Search Results (72,730)

Smart cities have emerged as a strategic approach to enhancing urban sustainability through the integration of digital technologies, intelligent infrastructure, and data-driven governance. However, limited research has comprehensively examined how technological, institutional, governance, and contextual factors interact to influence sustainability outcomes in industrial-city settings, particularly in Saudi Arabia. This study develops an integrated conceptual framework for sustainable smart industrial cities through a qualitative research design based on a systematic screening and synthesis of the literature, complemented by a contextual analysis of the Dammam Metropolitan Area (DMA) as an illustrative urban-industrial case under Saudi Vision 2030. A total of 86 articles, reports, and website materials were identified and screened, of which 49 sources met the inclusion criteria and were synthesized to develop the proposed framework. The analysis examined five interrelated components: smart-city applications, institutional governance, implementation conditions, contextual barriers and opportunities, and sustainability outcomes across three dimensions: environmental, social, and economic. The findings suggest that the effectiveness of smart-city applications depends not only on technological innovation but also on institutional governance, digital readiness, stakeholder participation, integrated planning, and supportive regulatory environments. The study further identifies fragmented governance, limited institutional coordination, cybersecurity risks, and high infrastructure costs as key implementation barriers, while highlighting opportunities associated with Saudi Vision 2030, digital-transformation initiatives, and circular-economy principles. The principal contribution of the study is the development of an integrated conceptual framework that explains the interactions among these five components and provides a theoretical foundation for future empirical validation, as well as conceptually informed guidance for policy development and planning in Saudi Arabia and comparable industrial-city contexts. Full article

While green bonds are increasingly used to channel capital towards environmentally responsible projects, their effectiveness depends not only on market growth, but also on the credibility of the institutional frameworks that support the green label. Through focused bibliometric positioning, scoping synthesis and comparative document analysis, the paper examines how seven major green-bond frameworks organise credibility: the EU European Green Bond Standard, the ICMA Green Bond Principles, the Japan Green Bond Guidelines, the ASEAN Green Bond Standards, China’s catalogue-plus-principles framework, India’s Sovereign Green Bond Framework and China’s Sovereign Green Bond Framework 2025. The findings show that frameworks converge in product grammar, including project selection, management of proceeds, reporting and external review, but diverge substantially in credibility architecture, especially regarding external review, supervision, refinancing governance and environmental additionality. Current frameworks generally make green-bond labels more transparent, comparable and verifiable, but not necessarily more additional in environmental terms. Important challenges therefore remain: fragmented standards, greenwashing risks, information asymmetries, weak impact-reporting comparability and limited safeguards against refinancing existing assets. The paper argues that the contribution of green bonds to the green transition depends on how credibility is institutionally organised. Full article

To address the limitations of traditional Voltage-Current (VI) trajectory features in appliance load identification—such as the difficulty in distinguishing similar appliances, weakened amplitude information, and the absence of dynamic characteristics—this paper proposes a dual-stage cyclic training method for load identification that integrates multi-feature reconstruction with Particle Swarm Optimization (PSO). First, to overcome the high similarity of original VI trajectories, a PSO-based threshold optimization algorithm is designed to reconstruct VI trajectories through reflection operations and normalization, thereby enhancing the geometric morphological differences among similar appliances. Second, to supplement dynamic impedance information and energy level features, conductance-time trajectories and mean-square current color-block maps are introduced to characterize dynamic impedance variations and energy level differences, respectively. Finally, a three-channel classification network based on ResNet18 is constructed, where the reconstructed VI trajectories, conductance-time trajectories, and mean-square current color-block maps are fused via RGB channels as inputs, forming a closed-loop “threshold optimization—feature reconstruction—cyclic training” framework. Experimental results on the PLAID dataset demonstrate that the proposed method achieves an identification accuracy of 98.29% and a macro-averaged F1-score of 97.93%. Comparative experiments verify the effectiveness of the reconstructed VI trajectories, the complementarity of multi-feature fusion, and the superiority of the combinatorial optimization network, significantly improving the identification of multi-state and similar-condition appliances. Full article

