Search Results (967)

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies. Full article

A strong safety culture is essential for managing human factors in complex systems and constitutes a strategic resource for supporting the sustainable operation of organizations. However, conventional approaches remain limited by unclear conceptual boundaries and a lack of mechanisms linking safety culture with other organizational safety elements. To address these gaps, this study develops a sustainable safety culture construction method grounded in accident causation theory. Using the 24Model, we establish a concise “culture–system–ability–acts” framework that operationalizes the pathways through which safety culture shapes organizational safety performance. The method integrates four components: conceptual clarification of safety culture, quantitative assessment, factor identification based on the 24Model, and Bayesian network analysis to quantify interdependencies among culture, systems, ability, and acts. Empirical evidence from coal mining enterprises shows that safety culture influences safety performance indirectly by shaping system implementation quality, workers’ safety ability, and safety-related actions. Enhancing “demand of safety training” substantially mitigated system deficiencies related to ineffective implementation of procedures, failure in enforcing procedures, lack of qualifications, and insufficient supervision. Improved training also strengthened workers’ knowledge of accident cases, consequences of violations, and technical standards, thereby reducing competence-related gaps and promoting more consistent safety supervision behaviors. Sensitivity analysis highlights the importance of reinforcing “safety responsibilities of line departments” and improving the dissemination of safety knowledge, particularly accident case knowledge. Overall, the findings empirically validate the dynamic “culture–system–ability–acts” transmission mechanism of the 24Model and provide a structured, quantitative pathway for advancing sustainable safety culture development. Full article

Floating offshore production systems play a critical role in offshore resource development, where the structural integrity and operational safety of risers, umbilical cables, and mooring cables are of paramount importance. Focusing on the failure risks of these key components under harsh marine environments, this paper systematically reviews the coupled mechanisms of wave-induced loading, electrochemical corrosion, and material fatigue. Unlike traditional reviews on offshore pipelines and cables, this study not only examines the mechanical performance of deepwater pipelines and cables along with representative research cases but also discusses corrosion mechanisms in marine environments and corresponding repair and mitigation strategies. In addition, recent advances in machine learning-based digital twin frameworks and real-time monitoring technologies are reviewed, with an analysis of representative application cases. The findings indicate that interdisciplinary material innovations combined with data-driven predictive models are essential for addressing maintenance challenges under extreme ocean conditions. Furthermore, this review identifies existing research gaps in data fusion for monitoring technologies and outlines clear directions for the intelligent operation and maintenance of future deep-sea infrastructure. Full article

The Estimated Time of Arrival (ETA) of vessels is a vital operational indicator for voyage planning, fleet deployment, and resource allocation. However, most existing studies focus on short-distance liner services with fixed routes, while ETA prediction for long-distance tramp bulk carriers remains insufficiently accurate, often resulting in operational inefficiencies and charter party disputes. To fill this gap, this study proposes a data-driven stacking ensemble learning framework that integrates Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) as base learners, combined with a Linear Regression meta-learner. This framework is specifically tailored to the unique complexities of tramp shipping, advancing beyond traditional single-model approaches by incorporating systematic feature engineering and model fusion. The study also introduces the construction of a comprehensive multi-dimensional AIS feature system, incorporating baseline, temporal, speed-related, course-related, static, and historical behavioral features, thereby enabling more nuanced and accurate ETA prediction. Using AIS trajectory data from bulk carrier voyages between Weipa (Australia) and Qingdao (China) in 2023, the framework leverages multi-feature fusion to enhance predictive performance. The results demonstrate that the stacking model achieves the highest accuracy, reducing the Mean Absolute Error (MAE) to 3.30 h—a 74.7% improvement over the historical averaging benchmark and an 11.3% reduction compared with the best individual model, XGBoost. Extensive performance evaluation and interpretability analysis confirm that the stacking ensemble provides stability and robustness. Feature importance analysis reveals that vessel speed, course stability, and remaining distance are the primary drivers of ETA prediction. Additionally, meta-learner weighting analysis shows that LightGBM offers a stable baseline, while systematic deviations in XGBoost predictions act as effective error-correction signals, highlighting the complementary strengths captured by the ensemble. The findings provide operational insights for maritime logistics and port management, offering significant benefits for port scheduling and maritime logistics management. Full article

