Search Results (2,343)

Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level scheduling, and ergonomic constraints. This study proposes a Q-learning-based hyper-heuristic genetic algorithm (QLHH-GA) to solve the cost-oriented ergonomic mixed-model human–robot collaborative assembly line balancing and scheduling problem. The proposed approach integrates bio-inspired evolutionary mechanisms of population variation and selection with adaptive, Q-learning-guided low-level heuristic selection. The Q-learning layer uses performance feedback to adapt the search strategy to different solution states while maintaining solution feasibility. A mixed-integer linear programming (MILP) model is also developed to minimize the total operating cost, including station opening, labor, robot operation, and energy consumption costs, while enforcing station-level energy expenditure (EE) limits. Computational experiments conducted using benchmark instances of varying sizes and a literature-based industrial case study demonstrate that QLHH-GA produces solutions comparable to those obtained by the MILP model on small-scale instances and maintains strong solution quality on larger instances, for which exact optimization becomes computationally prohibitive. These findings demonstrate the scalability and effectiveness of reinforcement-learning-guided hyper-heuristic search for designing cost-efficient and ergonomically constrained human–robot collaborative assembly lines. Full article

Spatial literacy and spatial thinking are fundamental skills for understanding space, interpreting geographical phenomena, and enabling active citizenship in a society increasingly driven by geospatial information. This review examines how digital tools and geospatial technologies contribute to cultivating spatial literacy and thinking within primary geography education. Following an integrative, PRISMA-informed review methodology combining searches of Scopus, Web of Science, and ERIC (n = 1373 records identified), this study draws on a corpus of 141 studies—comprising an original set of sources supplemented by newly identified, priority-ranked literature—examining digital maps, Geographic Information Systems (GIS), Web GIS, StoryMaps, collaborative mapping, digital games, and augmented and virtual reality in geography teaching. This review proposes an explicit conceptual model clarifying the hierarchical and complementary relationships among spatial skills, spatial thinking, critical spatial thinking, spatial literacy, and spatial citizenship, together with a four-dimensional typology distinguishing technological platforms, representational formats, pedagogical approaches, and learning activity types; both are proposed as synthesizing heuristics for organizing the reviewed evidence and are offered as a basis for future empirical validation rather than as established models. The findings suggest that pedagogically grounded digital integration strengthens spatial skills, shifts learning toward active, participatory models, and helps cultivate spatial citizenship, though the strength of this evidence varies considerably by tool and by educational level, with primary-specific support remaining comparatively limited for higher-order outcomes such as critical spatial thinking. Crucially, the effective integration of these technologies depends on structured teacher training—addressing self-efficacy and confidence-related barriers alongside technical skills—and the adoption of innovative instructional approaches. Ultimately, digital tools provide effective means of deepening geographical learning and strengthening spatial literacy in primary schools. Realizing this potential, however, depends on matching the selection and use of each tool to the specific dimension of spatial learning it is evidenced to support. Full article

The Multi-Node Allocation (MNA) problem in Internet of Things (IoT) networks arises when application requirements exceed the capacity of a single node, requiring job distribution across multiple devices. This problem is challenging in large-scale heterogeneous environments once it involves optimizing resource utilization, bandwidth consumption, and latency within a rapidly expanding search space. This paper proposes two scalable approaches: a greedy heuristic called Demand Index Multi-Node Allocation (DI-MNA) and a hybrid evolutionary algorithm (NSGA-Hyb) that combines DI-MNA with NSGA-III. Both methods use bounded combinatorial exploration and a normalized demand index to guide the search efficiently. The approaches are evaluated on IoT networks ranging from 10 to 1000 nodes under different workload conditions and compared with an optimal Branch and Bound (B&B) algorithm for small instances. Results show that DI-MNA achieves near-optimal solutions in small networks while maintaining low computational cost as network size grows. In large-scale scenarios, DI-MNA consistently matches or outperforms the evolutionary methods and sustains runtime speedups of up to $51\times$ over NSGA-Hyb and more than $7.8\times {10}^6$ over B&B. These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks. Full article