Background: The psychological health of healthcare workers is a critical global public health concern. Identifying associated risk factors and their interrelationships is key to informing the development of effective interventions. This study aimed to construct a risk factor model for psychological health among healthcare workers and to examine the strength and patterns of associations between various factors and psychological health outcomes using structural equation modeling. Methods: A cross-sectional survey was conducted among 930 healthcare professionals. Structural equation modelling (SEM) was used to test the theoretical hypotheses, with bootstrapping used for path significance and multiple indicators multiple causes (MIMIC) models used for robustness validation. Results: The social environment and job demand risks were greater than the negative events and psychological adjustment risks. The final model explained 62.2% of the variance in psychological health. The social environment and organizational management functioned as distal risk factors, exerting indirect effects. Job demands, negative events, and job resource risks were associated with psychological health through psychological adjustment. Most of the hypothesized direct associations with the outcomes–burnout, anxiety, depression, suicidal ideation, and turnover intentions—were significant, except for the direct effects of job resource risk on anxiety and job demand/resource risk on suicidal ideation. MIMIC analysis confirmed the stability of the model. Conclusions: The proposed model clarifies how multilevel risk factors interact to affect healthcare workers’ psychological health. The findings support the development of comprehensive, prioritized interventions, including strengthened legal protections, dynamic staffing schemes in high-pressure departments, and routine psychological resilience training for staff. Full article

Background: First-trimester screening for preterm preeclampsia relies on validated competing-risks models integrating maternal characteristics with biochemical and biophysical markers to generate individualized risk estimates. Large language models (LLMs) are increasingly used for pregnancy-related health information, yet their alignment with established clinical risk models remains unclear. Objective: to perform an exploratory, local perturbation-based assessment of how LLM-generated numerical risk estimates reproduce the direction and relative magnitude of established preeclampsia predictor effects compared with the Fetal Medicine Foundation (FMF) competing-risks model. Methods: A total of 129 synthetic clinical scenarios were generated from a low-risk reference pregnancy using a one-factor-at-a-time perturbation approach across 16 risk factors represented as 22 predictors. Eight LLMs (Claude, GPT, DeepSeek, Gemini, Copilot, Meta, Mistral, and Grok) estimated the probability of preeclampsia requiring delivery before 37 weeks. Corresponding risks were calculated using the FMF model. Model behavior was analyzed in logit space using a local perturbation-based modeling approach inspired by Local Interpretable Model-Agnostic Explanations (LIME) to derive feature-effect coefficients. Agreement with the FMF model was assessed using correlation, directional concordance, cosine similarity, and normalized root mean squared error, summarized using an exploratory composite score. Results: The FMF model identified mean arterial pressure, placental growth factor, parity, uterine artery pulsatility index, and chronic hypertension as dominant predictors. Alignment between LLM outputs and the FMF model was heterogeneous, with composite scores ranging from 0.59 to 0.82. Models with higher descriptive scores preserved predictor directionality (up to 90.9%) and rank ordering, but agreement in magnitude and scaling was limited (R²: 0.44–0.59). Intermediate models showed preserved directionality with reduced magnitude agreement, while lower-scoring models demonstrated more frequent sign inconsistencies and minimal variance explained. Conclusions: LLMs demonstrated partial, prompt-specific alignment with the FMF model in this local perturbation analysis, particularly for predictor direction and relative importance, but did not consistently reproduce quantitative effect sizes. This approach was intended to characterize local model behavior around a predefined reference case rather than evaluate clinically realistic combinations of interacting risk factors or global clinical prediction performance. Given the evolving nature of LLMs, ongoing reassessment using standardized approaches is required. Full article

Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliability. However, power tower detection in satellite remote sensing imagery remains challenging because of the substantial scale differences between distribution and transmission towers, the weak feature representation of small objects, and interference from complex backgrounds. To address these challenges, this paper proposes MS-YOLO, a power tower detection algorithm for satellite remote sensing imagery based on multi-scale feature extraction and small object enhancement. First, the poly kernel inception bottleneck (PKI_Bottleneck) module is introduced into the YOLOv9 backbone, enhancing the extraction of scale-diverse features and contextual cues while limiting interference from complex backgrounds. Second, the dual-branch semantic-spatial synergy attention (DSSA) module is introduced. By decoupling deep semantic and shallow spatial information, it effectively preserves small object features while suppressing environmental noise, enhancing the perception capability for small tower objects. Finally, a dynamic focal-weighted intersection over union (DFW-IoU) loss function is introduced to optimize the balance between easy and difficult samples, compelling the model to prioritize small objects and challenging samples during gradient updates. Experimental results demonstrate that MS-YOLO achieves mAP50 values of 79.1% and 95.7% on the two datasets used in this paper, representing improvements of 4.1% and 3.4% over baseline model. These results validate the effectiveness of the improved model for power tower detection in complex remote sensing scenarios. Full article

Transmission and distribution networks are evolving into distributed smart grids, making accurate defect localization of line components increasingly important for power inspection. However, traditional manual inspection is inefficient and unreliable in complex scenarios, while most existing deep learning methods rely only on two-dimensional visible-light images and struggle to capture spatial structure and occlusion relationships. To address these limitations, this paper proposes a defect detection network that integrates monocular relative-depth information with adaptive progressive feature fusion. The framework adopts a dual-branch architecture, where visible-light images provide appearance information and relative-depth maps generated from the corresponding RGB frames using Depth Anything V2 encode auxiliary spatial relationships. Because both modalities originate from the same image frame, no additional depth sensor or cross-sensor temporal synchronization is required. An adaptive progressive fusion module performs coarse-to-fine cross-modal interaction, and a coordinate attention mechanism is introduced to enhance positional encoding and suppress background interference. Experiments on a self-constructed dataset of 9838 RGB images paired with estimated relative-depth maps covering five typical defect categories show that the proposed method achieves 92.4% mAP@50 and 67.1% mAP@50–95. The results demonstrate its effectiveness in improving defect localization, small-object detection, and robustness across the evaluated transmission and distribution line scenes. Full article

The mandibular glands and the tergal glands have been suggested to play a crucial role in queen pheromone production. However, very few studies have linked changes in mandibular and tergal gland morphology to variations in their pheromone secretions. In this study, we aimed to reveal the age-related morphological development of these glands based on the morphological images and traits. We provided detailed information about how the mandibles, the mandibular glands, and the tergal glands changed in Apis cerana virgin queens aged 0 to 10 days. We detected only slight variations in mandible size (length and width of bilateral mandibles) across different age groups. However, the age of virgin queens had a significant effect on the morphological development of their mandibular and tergal glands, as reflected by the mandibular gland area and the morphology of tergal gland cell nuclei. The mandibular gland area of the 7-day-old virgin queens was significantly larger than that of the other age groups. Concurrently, the development of tergal glands reached a stable plateau in queens aged 5 to 7 days. In addition, significant differences were also detected in the surface area of tergal gland cells and the cell nucleus, and the cell nucleus diameter among tergites III, IV, and V. Compared to tergites IV and V, the cell nucleus surface area and the nucleus/cytoplasm index in tergite III exhibited greater variations across different age groups. These results are valuable for expanding our understanding of exocrine gland development in A. cerana queens. Full article

