Search Results (682)

Occupant-Centric Control (OCC) aims to achieve a balance between personalized comfort and energy efficiency; however, current strategies often optimize either thermal comfort or indoor air quality (IAQ) in isolation. This study presents a model predictive control (MPC) framework that integrates incremental learning of individual thermal preferences with IAQ and energy co-optimization in office buildings. An incremental Naive Bayes classifier updates personalized temperature preference bands. Gray-box models, including an RC-network for temperature and a CO₂ mass-balance model, provide multi-step forecasts calibrated via genetic algorithm cross-validation. These learned preferences, along with a CO₂ limit, are enforced as constraints within the MPC, which minimizes HVAC energy use, supported by a supervisory layer for preventing inefficient operation and allowing manual override. Python–EnergyPlus co-simulations for an open office and a meeting room demonstrate that the framework maintains CO₂ concentrations below 1000 ppm and keeps 95% of temperatures within comfort ranges. Compared with baseline control, HVAC energy use decreased by 66% in winter and 56% in summer for the open office and by 44% in winter and 57% in summer for the meeting room. The proposed framework provides a reproducible approach for HVAC control that simultaneously enhances comfort, indoor environmental quality, and energy performance. Full article

Microwave, millimeter-wave, and terahertz devices are fundamental to modern 5G/6G communications, automotive imaging radar, and sensing systems. As essential RF front-end elements, passive microwave array components on static platforms remain constrained by fixed geometry and single-frequency excitation, leading to limited spatial resolution and weak interference suppression. Phase-steered arrays offer angular control but lack range-dependent response, preventing true two-dimensional focusing. Frequency-Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) architectures introduce element-wise frequency offsets to enrich spatial–spectral degrees of freedom, yet conventional linear or predetermined nonlinear offsets cause range–angle coupling, periodic lobes, and restricted beamforming flexibility. Existing optimization strategies also tend to target single objectives and insufficiently address target- or scene-induced perturbations. This work proposes a nonlinear frequency-offset design for passive microwave arrays using a Dingo–Gray Wolf hybrid intelligent optimizer. A multi-metric fitness function simultaneously enforces sidelobe suppression, null shaping, and frequency-offset smoothness. Simulations in static scenarios show that the method achieves high-resolution two-dimensional focusing, enhanced interference suppression, and stable performance under realistic spatial–spectral mismatches. The results demonstrate an effective approach for improving the controllability and robustness of passive microwave array components on static platforms. Full article

A window-embedded broadband vehicle-mounted antenna for frequency modulation (FM) broadcast application is proposed. Antenna miniaturization at sub-gigahertz frequencies remains challenging due to the inherently long wavelengths, which impose strict constraints on compactness, bandwidth, and structural weight. A promising strategy to alleviate this problem is to use the vehicle itself as an effective radiator to enhance the bandwidth and maintain good radiation performance. In this work, the potentialities of the radiation patterns offered by the vehicle are analyzed by using the characteristic mode theory (CMT). A compact T-shape coupling element, with dimensions of 0.2λ₀ × 0.08λ₀ × 0.01λ₀, is employed to simultaneously excite multiple significant characteristic modes, thereby broadening the operating band. Both simulated and measured results validate that the proposed antenna can cover the FM broadcast operating band from 87 MHz to 108 MHz, with the 1:10 scaled prototype achieving a maximum measured gain of 7.4 dBi at 950 MHz. The proposed antenna and design strategy have advantages in radio broadcasting, radio navigation, and military and law enforcement communication systems for its low-cost, compact, and easy conformal structure. Full article

