Search Results (22,916)

Amid escalating regulatory and stakeholder pressures, corporate environmental practices emerge as strategic competitive advantages. Yet, research lacks depth on the interactions among PLU (power, legitimacy, and urgency) attributes and resource-constrained decision pathways. Integrating stakeholder theory and the attention-based view (ABV), a pressure–attention–action model is developed in this study to explain the voluntary adoption of ultra-regulatory proactive environmental practices (PEPs). An analysis of 503 Taiwanese firms using partial least squares structural equation modeling (PLS-SEM) reveals that (1) stakeholder legitimacy (β = 0.146, p < 0.01) and urgency (β = 0.215, p < 0.001) significantly increase perceived stakeholder pressure, whereas power exhibits no significant effect (β = 0.067, p > 0.05); (2) firm size positively moderates the pressure–resource linkage (β = 0.239, p < 0.001); and (3) urgency triggers partial mediation (57.4% VAF) through pressure and resources to drive proactive environmental practices. Firm size moderates pressure–resource linkages, with urgency prompting resource reallocation for environmental proactivity across scales. A dynamic PLU assessment tool and scale-sensitive strategies are proposed, challenging power-centric paradigms and aiding SMEs through collaborative networks. Limitations of the study include cross-sectional data and a regional focus, necessitating longitudinal and cross-industry validation. Full article

Accurate photovoltaic (PV) power output forecasting is critical for ensuring stable operation of modern power systems, yet it is constrained by high-dimensional redundancy in input weather data and the inherent heterogeneity of output scenarios. To address these challenges, this paper proposes a novel cascaded data-driven forecasting approach that enhances forecasting accuracy through systematically improving and optimizing the feature extraction, scenario clustering, and temporal modeling. Firstly, guided by weather data–PV power output correlations, the Deep Autoencoder (DAE) is enhanced by integrating Pearson Correlation Coefficient loss, reconstruction loss, and Kullback–Leibler divergence sparsity penalty into a multi-objective loss function to extract key weather factors. Secondly, the Fuzzy C-Means (FCM) algorithm is comprehensively refined through Mahalanobis distance-based sample similarity measurement, max–min dissimilarity principle for initial center selection, and Partition Entropy Index-driven optimal cluster determination to effectively cluster complex PV power output scenarios. Thirdly, a Long Short-Term Memory–Temporal Pattern Attention (LSTM–TPA) model is constructed. It utilizes the gating mechanism and TPA to capture time-dependent relationships between key weather factors and PV power output within each scenario, thereby heightening the sensitivity to key weather dynamics. Validation using actual data from distributed PV power plants demonstrates that: (1) The enhanced DAE eliminates redundant data while strengthening feature representation, thereby enabling extraction of key weather factors. (2) The enhanced FCM achieves marked improvements in both the Silhouette Coefficient and Calinski–Harabasz Index, consequently generating distinct typical output scenarios. (3) The constructed LSTM–TPA model adaptively adjusts the forecasting weights and obtains superior capability in capturing fine-grained temporal features. The proposed approach significantly outperforms conventional approaches (CNN–LSTM, ARIMA–LSTM), exhibiting the highest forecasting accuracy (97.986%), optimal evaluation metrics (such as Mean Absolute Error, etc.), and exceptional generalization capability. This novel cascaded data-driven model has achieved a comprehensive improvement in the accuracy and robustness of PV power output forecasting through step-by-step collaborative optimization. Full article

Accurate State of Health (SOH) estimation is critical for ensuring the safe operation of lithium-ion batteries. However, current data-driven approaches face significant challenges: insufficient feature extraction and ambiguous physical meaning compromise prediction accuracy, while initialization sensitivity to noise undermines stability; the inherent nonlinearity and temporal complexity of battery degradation data further lead to slow convergence or susceptibility to local optima. To address these limitations, this study proposes an enhanced CNN-BIGRU model. The model replaces conventional random initialization with a Hiking Optimization Algorithm (HOA) to identify superior initial weights, significantly improving early training stability. Furthermore, it integrates an Attention mechanism to dynamically weight features, strengthening the capture of key degradation characteristics. Rigorous experimental validation, utilizing multi-dimensional features extracted from the NASA dataset, demonstrates the model’s superior convergence speed and prediction accuracy compared to the CNN-BIGRU-Attention benchmark. Compared with other methods, the HOA-CNN-BIRGU-Attention model proposed in this study has a higher prediction accuracy and better robustness under different conditions, and the RMSEs on the NASA dataset are all controlled within 0.01, with R2 kept above 0.91. The RMSEs on the University of Maryland dataset are all below 0.006, with R2 kept above 0.98. Compared with the CNN-BIGRU-ATTENTION baseline model without HOA optimization, the RMSE is reduced by at least 0.15% across different battery groups in the NASA dataset. Full article

Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics. Full article

Change detection technology holds significant importance in disciplines such as urban planning, land utilization tracking, and hazard evaluation, as it can efficiently and accurately reveal dynamic regional change processes, providing crucial support for scientific decision-making and refined management. Although deep learning methods based on computer vision have achieved remarkable progress in change detection, they still face challenges including reducing dynamic background interference, capturing subtle changes, and effectively fusing multi-temporal data features. To address these issues, this paper proposes a novel change detection model called OFNet. Building upon existing Siamese network architectures, we introduce an optical flow branch module that supplements pixel-level dynamic information. By incorporating motion features to guide the network’s attention to potential change regions, we enhance the model’s ability to characterize and discriminate genuine changes in cross-temporal remote sensing images. Additionally, we innovatively propose a dual-domain attention mechanism that simultaneously models discriminative features in both spatial and frequency domains for change detection tasks. The spatial attention focuses on capturing edge and structural changes, while the frequency-domain attention strengthens responses to key frequency components. The synergistic fusion of these two attention mechanisms effectively improves the model’s sensitivity to detailed changes and enhances the overall robustness of detection. Experimental results demonstrate that OFNet achieves an IoU of 83.03 on the LEVIR-CD dataset and 82.86 on the WHU-CD dataset, outperforming current mainstream approaches and validating its superior detection performance and generalization capability. This presents a novel technical method for environmental observation and urban transformation analysis tasks. Full article

Urban greenspace exposure associates with improved health outcomes, particularly chronic disease mitigation. Based on the need to characterize spatial heterogeneity in the health benefits of urban greenspaces, this study quantified the association between greenspace accessibility and chronic disease mortality in London, while examining the modulating effects of urbanization and age. Utilizing nighttime light (NTL) data to define urbanization gradients and road-network analysis to measure greenspace accessibility, we applied geographically weighted regression (GWR) across 983 neighborhoods. Key findings reveal that over 60% of central London residents live within 300 m of greenspace, yet 20% fall short of WHO standards. Greenspace accessibility showed significant negative associations with standardized mortality ratios for both cancer (β = −0.0759) and respiratory diseases (β = −0.0358), and this relationship was more pronounced in highly urbanized areas and neighborhoods with higher working-age populations. Crucially, central urban zones show amplified effects: a 100 m accessibility improvement was associated with a potential reduction in cancer deaths of 1.9% and in respiratory disease deaths of 2.4% in high-sensitivity areas. Urbanization levels and working-age population proportions exert significantly stronger moderating effects on greenspace–respiratory disease relationships than on cancer outcomes. While observational, our findings provide spatially explicit evidence that the greenspace–mortality relationship is context-dependent. This underscores the need for precision in urban health planning, suggesting interventions should prioritize equitable greenspace coverage in highly urbanized cores and tailor functions to local demographics to optimize potential co-benefits. This study reframes understanding of greenspace health benefits, enhances spatial management precision, and offers models for healthy planning in global high-density cities. Full article

Human biomonitoring (HBM) is a vital tool for assessing chemical exposure in populations and informing evidence-based public health policy. For smaller countries such as Latvia, establishing a national HBM program presents specific challenges, including limited prior experience, national data gaps, and resource constraints. This study explores the expert experiences and reflections gathered during the development of Latvia’s national HBM chemical prioritization process. Semi-structured interviews were conducted with eight experts who were directly involved in evaluating and selecting substances for inclusion in the program. The focus of this study is not on the outcomes of the prioritization itself—published elsewhere—but rather on the strategies applied, challenges encountered, and lessons learned in navigating the prioritization process. A qualitative content analysis identified several key themes, including limitations in data availability, institutional coordination challenges, differences in expert opinion, and the complexity of adapting international methodologies to the national context. Despite these obstacles, the process benefitted from interdisciplinary collaboration, iterative methodological refinement, and the strategic use of international frameworks. The findings offer practical insights for countries with limited resources that are initiating or refining their national HBM programs. This study highlights the importance of national data infrastructure, stakeholder engagement, and tailored methodological approaches to ensure an effective and context-sensitive prioritization process. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

This study investigated consumer perception of food insecurity by refining data collected from social media platforms. Text mining and TF-IDF were used to extract core keywords closely related to food insecurity and analyze their meanings. In addition, time series analysis and sentiment analysis were used to examine temporal and emotional changes. The analysis results showed that keywords, such as health, stress, mental, and depression, appeared frequently, indicating that food insecurity is closely related to psychological and mental problems. In addition, consumers showed high emotional sensitivity to essential nutrients, such as vitamin D, magnesium, calcium, and omega. Furthermore, stress indices and mental and physical response indices increased simultaneously during this period, indicating that food insecurity is a factor that causes emotional and physical responses. The results of the sentiment analysis showed that negative emotions (anxiety, fear, and sadness) were higher than positive emotions, suggesting that discussions related to food insecurity have a negative emotional impact. Full article

