Search Results (4,922)

Fatigue remains a persistent contributor to safety incidents in construction; however, limited research has examined how industry professionals perceive and prioritize fatigue monitoring approaches in real-world settings, particularly within the context of increasing digitization and data-driven safety management. To address this gap, this study conducted an exploratory survey of 103 construction professionals, including workers, supervisors, safety personnel, and project managers, to assess their familiarity with subjective, objective, and hybrid fatigue monitoring methods, along with their implementation preferences and perceived challenges. Descriptive statistical analysis and qualitative interpretation were used to evaluate familiarity levels and method preferences. The results indicate that subjective approaches, such as self-assessments and rating-based check-ins, are more widely recognized (mean ≈ 2.1/5), while awareness of objective, sensor-based systems remains lower (≈1.5/5). Despite this disparity, approximately 38% of respondents preferred hybrid approaches that integrate subjective inputs with wearable or physiological data, and a similar proportion perceived these approaches as the most reliable for operational decision-making. Additionally, more than 85% of participants indicated that fatigue monitoring could moderately to significantly improve job-site safety. These findings suggest that successful adoption depends on usability, user acceptance, and the effective integration of digital monitoring tools into construction workflows. Full article

Tourism drives economic growth but also intensifies environmental pressure at travel destinations, particularly by exacerbating local challenges in waste management. Rather than merely testing the theoretical validity of the norm activation model (NAM), this study utilizes its key constructs—specifically moral and accountability variables—as a strategic framework to examine the psychological drivers of waste reduction in the urban context of Vientiane, Laos. Data from 382 domestic tourists were analyzed using ordinary least squares regression. Ascription of responsibility (AR) (β = 0.219, p < 0.001) was the strongest predictor of intention, followed by personal norm (PN) (β = 0.173, p < 0.01) and actual waste management behavior (β = 0.160, p < 0.01). Notably, environmental knowledge and awareness of consequences—factors often emphasized in traditional environmental campaigns—had no significant influence. The findings demonstrate that, in addressing urban waste challenges in developing regions, fostering internalized moral sentiments (AR and PN) is far more effective than mere pro-environmental education. This study concludes that sustainable waste management may benefit from operationalized interventions that activate personal accountability rather than relying solely on general environmental awareness. Full article

This study examines how night sky brightness (NSB) in Hong Kong has evolved over the past decade. It combines recent datasets covering 2019–2023 with the earlier dataset analyzed in a previous study (2010–2013) . This study emphasizes the importance of long-term monitoring in the context of light pollution variations resulting from urban development and increasing public awareness. Photometric data were collected nightly and continuously from multiple locations equipped with a Sky Quality Meter, covering both urban and suburban settings. The in situ observation frequency was at sub-minute intervals, characterizing nighttime profiles with a temporal resolution that other monitoring systems (e.g., satellites) cannot provide. Analysis reveals that Hong Kong’s night skies are substantially brighter than the International Astronomical Union’s (IAU) dark sky standard, with urban areas exceeding 100× the standard brightness on average. By comparing early- and late-night observations, we establish a robust indicator for assessing the direct impact of light pollution, concluding that early evening skies are brighter than late-night skies due to the variation in artificial lighting. Urban regions demonstrated more pronounced post-midnight darkening, a trend consistent with increased light pollution awareness and enhanced compliance with late-night lighting protocols. Additionally, this study introduces remotely sensed infrared (IR) sky temperature as a novel cloud amount indicator, demonstrating a strong positive correlation between cloud amount and NSB, particularly in urban areas. Our findings highlight the urgent need for effective light pollution mitigation strategies in rapidly developing cities like Hong Kong, offering valuable insights for similar initiatives worldwide. Full article