Marine oil spills severely threaten marine ecosystems, the coastal economy, and marine engineering structures. Because it enables all-weather and all-time acquisition of rich target information, fully polarimetric synthetic aperture radar (FP SAR) is widely used for monitoring marine oil spills. However, the differences in the scattering characteristics among oil types can cause variability in the information contained in the features extracted using FP SAR. Herein, RADARSAT-2 images obtained from a rare oil-on-water experiment conducted in the Norwegian North Sea were used to compare the distribution differences in polarimetric features based on the oil slick type and incident angle. Results showed that the incident angle exerted some influence on polarimetric features and the detection performance for oil spills with a low oil–water contrast, particularly at large incident angles. The polarimetric features related to scattering mechanisms exhibited good robustness and effectiveness across various incident angles. The polarimetric feature that combines the scattering entropy H and modified anisotropy A₁₂ exhibited strong overall performance and high suitability for extracting information on oil spills at different incident angles. This study demonstrates that incorporating appropriate polarimetric features according to the incident angle enables the identification of different oil slick types and facilitates oil spill detection and monitoring. Full article

The Chushandian Irrigation Area (CSDIA) lacks a comprehensive drought index integrating meteorological and hydrological information, hindering accurate drought assessment and sustainable water resource management under changing climatic conditions. To address this, a multivariate standardized drought index (MSDI) based on a Copula function was developed, combining precipitation and runoff. Optimized run theory identified compound drought events, and cross-wavelet power spectrum explored large-scale climate drivers. Results show that MSDI correlates strongly with both the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) (Pearson’s r > 0.75, p < 0.01) at the monthly scale, effectively capturing drought onset, duration, and termination. From 1960 to 2018, 110 compound drought events were identified, characterized by short durations (mean 3.82 months) and low intensities (mean 4.43). The most severe event (August 1960–October 1961, duration 15 months, intensity 22.72) has a return period of about 40 years. Among nine teleconnection factors, ENSO is the dominant driver, followed by sunspot activity (SSI). BEAST change-point detection revealed a shift toward drought intensification after 1990, underscoring the need for adaptive water management strategies. These findings provide scientific support for sustainable drought monitoring, climate-resilient agricultural planning, and adaptive water management in CSDIA, contributing to the broader goal of ensuring food security and water sustainability in monsoon-dependent irrigation systems. Full article

Based on agency theory, this study examines the association between the optimistic tone of earnings conference calls and the cost of equity capital using an unbalanced panel of 342 non-financial FTSE All-Share companies (987 firm-year observations) over the period 2010–2024. Earnings conference call tone is measured using the financial sentiment dictionary and analysed using NVivo 14 software. The cost of equity capital is estimated using an implied cost of equity model. Panel specification is determined using appropriate panel-data diagnostic tests, while robustness is assessed through lagged-tone regressions, an alternative cost of equity measure, and two-stage least-squares (2SLS) estimation to address potential endogeneity. The results show a significant negative association between optimistic earnings conference call tone and the cost of equity capital (β = −7.787, p < 0.01). A statistically significant reverse association is also documented: A statistically significant reverse association is also documented: a lower cost of equity is associated with a more optimistic tone in subsequent conference calls (β = −0.001, p < 0.01). This result is interpreted as evidence of an association rather than a causal effect. Both results remain robust across alternative model specifications, lagged-tone analyses, alternative cost of equity measures, and endogeneity controls. The findings indicate that positive and transparent voluntary communication, particularly through earnings conference calls, is associated with lower information asymmetry and a lower cost of equity capital. Firms that have not yet adopted this communication channel may consider incorporating earnings conference calls into their investor-relations strategies to enhance voluntary communication with investors. This study contributes to the disclosure literature by documenting statistically significant associations between earnings conference call tone and the cost of equity capital under two model specifications in the UK market and by providing comprehensive robustness evidence supporting the stability of the reported associations. Full article