Volumetric video streaming holds transformative potential for telemedicine, enabling immersive remote consultations, surgical training, and real-time collaborative diagnostics. However, transmitting sensitive patient data (e.g., 3D medical scans, surgeon head/gaze movements) raises critical privacy risks, including exposure of biometric identifiers and protected health information (PHI). To address the above concerns, we propose SecureTeleMed, a dual-track encryption scheme tailored for volumetric video based telemedicine. SecureTeleMed combines viewport obfuscation and region of interest (ROI)-aware frame encryption to protect both patient data and clinician interactions while complying with healthcare privacy regulations (e.g., HIPAA, GDPR). Evaluations show SecureTeleMed reduces privacy leakage by 89% compared to baseline encryption methods, with sub-50 ms latency suitable for real-time telemedicine applications. Full article

The sustainability of coastal ecosystem resources hinges on collective action; however, conservation programs often fail when the underlying values that shape human behavior are overlooked. Anchored in behavioral change theory and common-pool resource governance, this study explores artisanal fishers’ subconscious value orientations as drivers of conservation. Relational value, as defined by IPBES, was used to assess the strength of the relationship between artisanal fishers and coastal resources. Principal Component Analysis of survey data revealed three value components, Natural Legacy Value (NLV), Non-Economic Value (NEV), and Economic Value (EV), and two conservation orientations, tangible and intangible. Relational valuation, blending intrinsic and instrumental motives, strongly influences conservation attitudes. NEV correlates with religion and intangible measures (knowledge, cultural practices) (R = 0.153, p < 0.05), while EV supports both tangible and intangible strategies but none of the demographic factors, indicating strategic leverage points for inclusive engagement. Conversely, NLV’s negative association with tangible measures reflects cultural sensitivities that can hinder compliance. The results suggest that embedding value-sensitive approaches into co-management frameworks can foster trust, reciprocity, and behavioral change, key elements in Ostrom’s design principles. This study contributes to sustainability science by linking socio-psychological drivers to governance strategies for promoting coastal socio-ecological systems resilience. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

Infectious keratitis (IK), including bacterial, fungal, parasitic, and viral etiologies, continues to represent a significant cause of ocular morbidity in the United States and around the world. Corneal scraping for smears and cultures remains the gold standard in diagnosing IK; however, molecular diagnoses, including metagenomic deep sequencing (MDS), are promising emerging diagnostic tools. Despite recent interest in procedural treatment such as riboflavin photoactivated chromophore corneal collagen cross-linking (PACK-CXL) and Rose Bengal photodynamic antimicrobial therapy (RB-PDAT), medical treatment advances have remained stagnant. Methods: This review highlights IK pathogens obtained from corneal cultures at Bascom Palmer Eye Institute (BPEI) from 2011 to 2021 and provides the current BPEI algorithms for initial management of IK or as a referred clinically worsening patient. The roles of corticosteroid therapy, PACK-CXL, and RB-PDAT for IK are also summarized. Results: A total of 9326 corneal cultures were performed at BPEI between 2011 and 2021, and only 3609 (38.7%) had a positive organism identified, of which bacteria were the most common (83.4%). Fortified vancomycin and tobramycin are recommended as first-line medical therapy for IK patients based on culture sensitivity data for the top Gram-negative (Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. PACK-CXL and RB-PDAT may benefit IK patients with corneal melting and fungal IK, respectively. Conclusions: Drug holidays, minimizing contamination, and optimizing sample order are crucial to maximizing corneal culture positivity. PACK-CXL and RB-PDAT are promising procedural advancements for IK therapy. Full article

Urban areas face escalating hydrological risks due to climate change, urban sprawl, and aging stormwater infrastructures. In this context, Nature-Based Solutions (NbSs), especially Sustainable Urban Drainage Systems (SUDSs), have emerged as viable strategies to enhance water resilience and sustainability. However, the literature still lacks standardized and scalable methodologies for their design and performance monitoring. This study conducts a systematic review following the PRISMA protocol, combined with bibliometric and co-occurrence analyses, to identify prevailing approaches in the sizing and monitoring of NbS-based SUDSs. Based on the peer-reviewed literature indexed in Scopus and Web of Science from 2020 to 2024, the findings reveal an increasing integration of hydrological modeling with artificial intelligence, remote sensing, and IoT-based real-time monitoring. Despite this progress, challenges remain in methodology validation, data availability, and system adaptability. The review underscores the need for hybrid, context-sensitive frameworks that integrate empirical and simulated data to support decision-making in urban drainage planning and management. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article