The deployment of deep-learning vulnerability detectors in production remains difficult. Models are large, false-positive rates are high, output is opaque, and a persistent gap separates benchmark performance from real-world utility. The objective of this work is to close part of that gap by combining a learned detector with interpretable rules so that accuracy, efficiency, and actionability are achieved together. A hybrid framework is therefore presented in which a lightweight edge-conditioned GNN of 71,810 parameters, named FastVulnGNN, trained in 96.2 s on a single CPU core, is paired with rule-based heuristic detection for six C/C++ vulnerability classes, namely buffer overflows, format-string defects, null-pointer dereferences, double-free errors, integer overflows, and race conditions. On the MegaVul dataset, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are obtained by the GNN component. On a production codebase of 499 files and 312,758 lines of code, the full hybrid scan completes in 5.5 s, which corresponds to about 57,000 lines per second, without any GPU hardware. Per-file risk tiers and pattern-level explanations are produced, and these are suitable for continuous-integration use. The significance of this work lies in demonstrating that a deployable, explainable detector can be assembled from compact components, and an edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are reported to characterize the approach. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article

Machine learning (ML) models have achieved remarkable success in areas ranging from healthcare to autonomous systems. Yet, their inherent complexity frequently obscures the reasoning behind their decisions, undermining transparency, accountability, and user trust. Compounding this issue is the absence of a universal methodology for comparing and ranking interpretability across diverse ML models and interpretability techniques. This paper addresses this gap by introducing a configurable framework of quantitative metrics to evaluate and rank interpretability. Our approach offers a structured, heuristic basis for assessing model clarity, decision logic, and accessibility, enabling practitioners to systematically compare interpretability across a wide range of algorithms and techniques. The resulting scores are intended as a practical, domain-configurable heuristic guide for comparison rather than a universal notion of interpretability. Full article

Efficient task offloading in UAV-assisted heterogeneous mobile edge computing (MEC) networks is increasingly challenged by the co-existence of operationally distinct workload scenarios—including high-demand bursts, resource-constrained periods, and balanced operational states—each demanding fundamentally different assignment strategies. In such networks, mobile executor nodes (e.g., UAVs or vehicle-mounted edge servers) must be dispatched to the vicinity of geographically distributed tasks, making assignment decisions jointly dependent on node mobility, the quality of sensing data, and dynamic resource availability. Conventional approaches based on combinatorial optimization with fixed parameters or greedy heuristics fail to adapt to these varying conditions, leading to resource depletion under sequential workloads or underutilization under high-demand bursts. To address these limitations, this paper proposes SAGE (Scene-Adaptive Graph-Enhanced offloading), a task-offloading framework that combines a heterogeneous graph neural network (HeteroGNN) with a dueling double DQN meta-controller and a mixed-integer linear programming (MILP) solver. At the state-representation level, a heterogeneous bipartite graph is constructed over mobile executor nodes and tasks, with type-specific projection layers encoding the semantic features of each node type and three-dimensional edge features—comprising task success probability, normalized service distance, and link quality—integrated via edge-gated message passing. At the decision level, the meta-controller perceives the current workload scenario through a seven-dimensional situational state vector fused with the graph embedding, selects an appropriate offloading strategy from a learned discrete action space, and drives the MILP solver to perform task-chain assignment under the selected configuration. Experiments on 60 fixed evaluation episodes spanning three representative workload scenarios demonstrate that SAGE achieves an overall reward improvement of 15.9% over the best fixed-strategy baseline, reduces the resource depletion rate to 16.7%, and maintains a high-priority task completion rate of 84.7%. Particularly under resource-constrained conditions, SAGE reduces the reward deficit by 72.3% relative to the best fixed strategy (from $-0.531$ to $-0.147$), demonstrating strong scene-adaptive decision-making capability. Full article

