Search Results (145)

Most existing driver behavior classification technologies have focused on highways and other primary road infrastructures, despite secondary roads accounting for a disproportionately large number of traffic fatalities worldwide. Compared with highways, secondary roads present greater variability in road geometry, infrastructure quality, and traffic interactions, making driver behavior recognition considerably more challenging. This paper investigates the classification of driver behavior on secondary roads using machine learning techniques. Naturalistic driving data obtained from the publicly available UAH-DriveSet dataset were analyzed using two complementary feature groups describing lane detection and traffic status. Four supervised machine learning algorithms, namely, Logistic Regression (LR), gradient boosting (GB), Random Forest (RF), and Artificial Neural Networks (ANNs), were evaluated to classify driving behavior into three categories: Normal, Aggressive, and Drowsy. The extracted features were first analyzed through statistical profiling and exploratory feature analysis before training and evaluating the classification models. The experimental results show that gradient boosting consistently achieved the highest performance for both feature groups, attaining an overall classification accuracy of approximately 67% while providing balanced precision, recall, and F1-scores across all behavioral classes. Logistic regression and random forest produced competitive but lower performance, whereas the Artificial Neural Network yielded the lowest classification accuracy. The obtained results demonstrate the effectiveness of ensemble learning methods for driver behavior recognition under secondary-road conditions and highlight their potential for integration into intelligent driver monitoring and Advanced Driver Assistance Systems (ADASs). By enabling earlier identification of aggressive and drowsy driving behaviors on secondary roads, the proposed approach could support timely driver warnings and safety interventions, potentially reducing accident risk. Furthermore, the findings provide a benchmark for future machine learning models designed for real-world secondary-road environments, where driving conditions are more variable and challenging than on highways. Full article

Xylem sap is increasingly recognized as a dynamic biological matrix reflecting whole-plant physiological status, but its biochemical variation under field conditions remains insufficiently characterized. We investigated oxidative stress markers, osmolytes, antioxidant enzymes, and redox-related enzymes in xylem sap from three focal tree individuals representing Fraxinus excelsior, Populus nigra, and Pinus sylvestris. Sap was collected by passive stem tapping using a custom-built apparatus, and biochemical patterns were evaluated using multivariate statistical and machine-learning approaches. The three focal trees showed distinct biochemical profiles within the present dataset. The focal P. nigra individual was associated with relatively higher antioxidant enzyme activities, whereas the focal F. excelsior and P. sylvestris individuals were more closely associated with oxidative-damage and metabolic-adjustment traits. Precipitation and wind direction were retained as the main meteorological variables associated with biochemical variation, with wind direction interpreted as an atmospheric correlate rather than a direct physiological driver. Exploratory machine-learning analyses highlighted catalase and selected meteorological variables as influential predictors. Overall, the findings support the potential of xylem sap for integrative ecophysiological monitoring while emphasizing the exploratory nature of patterns derived from repeated measurements of three focal trees. Full article

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions. Full article

Against the global green transition and tightening ESG disclosure requirements, corporate greenwashing undermines capital market transparency and environmental governance. However, existing studies are limited in semantic quantification, model interpretability, and multidimensional feature interaction analysis. Using Chinese heavily polluting listed firms from 2020 to 2024, this study constructs a greenwashing indicator based on the divergence between environmental textual semantics in annual reports and substantive ESG performance and develops a comprehensive feature system including firm characteristics, corporate governance, green development, and external pressure. Seven machine learning models are employed for prediction, and SHAP is used to interpret nonlinear effects and key drivers. The random forest model achieves the best performance, with green development contributing 37.7% (RF) and 48.3% (SHAP). Key variables include environmental disclosure quality (Eidq), CSR disclosure (CSR), monitored pollution status (Monitored), and firm size (Size), with identified thresholds and interaction effects. EBM robustness tests confirm the reliability of results, providing evidence for greenwashing regulation and ESG disclosure standardization. Full article