Pesticides have been widely applied in agricultural practices over the past decades to protect crops from pests and other harmful organisms. However, their extensive use results in the contamination of soil, water, and agricultural products, posing significant risks to human and environmental health. Exposure to pesticides can lead to skin irritation, respiratory disorders, and various chronic health problems. Moreover, pesticides frequently enter surface water bodies such as rivers and lakes through agricultural runoff and leaching processes. Therefore, developing effective analytical methods for the rapid and sensitive detection of pesticides in food and water is of great importance. Electrochemical sensing techniques have shown remarkable progress in pesticide analysis due to their high sensitivity, simplicity, and potential for on-site monitoring. Two-dimensional (2D) carbon nanomaterials have emerged as efficient electrocatalysts for the precise and selective detection of pesticides, owing to their large surface area, excellent electrical conductivity, and unique structural features. In this review, we summarize recent advancements in the electrochemical detection of pesticides using 2D carbon-based materials. Comprehensive information on electrode fabrication, sensing mechanisms, analytical performance—including sensing range and limit of detection—and the versatility of 2D carbon composites for pesticide detection is provided. Challenges and future perspectives in developing highly sensitive and selective electrochemical sensing platforms are also discussed, highlighting their potential for simultaneous pesticide monitoring in food and environmental samples. Carbon-based electrochemical sensors have been the subject of many investigations, but their practical application in actual environmental and food samples is still restricted because of matrix effects, operational instability, and repeatability issues. In order to close the gap between laboratory research and real-world applications, this review critically examines sensor performance in real-sample conditions and offers innovative approaches for in situ pesticide monitoring. Full article

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived. Full article

This study proposes HGBRCond, a machine learning model for conductivity prediction in controlled biodegradation processes. Eight regression algorithms were evaluated using experimental data (n = 424) from a micro-pilot treatment system. HGBRCond, based on Histogram-Gradient Boosting Regression (best performing ML model), achieved optimal performance (R² = 0.877 ± 0.011, RMSE = 10.235 ± 0.54 µS/cm) through 10-fold cross-validation. Unlike standard HGBR and previous conductivity models that lack comprehensive validation frameworks, HGBRCond integrates rigorous statistical validation (cross-validation, sensitivity analysis, confidence intervals) with multi-level interpretability (Morris screening, SHAP analysis, feature importance), achieving a 6.8% performance improvement over standard gradient boosting approaches while addressing mechanistic interpretability gaps present in prior work. However, limitations constrain direct potential industrial applicability: limited dataset (n = 424), narrow conductivity range (285–360 µS/cm), strong dissolved oxygen dependence, sensitivity across two critical parameters, constant flowrate, and validation restricted to controlled conditions. These constraints require model recalibration for potential industrial application. Future work will focus on model validation across extended operational ranges using industrial samples and full-scale testing to establish applicability beyond controlled experimental settings. Full article

Plant extracts are rich in various bioactive compounds, such as polyphenols, flavonoids, tannins, terpenoids, phenolic acids, saponins, alkaloids, and polysaccharides. Antioxidant polyphenols are increasingly attracting attention, not only as dietary components but also as valuable food industry byproducts. Resveratrol, present in a wide range of plants, is well recognized for its diverse biological activities, including antioxidant, antitumor, cardioprotective, and neuroprotective effects. Given the importance of intercellular communication in these physiological processes, gap junctions (GJs) composed of connexin (Cx) family proteins are of particular interest because they provide a direct pathway for electrical and metabolic signaling and are key players in maintaining normal organ function and cell development. Aberrations of GJ intercellular communication (GJIC) may result in the progression of cardiovascular and neurological diseases and tumorigenesis. Cx43 and Cx45 play crucial roles in cardiac excitation and contraction, and alterations in their expression are associated with disrupted impulse propagation and the development of arrhythmias. In this study, for the first time, we performed a comparative analysis of the effect of resveratrol on Cx43 and Cx45 GJIC using molecular modeling, a dual whole-cell patch-clamp technique to directly measure GJ conductance (g_j), and other approaches. Our results revealed that resveratrol accomplished the following: (1) inhibited GJ g_j in Cx43- but enhanced it in Cx45-expressing HeLa cells; (2) exerted dose- and time-dependent changes in Cx expression and plaque size; (3) reduced cell viability and proliferation; (4) and altered Cx43 phosphorylation patterns linked to gating and plaque stability. Overall, resveratrol modulates GJIC in a dose-, time-, and connexin type-specific manner. Full article