Colorectal cancer is the third most commonly diagnosed cancer worldwide, and early detection of polyps via colonoscopy is essential for improving patient survival. However, automatic polyp segmentation faces three key challenges: balancing global context with local detail, delineating ambiguous boundaries under low contrast, and handling large variations in polyp size and morphology. To address these challenges, we propose WMA-Net, a Wavelet-Guided Mamba-Attention Network that uses wavelet-domain semantic–boundary separation as the organizing design principle. Rather than introducing a new individual operator, the contribution lies in how existing components—wavelet decomposition, Mamba state space modeling, multi-directional pixel difference convolution, and uncertainty-aware reverse attention—are combined and coordinated within one boundary-aware framework. The architecture integrates pixel difference convolution for multi-directional edge detection, frequency-selective cross-scale fusion with dual-stream wavelet-domain processing, Mamba-based multi-scale aggregation with linear complexity, and uncertainty-aware progressive boundary refinement. Extensive experiments on five public polyp benchmarks demonstrate state-of-the-art performance on four out of five datasets. On the seen datasets, WMA-Net achieves mean Dice scores of 94.4% on CVC-ClinicDB and 93.6% on Kvasir-SEG. On the unseen datasets, WMA-Net attains 91.7% on CVC-300, 82.3% on CVC-ColonDB, and 83.8% on ETIS-LaribPolypDB, demonstrating robust cross-dataset generalization. Comprehensive ablation studies validate the effectiveness and synergy of each proposed module. Full article

Triple-negative breast cancer is defined by the absence of druggable receptor targets and by a biologically dynamic phenotype that renders static, single-timepoint biomarker strategies fundamentally inadequate. Current predictive markers, including PD-L1 expression, tumor mutational burden, and genomic profiling, fail to capture the therapy-induced transcriptional reprogramming, spatial heterogeneity, and drug-tolerant persister states that drive resistance and relapse. In this review, we argue that RNA, particularly non-coding RNA (ncRNA), represents a complementary and state-aware platform for biomarker development in TNBC, capable of capturing transcriptional adaptation, regulatory threshold dynamics, and cell state transitions that static genomic markers cannot fully detect. Unlike messenger RNAs, which reflect active transcriptional programs, long non-coding RNAs and circular RNAs modulate the stability of state transitions and are specifically induced under conditions of therapeutic stress, immune exclusion, and drug tolerance, which are properties that make them suitable as potential early and sensitive indicators of adaptive reprogramming. We review the biological rationale for RNA as a state-aware readout across five dimensions: tumor plasticity, immune context, stress response, therapy adaptation, and microenvironment composition. An examination is conducted regarding how spatial transcriptomics can map RNA-defined resistant niches within TNBC, how serial liquid biopsy RNA measurements, including extracellular vesicle RNA and circulating tumor RNA, enable temporal monitoring of transcriptional state shifts before radiologic progression, and what analytical and clinical standards deployable RNA assays must meet. Finally, a state-guided adaptive management framework is proposed in which RNA signatures function as iteratively updated measurement layers informing therapy selection, on-treatment monitoring, and early resistance detection. This review outlines trial design models and defines the validation standards required before RNA-guided adaptation can enter clinical practice. Full article

Addressing building vulnerability to climate hazards requires advanced tools to support adaptation decisions. To this end, the current study presents an Agentic Artificial Intelligence (Agentic AI) Optimization framework to enhance the climate resilience of existing buildings, bridging policy guidelines and a practical tool for optimized and context-aware retrofit strategies. Aligned with EU Guidance, the framework operationalizes a Climate Vulnerability Assessment (CVA) within a Multi-Objective Optimization (MOO) engine through a multi-agent architecture. Specialized subagents, including Requirements, Cost, Strategy, and XAI Agents, collaborate to understand user goals, manage budget constraints, optimize strategies, and produce explainable reports. Two metaheuristic optimizers, such as Multi-Objective Invasive Weed (MO-IWO) and Grey Wolf (MO-GWO), were coupled with Multi-Criteria Decision Making (MCDM) models to minimize building vulnerability and adaptation costs against multiple climate hazards (e.g., heat waves and heavy precipitation). Results show that, despite MO-GWO’s lower computational burden, MO-IWO performed more robustly and is selected as the superior optimizer for integration into the Agentic AI system. Ultimately, the framework provides a scalable approach to asset management, significantly improving decision-making for building retrofits. Full article

