Search Results (9,157)

Traditional Digital Twins (DTs) in energy sectors lack cyber-threat awareness, while cybersecurity DTs overlook downstream physical impacts. Loosely coupled co-simulations attempt to bridge this gap but introduce computational lags that mask critical cross-domain vulnerabilities. To address these limitations, this paper proposes a unified, tightly coupled Virtual Digital Twin (VDT) framework that integrates energy systems and cybersecurity domains into a single environment. The methodology models the precise mathematical, thermal, and electrical constraints of key assets to capture cross-domain feedback loops. Specifically, a power transformer and a microgrid-connected inverter serve as case studies to map cyberattack vectors directly onto physical definitions. Numerical simulation evaluates multiple threat scenarios, including supervisory, measurement, and physical-level (harmonic) attacks on the transformer, alongside short-circuit and hybrid phase-harmonic attacks on the inverter. Results show how subtle digital disruptions propagate past communication layers to induce physical degradation and operational stress. By explicitly detailing the governing equations and providing sensitivity analyses, this work delivers a transparent, high-fidelity methodology for protecting critical cyber–physical infrastructures from asset-destructive manipulations. Full article

Geometric misalignment degrades industrial cone-beam computed tomography (CBCT), particularly when a dedicated calibration phantom cannot be deployed during object acquisition. This study presents a three-stage, scan-specific geometric refinement framework that searches a bounded five-coordinate correction space around a nominal geometry. Coarse candidates are screened using the normalized residual between a geometry-corrected center-of-mass trajectory and its best-fitting low-order periodic model. Translation- and rotation-dominant coordinates are then refined within system-specific physical bounds, and an energy-normalized nuclear-norm score of corrected row-wise sinograms is used for local correlation refinement. The periodic and low-rank terms are treated as object-dependent surrogate objectives rather than as standalone guarantees of physical parameter identifiability. An exact-ASTRA implementation check using a Shepp–Logan volume verified the detector-plane reindexing convention: applying the injected correction reduced valid-mask projection discrepancy to 35.2%, 13.7%, and 8.66% of the uncorrected values for small, medium, and large perturbations, respectively, with round-trip resampling NRMSE of 0.022–0.023 and a mean valid fraction of 98.4%. Three industrial datasets acquired with horizontal gantry CT, temperature-stage in situ CT, and vertical micro-CT provided comparative reconstruction evidence. In addition, a controlled reduced-resolution industrial object reprojection benchmark was used for direct comparison with MI-PSO, PR, and a stability-regularized implementation of the public epipolar-consistency formulation (Open-ECC-R). Over 20 fixed-ROI axial slices, Open-ECC-R increased the mean SSIM from 0.6852 ± 0.0454 for the uncalibrated reconstruction to 0.8450 ± 0.0155 and reduced the NRMSE from 0.5180 ± 0.0526 to 0.1671 ± 0.0125. The proposed method achieved the highest mean SSIM of 0.9933 ± 0.0002 and the lowest NRMSE of 0.0321 ± 0.0009. For the Bluetooth earphone dataset, local sagittal and axial MTF₅₀ estimates increased from 0.84 to 0.94 lp/mm and from 0.45 to 1.05 lp/mm, respectively. These results support scan-specific image-quality refinement around a nominal geometry while avoiding unsupported claims of absolute parameter recovery. Full article

