Search Results (6,004)

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

Intensive horticultural and fruit production in Chile relies on pesticides, raising concerns about compliance with residue limits and the continued availability of highly hazardous pesticides (HHPs). Recent national monitoring data from Chile indicate frequent detections of HHPs in plant-based foods and repeated exceedances of Maximum Residue Limits (MRLs). This study analyzed official datasets from Chile’s Ministry of Agriculture, combining food residue monitoring data from 2015 to 2023 with pesticide sales and import statistics as additional indicators of availability. Active ingredients were standardized to ISO names and CAS numbers and classified for HHP status based on FAO/WHO hazard criteria, with cross-referencing to the Pesticide Action Network (PAN). The results present surveillance indicators focusing on detection rates and MRL exceedance proportions. Between 2015 and 2023, residues were identified in 82.8% of the collected samples. The most frequently detected residues overall included fludioxonil, acetamiprid, pyrimethanil, fenhexamid, and boscalid, indicating a detection profile primarily characterized by fungicides with substantial contributions from insecticides. When restricting to HHPs classified residues, the most frequently detected HHPs included tebuconazole, captan, iprodione, spirodiclofen, chlorantraniliprole, and carbendazim, indicating a detection profile primarily characterized by fungicides, with significant contributions from insecticides. Records of exceedances were concentrated within a limited subset of residues, predominantly acetamiprid and dithiocarbonates, and were most frequently associated with apples, table grapes, cherries, blueberries, pears, and certain vegetables, notably leafy vegetables. The active ingredients classified within HHPs included fludioxonil, fenhexamid, tebuconazole, cyprodinil, and lambda-cyhalothrin. The findings support agronomic decision-making by emphasizing GAP/PHI reinforcement, targeted monitoring, and IPM-based substitution options for activities involving recurrent HHP detection. Full article

Botswana’s semi-arid climate makes it vulnerable to climate change, particularly drought, which threatens agricultural productivity. This study assesses drought impact on Botswana’s agricultural sector using Climate Hazards Center Infrared Precipitation with Station (CHIRPS) rainfall data and Climate Hazards Center Infrared Temperature with Station (CHIRTS) temperature data (25 km) to compute the Standardized Precipitation Index (SPI), Standardized Temperature Condition Index (STCI) and Standardized Precipitation Evapotranspiration Index (SPEI) at seasonal/annual time scales (1, 3, 6 and 12 months). The indices are used to assess their ability to predict crop yields using national data during Botswana’s rainy season, while employing univariate and multivariate statistical models. Statistical models also linked historical drought patterns to yield variability with the Percentage Area Affected (PAA) by drought, identifying key predictors. A majority of the crops (sunflower, maize, sorghum and pulses) showed variability which was best explained by SPEI 6 more particularly under the PAA multivariate models, with the highest and moderate explanatory power (R²) found in sunflower (0.48) and maize (0.43). However, variability in millet was best explained by SPI-3, although the R² was low (0.26). Other crops displayed positive coefficients within the models, which may be attributed to the varieties grown being drought tolerant. Nevertheless, the impacts from drought, which resulted in low yields, were shown by the negative coefficients across most crops. For a more holistic approach, the study also employed questionnaire data to capture first-hand local knowledge. The results showed drought to be among the indicators of climate change that were mostly perceived as well as its effects, in which yield decline, crop damage and crop pests and diseases were among the most perceived effects. Overall, this highlighted the sector’s vulnerability to the changes in climate. The study therefore underscores the need for integrated drought early warning systems, adaptive agricultural/water management and insights for policymakers to enhance drought resilience in Botswana, aligning with global sustainability goals. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

In this paper, a new probability model is suggested, known as the Unit Linear Failure Rate Distribution (ULFRD), which is used to analyse data expressed on a unit interval (0, 1), e.g., proportions, rates, and normalised indices. The proposed model is a transformation of the classical linear failure rate distribution to finite domains and gives us the opportunity to have shapes with a variety of shapes that can model any hazard rate behaviour, such as bathtub-shaped ones that are common in reliability research. Various fundamental statistical features of the distribution are obtained. The parameter estimation is analysed under Type-II censoring, where maximum likelihood and Bayesian estimations are used. Bayesian estimates are obtained under a symmetric and an asymmetric loss of a Metropolis–Hastings within a Gibbs approximation. The analyses of the estimates’ performance are performed via a simulation study of various sample sizes and censoring plans. Lastly, the generalisability of the proposed model is also demonstrated with two real datasets in the socioeconomic and reliability settings. The findings prove that the ULFRD offers a flexible and competitive alternative to model-bound data. Full article

