Search Results (1,840)

This paper addresses the flexible job-shop scheduling problem (FJSP) as a constrained combinatorial optimization task with a large discrete action space. Although action-masked reinforcement learning has shown promise for such problems, the effect of structured vector-state encoding in scheduling has received less attention. The main contribution of this work is a structured block-wise state representation and a multi-branch feature extraction module for action-masked Proximal Policy Optimization (PPO). The proposed representation decomposes the scheduling state into three heterogeneous components capturing resource availability, operation readiness, and temporal attributes of operation–machine alternatives. Instead of flattening these signals into a single vector, the proposed encoder processes each block separately before aggregation, with the aim of preserving semantic structure during policy learning. To isolate the effect of representation design, we compare the proposed multi-branch encoder with a baseline single-branch multilayer perceptron under identical PPO hyperparameters and training conditions. Experiments on the Brandimarte MK benchmark suite show that the proposed architecture yields a lower best-achieved makespan on nine of ten instances and improves the best baseline result by up to 27.84%. Additional validation on selected Behnke and Geiger instances indicates that the BR encoder’s advantage extends to larger FJSP cases while preserving sub-second inference. Full article

Architectural typology classification plays an important role in large-scale documentation and analysis of urban cultural heritage. Recent advances in computer vision enable automated approaches for detecting and categorizing buildings from street-level imagery, yet the suitability of different detection paradigms for architectural typology analysis remains insufficiently explored. Despite recent advances in computer vision for architectural analysis, no systematic comparative study has evaluated closed-set CNN-based detectors against open-vocabulary vision–language grounding models for urban architectural typology classification. This study presents a comparative evaluation of closed-set convolutional object detectors and open-vocabulary vision–language grounding models for the classification of Athenian architectural typologies. A dataset of 3349 street-view images containing 11,111 annotated building instances was compiled and organized into five typological categories: Neoclassical, Neoclassical-Eclectic, Interwar-Eclectic, Interwar, and Postwar. The experiments compare several YOLO-based detection configurations with Grounding DINO under zero-shot inference, parameter-efficient adaptation (e.g., Kiw Rank Adaptation—LoRA), and full fine-tuning. Results show that supervised YOLO-based models achieve robust detection and classification performance with high localization accuracy and consistent typology discrimination in dense urban scenes. In contrast, open-vocabulary grounding models demonstrate limited reliability in zero-shot settings and require substantial adaptation to approach comparable performance levels. Analysis of confusion patterns further reveals that most classification errors originate from intrinsic architectural similarities between transitional styles rather than from model instability. The findings highlight the advantages of supervised object detection frameworks for scalable urban heritage documentation and provide insights into the current limitations of vision–language models for fine-grained architectural typology classification. Full article

Role-Based Access Control (RBAC) is the de-facto mechanism for preserving Kubernetes and other cloud-native container platforms, however real deployments occasionally drift away from the principle of least privilege as clusters, teams, and services improve. This paper introduces an automated RBAC hardening framework that formulates least-privilege policy design as a limited optimization problem over RoleBindings and ClusterRoleBindings. The objective combines (i) a permission-risk score for namespaced and cluster-scoped actions with (ii) an operational complexity term that discourages overly large binding sets. Solid limitations encode functional requirements as well as practical security policies, which includes namespace allowlists, role scoping rules, administrative restrictions on cluster-wide bindings, binding budgets, and separation-of-duty requirements expressed by utilizing capability classes. To allow optimizer-agnostic search while protecting Kubernetes RBAC semantics, we analyze candidate policies by utilizing a unified penalty-based fitness function that compines risk, complexity, and constraint violations into a single scalar value. We utilized ten metaheuristic as a benchmark including baseline search paths on a Kubernetes-inspired instance and report feasibility and least-privilege quality metrics (precision, recall, F1, and over-privilege ratio) parallel to RB/CRB counts and excess risk as a structural indicators. Outcomes present that feasibility is the prime challenge, and is restricted to a subset of optimizers reliably arrives to entirely feasible and compact arrangements within the exact budget, indicating the practicality of metaheuristic enhancement for systematic RBAC reduction in containerized cloud computing environments. Full article