Environmental chemical exposures are increasingly recognized as contributors to metabolic dysfunction, particularly when they occur as chronic, low-dose mixtures rather than as isolated high-dose toxicants. However, current approaches often focus on exposure intensity, single-compound hazard or isolated biomarker associations, and provide limited explanation for why individuals with comparable exposure profiles may develop markedly different metabolic outcomes. This semi-systematic review proposes an Exposure–Adaptive Capacity (EAC) framework to interpret the metabolic consequences of environmental chemical mixtures through the interaction between exposure burden and host adaptive capacity. A structured literature search covered PubMed/MEDLINE, Scopus and Web of Science records published through 30 June 2026; a reviewer-triggered PubMed/MEDLINE update was executed on 30 July 2026 using harmonized British and American dyslipidaemia/dyslipidemia terms, explicit eligibility domains and evidence-mapping procedures. The review integrates epidemiological, mechanistic, toxicological and translational evidence related to environmental chemicals, metabolic dysfunction, mitochondrial impairment, oxidative stress, inflammation, endocrine disruption, metabolic resilience and biomarkers. The evidence indicates that several chemical classes, including per- and polyfluoroalkyl substances, bisphenols, phthalates, pesticides, persistent organic pollutants and selected metals, converge on mitochondrial bioenergetics, redox regulation, inflammatory signalling, endocrine and nuclear receptor activity, nutrient-sensing networks and adipose tissue function. The EAC framework defines exposure burden as the cumulative biological pressure imposed by chemical mixtures and adaptive capacity as the organism’s functional ability to buffer, compensate for or recover from exposure-induced metabolic stress. To make the framework empirically testable, we specify measurable domains for exposure burden, adaptive capacity and EAC mismatch, and distinguish biomarkers of exposure, early biological effect, adaptive capacity, metabolic dysfunction and vulnerability. A quotient-based expression is retained only as a heuristic representation, while empirical testing is proposed through exposure-by-adaptive-capacity interaction models and complementary multidimensional approaches. The framework provides a structured basis for future exposomic, epidemiological and translational studies by shifting attention from exposure alone to the balance between environmental pressure and biological resilience. Full article

The increasing penetration of distributed energy resources (DERs) introduces significant operational uncertainties, challenging the reliability and robustness of conventional protection coordination in distribution networks. Traditional optimization methods can obtain high-quality relay settings, but their iterative computational burden limits their direct use for real-time adaptation. To address this issue, this paper proposes a deep learning-based preventive relay setting calibration framework for enhancing protection coordination robustness in DER-rich distribution networks. The proposed method adopts an offline–online architecture. In the offline stage, a refined heuristic algorithm is integrated with ETAP-based fault analysis to generate a comprehensive dataset of high-quality optimized time-multiplier settings (TMSs) and pickup current settings (PCSs) under a wide range of DER-generation and load-demand scenarios. Subsequently, a convolutional neural network (CNN) is trained to learn a nonlinear mapping from multidimensional fault-current signatures to the corresponding optimized relay-setting vectors. In the online stage, the trained CNN serves as a predictive surrogate model, rapidly recommending coordinated relay settings for the current operating condition. The framework is validated using a 16-bus distribution system and the IEEE 37-bus test feeder. The results show that the proposed method can restore correct primary–backup relay operating sequences and maintain coordination time intervals (CTIs) within the required 0.2–0.4 s range under the studied DER-rich operating scenarios. This CNN-based preventive calibration approach provides a rapid, adaptive decision-support tool to improve protection coordination robustness against DER-induced operating uncertainties. Full article