We develop a tensor-based dynamical framework for monetary flows in multi-sector, multi-agent economies and quantify the information destroyed when the monetary state is reduced to a scalar aggregate. The state is a third-order tensor encoding capital flows across sectors, agent classes, and time; deviations from equilibrium obey a tensor-indexed Langevin (multivariate Ornstein–Uhlenbeck) equation with a coupling operator and channel-specific friction rates. Using standard Lyapunov theory, we assemble a stability and convergence framework for the induced vectorized system, with a bound stated so as to remain valid for the non-normal system matrices generated by asymmetric economic coupling, and characterize the stochastically forced case in the mean-square sense. Shannon entropy, Kullback–Leibler divergence, and sector–agent mutual information measure the structural information discarded by scalar aggregation. We then study a stylized, heuristically calibrated $3\times 3$ economy subject to a shock inspired by the 2007–2009 crisis; we emphasize at the outset that the figures reported below are properties of that calibration and are not empirical estimates. In this scenario Finance absorbs an 18.9% peak capital loss while Manufacturing and Services suffer 5.8% and 3.9% secondary drops, against an aggregate contraction of only 8.6%; the Kullback–Leibler divergence of the sector–agent flow distribution recovers systematically later than the aggregate signal, a lag that is positive in 96.6% of a 1000-draw Monte Carlo ensemble, although its magnitude is calibration-dependent. Under a symmetric exit rule, a deficit-targeted stimulus restores equilibrium substantially faster than a share-weighted uniform stimulus in 100% of the ensemble while spending strictly less—its realized expenditure saturates below the uniform budget because it self-terminates as deficits close—and attains integrated disequilibrium within 18% of the exact linear-quadratic optimum at equal control effort while requiring no knowledge of the system matrix. The ordinal conclusions—aggregation masks the epicenter, structure lags the aggregate, and deficit targeting dominates uniformity—are robust across a wide neighborhood of the calibration, and identify the disaggregated state as the object that stabilization policy needs and that scalar aggregation destroys. Full article

Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article

Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and Image Understanding), an original hybrid pipeline for the automatic extraction of man-made infrastructure from high-resolution satellite imagery. The primary methodological contribution lies in the sequential integration of four technologically heterogeneous components: a per-pixel Random Forest classifier, a guided image modulation step, edge detection via the Mumford–Shah variational functional solved through the Ambrosio–Tortorelli approximation, and final object delineation via the Segment Anything Model (SAM). Each component does not operate independently but conditions and informs the next: The RF probability map guides the modulation, which in turn directs the sensitivity of the variational step exclusively towards regions of interest; the AT edges provide spatial prompts to SAM, for which its masks are finally filtered by the RF probability in an adaptive manner through a Gaussian Mixture Model. This progressive conditioning scheme constitutes the architectural core of GANCIU and distinguishes it from approaches that combine classification and segmentation in parallel or in purely sequential fashion with each stage conditioning the next but without any reverse correction between them. The Random Forest classifier was trained on 44 manually annotated scenes, geographically disjoint from the twelve independent scenes used for quantitative validation. This validation, based on an instance matching protocol (precision, recall, F1 score, and IoU), confirms the contribution of the full pipeline over a Random-Forest-only baseline: Pooled false positives fall by close to two orders of magnitude (from 8320 to 209), while true positives rise nearly twentyfold (from 5 to 95), with a mean IoU of 0.742 ± 0.060 on correctly matched objects. Notably, the entire pipeline—including SAM-based segmentation—runs end-to-end on a modest, GPU-free consumer laptop (four logical CPU cores, under 16 GB RAM), demonstrating that competitive infrastructure-extraction performance does not require specialised computing hardware. Full article

European urban policy increasingly requires city benchmarks that are people-centred, multidimensional and methodologically defensible, yet perception-based environmental evidence is rarely aggregated without arbitrary weighting. Accordingly, this paper develops and justifies a synthetic index of perceived urban environmental performance (UEP) for European cities. Specifically, using the environmental module of the 2023 Eurostat Urban Audit Perception Survey (UAPS) for 83 Functional Urban Areas (FUAs), four satisfaction indicators (air quality, noise, cleanliness and green spaces) are aggregated with a benefit-of-the-doubt (BoD) composite indicator that assigns each city endogenous, self-favouring weights. Standard, weight-restricted and cross-efficiency variants are estimated, benchmarked against an equal-weight comparator, and embedded in an exploratory spatial data analysis. The results demonstrate that nine cities form the efficient frontier, led by Oulu, Luxembourg and Zurich, while Skopje, Naples and Athens anchor the lower tail. Additionally, a robust North–South gradient emerges, and green space satisfaction is the dominant structural driver of composite scores, partially compensating weak air quality in many cities. The study addresses perception-based BoD benchmarking—uncovering dimension-specific environmental governance deficits masked by national indicators—and complements objective environmental monitoring for European Union (EU) cohesion and climate-neutrality policy. Full article

