2026 October - 94 articles
Super-resolution (SR) facilitates high-fidelity mapping beyond the spatial resolution capacity of clinical neuro-oncology MRI. While state-of-the-art deep learning algorithms have augmented this domain, such methods exclusively learn from spatial or...
We evaluate supervised classification models on European Union public tenders, pairing random forest and gradient boosting with explainable artificial intelligence (XAI) and a rule-based system. Using TED data processed with Apache Spark, we modeled...
Automatic modulation classification (AMC) is a core task in cognitive radio and spectrum monitoring. Mainstream deep-learning-based AMC models typically contain hundreds of thousands to millions of parameters, making them difficult to deploy on resou...
This study develops a digital-thread framework for predictive assessment of digital transformation in heavy equipment manufacturing and evaluates it through resource-constrained discrete-event simulation (DES) of the VFP-1600 route. The model represe...
Distributed graph neural network training usually starts with an offline partitioner such as METIS, which fixes the layout before any workload is seen. SoftPart GNN is a distributed GraphSAGE runtime on PyTorch DistributedDataParallel (DDP) without o...
Recent advances in neural speech synthesis and voice conversion have increased the risk of logical-access spoofing attacks against voice-authentication systems. This study presents an interpretable, enrollment-conditioned framework that separates spe...
Single-cell multi-omics clustering requires integrated representations that preserve shared biological states and modality-specific signals. Graph-based generative methods often couple clustering and reconstruction through a common latent pathway and...
This study examines the computational scaling of mixed-integer linear programming (MILP) for 12-bar blues guitar-solo generation. A Python/PuLP implementation uses CBC to select and order licks under duration, turnaround, repetition, pause, and conne...
Digital twins are expected to improve supply chain decisions by linking physical operations with models that simulate and optimize them. This literature review, guided by PRISMA 2020, analyzes 104 studies (2016 to 2026) on the enabling technologies o...
Background: Choosing the number of clusters is still one of the least settled steps in a K-means analysis. The within-cluster sum of squares (WCSS) elbow plot is easy to understand, yet visual inspection is difficult to reproduce, and different valid...
The spread of infectious diseases under wartime and resource-constrained conditions creates a need for analytical tools that support both population-level epidemiological monitoring and preliminary patient-level image analysis. This study develops an...
Lightweight single-image super-resolution (SISR) requires effective local structural representation and broader contextual modeling under a restricted computational budget. We propose DPALSR, a Dual Prior-Aware Lightweight Super-Resolution Network th...
Inspired by bio-medical applications, a new mathematical model of the equilibrium of a heterogeneous Timoshenko plate with two thin rigid inclusions connected by a hinge joint is studied. It is assumed that one inclusion is not delaminated from the p...
Transparent detection of underground space is crucial to urban safety and resource development, and ground-penetrating radar (GPR) inversion is the core technology to achieve this goal. In recent years, GPR inversion has undergone major changes, shif...
To address the challenges posed by the structural complexity and frequent resource conflicts in flexible job shop scheduling (FJSP) at practical power grid inspection bases, this paper formulates a makespan-minimization optimization model and propose...
Probabilistic graphical models (PGMs) provide a powerful framework for modelling complex systems, but inference over loopy graphs requires approximate methods whose accuracy depends on how factors are clustered in the graphical representation. Existi...
This systematic review examines the evolution of Artificial Bee Colony (ABC)-based scheduling research between 2011 and 2025, based on 197 peer-reviewed journal articles systematically identified through a search of the Web of Science Core Collection...
Physics-informed neural networks (PINNs) have recently emerged as an effective surrogate to classical numerical methods for solving partial differential equations (PDEs) in a wide range of physical systems. When augmented with supervised data, PINN p...
This paper presents a set of algorithms and models for the comprehensive assessment of the environmental situation in urbanised areas subject to anthropogenic impacts from industrial and transport facilities. The proposed mechanism combining fuzzy lo...
Text-to-speech (TTS) synthesis for low-resource languages such as Kazakh is still difficult: the language is agglutinative, uses an extended Cyrillic alphabet and has few annotated speech corpora. We present Kazakh TTS systems trained on a new corpus...
The process of image denoising is very crucial in the field of image processing, since noise tends to adversely affect both the appearance and structure of images that are applied in different fields of study such as science and engineering. In this...
Cross-context accuracy in electroencephalography (EEG)-based emotion recognition is usually reported without the model-selection rule, chance-level test, or reliability reference needed to interpret it. We quantify the consequences in simulations wit...
In the 2.4 GHz band, the channel of a short-range link is usually selected at one end; in Bluetooth Low Energy, a peripheral’s channel classification is advisory. This paper examines, for one instrumented case, whether the two ends of a link pe...
Accurate identification of transformer insulation faults is essential for reliable power-system operation, and dissolved gas analysis (DGA) remains an important diagnostic tool. However, conventional Rogers’ Ratio interpretation relies on fixed...