Background/objective: Given the increasing complexity of the luminal breast cancer landscape, a proper characterization is required in everyday clinical practice, and the recurrence after the standard first-line treatment with CDK4/6 inhibitors with/without endocrine therapy should be managed. Method: The Hermione Exchange Educational Program was held in Milan, Italy, between September 2024 and January 2025. Two questionnaires were proposed regarding the use of targeted treatment or chemotherapy after progression from CDK4/6 inhibitors. The lecture and use cases enhanced the discussion during the workshops. Results: From the surveys, it emerged that most participants (69%) considered liver metastases at CDK4/6-inhibitor progression as a key reason to initiate chemotherapy, while lung progression influenced this choice for 50% of participants. Liver involvement guided the use of targeted therapy for 56%, and attitudes were divided on whether the duration of first-line CDK4/6 therapy should affect decisions (44% in agreement vs. 38% in disagreement). The willingness of patients to receive chemotherapy (88%) and comorbidities (81%) were significant drivers. Almost all participants agreed that both the duration of response and the molecular status were key aspects to consider when choosing a second line of therapy, along with the general clinical condition of the patient. In the lecture, tissue and liquid biopsy are considered powerful tools to describe tumor molecular features over time; such complexity should be harnessed by a close dialogue between oncologists, molecular biologists, and pathologists to optimize the therapeutic choice according to the mutational status of patients. The use cases illustrate three patients with visceral progression, non-visceral progression within 12 months, and non-visceral progression after 12 months following CDK4/6 inhibitors. Conclusion: Genomic testing should be considered at diagnosis and repeated during treatment to monitor the disease. The clinical experience acquired over the years must be integrated with new molecular knowledge. Full article

Mountain stream ecosystems are often considered among the least disturbed freshwater environments; however, increasing land-use pressures may affect their ecological integrity even under apparently high-water quality conditions. This study aimed to assess the relative influence of landscape, physicochemical, and hydromorphological factors on benthic macroinvertebrate communities in three sub-catchments (Ambroz, Jerte, and Tiétar) of the Sierra de Gredos (Central Spain). A total of 33 sampling sites were surveyed, and macroinvertebrate assemblages were analyzed in relation to environmental variables using partial Redundancy Analysis (pRDA) and variance partitioning. All sites were classified as having “Excellent” ecological status based on the Iberian Biological Monitoring Working Party (IBMWP) index. However, multivariate analyses revealed clear spatial patterns and responses to environmental gradients. Results indicated that catchment-scale landscape characteristics defined the pool of potential colonizers, while local physicochemical and hydromorphological conditions acted as secondary filters structuring macroinvertebrate assemblages. Landscape variables explained the largest fraction of variance in community structure (30.6%), followed by physicochemical parameters (29.0%) and hydromorphological indices (24.9%), with a significant shared component (16.5%) indicating interactions among drivers. Agricultural land use, particularly in the Jerte sub-catchment, was associated with shifts in community composition, favoring tolerant taxa such as Diptera, while sub-catchments dominated by natural vegetation supported higher richness of sensitive groups, including Ephemeroptera and Plecoptera. These findings highlight the importance of multi-scale processes in structuring mountain stream communities and reveal limitations of traditional biotic indices in detecting early ecological changes. The results support the integration of catchment-scale variables into ecological assessment frameworks and emphasize the need for preventive, basin-scale management strategies to maintain ecological integrity under increasing anthropogenic pressure. Full article

Cetacean mortality serves as a critical indicator of marine ecosystem health, reflecting the cumulative impacts of climate-driven environmental shifts and anthropogenic pressures. However, long-term national-scale assessments remain limited in Thailand. This study aimed to assess population status, analyse spatiotemporal mortality patterns, and evaluate the influence of environmental drivers and anthropogenic stressors in Thai waters over the past two decades. Secondary data from multiple sources were analysed using generalised linear models with a negative binomial distribution. A total of 29 cetacean species were recorded, with an estimated population of approximately 3000 individuals. Mortality was documented in 24 species and showed an increasing trend over time. Coastal species, particularly Irrawaddy dolphins and finless porpoises, accounted for the majority of deaths (56%). Mortality patterns varied significantly by region (p < 0.05) but not by season, with the highest levels observed in the Upper Gulf of Thailand. Environmental factors were significantly associated (p < 0.05) with mortality, including wind speed in the Andaman Sea and extreme conditions (drought and heavy rainfall) in the Upper Gulf. In the Lower Gulf of Thailand, mortality was significantly associated with a combination of environmental (sea surface temperature and wind speed) and anthropogenic factors (fishery production). Overall, environmental variability appeared to exert a stronger influence than anthropogenic stressors. These findings highlight the requirement for targeted monitoring in high-risk regions and periods, alongside improved investigation of mortality causes to support effective conservation strategies. Full article

