2026 September - 105 articles
Metaheuristic-assisted permanent magnet synchronous motor (PMSM) inverter fault diagnosis is often compared using endpoint accuracy alone, while search dynamics, computational demand, and validation dependence remain under-reported. This study presen...
Optical vortex beams that carry orbital angular momentum are widely used in optical communication, quantum information, and optical manipulation, where precise detection of the topological charge is essential, but existing machine learning approaches...
The design of modern railway vehicles is increasingly inseparable from advanced computation and programming. As engineering challenges become more complex, sophisticated numerical algorithms and powerful computational tools enable designers to solve...
Existing methods for human action recognition from dynamic point clouds commonly rely on farthest point sampling and dense spatiotemporal neighborhood queries. The resulting local geometric computations are expensive, which complicates deployment in...
Electroencephalography (EEG) decoding pipelines are commonly tailored to individual brain–computer interface (BCI) paradigms and datasets, making dataset-specific peak performance difficult to distinguish from consistency under a shared design....
EvoSort evolves compiled C++ sorting routines. A language model proposes mutations of a baseline sorter, a per-context upper confidence bound (UCB1) bandit picks which operator to try, and a four-gate harness decides whether a candidate may be timed...
The fast development of Internet of Things (IoT) gadgets has brought both a new level of connectivity and automation, at the same time making the networks more vulnerable to more sophisticated cyber attacks. Existing intrusion detection systems have...
Assessing chart understanding requires distinguishing responses to local visual evidence from associations with a chart’s overall shape. We present Martingale Doppelgänger-Eval, an interventional audit framework that connects executable ed...
Forests play a critical role in biodiversity and in the conservation of ecosystems. Reliable and efficient forest inventories are therefore essential for sustainable forest management. Handheld mobile laser scanning (HMLS) is operationally attractive...
Reliable acquisition of vital signs is essential in prehospital emergency care; however, conventional contact-based sensors may delay assessment, cause patient discomfort, increase cross-contamination risk, and perform poorly during motion. To addres...
Retrofit condition monitoring of industrial manipulators should distinguish actual mechanical anomalies from signal changes produced by payload, speed, program phase, and transient motion. This study presents a hybrid detector for a six-axis machine-...
Existing top-down carbon emission estimation studies often rely on single models or conventional ensemble approaches, which may limit their ability to capture complex nonlinear relationships under small-sample conditions. To address this limitation,...
Reliability assessment of ship equipment is often constrained by insufficient or unavailable failure data, particularly for highly reliable components with extremely low failure frequencies. To support the use of reference information from similar eq...
Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the...
Student engagement within Learning Management Systems (LMSs) provides valuable behavioural data for understanding and predicting learning outcomes. However, predicting students’ forum consumption behaviour remains challenging because LMS datase...
OCR for low-resource languages is still held back by the same small number of issues: too little labeled image-text data, too few benchmarks, and thin language-specific tooling. Kurdish Sorani is a particularly awkward case. It is written in a modifi...
Facial Expression Recognition (FER) models frequently struggle with intrinsic emotional label ambiguities caused by overlapping facial action units, leading to high misclassification rates among confusable categories such as fear and disgust. Simulta...
To address charging-resource contention and task-allocation conflicts in multi-UAV operations supported by a single mobile charging vehicle (MCV), this paper develops GEMS-DQN (Global-Enhanced Multi-Agent Scheduling Deep Q-Network), a centralized dis...
We investigate human overreliance on artificial intelligence (AI), a phenomenon in which users fail to critically verify AI outputs despite knowing the system is imperfect. To analyze this interaction, we model a cybersecurity scenario in which a hum...
Bridge and pavement management systems typically evaluate structural condition and roadway performance separately, limiting network-level consideration of infrastructure condition and traffic exposure. This study develops the Mobility Priority Index...
Brain tumor classification from MRI is an important task in medical image analysis. This study introduces a new hybrid deep learning method that integrates Gabor filtering for the improvement in texture feature extraction, the Vision Transformer (ViT...
The rapid aging of social infrastructure constructed during Japan’s high-growth period presents a critical national challenge. Following a major tunnel collapse in 2012, Japan mandated a uniform “once-every-5-years” visual inspectio...
Cardiovascular diseases remain a major global health burden, making the development of accurate and interpretable prediction models important for clinical decision support. In this study, the metaheuristic Ivy Algorithm was employed to perform two-st...
Epilepsy affects approximately 50 million people worldwide, and timely, accurate seizure detection is essential for clinical management. Electroencephalogram (EEG) signals carry rich temporal and spectral information about brain dynamics, yet manual...
Decentralized Layer-2 sequencing requires fixed-size committees to balance performance, fairness, and resilience while downstream transaction tasks remain dependency-constrained. This study formulates committee selection as a bounded seven-objective...
Last-mile delivery routing requires simultaneous control of distance, travel time, service deadlines, vehicle capacity, workload balance, operating cost, emissions, and uncertainty. This study provides a controlled low-budget comparison of ten classi...