Hurricanes create compound hazards such as storm surge, flooding, and wind-driven debris that can degrade roadway capacity, fragment network connectivity, and hinder evacuation and shelter operations. From a sustainability perspective, improving evacuation planning is essential for reducing disaster-related losses, protecting vulnerable populations, and strengthening the resilience of coastal communities facing intensifying climate-driven hazards. This paper develops a micro-scale, agent-based evacuation modeling framework to assess evacuation performance under baseline and compound-hazard conditions, with emphasis on municipal decision support. The framework is demonstrated for Westbrook, Connecticut, at the census block-group scale in AnyLogic by integrating household locations, vehicle availability, road-network connectivity, and shelter capacities from publicly available datasets. Evacuation propensity and destination choice are parameterized using survey data, enabling empirically grounded decisions for in-town versus out-of-town evacuation among household-vehicle agents. Compound disruptions are represented through flood-related road closures derived from SLOSH storm-surge outputs and stochastic wind-related disruptions that dynamically constrain accessibility during the simulation. Scenarios are evaluated for Saffir–Simpson Category 1–2 and Category 3–4 hurricanes under baseline and compound conditions. Model outputs quantify normalized evacuation time, congestion and critical intersections, shelter demand and unmet capacity, evacuation failure, and spatial heterogeneity across block groups. Results indicate that compound flooding substantially increases evacuation times and failure rates, with the largest performance degradation concentrated in higher-vulnerability areas. Optimization experiments further compare the effectiveness of behavioral shifts, shelter-capacity expansion, and earlier departure timing in reducing delays and unmet shelter demand. Overall, the proposed framework provides transparent, reproducible, and scalable analytics that town engineers and emergency planners can use to evaluate evacuation readiness under compound hurricane impacts. Full article

Background: Avian pathogenic Escherichia coli (APEC) contributes substantially to colibacillosis outbreaks in chickens. Because APEC cells readily attach to surfaces and develop biofilms, they pose a notable hazard to poultry production and food safety. This study investigated the antibiofilm and anti-adhesion activities of deep eutectic solvent-based emulsion containing Piper betle L. extract (DEPE) and hydroxychavicol, a pure compound isolated from P. betle leaves against APEC. Methods: Antibiofilm and anti-adhesion activities of DEPE and hydroxychavicol against APEC were investigated. Molecular docking and dynamics simulation of DEPE and hydroxychavicol was conducted. In addition, anti-adhesion activity of DEPE on chicken meat during storage was evaluated. Results: DEPE and hydroxychavicol significantly inhibited biofilm formation at sub-MIC, with DEPE achieving up to 80% inhibition and hydroxychavicol up to 69%. At 8 × MIC, DEPE and hydroxychavicol diminished the viability of both early and established biofilms. Furthermore, DEPE and hydroxychavicol reduced APEC adhesion on the surface as observed by SEM. In silico analyses demonstrated the stable binding of hydroxychavicol to adhesion-related proteins, particularly EcpA and FimH, suggesting a possible mechanism for its anti-adhesion activity. At day 5, DEPE at 4 × MIC significantly reduced 63% bacterial adhesion to chicken meat surfaces during storage, while maintaining the meat’s color. Conclusions: These findings indicate that DEPE and hydroxychavicol are promising candidates for limiting APEC biofilm formation and surface attachment and may serve as alternative antibacterial agents in poultry-related food safety applications. Full article