Aiming at the multi-constraint three-dimensional bin packing problem for mixed vehicle types in urban logistics, where traditional exact algorithms are limited by NP-hard computational complexity and practical engineering constraints, this study proposes a progressive optimization framework for vehicle loading and fleet allocation optimization. First, a heuristic loading algorithm based on the extreme point method and greedy strategy is developed to maximize single-vehicle loading efficiency by balancing space and weight utilization. Second, an NSGA-II based evolutionary framework with sequential encoding is constructed to minimize fleet size while improving loading balance for single-vehicle-type optimization. Third, a three-stage hybrid algorithm integrating greedy packing, enumerative search, and tail vehicle replacement is designed to optimize mixed-vehicle fleet composition and minimize total transportation cost. Experimental results demonstrate that the proposed heuristic achieves high composite loading performance across vehicle types, and the evolutionary framework significantly reduces fleet size compared with theoretical lower bounds. Under mixed-fleet optimization, the model identifies cost-effective vehicle configurations that outperform single-type dispatching strategies. Sensitivity analysis reveals that cargo composition, particularly the number of fragile items, is the most critical factor affecting system performance, while validation on 16 vehicle types confirms the robustness and practical generalizability of the method. This study verifies the effectiveness and stability of heuristic-evolutionary hybrid optimization methods, providing a reliable decision-making reference for logistics enterprises in vehicle selection, cargo allocation, and transportation planning. Full article

Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. The proposed framework combines image preprocessing, multiple convolutional neural networks trained under different conditions, and a second-stage classifier that integrates the CNN outputs. Several machine learning models and feature selection techniques were evaluated using publicly available mammography datasets. Results: The ensemble approach consistently outperformed the individual CNN models. The MLP classifier achieved the best overall balance between precision and recall, while the heuristic fusion method provided the highest sensitivity. Feature selection reduced model complexity while maintaining comparable performance, and cross-validation confirmed the robustness of the proposed methodology. Combining complementary information from multiple CNNs with classical machine learning improves diagnostic performance and provides a robust framework for computer-aided breast cancer diagnosis. The proposed two-stage ensemble offers an effective and interpretable approach for mammographic breast cancer classification. A demonstration application incorporating Grad-CAM explainability further supports its potential use as a clinical decision-support tool. Full article

Trust Management Systems (TMSs) have recently emerged as a behavioural complement to identity-based approaches in Industrial IoT (IIoT) cybersecurity by evaluating node trustworthiness. Deep Learning (DL)-based TMSs offer favourable detection over heuristic and Machine Learning (ML) models. The computational density of DL models introduces a trade-off between inference fidelity and deployment feasibility, particularly in Edge-Fog-Cloud (EFC) IIoT architectures where latency and resources are constrained. This work proposes an EFC architectural framework that relocates DL inference to the Fog layer, reducing Cloud communication latency and Edge resource exhaustion. A lightweight Long Short-Term Memory (LSTM)-based model derives continuous trust scores from header-derived, flow-aggregated features, with inference latency bounded through fixed-size sliding windows and stateless execution. The system is trained and evaluated on CIC-IoT-2023 across Denial-of-Service (DoS), Distributed DoS (DDoS), Mirai, and benign scenarios. System scalability is assessed through ns-3 network simulation under benign conditions, with full-system behaviour further evaluated under benign, DoS, and Mirai scenarios. Offline evaluation achieves F1-score 0.9996, accuracy 0.9997, ROC-AUC 0.9999, and PR-AUC 0.9997. Architectural evaluation yields a mean inference latency of 0.049 ms, a maximum enforcement latency of 0.120 ms, and a 302 kB deployment footprint. System simulation confirms a benign False Positive Rate (FPR) 0.07% and a maximum detection latency of 0.22 ms. DoS achieves recall 0.99999 and FPR 0.00186, and Mirai achieves recall 0.99997 with FPR 0. This demonstrates that DL-based trust inference is achievable on resource-constrained Fog nodes, establishing the work as a viable solution for trust evaluation in EFC IIoT deployments. Full article