We formulate the Peaceman–Rachford (PR) and Douglas–Rachford (DR) operator splitting schemes for multidimensional parabolic evolution equations in the tensor-train (TT) format. The resulting TT-PR and TT-DR algorithms maintain the evolvin...
The increasing penetration of electric vehicles (EVs) creates new challenges for coordinated planning of charging and battery-swapping infrastructure. This study aims to develop a joint location and capacity planning framework for electric vehicle ch...
Research on low-carbon construction in power transmission and distribution (PTD) projects has mainly focused on carbon accounting and emission-reduction assessment, with limited consideration of construction method differences and shared resource con...
Accreditation and quality assurance in technical and vocational education and training (TVET) remain largely dependent on manual review and expert judgment. This study presents AccreditNet, an explainable tabular AI model embedded in a broader accred...
In television dance competitions such as Dancing with the Stars (DWTS), fair and transparent outcome determination is crucial for addressing public concerns regarding contest fairness. However, existing studies face challenges due to the opacity of f...
The rapid growth in traffic volumes has increased the demand for traffic-flow forecasting models with stronger prediction capability. Traditional methods that rely on local feature extraction and static spatial graph construction can no longer fully...
Underground coal-mine equipment operates under broadband noise, high dust, humidity, and methane. Acoustic sensing is uniquely suited to this environment: it captures vibration, friction, and airflow signatures without physical contact, incurs low se...
In the digital education system, the increasing demand for measuring lexical and semantic text similarity has led to the need for developing scalable and intelligent automated grading systems. Traditional assessment methods used for short-answer grad...
Efficient scheduling of special materials is essential for improving the operational efficiency of material transportation systems. However, multiple processing stages, heterogeneous transportation resources, and complex transfer paths pose significa...
To address amplitude scaling, phase rotation, local misalignment, and ambiguous decision boundaries under low-SNR and non-cooperative reception conditions, we propose an Attention RadioAlign Bayesian Residual Network (ABRNet) for automatic modulation...
Hyperspectral images contain abundant spectral information and provide fine-grained spatial representations. However, their high dimensionality, severe spectral redundancy, subtle inter-class differences, mixed boundary regions, and limited labeled s...
Operational production planning has become a key factor in a company’s competitiveness, enabling efficient resource management and a rapid response to fluctuations in demand. However, many manufacturing organizations base their decisions on a c...
Mountainous forest-fire surveillance with multiple UAVs is constrained by rugged terrain, time-varying winds, and temperature-dependent battery derating, which jointly affect endurance and route feasibility. Most existing patrol models simplify these...
In complex three-dimensional airspace, UAV trajectory planning is subject to stringent real-time constraints and must rapidly generate collision-free, near-optimal and curvature-continuous feasible flight paths within a limited computational window....
Spanning structures that must be maintained, not merely rebuilt, underlie sensor fabrics, peer-to-peer overlays, and software-defined networks. We give a complete treatment of distributed minimum-spanning-tree (MST) construction and maintenance built...
Published gaining-sharing knowledge (GSK) variants adapt scalar parameters and donor/selection policy to a single operating point. Dimension-Tiered GSK (DT-GSK) selects its configuration by dimension: an adaptive scaffold serves every tier, a determi...
Explainable artificial intelligence (XAI) is increasingly important in scientific and engineering applications where predictive performance alone is insufficient and model outputs must also be physically credible, transparent, and reliable under unse...
The COVID-19 pandemic exposed critical fragilities in health systems, particularly regarding the equitable distribution of resources and the logistical capacity of early warning systems. This study addresses the optimization of the Sentinel Surveilla...
Existing video person re-identification (ReID) methods still face three major challenges: limited spatial perception from RGB-only inputs, equal-weight local feature extraction under occlusion, and ineffective fusion of heterogeneous motion and appea...
Traditional evaluation of Networks-on-Chip is based on aggregate metrics. Most of these metrics (latency, throughput, hop count, packet loss, energy consumption, buffer occupancy, thermal or reliability summaries) compress detailed execution traces i...
The shoe last is central to footwear design. Modern footwear CAD operates on parametric digital lasts, yet much last geometry—legacy collections and lasts that skilled last makers still sculpt by hand and copy by pantograph turning—exists...
Microbiota dysbiosis is closely associated with a wide range of human diseases, yet wet-lab validation remains costly and time-consuming. Therefore, this study aims to develop an efficient framework for predicting potential microbe–disease asso...
Multi-criteria decision-making methods conventionally rank alternatives by aggregating normalized criterion values into a composite score, an approach that depends on the normalization scheme, requires externally determined criterion weights, and eva...
The Newton Downhill Optimizer (NDO) provides a compact derivative-free search framework, but its random dimension mask, uniform treatment of individuals, and limited use of supplementary global-best candidates may restrict performance on high-dimensi...
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