Search Results (4,407)

Digital twin (DT) technology has expanded far beyond its industrial origins, increasingly finding application across environmental and territorial domains. This review provides a structured mapping of DT deployments at environmental and territorial scales over the period 2020–2025, examining 117 peer-reviewed publications (109 applied studies and 8 review articles) through a structured 16-parameter classification framework. The review traces three major conceptual shifts in the DT paradigm: from industrial assets to living entities, from discrete systems to Earth-scale representations, and from closed deterministic models to ecological and systemic frameworks, as reflected in the emergence of ecological digital twins (EcoDTs), environmental digital twins (EDTs), and territorial digital twin (TDT) definitions. The results reveal a clear growth trajectory in DT applications across themes, with urban systems as the most consolidated application domain, and progressive diversification into marine, coastal, forestry, river/lake, and Earth system applications from 2022 onward. Institutional actors dominate production in this space, aligned with European flagship initiatives such as Destination Earth (DestinE) and the European Digital Twin of the Ocean (EDITO). The findings position and expand the notion of territorial digital twins as an evolving paradigm, underscoring both the momentum generated by EU digital and environmental policy and the need for integrated tools to answer and respond to key environmental challenges. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Plant-based food systems increasingly rely on heat-induced gelation of protein–starch mixtures, yet no focused synthesis has linked legume protein composition to mixed gel structure and function. This review critically analyses heat-induced gelation mechanisms in legume protein–starch systems, using the legumin-to-vicilin (L:V) ratio and starch origin as integrating design parameters. Legume storage proteins range from legumin-rich faba bean and Lupinus angustifolius, which form dense, disulfide-stabilised networks with high storage moduli, to vicilin-dominated mung bean, which produces weaker gels reliant on starch reinforcement. Pulse starches, characterised by high amylose content (24–45%), C-type crystallinity, and rapid amylose retrogradation upon cooling, act as a parallel gel-forming phase whose contribution scales inversely with protein network strength. Four protein–starch interaction modes, namely segregative phase separation, water competition, granule filler effects, and molecular complexation, jointly determine microstructure and rheological behaviour. A three-axis compositional framework defined by the L:V ratio, starch amylose content, and protein-to-starch ratio maps the gel design space. Variables favouring plant-based meat analogue performance, including high elastic modulus, yield stress, and hardness, are systematically opposed by dysphagia food requirements, including low yield stress, adequate lubrication, and soft fracture. This demonstrates that both application domains traverse the same compositional space in opposite directions. Critical research gaps include chickpea and lentil performance in meat analogue systems, mechanistic modelling of protein-matrix-mediated starch digestibility, and retrogradation kinetics during food storage. Full article

Synthetic aperture radar (SAR) object detection has long been affected by spatial speckle interference, spectral energy imbalance, and structural bias in cross-scale feature fusion. In this article, we propose the Multi-Domain Interference-Suppressed Detection Transformer (MDIS-DETR), a unified multi-domain interference-suppressed detection framework built on the Real-Time Detection Transformer (RT-DETR) architecture. Specifically, spatial-domain interference is suppressed by learnable fusion of complementary denoising responses at the input stage. Furthermore, frequency-domain interference is suppressed by polarization-guided attention together with adaptive frequency refinement within the encoder. In addition, structural-domain interference is suppressed by non-sequential cross-scale interaction to enhance multi-scale consistency. Extensive experiments on multiple SAR benchmarks demonstrate that MDIS-DETR establishes state-of-the-art (SOTA) performance across datasets. Notably, on SARDet-100K, currently the largest SAR detection dataset with a scale comparable to the Common Objects in Context (COCO) dataset, it achieves 58.82% mAP, surpassing the RT-DETR baseline by 4.58%. Full article

Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on. Full article

Traditional over-the-air (OTA) testing typically requires the device under test (DUT) to be positioned at the geometric center of the anechoic chamber, which limits the flexible evaluation of modern wireless terminals. Although the spherical vector wave (SVW) method provides a rigorous electromagnetic mode expansion, its direct use in eccentric testing scenarios is prone to coefficient-domain overfitting. In the conventional coefficient-domain formulation, the increased involvement of high-order evanescent modes can lead to overfitting of physically insignificant coefficients, resulting in unstable and oscillatory reconstruction. To explain this behavior, an analytical periodicity model is developed and validated by numerical simulations, showing good agreement across all tested configurations. To overcome this limitation, this paper develops a unified 3D eccentric spatial–spectral composite operator for eccentric field synthesis by directly incorporating the three-dimensional offset into the field evaluation process. The proposed operator maps probe excitation weights to the translated 3D local test-zone field samples, thereby reformulating the synthesis problem from coefficient-domain fitting to field-domain matching. This field-domain formulation naturally downweights high-order modal components with negligible local-field contributions, thereby improving numerical stability. Numerical simulations in a 3D multi-probe anechoic chamber (MPAC) demonstrate that, under significant eccentric conditions, the conventional SVW method essentially fails, while the plane wave synthesis (PWS) method achieves less accurate reconstruction than the proposed scheme. In contrast, the proposed scheme maintains stable, oscillation-free reconstruction and consistently outperforms PWS by 5 to 15 dB across all evaluated scenarios. This work provides a promising solution for flexible 3D OTA evaluation of large-scale wireless terminals. Full article