Object detection in optical remote sensing imagery faces persistent challenges from severe instance overlap, extreme spatial density, and motion or atmospheric blur. These degradations cause conventional detectors to over-mix neighboring instance features and fail to separate closely packed objects. To address these limitations, we propose SARC-DETR, a detection framework that augments the RT-DETR architecture with two complementary plug-in modules: Similarity Adaptive Convolution (SAC) and Receptive Field Cross Convolution (RCC). SAC introduces a reproducing-kernel-Hilbert-space (RKHS) motivated similarity gate that selectively suppresses responses inconsistent with local feature prototypes, thereby reducing cross-instance interference in overlapped and blurred regions. RCC constructs a large directional receptive field through orthogonal strip-based aggregation and content-adaptive fusion, enabling efficient long-range context capture without quadratic complexity overhead. Both modules can be integrated into existing DETR-style detectors without modifying the detection head or training protocol. On VisDrone2019-DET, SARC-DETR improves ${\mathbf{AP}}^{\mathbf{val}}$ from 29.7 to 34.8, ${\mathbf{AP}}_{50}^{\mathbf{val}}$ from 49.5 to 56.2, and ${\mathbf{AP}}_S^{\mathbf{val}}$ from 19.2 to 24.8. On DIOR, AP rises from 57.9 to 68.4, and on NWPU VHR-10, from 44.4 to 66.5, demonstrating robust cross-dataset generalization. After structural reparameterization, the additional overhead is less than 0.75 M parameters and 0.36 G FLOPs, confirming deployment suitability for UAV and satellite-based remote sensing applications. Full article

Large Language Models have become important in our lives, and academia is not agnostic to this trend, offering tools like text rephrasing and summarisation. However, this integration raises significant concerns regarding the integrity of science. In this paper, we investigate hallucinations of LLMs when generating scientific references. Using nine LLMs, we generated a dataset of 74,196 $\mathrm{B}\mathrm{I}\mathrm{B}{\mathrm{T}}_{\mathrm{E}}\mathrm{X}$ references to quantify and analyse fabricated references, focusing on distinguishing between intrinsic and extrinsic hallucinations. Also, we extracted and analysed 127,063 references from 3541 published papers in 2023 to assess the prevalence of fake bibliographic data. Our manual verification process identified eight instances of fabricated references. While the overall rate is statistically low, the mere existence of fabricated content in the peer-reviewed literature is a critical integrity issue, demonstrating a vulnerability in current academic validation systems. The significance of our finding is not the statistical prevalence but rather the necessity for rigorous, human-validated processes to prevent the injection of spurious citations regardless of their source. Full article

The rapid development of synthetic aperture radar (SAR) imaging technology has significantly enhanced maritime monitoring capabilities; however, SAR ship detection remains constrained by the limited scale and representation capacity of existing rotated bounding box datasets. Most publicly available datasets rely on horizontal annotations, which introduce redundancy and localization ambiguity in densely distributed and nearshore scenarios. Although rotated bounding boxes provide more precise geometric representation, large-scale multi-source rotated SAR datasets are still insufficient to support robust model training. To address this limitation, we construct a large-scale multi-source rotated SAR ship dataset (LMRD) consisting of 13,024 high-resolution image chips with over 38,000 annotated ship instances, covering multiple satellite sources, polarization modes, and diverse maritime environments, including offshore, nearshore, complex coastal, and densely distributed port scenes, thereby enhancing scene diversity and annotation precision. Furthermore, independent of the dataset construction, we propose a multi-domain feature fusion (MDF) framework built upon Oriented RCNN, which integrates high-frequency information and visual saliency cues to improve feature representation under complex backgrounds. Experimental results on the LMRD demonstrate that, compared with the baseline Oriented RCNN, the proposed MDF framework achieves a 2.7% improvement in average precision. Additional analysis indicates that the dataset characteristics and the multi-domain fusion strategy contribute to performance enhancement at different stages of the detection pipeline, validating the effectiveness of the proposed dataset for rotated ship detection while demonstrating the complementary role of multi-domain feature enhancement. Full article

Identifying similarities in data is fundamental to discovery in science. Measuring or ranking similarity is a key way of reducing the dimensionality of data, is at the heart of many data intensive algorithms and can also be used directly for some applications. This paper extends our understanding of a relatively simple similarity problem. Our primary application is spectral-based fault localisation (SBFL), in which a computer program is run with a large number of test cases and data is collected on which statements are executed in each test case. For each statement, the set of test cases in which it is executed is compared to the set of test cases that failed, and this is used to rank the statements to help locate bugs, an instance of what we call the similarity to a single set (STASS) problem. This paper is primarily theoretical but some contributions are validated with SBFL experiments. Set similarity is equivalent to similarity of binary vectors or two-by-two contingency tables. The problem is also equivalent to converting two-dimensional data with a “partial order”, such as points on a rectangular grid, to a one-dimensional total order. Even when the raw data is not binary, we are often interested in comparing binary classifiers for the data, such as diagnostic tests, and comparing binary classifiers is an instance of the STASS problem. More than a hundred set similarity measures have been proposed in the literature and hundreds of thousands have been evaluated for SBFL, but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties and forms of symmetry that similarity measures can have. It refines previously identified properties so they are no longer incompatible, identifies new forms of symmetry, defines ordering relations over similarity measures, and proposes a new statistic that can be used to help choose a good similarity measure for a given domain. Full article