To scientifically evaluate the health of river aquatic ecosystems in the Qianxinan Buyi and Miao Autonomous Prefecture, southwestern Guizhou, systematic surveys of benthic macroinvertebrate and periphytic algal communities were conducted in representative rivers during October 2024 (autumn) and April 2025 (spring), coupled with concurrent water quality monitoring. Reference sites were selected based on water quality indicators and habitat conditions. Core parameters were identified through correlation analysis, discriminatory ability analysis, and distribution range analysis to construct a Benthic Index of Biotic Integrity (B-IBI) and a Periphytic Algae Index of Biotic Integrity (P-IBI) suitable for the region. These indices were then applied to assess the ecological health of the rivers. Additionally, stepwise regression analysis was employed to investigate the key environmental drivers influencing the two biotic integrity indices. The results indicated that: (1) In terms of species composition, the benthic macroinvertebrate community structure was relatively simple, dominated by arthropods, particularly chironomid larvae. Bacillariophyta and Cyanophyta consistently dominated the periphytic algae community. (2) Assessments using both B-IBI and P-IBI showed that the overall river health in spring was slightly better than in autumn. However, more than half of the sampling sites were rated as “fair” or worse in both seasons. The reference sites (S2, S10) consistently exhibited “excellent” or “good” health, while the impaired sites showed significant spatial heterogeneity. Discrepancies between B-IBI and P-IBI ratings at some sites revealed differential responses of the two biological communities to environmental stressors. (3) Stepwise regression analysis unveiled a seasonal shift in key environmental drivers. The primary factor affecting the B-IBI in autumn was biochemical oxygen demand (BOD₅), which shifted to total phosphorus (TP) and ammonia nitrogen (NH₄⁺-N) in spring. For the P-IBI, the main factor changed from dissolved oxygen (DO) in autumn to chemical oxygen demand (COD) in spring. These findings confirm the applicability of the B-IBI and P-IBI systems in this region, and indicate that multi-assemblage integrated assessments can contribute to understanding the health status of river ecosystems in the Qianxinan Prefecture. This study could serve as a scientific reference for the protection, management, and restoration of local river ecosystems. Full article

Urban drainage systems (UDSs) are critical built assets increasingly challenged by short-duration extreme rainfall, aging infrastructure, and rising surcharge risk. Physics-based hydrodynamic models are widely used for system assessment, but their high computational cost limits real-time operational prediction. Existing data-driven prediction approaches improve computational efficiency, but often rely mainly on sensor inputs and provide limited asset-level interpretation. This study develops an explainable digital twin for real-time prediction of storm-driven water level response in a separate sewer network in the Yangtze River Delta, China. The framework integrates 5 min monitoring and SCADA data, including water level, flow, pump status, and rainfall, with GIS and as-built asset information, including pipe geometry, hydraulic capacity, catchment characteristics, and network connectivity. A hybrid TCN-LSTM model was developed to predict water level and surcharge risk probability at 15–60 min lead times. A surrogate-based SHAP module was used to explain model predictions at the node and subcatchment scales. Multi-source fusion reduced the RMSE by approximately 18% compared with sensor-only baselines. The SHAP results showed that the pipe capacity-related variables and upstream contributing area were the main drivers of surcharge onset. The framework provides interpretable, operationally relevant predictions to support the resilience-oriented management of urban drainage systems. Full article

Groundwater contamination by dissolved metals and metalloids in the Chiang Mai Basin is an important drinking-water concern, yet the coupled depth patterns, hydrogeochemical controls, composite contamination status, and screening-level health implications have not previously been assessed in an integrated basin-scale framework. This study evaluated 120 groundwater samples from alluvial wells classified by depth as shallow (≤30 m, n = 40), intermediate (31–60 m, n = 35), and deep (>60 m, n = 45). Samples were analyzed for nine dissolved metals and metalloids (Fe, Mn, As, Cd, Pb, Cr, Zn, Hg, and Se) together with pH, Eh, and total dissolved solids (TDS). The highest exceedance frequencies were observed for Fe (72.5% of samples, >0.3 mg/L acceptability threshold), Mn (65.0%, >0.08 mg/L), and As (45.8%, >10 μg/L). Fe and Mn increased significantly with depth, whereas As was enriched in deep wells but showed no statistically significant depth dependence. Pearson correlation and principal component analysis consistently identified a dominant redox-associated component in which Fe, Mn, and As covaried negatively with Eh, supporting redox-sensitive co-enrichment in deeper groundwater. Contamination factors calculated relative to selected global groundwater background values were >6 for all seven evaluated metals (Fe, Mn, As, Cd, Pb, Cr, and Zn), and the overall pollution load index (PLI) was 9.11, with the highest depth-specific PLI in deep wells (10.42). These indices are interpreted here as background-relative screening tools rather than stand-alone regulatory measures. A screening-level ingestion risk assessment identified arsenic as the dominant toxicological driver, with hazard quotients (HQ) of 1.97 for adults and 4.60 for children, and an estimated lifetime cancer risk (LCR) of 8.87 × 10^–4. The results support targeted monitoring of deeper wells, routine screening for As and Mn, and treatment strategies that can address the co-occurring Fe–Mn–As assemblage in alluvial groundwater. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