Online hate speech poses a growing socio-technological threat that undermines democratic resilience and obstructs progress toward Sustainable Development Goal 16 (SDG 16). This study examines the regulatory and behavioral dimensions of this phenomenon through a combined legal analysis of platform governance and an empirical survey conducted on Meta platforms, based on a sample of young Hungarians (N = 301, aged 14–34). This study focuses on Hungary as a relevant case study of a Central and Eastern European (CEE) state. Countries in this region, due to their shared historical development, face similar societal challenges that are also reflected in the online sphere. The combination of high social media penetration, a highly polarized political discourse, and the tensions between platform governance and EU law (the DSA) makes the Hungarian context particularly suitable for examining digital resilience and the legal awareness of young users. The results reveal a significant “awareness gap”: While a majority of young users can intuitively identify overt hate speech, their formal understanding of platform rules is minimal. Furthermore, their sanctioning preferences often diverge from Meta’s actual policies, indicating a lack of clarity and predictability in platform governance. This gap signals a structural weakness that erodes user trust. The legal analysis highlights the limited enforceability and opacity of content moderation mechanisms, even under the Digital Services Act (DSA) framework. The empirical findings show that current self-regulation models fail to empower users with the necessary knowledge. The contribution of this study is to empirically identify and critically reframe this ‘awareness gap’. Moving beyond a simple knowledge deficit, we argue that the gap is a symptom of a deeper legitimacy crisis in platform governance. It reflects a rational user response—manifesting as digital resignation—to opaque, commercially driven, and unaccountable moderation systems. By integrating legal and behavioral insights with critical platform studies, this paper argues that achieving SDG 16 requires a dual strategy: (1) fundamentally increasing transparency and accountability in content governance to rebuild user trust, and (2) enhancing user-centered digital and legal literacy through a shared responsibility model. Such a strategy must involve both public and private actors in a coordinated, rights-based approach. Ultimately, this study calls for policy frameworks that strengthen democratic resilience not only through better regulation, but by empowering citizens to become active participants—rather than passive subjects—in the governance of online spaces. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Law Enforcement Agencies (LEAs) typically analyse vast collections of media files, extracting visual information that helps them to advance investigations. While recent advancements in deep learning-based computer vision algorithms have revolutionised the ability to detect multi-class objects and text instances (characters, words, numbers) from in-the-wild scenes, their association remains relatively unexplored. Previous studies focus on clustering text given its semantic relationship or layout, rather than its relationship with objects. In this paper, we present an efficient, modular pipeline for contextual scene text grouping with three complementary strategies: 2D planar segmentation, multi-class instance segmentation and promptable segmentation. The strategies address common scenes where related text instances frequently share the same 2D planar surface and object (vehicle, banner, etc.). Evaluated on a custom dataset of 1100 images, the overall grouping performance remained consistently high across all three strategies (B-Cubed F1 92–95%; Pairwise F1 80–82%), with adjusted Rand indices between 0.08 and 0.23. Our results demonstrate clear trade-offs between computational efficiency and contextual generalisation, where geometric methods offer reliability, semantic approaches provide scalability and class-agnostic strategies offer the most robust generalisation. The dataset used for testing will be made available upon request. Full article

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient. Full article

The increasing prevalence of deep learning models in industry has highlighted the critical need to protect the intellectual property (IP) of these models, especially generative adversarial networks (GANs) capable of synthesizing realistic data. Traditional IP protection methods, such as watermarking model parameters (white-box) or verifying outputs (black-box), are insufficient against non-public misappropriation. To address these limitations, we introduce EGAN (Encrypted GANs), which secures GAN models by embedding a novel self-adversarial mechanism. This mechanism is trained to actively maximize the feature divergence between authorized and unauthorized inputs, thereby intentionally corrupting the outputs from non-key inputs and preventing unauthorized operation. Our methodology utilizes key-based transformations applied to GAN inputs and incorporates a generator loss regularization term to enforce model protection without compromising performance. This technique is compatible with existing watermark-based verification methods. Extensive experimental evaluations reveal that EGAN maintains the generative capabilities of original GAN architectures, including DCGAN, SRGAN, and CycleGAN, while exhibiting robust resistance to common attack strategies such as fine-tuning. Compared with prior work, EGAN provides comprehensive IP protection by ensuring unauthorized users cannot achieve desired outcomes, thus safeguarding both the models and their generated data. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a growing global health challenge, driven in part by how antibiotics are accessed, distributed, and used within complex value chains. In peri-urban India, these supply chains involve a range of formal and informal actors and practices, making them a critical yet underexamined focus for antimicrobial stewardship efforts. While much research has focused on the manufacturing and regulatory end, less is known about how antibiotics reach consumers in rural and peri-urban settings. This study aimed to map the human antibiotic value chain in West Bengal, India, and to analyse how formal and informal governance structures influence antibiotic use and stewardship. Methods: This qualitative study was conducted in two Gram Panchayats in South 24 Parganas district, West Bengal, India. Semi-structured interviews were carried out with 31 key informants, including informal providers, medical representatives, wholesalers, pharmacists, and regulators. Interviews explored the structure of the antibiotic value chain, actor relationships, and regulatory mechanisms. Data were analysed thematically using a value chain governance framework and NVivo 12 for coding. Results: The antibiotic value chain in rural West Bengal is highly fragmented and governed by overlapping formal and informal rules. Multiple actors—many holding dual or unofficial roles—operate across four to five tiers of distribution. Informal providers play a central role in both prescription and dispensing, often without legal licences but with strong community trust. Informal norms, credit systems, and market incentives shape prescribing behaviour, while formal regulatory enforcement is inconsistent or absent. Conclusions: Efforts to promote antibiotic stewardship must move beyond binary formal–informal distinctions and target governance structures across the entire value chain. Greater attention should be paid to actors higher up the chain, including wholesalers and pharmaceutical marketing networks, to improve stewardship and access simultaneously. This study highlights how fragmented governance structures, overlapping actor roles, and uneven regulation within antibiotic value chains create critical gaps that must be addressed to design effective antimicrobial stewardship strategies. Full article

Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and sparse measurements, while existing data-driven methods often neglect critical physical constraints inherent to power systems. To address these limitations, this paper proposes a physics-constrained Graph Attention Network (GAT) framework for distribution system state estimation (DSSE) that synergistically integrates data-driven learning with physical domain knowledge. The proposed method comprises three key components: (1) a Gaussian Mixture Model (GMM)-based data augmentation strategy that captures the stochastic characteristics of loads and distributed generation to generate synthetic samples consistent with actual operating distributions; (2) a GAT-based feature extractor with topology-aware admittance matrix embedding that effectively learns spatial dependencies and structural relationships among network nodes; and (3) a physics-constrained loss function that incorporates nodal power and voltage limit penalties to enforce operational feasibility. Comprehensive evaluations on the real-world 141-bus test system demonstrate that the proposed method achieves mean absolute error (MAE) reductions of 52.4% and 45.5% for voltage magnitude and angle estimation, respectively, compared to conventional Graph Convolutional Network (GCN)-based approaches. These results validate the superior accuracy, robustness, and adaptability of the proposed framework under challenging measurement conditions. Full article

Financial fraud, as a salient manifestation of corporate governance failure, erodes investor confidence and threatens the long-term sustainability of capital markets. This study aims to develop and validate SFG-2DCNN, a multimodal deep learning framework that adopts a configurational perspective to diagnose financial fraud under class-imbalanced conditions and support sustainable corporate governance. Conventional diagnostic approaches struggle to capture the higher-order interactions within covert fraud patterns due to scarce fraud samples and complex multimodal signals. To overcome these limitations, SFG-2DCNN adopts a systematic two-stage mechanism. First, to ensure a logically consistent data foundation, the framework builds a domain-adaptive generative model (SMOTE-FraudGAN) that enforces joint distribution alignment to fundamentally resolve the issue of economic logic coherence in synthetic samples. Subsequently, the framework pioneers a feature topology mapping strategy that spatializes extracted multimodal covert signals, including non-traditional indicators (e.g., Total Liabilities/Operating Costs) and affective dissonance in managerial narratives, into an ordered two-dimensional matrix, enabling a two-dimensional Convolutional Neural Network (2D-CNN) to efficiently identify potential governance failure patterns through deep spatial fusion. Experiments on Chinese A-share listed firms demonstrate that SFG-2DCNN achieves an F1-score of 0.917 and an AUC of 0.942, significantly outperforming baseline models. By advancing the analytical paradigm from isolated variable assessment to holistic multimodal configurational analysis, this research provides a high-fidelity tool for strengthening sustainable corporate governance and market transparency. Full article

This work addresses computational inefficiency in ultra-wide-area remote sensing image (RSI) object detection. Traditional homogeneous tiling strategies enforce computational symmetry by processing all image regions uniformly, ignoring the intrinsic spatial asymmetry of target distribution where target-dense coexist with vast target-sparse areas (e.g., deserts, farmlands), thereby wasting computational resources. To overcome symmetry mismatch, we propose a heat-guided adaptive blocking and dual-model collaboration (HAB-DMC) framework. First, a lightweight EfficientNetV2 classifies initial 1024 × 1024 tiles into semantic scenes (e.g., airports, forests). A target-scene relevance metric converts scene probabilities into a heatmap, identifying high-attention regions (HARs, e.g., airports) and low-attention regions (LARs, e.g., forests). HARs undergo fine-grained tiling (640 × 640 with 20% overlap) to preserve small targets, while LARs use coarse tiling (1024 × 1024) to minimize processing. Crucially, a dual-model strategy deploys: (1) a high-precision LSK-RTDETR-base detector (with Large Selective Kernel backbone) for HARs to capture multi-scale features, and (2) a streamlined LSK-RTDETR-lite detector for LARs to accelerate inference. Experiments show 23.9% faster inference on 30k-pixel images and reduction in invalid computations by 72.8% (from 50% to 13.6%) versus traditional methods, while maintaining competitive mAP (74.2%). The key innovation lies in repurposing heatmaps from localization tools to dynamic computation schedulers, enabling system-level efficiency for Ultra-Wide-Area RSIs. Full article