This study maps the intellectual structure and thematic evolution of buy now, pay later (BNPL) research published between 2010 and 2025, with particular attention to how impulsive buying and post-purchase regret are positioned within the broader BNPL knowledge domain. Drawing on an integrated bibliometric science mapping and focused behavioral synthesis approach, the study first mapped a broad Scopus dataset of BNPL-related digital consumer credit and deferred payment research published between 2010 and 2025. This dataset was used for performance analysis and VOSviewer-based science mapping. A second, narrower PRISMA-guided screening process was then applied to identify empirical studies that directly examined BNPL-related behavioral and psychological outcomes, resulting in 13 studies retained for focused qualitative synthesis. The bibliometric findings show that BNPL scholarship expanded sharply after 2020, with research concentrated in marketing, consumer behavior, fintech, and digital commerce outlets. The science mapping results reveal a fragmented field structured around digital finance adoption, impulsive consumption, consumer vulnerability, and emerging ethical and regulatory concerns. The systematic synthesis further indicates that BNPL-related mechanisms, including installment framing, urgency cues, perceived affordability, and reduced payment salience, are consistently associated with impulsive buying tendencies. However, post-purchase regret remains underexamined and is rarely modeled as a distinct emotional outcome. By integrating bibliometric evidence with behavioral synthesis, this study clarifies how BNPL research has developed, where conceptual fragmentation remains, and why future studies should connect digital payment design, cognitive distortions, impulsive purchasing, and post-purchase emotional consequences within more comprehensive theoretical models. The study contributes by offering a structured research agenda for advancing responsible BNPL scholarship, consumer protection, and future digital finance research. Full article

This paper designs a statistical channel state information-based pinching antenna system for short-packet communication (SPC). To maximize the average maximal achievable rate (MAR) under physical collision-avoidance constraints, we formulate a highly non-convex geometry optimization problem, which is solved by our proposed novel phase-domain proximal policy optimization (PPO) framework. Unlike conventional coordinate-based approaches, the agent operates in a dual-component trigonometric phase domain, and the generated phase actions are mapped to feasible antenna positions via a customized phase-domain action mapping, which fundamentally avoids the $0/2\pi$ phase discontinuity and ensures stable learning. To evaluate the reliability of SPC, we derive a tractable statistical characterization of the received signal-to-noise ratio based on a mixture Gamma approximation over spatially correlated Rician fading channels, leading to a closed-form approximation for the average block error rate (BLER). A bisection search algorithm is further developed to minimize the required blocklength under the target reliability constraint. Simulation results demonstrate that the proposed phase-domain PPO scheme significantly outperforms the conventional algorithms in terms of average MAR, average BLER, and blocklength efficiency, with the performance gain becoming more pronounced as the number of antennas per waveguide increases. Full article

Foggy traffic scenes pose significant challenges for object detection because reduced contrast, blurred object boundaries, and the loss of local details weaken discriminative feature representations. These degradations are particularly detrimental to lightweight detectors used in intelligent transportation and vehicle perception systems, where both accuracy and real-time efficiency are required. To address this problem, this paper proposes YOLO-GCM, a lightweight detector-side feature enhancement framework built upon YOLO11n. Instead of relying on an external image dehazing stage, YOLO-GCM improves the internal feature representation of the detector through three complementary modules: a gated additive feature block (GAFB) for adaptive channel-wise feature selection and noise suppression, a context-aware feature enhancement module (CAFEM) for strengthening high-level semantic context, and a multi-scale adaptive fusion (MSAF) module for enhancing cross-scale feature interaction. By integrating these modules into a unified one-stage detector, the proposed method improves detection robustness under low-visibility traffic conditions while maintaining a compact architecture. Experiments on the FoggyCar dataset show that YOLO-GCM achieved 89.81% mAP@0.5 and 67.99% mAP@0.5:0.95, outperforming standard YOLO baselines and dehazing-assisted detection pipelines under a consistent evaluation protocol. Additional evaluation on Foggy Cityscapes further verified the generalization capability of the proposed method under domain shift. The results demonstrate that detector-side feature enhancement provides an effective and efficient alternative to multi-stage dehazing-plus-detection pipelines for foggy traffic object detection. These findings can provide useful guidance for the development of robust and efficient perception modules in roadside monitoring, intelligent transportation systems, and vehicle-assisted driving applications under adverse weather conditions. Full article