Background: Pulmonary fibrosis (PF) is a major global health issue associated with substantial morbidity across all age groups. One of the important etiological factors contributing to PF is drug-induced lung injury, which can result from both direct and indirect damage to the pulmonary parenchyma caused by various pharmacological agents, including chemotherapeutics, antirheumatic drugs, cardiovascular medications, and certain antimicrobial agents. The aim of our study was to assess the structure of antibacterials involved in drug-induced PF (DIPF) and analyze signals of DIPF, calculating the reporting odds ratio (ROR) and proportional reporting ratio (PRR) using spontaneous reports (SRs) extracted from the Russian National Pharmacovigilance database. Methods: A retrospective, descriptive pharmacoepidemiological analysis of SRs from the AIS database for the period 1 April 2019–31 March 2025 was conducted. Results: A total of 130 SRs with data on DIPF associated with antibacterial agents were identified, with patients’ mean age of 59.1 ± 14.46 years. Death was reported in 65 SRs (50%) with a mean age of 53.0 ± 13.66 years. Next, antibacterials were identified as leaders: sulfamethoxazole (used alone or in combination with trimethoprim, 20.7% (n = 50)), azithromycin (18.2%, n = 44), levofloxacin (12.4%, n = 30), doxycycline (11.6%, n = 28), and cefuroxime (10.7%, n = 26). Disproportionality analysis performed with PRR and ROR calculation revealed the strongest association with DIPF for cefuroxime (PRR = 15.11, 95% confidence interval, CI: 10.25–22.27; ROR = 15.31, 95% confidence interval, CI: 10.33–22.68). Conclusions: Cefuroxime was revealed as a drug with an unexpected but robust safety signal for DIPF, warranting heightened clinical awareness and further investigation. The observed associations between antibacterial agents and DIPF should be interpreted with caution, as they may reflect protopathic bias (antibiotics prescribed for early symptoms of unrecognized pulmonary fibrosis) or context-dependent biological effects rather than true pro-fibrotic drug properties. Our findings do not establish causality but rather generate safety signals that warrant validation through prospective studies with detailed clinical phenotyping and mechanistic investigations using human cell lines. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts across vehicle models and sensor configurations. This paper proposes MVCA-AD (Multi-View Context Augmentation for Anomaly Detection) for single-channel crash-test curves. MVCA-AD generates multiple context-rich views using deterministic time- and frequency-domain transformations to amplify subtle anomalous patterns under limited labeled supervision. A trend-aware modulation module and cross-view attention fuse these views to improve sensitivity to critical segments such as impact spikes and gradual transitions while remaining robust to noise. Experiments on three subsets derived from physical full-scale crash tests show that MVCA-AD improves Precision, Recall, F1-score, and area under the ROC curve (AUC) over strong baselines and achieves stable performance under event-level grouped evaluation across heterogeneous head and B-pillar crash-test signals. The proposed approach supports crash-test data quality control by automatically identifying abnormal curves for downstream crashworthiness assessment workflows. Full article

Field-service operations for utility companies require routing technicians across multiple depots while guaranteeing same-day response to critical infrastructure customers, a constraint that standard multi-depot routing methods cannot structurally enforce. We introduce the MDVRP with Priority Requests (MDVRP-PR), formalised as a lexicographic optimisation problem that guarantees service to priority customers before maximising coverage and minimising route duration. A three-stage pipeline is proposed: hybrid DBSCAN-Hierarchical clustering for topology-aware depot assignment, an Enhanced Max-Min Ant System (MMAS) with priority-driven construction, lexicographic solution selection, and repair, and a Boundary Relocate post-optimisation stage with global cross-depot recovery. The approach is evaluated on a real-world applied case study from Algérie Télécom (Guelma, Algeria), comprising a single four-depot field-service instance scaled to three sizes (55, 90, and 150 customers) and assessed over 2135 controlled runs. On this case study, the proposed clustering method outperforms the MDVRP-adapted Sweep baseline by $22.9$ percentage points on the largest instance (n = 150; Friedman p < 0.001). The priority mechanisms sustain $100\%$ feasibility across all configurations, compared to complete collapse without them ($0/10$ seeds at $\ge 40\%$ priority), at a route-time overhead below $5\%$. Relative to the company’s current manual practice, the framework improves customer coverage by $16.1$ percentage points within 28 s, confirming its practical utility for daily deployment in this capacity-constrained, priority-sensitive routing context. Full article