Road traffic accidents remain a major global challenge, contributing to significant human and economic losses each year. In Portugal, the analysis and prevention of severe accidents are critical for optimizing the allocation of law enforcement resources and improving emergency response strategies. This study aims to develop and evaluate predictive models for accident severity using real-world data collected by the Portuguese Guarda Nacional Republicana (GNR) between 2019 and 2023. Four algorithms, Random Forest, XGBoost, Multilayer Perceptron (MLP), and Deep Neural Networks (DNN), were implemented to capture both linear and non-linear relationships within the dataset. To address the natural class imbalance, class weighting, Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling were applied. The models were assessed using Recall, F1-score, and G-Mean, with particular emphasis on detecting severe accidents. Results showed that DNNs achieved the best balance between sensitivity and overall performance, especially under SMOTE and class weighting conditions. The findings highlight the potential of classical machine learning and deep learning models to support proactive road safety management and inform resource allocation decisions in high-risk scenarios.This research contributes to sustainability by enabling data-driven road safety management, which reduces human and economic losses associated with traffic accidents and supports more efficient allocation of public resources. By improving the prediction of severe accidents, the study reinforces sustainable development goals related to safe mobility, resilient infrastructure, and effective disaster prevention and response policies. Full article

Background: The widespread use of antibiotics in dentistry has become a significant driver of the global rise in antibiotic resistance, posing serious risks to both oral and overall health. Objectives: This study aims to review antibiotic use in dentistry, elucidates the mechanisms of resistance development, identifies contributing factors, and discusses strategies to mitigate this growing global health threat. Methods: This narrative review examines current patterns of antibiotic prescribing in dentistry and evaluates evidence showing that antibiotics, although essential for preventing and managing odontogenic infections, are often prescribed unnecessarily or inappropriately. Results: The analysis highlights the growing resistance of key oral pathogens such as Streptococcus spp., Enterococcus faecalis, and Porphyromonas gingivalis, which increasingly limits the effectiveness of conventional treatments. Factors contributing to this trend include inconsistent adherence to clinical guidelines, patient pressure, and insufficient awareness of antibiotics stewardship among dental professionals. To address these challenges, the review emphasizes the importance of evidence-based prescription, strengthened stewardship programs, and the development of alternative therapies, including host-modulating agents and bacteriophage applications. Ongoing education and professional development are equally vital to enhance clinical judgment and promote responsible prescribing habits. Conclusions: Overcoming antibiotic resistance in dentistry requires coordinated effort among clinicians, researchers, educators, and policymakers. Expanding surveillance, enforcing stewardship-driven policies, and supporting innovation in therapeutic research are key to reducing antibiotic misuse and preserving their effectiveness. Through collective commitment and informed practice, the dental profession can play a crucial role in protecting antibiotic efficacy and promoting sustainable, high-quality patient care. Full article

Political rhetoric on immigration has increasingly framed it as a threat to public safety—fueling aggressive immigration enforcement strategies, including the expanded use of federal agents, mass deportations, and strict border controls. In particular, the immigration-crime narrative has been built on four key themes or “pillars,” which suggest that immigration (1) increases crime, (2) fuels gang violence, (3) is responsible for drug problems, and (4) requires mass deportation and strict border control policies to combat these issues and reduce crime. Using data from a 2025 Lucid survey and a review of existing literature, this article provides a clear and focused summary describing the extent to which these four claims of the immigration-crime narrative are supported by (1) public opinion and (2) findings from scientific research. As we highlight in the following sections, all four of these “pillars” of the immigration-crime narrative are in fact myths with no consistent empirical support. Full article