The development of sustainable and environmentally benign N-methylation methodologies is essential for enhancing sustainable synthetic practice in pharmaceutical manufacturing. In this study, we demonstrate that microwave heating (MWH) markedly enhanced the efficiency of cobalt-catalyzed N-methylation using methanol or methanol-d₄ as green C1 sources. Compared with conventional heating (CH), MWH enabled highly efficient syntheses of key pharmaceutical intermediates—including 6-dimethylamino-1-hexanol, imipramine hydrochloride, and butenafine hydrochloride—under milder conditions and shorter reaction times and without generating hazardous halogen-containing waste. UV–vis spectroscopic analysis revealed that MWH accelerated the transformation of Co(acac)₂ into catalytically active Co species by approximately four-fold, providing a mechanistic basis for the enhanced reactivity. We hypothesized that this effect was caused by the selective microwave heating of the catalyst, which in turn promoted the rapid generation of catalytically active species. Notably, MWH also significantly improved the N-trideuteromethylation of amines using methanol-d₄, achieving a 95% yield for imipramine-d₃ hydrochloride versus 32% under CH. Molecular dynamics simulations indicated that methanol-d₄ exhibited slower dipole relaxation and enhanced cluster fragmentation under microwave fields, improving catalyst–substrate contact, while kinetic isotope effects stabilized reactive intermediates. These synergistic effects account for the pronounced microwave promotion observed in deuterated systems. Overall, the combination of MWH and cobalt catalysis offers an energy-efficient, waste-minimizing, and environmentally benign strategy for the scalable synthesis of both methylated and deuterated amines. Full article

Organophosphate flame retardants (OPFRs) are widely used in consumer products and have attracted extensive attention due to their potential hazards. In this study, the concentration of OPFRs in cosmetic sponges, the migration of these compounds, and the assessment of dermal exposure risk are reported for the first time. Twelve OPFRs were detected in cosmetic sponges, with concentrations ranging from not detected (ND) to 9624 ng·g⁻¹ and a total detection frequency (DF) of 75.58% (n = 86). A migration experiment was designed to evaluate the skin load of OPFRs from cosmetic sponges using the Strat-M^TM artificial membrane, and the reliability of the method was verified. The daily exposure of females (age: 11–40 years) to OPFRs through dermal contact with cosmetic sponges under different use conditions and for different age groups was assessed. The use of wet cosmetic sponges resulted in persistent and higher OPFRs exposure. Although the calculation of the hazard ratio indicated an acceptable health risk from OPFRs contained in cosmetic sponges, the toxicity results based on the L-929 cell line highlight that the potential toxicity risk caused by the migration of OPFRs from cosmetic sponges cannot be neglected. Full article

Coprophagous flies can convert livestock manure into protein-rich larval biomass for animal feed, but manure-based rearing raises biosafety concerns. This study characterized the internal bacterial community dynamics across development in Aldrichina grahami and Boettcherisca peregrina reared on swine manure, aiming to identify developmental stages with a lower microbial hazard profile. Using 16S rRNA gene amplicon sequencing of pooled internal samples, we analyzed communities from third-instar larvae, dispersing-stage larvae, pupae at multiple time points, and newly emerged adults. Developmental stage strongly structured bacterial composition and altered richness in both species. Communities were dominated by Bacillota and Pseudomonadota, reflecting substrate origin, with pronounced turnover during metamorphosis and stage-specific dominance patterns, indicating developmental filtering rather than uniform microbial clearance. Crucially, dispersing larvae did not show the marked dominance signatures seen in later pupal or adult stages, supporting this stage as a pragmatic harvest window with a comparatively lower microbial-hazard indicator profile. Since downstream processing such as drying or heating will further reduce viable hazards, stage selection serves as an effective upstream control to lower the initial hazard burden entering production. Full article

Personal protective equipment (PPE) is critical for defending against airborne biological hazards; however, current standard testing protocols often rely on “black-box” aggregate metrics or qualitative visual inspections that fail to pinpoint localized vulnerabilities. This study proposes a novel, spatially resolved quantitative methodology combining a whole-body fluorescent aerosol exposure chamber with an entropy-based image processing algorithm. By establishing a robust linear calibration mode, we accurately mapped and quantified localized aerosol ingress through protective clothing interfaces. Dynamic human-in-simulant tests were conducted using three suit models on two subjects with distinct body morphologies over 2- and 5-min exposure durations. Quantitative results revealed two distinct morphological failure mechanisms. A well-fitted suit resulted in steady “ Steady Accumulation,” where the total body leakage mass increased consistently (e.g., from 3.29 to 4.19 μg/cm²) while maintaining stable standard deviation, indicating preserved structural integrity. Conversely, an oversized fit induced “Structural Instability” and an erratic “Bellows Effect.” This mismatch was characterized by a dramatic inflation in aerosol leakage standard deviation during extended dynamic movements, rather than a simple increase in the mean leakage. Ultimately, this study empirically proves that protective clothing efficacy is highly morphology-dependent. The proposed quantitative methodology provides a rigorous scientific tool for diagnosing localized interface failures, thereby facilitating targeted improvements in PPE design and occupational safety. Full article