Multi-modal sentiment analysis integrates linguistic, acoustic, and visual evidence, yet the reliability of these streams varies across samples because of missing observations, masking, measurement noise, and feature corruption. This paper presents a trainable reliability-aware evidential fusion framework that estimates not only sentiment predictions but also modality-specific evidence, predictive uncertainty, observable input quality, cross-modal disagreement, and normalized sample-dependent fusion weights. Each available modality is independently encoded and processed by an evidential classification head and a quality estimation head. Availability masks enforce exact exclusion of missing streams, while estimated quality, Dirichlet uncertainty, and Jensen–Shannon disagreement jointly regulate the contribution of each observed stream. The model is optimized end-to-end using fused classification, evidential regularization, clean–corrupted consistency, reliability-calibrated cross-modal alignment, and quality regression objectives. Experiments are conducted on both CMU-MOSI and CMU-MOSEI using their official speaker-independent splits. Binary classification follows the standard non-zero protocol, in which samples with sentiment score zero are excluded from Acc-2 and binary F1 evaluation; all labeled samples are retained for seven-class accuracy, mean absolute error, and correlation. The evaluation covers complete-input, every single- and double-modality missing pattern, graded and unseen corruption, combined missing-plus-corrupted conditions, calibration, selective prediction, statistical testing, and computational efficiency. All comparative values in the main tables are identified as local controlled adaptations under the common pipeline, while selected published reference values are reported separately to prevent provenance mixing. Across both datasets, the empirical results show that the proposed method preserves competitive complete-input performance while providing larger and more consistent gains as modality availability or integrity deteriorates. Full article

Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 $\mathsf{\mu }$m, producing increased measurement uncertainty within the spectral region containing much of the temperature information. To address these conditions, a modified Gray Body Emissivity method, referred to as the Robust Ratio Linear Fitting method, was developed using robust linear regression, spectral masking, and iterative temperature refinement. The algorithm was validated by comparing the retrieved temperatures with pyrometer measurements and the retrieved spectral emissivities with a high-accuracy spectral emissivity database collected using a SOC-100 hemispherical directional reflectometer. When applied to radiance measurements of a carbon phenolic sample heated using a plasma torch and measured with an ABB MR304 FTIR spectrometer, the algorithm retrieved temperatures with a mean absolute percentage error of 3.05% and spectral emissivities with a mean absolute percentage error of 3.13% relative to the SOC-100 reference measurements. Although the method is ineffective at lower temperatures where the peak of the Planck radiance lies within excluded spectral regions, the results demonstrate that the proposed approach provides accurate temperature and emissivity retrieval for high-temperature laboratory FTIR measurements acquired under these experimental conditions. Full article

Historically, mpox was thought to be a geographically constrained ‘disease of poverty,’ leading to decades of neglect by global health actors. Waning population immunity from the cessation of smallpox vaccination and prolonged scientific neglect created conditions that enabled the monkeypox virus (MPXV) to adapt cryptically. This ultimately contributed to the emergence of unprecedented global public health crises. This review aims to systematically trace the history, epidemiology, genomic evolution, and reservoir ecology of mpox in West Africa from 1970 to 2025. Following PRISMA guidelines, 110 articles met the inclusion criteria and were synthesized to map the virus’s trajectory. For nearly four decades, an “Era of Silence” (1970–2016) masked the silent enzootic circulation of MPXV within West African wildlife, primarily rodents and small mammals. This epidemiological quiescence ended with the 2017 re-emergence in Nigeria, which signaled a fundamental paradigm shift. The disease profile transitioned from sporadic, rural paediatric infections to sustained, urban and secondary transmission among young adult males. This shift was often associated with sexual networks, especially among men who have sex with men, and was characterized by novel clinical presentations, including genital and perianal lesions. Genomic analyses revealed that clade II diverged from clade I approximately 3500 years ago and is uniquely defined by the deletion of virulence factors, such as the complement-binding protein. Importantly, the clade IIb lineage, which triggered the 2022 global outbreak, exhibits accelerated microevolution consistent with APOBEC3-mediated hypermutation. This host-driven mutational signature provides genomic evidence supporting the hypothesis that clade IIb circulated cryptically within human-to-human transmission chains in West Africa as early as 2014. Ecologically, while no definitive reservoir has yet been identified, evidence suggests diverse rodents and an expanding host range. The transformation of mpox from a rare zoonosis to a global threat underscores the severe consequences of delayed intervention, demanding robust, integrated “One Health” surveillance. Full article