The interindividual phenotypic heterogeneity in Alpha-1 Antitrypsin Deficiency (AATD), despite a shared genetic etiology (the Z-allele of SERPINA1), is explained by the interaction of dual pathogenic mechanisms (gain-of-function vs. loss-of-function), additional genetic modifiers, and environmental or metabolic factors. Building on recent evidence suggesting divergent disease trajectories, we investigated whether pulmonary and hepatic impairments represent coupled manifestations or independent clinical dimensions within a large European cohort. Methods: This international multicenter study utilized the European Alpha-1 Research Collaboration (EARCO) registry (n = 1217). Pulmonary and hepatic severities were quantified using concurrent 0.0–10.0 composite indices. Independence was evaluated via partial Spearman correlations, multivariable multinomial regression, and geometric mapping across a continuous phenotypic space. Results: Cross-domain correlations between respiratory metrics and liver stiffness were near zero (r = −0.03), demonstrating statistical independence. Phenotypic dominance classification isolated distinct profiles; the lung-dominant group exhibited a higher age (57.0 vs. 54.0 years; p < 0.001) and tobacco exposure, while the liver-dominant group registered a higher body mass index (25.8 vs. 24.4 kg/m²; p < 0.001). Multivariable models identified age (OR 1.03; 95% CI 1.02–1.05) and smoking as independent predictors of lung dominance, whereas body mass index was independently associated with liver dominance (OR 1.04; 95% CI 1.01–1.07). Geometric mapping revealed advanced disease clusters at orthogonal margins rather than forming a systemic continuum. Conclusions: Hepatic and pulmonary impairments in AATD operate as independent clinical dimensions modulated by distinct metabolic and environmental factors. Risk stratification must transition toward organ-specific prognostic models. Full article

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

Adversarial camouflage has attracted growing research attention owing to its ability to execute multi-view, persistent attacks in real physical environments, outperforming conventional single-view adversarial patches. However, most existing methods are confined to non-targeted attacks, which induce arbitrary incorrect detection results without specifying target categories. This ambiguity weakens attack destructiveness and stealthiness, posing limitations for security evaluation of real-world vision systems. To address this gap, we present TACT, an approach built upon the full-coverage physical camouflage pipeline. By replacing the original category supervision with a predefined target class, TACT redirects the optimization gradient to guide 3D texture toward the target category features. Such a scheme only employs the inherent feature alignment mechanism of off-the-shelf object detectors, without redesigning network modules, defining novel loss functions, or modifying the rendering pipeline. Extensive experiments across digital and physical domains validate its effectiveness: on seven mainstream general-purpose object detectors, TACT-person achieves an average targeted attack success rate of 51.91%, and delivers cross-architecture and cross-version transferability. In physical tests, TACT-bird reduces mAP50-95 by 59.87% on YOLOv8, yet a TCER–TASR gap suggests that the physical pipeline acts as a low-pass filter: coarse-grained target classes transfer robustly while fine-grained ones suffer feature collapse. These results confirm the viability of native supervision redirection and reveal an empirical pattern: coarse-grained target classes transfer more robustly through the physical pipeline than fine-grained ones, suggesting that target class feature granularity consistently influences physical-domain attack effectiveness. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

Background/Objectives: Health literacy (HL) is recognized as an important social determinant of health. It supports healthy behaviors and effective health management throughout one’s life. For children and adolescents, developing HL influences their well-being, development, and ability to make informed health decisions. Nurses are strategically positioned to promote HL from an early age. To our knowledge, no prior synthesis has specifically examined nurse-led HL interventions targeting pediatric populations, highlighting the originality and relevance of this scoping review. The purpose of this review was to map and characterize nursing interventions aimed at improving HL outcomes in children and adolescents. Methods: A scoping review was conducted according to the Joanna Briggs Institute methodology, using a three-step search strategy, and reported in accordance with the PRISMA-ScR guidelines. Searches were conducted in MEDLINE, CINAHL, Scopus, Web of Science, and ProQuest with no date restriction, including studies published in Portuguese, English, or Spanish. Studies involving children and adolescents (ages 0–18) in any healthcare or community setting were eligible. Data on intervention characteristics and HL outcomes were extracted and analyzed descriptively, and no critical appraisal of the included sources was conducted. Results: A total of 44 studies were included. Interventions were predominantly school-based and focused on adolescents (n = 26), with a clear gap in early childhood (n = 2). Studies of early childhood primarily used storytelling and reading activities, whereas interventions targeting older children and adolescents more often employed participatory educational strategies, group-based approaches and digital platforms. The most frequently addressed topics were chronic disease management (n = 12), mental health (n = 7), and nutrition (n = 5). HL domains mainly focused on healthcare and health promotion, with fewer studies addressing disease prevention. Most interventions were conducted in school settings (n = 24), highlighting this context over those in primary care, community, and hospital settings. Conclusions: The results revealed nursing interventions used to promote HL, particularly in the management of chronic diseases, mental health and nutrition. However, the existing body of research is still limited. Key gaps include the absence of standardized measurement tools and the scarcity of longitudinal studies evaluating long-term outcomes. These limitations constrain the comparability and generalizability of findings, highlighting the necessity of more rigorous, methodologically robust research to support evidence-based practices. This scoping review comprehensively maps nurse-led interventions that promote HL among children and adolescents, identifying key priorities to guide future research in this area. Full article