Infrared and visible image fusion needs to preserve visible texture details and infrared thermal saliency, yet emphasizing one modality may suppress or distort useful information from the other, while cross-modal differences may also contain noise, pseudo-textures, or locally incompatible boundaries. We propose ARC${}^3$Fusion, which reformulates image fusion as a reliability-calibrated consensus–complementarity–conflict process to achieve a more effective balance between visible texture detail and infrared target saliency. A progressive shared encoder and a modality-specific residual adapter first produce comparable yet modality-aware features. Cross-Modal Explainable Residual Decomposition then estimates jointly supported consensus and represents the information unexplained by the opposite modality as candidate residuals. Trustworthy Complementarity Verification evaluates infrared residuals using source intensity and edge evidence, while visible residuals are examined using source cues and learnable frequency-pattern evidence. Cross-Modal Conflict Estimation further characterizes local incompatibility through co-activation, reliability, amplitude imbalance, edge-strength mismatch, and orientation mismatch. Conflict-Aware Routing finally coordinates consensus and verified residuals according to these relation cues. Unlike conventional shared–private decomposition that directly preserves private features, ARC${}^3$Fusion treats modality-specific residuals as candidates that must be verified and conflict-coordinated before fusion. Experiments on LLVIP, MSRS, and TNO demonstrate consistent fusion performance. On LLVIP, ARC${}^3$Fusion achieves the best EN, SF, AG, VIF, and SCD values of 7.158, 14.467, 4.331, 1.136, and 1.229, respectively. These results indicate that verifying modality-specific residuals and coordinating local conflicts improves the joint preservation of visible texture details and infrared thermal saliency. Full article

Sentiment analysis for Greek social media texts remains challenging because of limited annotated resources, linguistic variation, and domain-dependent sentiment expression. This study presents a topic-aware, lexicon-guided framework for sentiment classification across five reference domains in Greek social media. Domain-specific sentiment lexicons are activated according to the relevant domain and transformed into a structured representation comprising a token-level multi-channel lexical matrix and aggregate lexical descriptors. A fusion convolutional neural network combines these complementary components to classify sentiment while retaining explicit lexical evidence for inspection. The evaluation follows a leakage-free protocol: lexicons are constructed exclusively from the sentiment inner-training subset, validation data are used for model selection, and a held-out test set is reserved for final evaluation. The proposed fusion CNN achieved the strongest held-out sentiment result among the evaluated models, with an Accuracy of 0.8029 and a Macro-F1 of 0.7883, exceeding TF–IDF + Linear SVM and fine-tuned GreekBERT baselines in the present experimental setting. Ablation results show that the token-level lexical matrix and global descriptors provide complementary information. For domain routing, GreekBERT late fusion achieved an Accuracy of 0.9162 and a Macro-F1 of 0.9116. When lexicon activation used predicted rather than reference domains, the end-to-end sentiment pipeline achieved a Macro-F1 of 0.7569. These findings indicate that explicit domain-specific lexical knowledge can support an interpretable sentiment representation while making the effects of lexical coverage and topic-routing uncertainty visible. Full article

Conventional gummies are characterized by high sugar, sweetener, and additive content, which has increased the demand for healthier alternatives. In this context, the incorporation of unconventional ingredients has emerged as a promising strategy to improve nutritional profile and functional value. This review aimed to collect and analyze information on the nutritional, technological, and sensory properties of unconventional ingredients, such as fruits, vegetables, microalgae, natural sweeteners, coproducts, and other functional compounds, in gummy formulations. A literature search was conducted, from which 20 relevant studies were selected and analyzed. The results showed that fruit incorporation increased the content of fiber, vitamin, and antioxidant compounds, while vegetables improved the mineral composition and the presence of bioactive compounds. Similarly, agro-industrial coproducts were identified as valuable sources of functional compounds with potential for sustainable valorization. In addition, the alternative gelling agents exhibited favorable effects on the texture, gel-forming capacity, stability, and shelf life of the samples. Overall, these ingredients demonstrated nutritional, technological, sensory, and sustainability advantages. However, challenges related to industrial scalability and sensory evaluation remain. Full article

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article