Despite extensive global progress monitoring under the 2030 Agenda, existing Sustainable Development Goal (SDG) assessment frameworks remain structurally blind to within-country distributional disparities. This study addresses this gap by developing a methodologically transparent composite SDG index for multi-level governance assessment, applying it to 218 Nomenclature of Territorial Units for Statistics (NUTS 2) regions across the European Union over the period 2015–2022 (1744 region-year observations). In this context, the term “region-year observations” refers strictly to the balanced panel data structure, which is calculated by observing 218 distinct sub-national regions continuously over an 8-year period (218 regions × 8 years The index aggregates four dimensions—social, economic, educational, and institutional—using min-max normalization. The analysis yields three main results: (1) Spatial econometric analysis reveals strong, persistent positive spatial autocorrelation, with high-performing clusters concentrated in Northern and Western Europe and lagging clusters in Eastern and Southern peripheries. (2) A spatial error model identifies institutional governance quality as a consistent statistical predictor of sub-national SDG performance. The significance of the spatial error parameter (λ = 0.497) suggests that unobservable institutional and geographical common shocks systematically link neighboring regions. (3) Cluster analysis further distinguishes four regional archetypes: Disadvantaged, Leaders, Educated, and Transitional. These findings underscore the need for spatially aware SDG monitoring infrastructure and investment in institutional capacity as prerequisites for equitable governance, as integrating spatial dependencies is crucial to prevent national averages from masking severe regional developmental traps. Full article

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management. Full article

Micro- and nanoplastics (MPs/NPs) are increasingly recognized as persistent contaminants in agricultural systems, where repeated inputs from mulch films, biosolids, composts, irrigation water, and atmospheric deposition create sustained exposure pathways for crops. Although various studies report effects on crop growth and physiology, mechanistic interpretation remains limited because outcomes vary widely across experiments and are often discussed without appropriate attention to exposure context, particle properties, and evidentiary strength. This review advances an agroecosystem-centered, evidence-aware framework for interpreting how MPs/NPs influence crops from environmental entry to plant phenotype. We argue that crop responses cannot be inferred from polymer identity alone, but must be interpreted through the interacting effects of particle size, morphology, surface chemistry, weathering state, aggregation behavior, co-contaminant associations, and exposure matrix. Within this framework, crop responses are organized along a mechanistic chain linking environmental entry and plant contact, interface behavior at root and leaf surfaces, conditional barrier crossing and transport, ROS-centered stress signaling with hormonal and ionic regulation, and downstream effects on germination, root function, photosynthesis, biomass, productivity, and quality-related traits. Particular emphasis is placed on distinguishing surface association, supported internalization, and supported systemic translocation, because these categories carry distinct implications for edible-tissue occurrence, crop quality, and food-system relevance. Current evidence suggests that the soil–plant–food pathway is plausible and increasingly supported, but its interpretation remains constrained by uneven analytical rigor and limited field realism. Future progress will require realistic agricultural exposure designs, stronger polymer-specific confirmation, and closer integration of mechanistic evidence with agronomic and food-system endpoints. Full article

The digital transformation of agriculture is a critical pathway for promoting sustainable rural livelihoods in transition economies. This study examines the determinants of mobile agricultural application adoption among 327 smallholder farmers in Tajikistan, integrating the Technology Acceptance Model (TAM) with New Institutional Economics (NIE). We develop a formative Institutional Support Index (ISI) comprising cooperative membership, extension access, and regulatory familiarity. Using binary logistic regression and multi-model robustness checks (probit, LPM, IV-probit), we identify three core findings. First, perceived usefulness (PU) is the dominant positive driver (AME = +12.2 pp; p < 0.001). Second, perceived risk (PR) constitutes a significant psychological barrier (AME = −7.6 pp; p < 0.01), while perceived trust (PT) partially offsets this deterrent effect (AME = +6.4 pp; p < 0.01). Third, we document a “land ownership puzzle,” where land ownership exerts a robust negative conditional effect on adoption (AME = −14.2 pp; p < 0.01). This finding suggests a property-rights-based “conservatism bias” unique to transition contexts, where asset-protection motives increase the adoption threshold for landowners compared to tenants. Exploratory analysis indicates a tentative “Sensitization Effect,” in which institutional support may increase risk awareness in the absence of financial risk-sharing mechanisms. These results broaden the applicability of the TAM to post-Soviet transition environments and suggest that digital extension initiatives must incorporate risk-management tools to effectively assist smallholder farmers. Full article