Agricultural expert forums serve as important platforms for knowledge exchange, containing large volumes of text that embody professional expertise and practical experience. However, because experts often focus on different agricultural subfields and category semantics may overlap, traditional text classification methods that rely solely on textual content often show limited discriminative power. To address this issue, this paper proposes a text classification model for agricultural expert forums that integrates expert domain preferences with deep semantic representations. Specifically, the model incorporates an expert domain preference matrix and an expert semantic vector matrix into the deep semantic encoding of forum texts to construct interaction representations between experts and categories, thereby enabling collaborative modeling of textual semantics and expert domain preferences. To further improve classification performance, a category-level enhancement strategy based on domain attention is introduced at the output stage. This strategy uses candidate category masking and probability constraints to guide the model in adaptively aligning its predictions with experts’ long-term domain focus patterns. Experimental results on over 110,000 text sentences show that the proposed model improves average precision by approximately 12.7% and the overall F1 score by around 10.1%, with stable gains in both accuracy and recall. Compared with mainstream encoder-based classification models, the proposed model achieves an average accuracy improvement of 11.67%, demonstrating significant and robust performance advantages. Overall, the proposed model enhances both the accuracy and robustness of text classification in agricultural expert forums while improving interpretability, providing effective technical support for agricultural knowledge management and intelligent information retrieval. Full article

In response to degraded parking slot markings caused by wear and tear, water accumulation, or occlusion, which significantly impair the perception accuracy and localization robustness of automated parking systems, this paper proposes SlotNet, a lightweight enhancement network. The proposed method incorporates a skeleton-driven adaptive width completion algorithm to mitigate segmentation errors and restore the topological continuity of fractured parking slot lines. The network integrates three lightweight modules: Lightweight Reparameterized VGG (LightRepVGG) for enhancing the extraction of fine-grained structural features via structural reparameterization, Parallel Perceptual Structured Attention—Light (PASA_Light) for multi-scale feature fusion, and Adaptive Decoupled Detect and Segment (AdaDecDS) for anchor-free decoupled detection and segmentation. The experimental results show that SlotNet achieves an inference speed of 65.75 Frames Per Second (FPS). The mask average precision (mask mAP@0.5) reaches 90.2% under an Intersection over Union (IoU) threshold of 0.5, enabling robust completion and accurate detection of degraded parking slot lines. Compared with existing detection, SlotNet achieves a superior balance among accuracy, robustness, and real-time performance, making it suitable for deployment on embedded in-vehicle platforms. Full article

Underwater object detection remains challenging in adverse aquatic environments, where severe image degradation caused by turbidity, light scattering, color attenuation, and low illumination substantially reduces detection reliability. Although real-world underwater datasets are essential, their limited scale and environmental diversity make it difficult to cover the wide range of degraded conditions encountered in practice. To improve detection robustness without collecting additional annotations, we propose physics-aware diffusion synthesis (PADS), a framework that uses a small set of labeled real images to synthesize diverse physically plausible degraded underwater samples. PADS couples a ControlNet-conditioned latent diffusion generator with a physics-based underwater image-formation model inspired by Jaffe–McGlamery and Akkaynak optics. Semantic masks are first employed to preserve object layout during generation. Meanwhile, water-optics parameters are incorporated through cross-attention to guide the degradation process. In addition, the physical model enforces a color and attenuation consistency loss during training and serves as an SDEdit-style latent prior during synthesis. To further improve localization under degradation, especially for small objects, we introduce a training-only scale-aware focaler–NWD (SA-FNWD) bounding-box loss, which emphasizes normalized Wasserstein distance for small boxes while retaining IoU-based regression for larger objects. Experiments on the MOUD dataset demonstrate that detectors trained with PADS-synthesized data achieve substantially stronger robustness under severe degradation. At the harshest turbidity level, PADS retains 59.3% of clean accuracy compared with 14.9% for the copy–paste-based synthesis method and 14.1% for the pix2pix-based synthesis method. SA-FNWD further improves mAP@0.5:0.95 across degradation severities. These results show that physics-grounded diffusion synthesis provides the main robustness gain, while SA-FNWD offers a complementary small-object localization improvement with no inference overhead. Full article

