilience assessment. On the basis of identifying N-k fault uncertainty risks, establishing a resilience...
As smart grids evolve into complex cyber-physical systems, conventional static defenses struggle to address time-varying topologies and Advanced Persistent Threats (APTs). We propose the Security Framework for Resilient Smart Grids based on Self-Orga...
Time series forecasting in power systems is crucial for power supply planning and exerts a direct impact on the electricity market. Accurate forecasting can effectively mitigate decision-making risks. This paper proposes a forecasting method based on...
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