Foundation models are reshaping computational pathology by enabling scalable task-agnostic representations of histopathological whole-slide images (WSIs). Unlike earlier task-specific deep learning systems, pathology foundation models (PFMs) leverage massive whole-slide image repositories and self-supervised Vision Transformer architectures to achieve broad generalization and few-shot adaptability. Their evolution reflects a shift from weakly supervised approaches such as Clustering-Constrained Attention Multiple Instance Learning (CLAM) and hierarchical architectures such as Hierarchical Image Pyramid Transformer (HIPT) to large-scale efforts including foundation models, UNI, Virchow, Phikon, CONtrastive learning from Captions for Histopathology (CONCH), GigaPath, H-Optimus, Transformer-Based Pathology Image and Text Alignment Network (TITAN), and the Mayo Clinic Atlas. These models demonstrate impressive performance across diagnostic and prognostic benchmarks while also opening pathways for multimodal integration with genomics and clinical data. Yet significant barriers remain including inconsistent generalization across institutions, interpretability lagging behind clinical needs, and slow integration into routine laboratory workflows. Certain domains of anatomic pathology such as cytopathology, transplant pathology, frozen sections, and rare tumor subtypes remain particularly resistant to current models. Here, we review the development of PFMs, critically evaluate their strengths and limitations, and outline priorities for their safe and effective clinical translation. We argue that the next phase of PFM development will depend on rigorous benchmarking, pathologist-in-the-loop deployment, and multimodal fusion ensuring these models evolve from research tools into clinically robust systems. Full article

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution. Full article

Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions and total tardiness penalty are the primary objectives. We propose a Policy-based Rough Optimization with a Large Neighborhood Search (Pro-LNS) framework integrating Proximal Policy Optimization (PPO) and adaptive Large Neighborhood Search (LNS). PPO constructs a feasible schedule by selecting operation-machine assignments from job-readiness, machine-availability, earliest-completion, and critical-path features. This policy-generated schedule provides a structurally informed incumbent, enabling LNS to avoid unguided search and focus destroy-and-repair refinement on high-impact operations. Both phases use the same normalized scalarized carbon-tardiness objective, which guides PPO rewards and LNS removal, reinsertion, and acceptance while preserving precedence, eligibility, and capacity constraints. Experiments on small, medium, and large workcenter benchmarks show strong due-date performance and controlled carbon emissions. Under equal objective weighting, Pro-LNS achieves a median optimality gap of 6.12% relative to the exact formulation, with all instances within 14%, while requiring 4.08 s on average and at most 10.51 s. Comparisons with PPO-only, Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Genetic Algorithm (GA) schedulers show that Pro-LNS attains the best weighted scalarized objective across representative instance-weight settings. Friedman and Holm-corrected Wilcoxon tests confirm significant improvements over all competitors, with average weighted-objective gains of 4.90%, 7.25%, 8.81%, and 9.51% over PPO-only, A2C, SAC, and GA, respectively. These results demonstrate that Pro-LNS is an effective and computationally practical hybrid approach for carbon-aware, tardiness-sensitive flexible job shop scheduling. Full article

Large-scale engineering of the 3D scenes used in BIM, GIS, digital twins, and geospatial web delivery frequently suffer from significant geometric redundancy after export to mesh-based delivery formats, arising in part from the inconsistent reuse of geometry, where many repetitive components are stored as independent meshes rather than being fully instantiated. This paper proposes an affine–covariant mesh instancing framework designed to achieve a lightweight representation of watertight triangular solids. The core of the method lies in a canonicalization pipeline: each mesh is normalized via volume-centroid translation, principal-axis alignment derived from volume covariance, and anisotropic covariance whitening. This process effectively decouples the influence of translation, rotation, and non-uniform scaling, projecting diverse geometries into a unified canonical space. Within this space, geometric similarity is quantified by evaluating compact descriptors against user-defined tolerances. A greedy clustering strategy is then employed to group affine–similar models based on these descriptors. Finally, the scene is efficiently reconstructed by applying inverse affine transformations to the representative instance of each cluster. The output stores one shared geometry per cluster alongside per-instance $4\times 4$ transform matrices, preserving the original spatial layout while reducing redundant geometry storage. Experiments on four real-world engineering scenes demonstrate varying compression benefits. The results prove particularly effective for scenes containing unlinked repetitive parts and affine–similar parametric components, while also revealing a controllable trade-off between fidelity and compression rate. The method is therefore suitable as a post-export geometry-lightweighting step in mesh-based BIM/GIS integration, infrastructure digital twins, and large-scale 3D mapping workflows. Full article

Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately $3.5$–$4.5^{\circ }$. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–${12}^{\circ }$ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately $2^{\circ }$ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance ($3.{77}^{\circ }$ on CFV; $5.{38}^{\circ }$ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation. Full article