The accurate estimation of outflow discharge from Piano Key Weirs (PKWs) under unsteady flow conditions is critical for effective flood management and the safety of dams. While extensive research exists on PKWs under steady flow, their hydraulic behavior during unsteady flow remains poorly understood. This study addresses this gap by investigating a Type-A PKW using combined physical and numerical modeling. A total of eight steady-flow and fifty-three unsteady-flow experiments were conducted. The steady flow experiments covered a range of Q = 5.13–40.76 L/s (H = 1.29–10.45 cm), while the unsteady experiments employed hydrographs with peak discharges up to ~68 L/s. Outflow was estimated via the Modified Puls method (hydrological routing) and a validated 3D numerical model (hydraulic routing). The results revealed significant discrepancies between steady and unsteady stage-discharge relationships, with a mean relative error of up to 41.37% and instantaneous errors exceeding 150% during the rising limbs of hydrographs with high rates of change in discharge, associated with intensified unsteady flow effects. A validated looped stage-discharge curve was observed under unsteady conditions, showing lower discharge on the rising limb for the same head. The Modified Puls method exhibited high accuracy, with relative errors below 5% when compared to hydraulic routing results. Additionally, three comparative indices were proposed and used to evaluate the performance of outflow estimation methods. The findings underscore the importance of incorporating unsteady flow conditions in the design and analysis of PKWs, particularly in the context of climate change and increasing flood uncertainties. Full article

Mega-event-led urban waterfront regeneration has played a key role in shaping public open spaces, particularly in newly developed areas within the Chinese context. However, public perceptions and their influence on the use of newly built open spaces created through mega-event-led regeneration have not been examined in existing research. To address this gap, this study establishes an integrated assessment framework to evaluate the quality of urban waterfront open spaces. A mixed methods approach was adopted, including direct observations and 770 online questionnaires collected between July and October 2024 at the South Bank of the Qiantang River (SBQR) in Hangzhou, China. Spatial analysis and Importance–Performance Analysis (IPA) were employed to determine priority improvement areas that should inform future waterfront regeneration strategies. The results indicate that inclusiveness emerged as the most important factor for enhancing waterfront open space quality, while spatial aesthetics ranked the lowest. Among the sub-sub factors, elements related to improving water accessibility, enhancing natural surveillance, providing artificial shelters and diverse seating options, introducing distinctive water features, and shaping collective memory through digital technologies are the key priorities for improvement in the future urban waterfront regeneration policies. Finally, the study highlights that the intangible legacies of the Asian Games and the adaptive reuse of informal built heritage have the potential to reshape a distinctive new city image and collective memory, even in the absence of tangible and formally recognised heritage buildings. Full article

Pseudomonas has been revealed as an important member of plant probiotics, with its rich species diversity implying complementary plant growth-promoting (PGP) traits. However, information on Pseudomonas species in the microecology of Angelica sinensis and medicinal plants in general remains to be further investigated. This study examined the microecological characteristics, PGP traits, and their underlying molecular mechanisms of Pseudomonas. Filling this gap will provide an important reference for microbial community design centered on dominant functional bacterial genera. In this study, we characterized the microecological traits, PGP properties, and their underlying molecular mechanisms of Pseudomonas strains. Microbiome analysis identified Pseudomonas as the dominant genus in the rhizosphere and a core endophytic genus, exerting significant influences on both (path coefficients = 0.971, 0.872). Comparative phenomics suggested potential functional complementarity among different strains. Our observations revealed significant differentiation in PGP traits: P. umsongensis X08 showed exceptional performance in IAA and siderophore production (IAA: 1.24 mg/mL, siderophore halo diameter: 2.04 cm); P. frederiksbergensis X06 exhibited advantages in ACC deaminase activity and potassium solubilization; and P. allii X32 demonstrated high organic phosphorus solubilization capability (3.98 mg/L). Finally, genomic data revealed that P. allii X32 possesses a rich repertoire of PGP-related genes and metabolic pathways, providing a basis for establishing molecular mechanistic hypotheses for these traits. In summary, Pseudomonas strains from different species, which exhibit complementary probiotic functions without antagonism in the A. sinensis microecosystem, provide valuable microbial resources for the ecological cultivation of A. sinensis. Full article