Climate change education has expanded in curricular, institutional and media arenas, yet it still faces a central challenge: turning scientific knowledge into educational practices that transform perceptions, decisions and participation. This article argues that the challenge cannot be addressed by merely adding content about the climate system, because the climate crisis is experienced in specific territories, affects communities unequally and activates emotions that can either enable or inhibit action. Through a critical integrative review and research-reflection approach, the article connects three bodies of scholarship that are commonly developed in parallel: territorial and place-based learning, climate justice, and emotional literacy. Its conceptual novelty lies not in claiming that these components are individually new, but in specifying territorial memory as the mediating mechanism through which scientific evidence, unequal vulnerability, affective experience and collective action are combined in teacher education. On this basis, it presents the TEJA Model—territorialization, experience, justice and action—as a heuristic framework for designing, analyzing and evaluating teacher-education sequences. Its practical contribution is a five-phase process and an operational matrix that translate nearby risks such as floods, droughts and wildfires into inquiry, deliberation, emotional care and feasible collective action. The model is presented as a testable conceptual proposal rather than a validated intervention. The article concludes that relevant climate education must be scientific, human, territorial and political: it should recognize vulnerability, begin from lived places and address unequal responsibilities and collective capacities for action. Full article

Warehouse operations involve dynamic item flows, fluctuating demand, and heterogeneous layouts, making adaptive decision-making essential for efficient storage and order fulfillment. In this context, reinforcement learning (RL) provides a promising approach for learning adaptive warehouse control policies under stochastic environments. However, evaluating RL-based solutions in real warehouse settings is often costly and time-consuming, motivating the need for realistic and reproducible simulation environments. In this paper, we introduce a configurable warehouse simulation environment modeling stochastic item arrivals, order generation, and internal logistics operations across diverse layouts and workload conditions. Based on this environment, we construct a reproducible experimental testbed composed of multiple scenarios ranging from low-load to highly congested settings. The testbed is publicly released to support reproducible research and comparative evaluation within the research community. We formulate the warehouse management problem as a Markov decision process (MDP) and apply a Maskable Proximal Policy Optimization (Maskable PPO) agent to learn adaptive control policies. The RL-based approach is evaluated across the defined scenarios and compared against heuristic baseline strategies. Experimental results show that the proposed solution achieves performance comparable to a strong greedy first-in, first-out (FIFO) heuristic while improving order fulfillment by up to 13.5 percentage points under challenging workload conditions. These results demonstrate the ability of RL to learn robust warehouse control policies that adaptively optimize performance and maintain operational stability across a wide spectrum of distinct scenarios. Full article

Carbon fiber-reinforced polymer (CFRP) composites are increasingly used in aerospace, rail transportation, and energy engineering owing to their high specific strength and corrosion resistance. However, their complex and interacting damage mechanisms, including delamination and matrix cracking, present significant challenges for reliable structural health monitoring. Fiber Bragg grating (FBG) sensors offer distinct advantages for monitoring composite structures because of their compact size, immunity to electromagnetic interference, embeddability, and capability for distributed strain measurement. Nevertheless, the effectiveness of an FBG sensing network depends strongly on the spatial distribution of the sensing points. This study proposes a finite-element-assisted framework for evaluating and improving FBG sensor layouts for strain-field reconstruction and structural feature characterization of composite plates. The framework first reconstructs the spatial strain field from limited sensing data using interpolation and least-squares fitting methods, and then evaluates the performance of existing and candidate sensor layouts based on reconstruction errors and spatial coverage of structurally important regions. A strain-gradient-informed heuristic strategy is subsequently developed to improve sensor placement by combining high-gradient region identification, spatially uniform coverage, minimum-distance constraints, and predefined support-region monitoring requirements. The Fourier least-squares fitting method provides the lowest reconstruction error among the investigated approaches and is therefore adopted for subsequent layout evaluation and improvement. Finite-element simulations and experimental measurements are used to assess the reconstruction performance and identify the advantages and limitations of different sensor layouts under static and dynamic loading conditions. The results demonstrate that the proposed framework can effectively evaluate existing FBG layouts and provide a systematic basis for their improvement, while also revealing the trade-off between local strain-gradient resolution and global spatial coverage. The proposed framework provides practical guidance for the performance-oriented design and improvement of FBG sensor networks for structural health monitoring of composite structures. Full article