This paper presents a dynamic multi-period mixed-integer programming model for the Disaster Volunteer Task Assignment Problem (DVTAP) that advances the humanitarian logistics literature through an integrated treatment of features that have previously appeared only in isolation. Unlike prior formulations that assume volunteer surplus or steady-state conditions, our model reflects the acute-phase reality where tasks far exceed available volunteers and new task arrivals diminish over time as the disaster stabilizes. We incorporate makespan as an optimization objective alongside deprivation-weighted response time, skill matching, workload balance, and volunteer reliability. Ideal-nadir normalization ensures that all objective components contribute meaningfully regardless of their native units. The approach proceeds in two stages. First, we formulate and solve a single-period baseline MIP under volunteer surplus using the CBC solver at four scales (10 to 500 tasks). All four instances are solved to proven optimality, achieving 80 to 100% task coverage with skill-matching rates of 76.9 to 99.6%. Second, we develop a rolling-horizon algorithm that decomposes the multi-period problem into sequential epoch-level MIPs with state transitions, non-homogeneous Poisson task arrivals, fatigue accumulation, and task surplus conditions where the initial task-to-volunteer ratio exceeds 3:1. Computational experiments on three dynamic scenarios (up to 559 mean cumulative tasks) demonstrate that the algorithm achieves mean task completion rates of 84.21 ± 1.92% (Large-Dynamic), 93.74 ± 2.07% (Small-Dynamic), and 94.59 ± 2.03% (Medium-Dynamic) (mean ± standard deviation across 30 Monte Carlo replications) within a 15 h planning horizon, with per-epoch skill-matching rates of 11 to 20% (substantially lower than the static baseline due to triage-mode epochs that force all-volunteer assignment regardless of skill fit). The results reveal a clear regime transition: early epochs operate under severe task surplus where triage dominates, while later epochs transition to volunteer surplus where optimization of secondary objectives becomes feasible. Comparison against a skill-aware greedy heuristic confirms that the MIP’s advantage lies in global multi-objective coordination. This research contributes both a validated mathematical framework and a practical algorithmic approach for multi-period volunteer assignment under demand decay, extending prior work by Sperling and Schryenthrough explicit Poisson dynamics, fatigue state modeling, and makespan optimization. Full article

Nearby ($z<0.1$) TeV-detected, high-energy-peaked BL Lacertae objects (HBLs) are among the most prominent extragalactic sources of the highest-energy photons, sometimes detected at energies of ∼10 TeV or beyond. These objects show a strong and complex flux variability, with strong flares and exceptional outbursts, as well as very rapid and large-amplitude TeV-band variations on timescales down to a few minutes during such instances. The higher-energy component of broadband spectral energy distribution (SED) is stretched over the MeV–TeV domain and, generally peaking beyond 100 GeV, has a controversial origin, and different emission scenarios (one- or multi-zone synchrotron self-Compton, hadronic cascades, etc.) are proposed. This paper presents a review of the TeV-band timing and spectral results obtained in the framework of different observational campaigns for nearby HBLs, their implications for different emission scenarios, and basic results from the corresponding SED modelings. Finally, the prospect of filling the observational gaps above some threshold energy by means of the planned projects for the dedicated $\gamma$-ray observations and, consequently, solving the different persisting problems related to the innermost structure, particle acceleration, and emission mechanisms are also presented. Full article

In many real-world logistics systems, two-echelon distribution structures and clustered customer demands often coexist. However, traditional Two-Echelon Vehicle Routing Problems (2E-VRPs) mainly focus on the coordination between depots, satellites, and customers, while usually ignoring clustered customer service requirements. To fill this research gap, this study investigates a novel variant of the 2E-VRP, called the 2E-VRP with Clustered Customers (2E-VRP-CC). In this problem, customers in the second echelon are partitioned into predefined clusters, and all customers within a cluster must be visited consecutively by the same vehicle. For the problem, a Mixed-Integer Linear Programming (MILP) model is first established, followed by the development of an Adaptive Large Neighborhood Search (ALNS) algorithm integrated with a local search method. To validate the effectiveness of the proposed algorithm, comparisons are conducted on instance sets adapted from the literature. For the traditional 2E-VRP, which is a special case of the 2E-VRP-CC, the proposed algorithm is compared with existing methods in the literature. For the proposed 2E-VRP-CC, it is compared with the CPLEX solver. Extensive computational experiments demonstrate that the proposed algorithm can achieve high-quality solutions within relatively short computing times, confirming its effectiveness and efficiency. In addition, sensitivity analysis shows that the number of customer clusters has a significant impact on transportation costs. The results indicate that moderately increasing the number of customer clusters can effectively reduce operational costs and provide practical decision support for customer clustering design and two-echelon logistics planning. Full article