Pseudomonas aeruginosa bloodstream infections (PABSIs) are a major clinical challenge due to their association with significant mortality and antimicrobial resistance mechanisms. The COVID-19 pandemic changed antimicrobial practices, intensive care management, and patient risk profiles, potentially influencing the epidemiology and outcomes of PABSIs. In the post-pandemic period, practices were expected to revert to normal. The objective of this scoping review was to identify and summarize reported mortality rates and risk factors for PABSIs in studies published between 2023 and 2025. Literature searches were conducted across PubMed, Web of Science, Embase, and Scopus. Screening was performed in accordance with PRISMA-ScR guidelines. Twenty-two eligible studies were included. Mortality rates varied across the study setting and populations; however, several consistent predictors were consistently identified, including carbapenem exposure, multidrug-resistant Pseudomonas aeruginosa, hematologic disease or malignancy, corticosteroid therapy, sepsis or septic shock, mechanical ventilation, and higher severity-of-illness scores. Few studies have linked molecular mechanisms to patient outcomes, highlighting important gaps in knowledge. Notably, only a small number of studies included the post-pandemic period but did not analyze the data separately. Despite limited available evidence, critically ill and immunocompromised patients remain at greatest risk of death from PABSIs. This review highlights the need for a broader comparative analysis in future. Full article

Workforce adjustments, such as mass layoffs, are significant corporate events that can influence stock returns and volatility, yet their broader asset-pricing implications remain underexplored. We examine the impact of such workforce shocks on stock performance from an asset-pricing perspective. Grounded in production-based asset-pricing theory, incorporating labor adjustment costs and search-and-matching frictions, our study posits that disruptions in the labor force significantly affect firm risk and value. This focus addresses a clear gap. Previous research has not comprehensively evaluated workforce shocks as systematic risk factors in a cross-sectional asset-pricing model. Using an extensive dataset spanning 1990–2023 and covering thousands of layoff events, we construct a novel “workforce shock” factor and conduct the first large-scale empirical tests of its pricing relevance. Our analysis reveals that workforce shocks lead to lower stock returns and heightened volatility, effects especially pronounced in labor-intensive firms. Moreover, exposure to workforce shock risk carries a significant premium, indicating that these disruptions act as a systematic risk factor priced in the cross-section of equity returns. Overall, our study provides the first comprehensive evidence linking labor force disturbances to equity risk premia, underscoring the importance of incorporating labor market considerations into asset-pricing models. Full article

Parametrization of photovoltaic (PV) modules makes an important foundation for monitoring and fault diagnosis. This work focus on the sensitivity of parameters for the single-diode model (SDM), which fills the gap in existing research. The sensitivity analysis in this work provides a fundamentally new perspective on understanding parameter robustness as well as the prior knowledge for the parameter identification method. Based on insights into the sensitivity analysis, a novel parameter identification method is proposed, which combines analytical expressions with the grid search algorithm. The proposed method reduces the relative error of the extracted parameters in the simulated dataset, and the quantitative improvement of the reverse saturation current is significant (12.6% average reduction). This method achieves the state-of-the-art overall performance in the measured dataset, and the Friedman test confirms that this improvement is statistically significant (p < 0.05). The transition capability of the proposed method is excellent under varying operating conditions, which implies that it has the potential to be applied to the intelligent operation and maintenance of photovoltaic systems. Full article

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its direct link to energy poverty at the macro level has rarely been analysed, let alone with respect to structural changes in industry. Filling this gap, this paper evaluates whether reductions in industrial energy intensity result in reduced energy poverty, understood as the share of households unable to maintain adequate indoor thermal comfort. Empirical analysis relies on a balanced panel dataset and uses fixed-effects regression models to take into account unobserved country-specific and time-specific heterogeneity. In addition, potential endogeneity between industrial energy intensity and labour productivity is addressed by the instrumental variable approach using two-stage least squares. The main models also include key macroeconomic and social control variables: real GDP per capita, social benefit expenditure, electricity prices for households, and unit labour costs. The results yield a robust and statistically significant positive link between industrial energy intensity and energy poverty, suggesting that efficiency improvements in industry make a quantifiable difference in household energy deprivation. This effect even increases in strength after the correction for endogeneity, thereby corroborating the causal relevance of productivity-driven efficiency gains. The findings also show substantial heterogeneity between EU Member States, indicating that national structural features will determine baseline levels of energy poverty. However, no strong evidence is found for an indirect price-mediated transmission mechanism or for moderation effects bound to income levels or social expenditure. This study provides sound empirical evidence that industrial energy efficiency is an important but structurally conditioned lever to alleviate energy poverty in the European Union. The results emphasise the integration of industrial efficiency policies with social and institutional frameworks while designing strategies for a just and inclusive energy transition. Full article