During a tunnel fire, the foremost priority is the safe evacuation of passengers. Extreme temperatures and toxic combustion products can quickly lead to mass casualties, so evacuation support systems require fast forecasts of how hazardous conditions will evolve in space and time. This study investigates whether sparse sensor measurements can be used to reconstruct future tunnel-wide fire conditions on two-dimensional sections that are directly relevant to structural assessment and human exposure. To this end, we develop 2D ST-FAM, a data-driven forecasting model that maps time-resolved measurements from 75 tunnel sensors to future temperature, soot, and carbon monoxide (CO) fields derived from 108 computational fluid dynamics (CFD) fire simulations. The study is organized around three questions: whether the model can accurately reconstruct future tunnel fields from sparse measurements, whether this performance is maintained on both the vertical center plane and the horizontal breathing plane, and which physical quantities remain most challenging to predict. Results show high structural agreement with the CFD reference fields over the full 1800 s prediction horizon, with average structural similarity index (SSIM) values of 0.964 for temperature, 0.984 for CO, and 0.937 for soot. These findings indicate that sparse-sensor forecasting is feasible for tunnel-scale temperature and toxic-gas field prediction, while soot prediction remains comparatively more difficult because of its sharper spatial structures. Full article

This systematic review evaluates recent scientific and technological advances in water quality monitoring and pollution alarms for ports, based on records retrieved from seven databases following the PRISMA protocol. A total of 414 documents were screened, resulting in 141 articles (TRL 3) and 56 patents (TRL 4–5). Bibliometric, patentometric, and thematic analyses were conducted using Bibliometrix and ORBIT^®. Results show sustained growth in both academic and technological outputs, with a patent Compound Annual Growth Rate (CAGR) of 32%, compared with 13% for scientific publications, indicating accelerated translation from research to innovation. The conversion rate from scientific research to patenting increased from 14% (2010–2015) to 47% (2020–2023). Analysis of patent legal status reveals that 52% of patent families remain valid (48% granted; 4% pending), while 33% are lapsed, 13% revoked, and 2% expired, reflecting the dynamic and emerging character of the field. Technological ownership is highly concentrated, with China accounting for nearly all active patents, whereas scientific production is more geographically distributed. Thematic analysis identifies four main scientific clusters: environmental monitoring, chemical pollutants, seashore hazards, and eutrophication. The main technological domains of the patents are analysis of biological materials, control, and environmental technologies. Emerging areas of focus at TRL 3 and TRL 4–5 include microplastics, climate-change impacts, aquaculture risks, real-time sensing, IoT-enabled platforms, machine-learning analytics, autonomous monitoring systems, and bioindicator-based early-warning tools. This review provides a quantitative roadmap to support sustainable port operations, coastal ecosystem protection, and progress toward multiple synergistic United Nations Sustainable Development Goals (SDGs). Full article

The ice–rock interface shear mechanism is fundamental to understanding ice–rock avalanche hazards. This study conducts a series of direct shear tests under various normal stresses to analyze the mechanical response and acoustic emission (AE) evolution of the interface, establishing a shear strength prediction model. Results indicate that the roughness significantly affects mechanical properties and AE responses: as the roughness increases, the shear strength, cohesion, and internal friction angle improve significantly, while peak AE ringing counts and energy exhibit an increasing trend. During failure, the proportion of shear cracks decreases while tensile cracks increase, reflecting a shift in crack development modes driven by the roughness. Based on AE characteristics and stress–displacement relations, the shear failure process is categorized into five stages: initial, crack development, crack propagation, crack coalescence, and residual stages. Incorporating the effects of the roughness and cementation force, a shear mechanical model was established. Experimental data verify the model’s rationality; however, its applicability may be limited when the roughness is excessively high. Full article