Distributed navigation interference is a critical technique for degrading the performance of multi-element adaptive nulling antennas, where optimal spatial deployment of interference sources is pivotal to maximizing overall interference effectiveness...
This paper develops a reliability and availability model for a repairable two-tier k-out-of-n computer network system. Four analytical components are combined: event-triggered repair intensification, Gumbel–Hougaard copula-based restoration, a...
Reinforcement learning is increasingly deployed in autonomous systems whose reward functions encode operational priorities and decision-making objectives. However, observable behavior can enable external observers to infer sensitive reward parameters...
Distinguishing malignant pulmonary lesions from benign nodules, infection-related abnormalities, and normal findings remains difficult because structural and metabolic imaging patterns overlap. This study presents a multi-textural feature-fusion fram...
Simulation model parameter optimization requires selecting optimizers from task knowledge and translating those decisions into execution configurations that satisfy interface, authorization, and budget requirements. We propose ParaOpt-Agent, a knowle...
Open-vocabulary text-based person retrieval must match natural-language descriptions containing concepts that may be absent from task-specific training data while controlling unreliable generated evidence and inference cost. This paper formulates het...
Preferential voting allows participants to rank multiple alternatives according to their preferences rather than being restricted to selecting a single option. Such mechanisms are used in national and local elections and, in decentralized governance,...
In this paper, we introduce a new class of mappings called enriched
Depression is a widespread mental health disorder whose early diagnosis is often hindered by the reliance on subjective clinical assessments. This study proposes a subject-aware machine learning framework for detecting depression using wrist-worn act...
Monitoring forest diseases is essential for sustainable forestry management and ecological security. However, unmanned aerial vehicle (UAV)-based forest inspection remains challenging because diseased pine canopies are often small, densely distribute...
This study develops a safety-aware supervisory framework for industrial fed-batch bioreactors by integrating an adaptive digital twin, fuzzy multi-objective optimization, Agentic LLM reasoning, and an independent Safety Governor within a closed-loop...
Connecting culturally informed concept design to engineering assessment requires explicit links between generated images, human judgments, and physical-model inputs. This study presents an integrated human–artificial intelligence (AI) decision...
This study examines the structural, semantic, and temporal evolution of strategy- and competition-oriented web scraping research, focusing on the legal and governance boundaries of data-driven competitive advantage. A purposively delimited corpus of...
Persistent and infrequent (PI) flows refer to packet flows that last for a long time at low packet arrival rates in network traffic. It is crucial to accurately find these flows for uncovering stealthy network threats, such as advanced persistent thr...
Forestry helicopter spraying requires fuel-efficient allocation of coverage tasks to successive sorties. Coverage-path generation alone does not resolve this allocation under coupled pesticide depletion and load-dependent fuel consumption. We formula...
This paper presents an automated clamping error compensation method for robotic grinding of thermal barrier coatings (TBCs) on turbine blades. In the eye-to-hand robotic grinding cell, a fixed line-laser scanner acquires a point cloud after each blad...
Similar to supply chains (SCs), where bullwhip effect shocks spread backward, financial time series exhibit volatility driven by investors’ choices, macroeconomic context, and technological development, among other factors. While deep learning...
Smart home gateways execute routine automation and emergency workloads on centralized, resource-constrained controllers. Under overload or dynamic workloads, conventional static scheduling policies may allow routine jobs to delay high-criticality tas...
We propose an improved protocol that combines the code-bit option [z] of the Universal SNS framework with asymmetric sending probabilities across odd-indexed and even-indexed time windows. This approach increases the fraction of single-sided events t...
Electrified transport introduces loads whose location, timing, and flexibility depend on vehicle movement and user behavior. Its operation therefore links road networks, charging infrastructure, distribution grids, communication systems, and electric...
The Cognitive Federated Graph Learning (CFGL) framework is introduced to jointly optimise three interdependent resources in cognitive radio networks: (i) frequency channel allocation for secondary-user transmissions, (ii) routing paths for multi-hop...
Feature alignment domain adaptation (DA) methods such as CORAL and MMD are widely used to mitigate distribution shift between training (source) and deployment (target) domains. They are typically evaluated on a single downstream classifier, which imp...
Automated generation of multiple-choice questions (MCQs) represents a viable strategy for scalable assessment and instructional support, but current systems exhibit persistent limitations in distractor quality, reasoning validity, and deployment acce...
Code-to-code translation aims to automatically convert a program written in one programming language into a semantically equivalent, executable program in another language, and is a key enabling technology for legacy-system modernization, cross-platf...
Last-mile logistics is a critical component of e-commerce distribution, particularly in urban environments where delivery failures, address inconsistencies, traffic variability, and operational inefficiencies affect service performance. This study ev...
Rich and real-world variants of the well-known vehicle routing problem (VRP) involve customer demands that are uncertain and influenced by contextual factors. Classical metaheuristics rely on deterministic demand estimates, while simheuristics incorp...
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