Background and Objectives: Venous thromboembolic disease (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major cause of morbidity and mortality worldwide and imposes a substantial financial burden on health systems due to both the direct and indirect costs of acute management and long-term complications. This systematic review aimed to assess patient satisfaction with anticoagulation therapy for VTE and to highlight potential differences according to the type of anticoagulant. The review focused on factors influencing the patient experience, such as perceived efficacy, ease of use, adverse effects, and health-related quality of life. Materials and Methods: A systematic review, without quantitative meta-analysis, was conducted in accordance with PRISMA 2020 guidelines. Articles were identified through searches in major databases (PubMed, Scopus, Cochrane Library and others) using keywords including “patient satisfaction”, “anticoagulation”, “venous thromboembolic disease”, and “quality of life”. In total, 21 studies published between 2009 and 2025 met the inclusion criteria. The studies assessed patient satisfaction with different types of anticoagulation, including vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs), and low-molecular-weight heparin (LMWH) injections. Results: Across the included studies, patients generally reported higher levels of treatment satisfaction with DOACs compared with VKAs, mainly due to the absence of routine laboratory monitoring and fewer dietary restrictions. However, satisfaction varied according to age, sex, and clinical status. In specific patient populations, such as those with cancer-associated thrombosis, factors including fewer drug–drug interactions and perceptions of safety with LMWH appeared to influence treatment choice and satisfaction. Adverse effects, particularly bleeding, were identified as major drivers of dissatisfaction. Several studies suggested that higher treatment satisfaction was associated with better adherence, while quality of life appeared to improve in patients treated with DOACs in comparison with VKAs. Conclusions: Patient satisfaction is a critical component of successful VTE management. Overall, DOACs appear to be associated with higher treatment satisfaction than traditional therapies such as VKAs, although further high-quality research is needed to individualise anticoagulation strategies. Systematic incorporation of patient-reported satisfaction into clinical decision-making and into international guidelines may improve adherence, enhance quality of life, and ultimately increase the effectiveness of anticoagulation therapy. Full article

Yellow fever (YF) remains a re-emerging vector-borne zoonotic disease in tropical regions of the Americas despite the availability of an effective vaccine. In South America, the virus is maintained through a jungle transmission cycle involving Haemagogus and Sabethes mosquitoes and non-human primates (NHPs), which act as amplifying hosts and key epidemiological sentinels. This narrative review examines the current status of YF epizootics in South America, with a focus on the role of NHPs in viral circulation, early detection, and spillover risk to human populations. We synthesize recent evidence on epizootic patterns across endemic countries, the differential susceptibility of neotropical primates, and the ecological and environmental drivers influencing transmission, including deforestation, habitat fragmentation, and human encroachment into forested areas. In addition, we analyze current surveillance strategies, including wildlife monitoring, entomological and genomic surveillance, and their integration within a One Health framework. This review highlights that YF epizootics are expanding geographically and are closely linked to environmental change and human–ecosystem interactions. Strengthening integrated, multidisciplinary surveillance systems is essential to improve early detection, guide vaccination strategies, and prevent human outbreaks. These findings underscore the critical importance of operationalizing the One Health approach to enhance preparedness and response to YF in South America. Full article

Heavy-duty forklifts possess substantial kinetic energy during braking, which is currently wasted due to a lack of recovery in conventional systems. To ensure braking safety, an electro-hydraulic–mechanical compound braking system is necessary. However, the uncoordinated distribution between regenerative and mechanical braking torque leads to braking torque fluctuations, compromising safety, comfort, and recovery efficiency. This paper constructs a parallel hydraulic hybrid power system for heavy-duty forklifts based on a four-quadrant pump/motor, enabling braking energy recovery and reuse via the pump/motor and an accumulator. A compound braking strategy based on the ideal braking force distribution and multi-sensor information fusion is proposed. The system incorporates various sensors, including pressure, speed, flow, and pedal displacement sensors, to monitor system status and driver intention in real time, providing precise data for coordinated control. Feasibility is verified through AMESim simulation and real vehicle tests. The control system based on sensor feedback maximizes braking energy recovery while ensuring braking safety and comfort, achieving a 12.2% energy-saving rate and significantly improving the vehicle’s economy and range. Full article

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article