Sustainable forest governance requires fiscal instruments responsive to community preferences for ecosystem services, yet compensation standards are rarely checked against those preferences. China’s ecological public welfare forest program, which covers 120 million hectares, sets graded provincial compensation rates across internally differentiated communities. Using contingent valuation data from 315 households in Lin’an District, Zhejiang Province (2024), this study compares compensation with community-expressed preferences and asks whether that comparison holds across social groups. The aggregate community willingness to pay (WTP) is CNY 41.07 million per year, with a mean household WTP of CNY 210, and the actual provincial compensation of CNY 41.34 million falls within its confidence interval. Because both quantities carry estimation uncertainty, this interval overlap is not a formal test of equivalence and does not establish that the two are equal. The aggregate correspondence also masks substantial heterogeneity. Income and education generate WTP differentials exceeding 30 percent, and rural residents report valuations 16.6 percent below those of urban residents. This pattern is associated with economic capacity rather than institutional trust or environmental awareness. Lin’an is a most-likely case for such correspondence, since its urban–rural income ratio (1.54:1) lies well below the national average (2.31:1). Preference heterogeneity is therefore plausibly more pronounced elsewhere, although cross-regional replication would be required to confirm this. Graded fiscal instruments for forest resource governance, even where broadly consistent with aggregate preferences, warrant differentiation to sustain community support for conservation across heterogeneous populations. Full article

General gaze following aims to infer the region attended to by a person within a natural scene and offers a computational perspective on visual attention and social scene understanding. Existing direction-guided methods usually reduce gaze direction to a single vector or spatial mask. This deterministic treatment can obscure directional ambiguity, discard coexisting candidate directions, and propagate early estimation errors to gaze target localization when head cues are weak or multiple targets are plausible. To address these limitations, we propose an uncertainty-aware framework based on Circular Direction Distribution Learning (CDDL) and Probabilistic Gaze Geometry Modeling (PGGM). CDDL represents gaze direction as a 72-bin circular probability distribution under von Mises soft supervision, thereby preserving neighboring directional hypotheses before target localization. Rather than predicting a target space distribution directly, PGGM aggregates the direction distribution into 18 groups and projects the retained hypotheses into cone-like spatial probability fields, allowing spatial tolerance to expand with distance while preserving a channel-wise direction-to-region representation. The full probability volume is fused with the RGB scene image at the input level and processed by a ResNet-50-FPN network for gaze heatmap prediction. Controlled experiments on GazeFollow demonstrate competitive localization and support the contribution of direction-distribution learning and channel-wise geometric projection. Zero-shot transfer, dataset-level uncertainty and failure analyses, efficiency measurements, and qualitative results further indicate interpretable behavior together with domain-dependent and computational trade-offs. Full article

Background: Standardized institutional feeding systems determine nutritional adequacy entirely through how the provided diet is formulated, yet how completely that formulation is documented is rarely examined. Military operational rations are the most widely deployed example of such a system. Objectives: We aimed to map the global availability of publicly accessible ration composition data; to assess the compositional adequacy of rations with complete profiles; and to test whether dietary diversity predicts micronutrient adequacy where dietary choice is absent. Methods: A standardized seven-step search was applied across 69 countries grouped into ten geopolitical clusters. All analyses used planned ration composition from official and published sources, not the food actually consumed. Compositional adequacy was quantified as mean per-nutrient coverage of the Dietary Reference Intake, and a separate diversity score was correlated with this aggregate measure. Results: Complete micronutrient profiles were publicly accessible for only 8 of 69 countries (11.6%), with post-conflict states showing the highest data-absence rate (80.0%). Vitamin D was the only nutrient critically low across all eight complete-data rations, ranging from 2.9% to 30.0% of the Dietary Reference Intake—a compositional feature, not a measure of personnel status. The correlation between aggregate adequacy and dietary diversity was negligible (r = −0.042). Pakistan’s high aggregate score (90.29%) coexisted with vitamin D at 2.9%, showing how aggregate scores mask single-nutrient deficiencies. Conclusions: Per-nutrient compositional assessment is essential for evaluating standardized institutional feeding; low public data availability does not imply absent nutritional planning. Full article