Search Results (53,668)

Right-turning vehicles at intersections permitting right turn on red (RTOR) frequently conflict with pedestrians, posing significant safety risks. Existing studies often simplify vehicle trajectories by treating vehicles as centroid points, ignoring the spatial disparities between pedestrians and vehicles. To address this gap, we propose a conflict risk assessment framework based on front and rear wheel trajectories (FRWTs), which accounts for the dynamic differences in vehicle segments during turns. First, we partition vehicles into four segments (inner/outer and front/rear wheels) and develop a trajectory prediction model to quantify risk variations across these segments. Our analysis reveals that the inner front wheel poses the highest collision risk due to its speed, trajectory curvature, and pedestrian proximity. Next, we introduce three conflict interaction modes—hard interaction, no interaction, and soft interaction—and evaluate the applicability of conflict indicators (e.g., Time to Collision (TTC) and Post-Encroachment Time (PET)) under each mode. Using a Support Vector Machine (SVM) classification algorithm, we classify risk severity with high accuracy: 96% for hard interaction, 96% for no interaction, and 97% for soft interaction modes when TTC-PET dual indicators are employed. Our findings demonstrate that FRWT-based modeling significantly improves conflict risk assessment accuracy compared to centroid-point approaches. This work provides actionable insights for proactive traffic safety management and supports the development of targeted conflict mitigation strategies at RTOR intersections. Full article

Brain–computer interfaces (BCIs) have successfully been used for stroke rehabilitation by pairing movement intentions with, e.g., functional electrical stimulation. It has also been proposed that BCI training is beneficial for people with cerebral palsy (CP). To develop BCI training for CP patients, movement intentions must be detected from single-trial EEG. The study aim was to detect movement intentions in CP patients and able-bodied participants using different classification scenarios to show the technical feasibility of BCI training in CP patients. Five CP patients and fifteen able-bodied participants performed wrist extensions and ankle dorsiflexions while EEG was recorded. All but one participant repeated the experiment on 1–2 additional days. The EEG was divided into movement intention and idle epochs that were classified with a random forest classifier using temporal, spectral, and template matching features to estimate movement intention detection performance. When calibrating the classifier on data from the same day and participant, 75% and 85% classification accuracies were obtained for CP- and able-bodied participants, respectively. The performance dropped by 5–15 percentage points when training the classifier on data from other days and other participants. In conclusion, movement intentions can be detected from single-trial EEG, indicating the technical feasibility of using BCIs for motor training in people with CP. Full article

In line with the global goal of achieving climate neutrality, a flexible energy system capable of accommodating the uncertainties induced by renewable energy sources becomes vitally important. This paper investigates the electricity demand flexibility characteristics and develops demand-response (DR) control strategies for grid-interactive buildings. First, a building’s flexible loads are classified into three types, interruptible loads (ILs), shiftable loads (SLs), and adjustable loads (ALs). The load flexibility characteristics, including real-time response capabilities, the time window range, and the adaptive adjustment ratios, are investigated. Second, DR control strategies and their features, which form the basis for achieving different optimization objectives, are detailed. Finally, three DR optimization objectives are proposed, including maximizing load reduction, maximizing economic benefits, and ensuring stable load reduction and recovery. Through case studies of a residential building and an office building, the results demonstrate the effectiveness of these DR strategies for load reduction and cost savings under different DR objectives. For the residential building, our results showed that over 50% of the electricity load could be shifted, resulting in electricity bill savings of over 17.6%. For office buildings, various DR control strategies involving zone temperature resetting, lighting dimming, and water storage utilization can achieve a total electricity load reduction of 28.1% to 63.6% and electricity bill savings of 7.39% to 26.79%. The findings from this study provide valuable benchmarks for assessing electricity flexibility and DR performance for other buildings. Full article

Background: Heart failure (HF) is highly prevalent among patients on maintenance hemodialysis (HD) and contributes substantially to morbidity and mortality. This study aimed to evaluate the prevalence, clinical characteristics, and prognostic impact of HF in a chronic HD population. Methods: A single-center observational study was conducted on 271 HD patients (January 2022–September 2024). HF was defined and classified according to 2021 ESC criteria using echocardiography and NT-proBNP. Clinical, laboratory, and dialysis parameters were compared between HF and non-HF patients. Predictors of HF were assessed using multivariable logistic regression, and survival analyses were performed using Cox regression and Kaplan–Meier curves. Results: HF was identified in 75% of patients: 45% had a preserved EF, 31% had a mildly reduced EF, and 24% had a reduced EF. HF patients were older, had higher NT-proBNP, lower EF, more atrial fibrillation, CAD, and increased interdialytic weight gain. In the multivariable analysis, a reduced EF (OR = 0.77, p = 0.001), older age (OR = 1.12, p = 0.001), and UF rate (OR = 1.31, p = 0.02) were found to independently predict HF. During the 34-month follow-up, HF was found to be associated with significantly higher all-cause and cardiac mortality and more frequent HF-related hospitalizations (log-rank p < 0.001). In the multivariable Cox regression, two variables were found to independently predict all-cause death, NT-proBNP (per 1000 pg/mL) (HR 1.030, p = 0.029) and a lower EF: (HR 0.97, p = 0.019). For cardiac death, a higher NT-proBNP (HR 1.038, p = 0.033) and a lower EF (HR 0.933, p = 0.001) together with a lower BMI (HR = 0.929, p = 0.028) persisted as independent predictors. Conclusions: HF is extremely common in HD patients and identifies a subgroup with distinct clinical characteristics and poor prognosis. NT-proBNP and left ventricular ejection fraction are key independent predictors of mortality, underscoring the importance of early cardiac evaluation and integrated volume and dialysis management to improve outcomes. Full article

This study examines how different types of word-of-mouth (WOM) influence online purchase intention (OPI) among rural residents, an area not yet fully explored. Based on social tie strength theory, we classify WOM into “quantity-sourced” (e.g., friends, family, general consumers) and “quality-sourced” (e.g., influencers, celebrities, professionals). We propose a chain mediation model involving social distance (SD) and perceived value (PV). Using survey data from 1005 rural residents in Jiangxi Province, China, and analyzing the data with structural equation modeling (SmartPLS 4), we find that quantity-sourced WOM positively affects OPI (β = 0.135), while quality-sourced WOM negatively affects it (β = −0.166). Mechanism analysis shows SD is a key mediator: quantity-sourced WOM shortens SD, thereby increasing OPI (β = 0.152), whereas quality-sourced WOM widens SD, reducing OPI (β = −0.047). PV mediates between quantity-sourced WOM and OPI (β = 0.043), but it shows no significant mediation between quality-sourced WOM and OPI (β = −0.002). Additionally, SD and PV serve as chain mediators between both types of WOM and OPI. These findings extend WOM theory to rural contexts and offer practical insights for governments and e-commerce platforms to develop differentiated WOM strategies and build localized WOM networks. Full article

The noise produced by high dam flood discharge is prolonged and propagates over a great distance, significantly impacting the lives of nearby residents. However, accurately predicting and mitigating this noise remains challenging due to the complex nature of its sources and the lack of comprehensive models that are capable of deconstructing the overall sound field. This study systematically investigates the propagation characteristics and generation mechanisms of environmental noise from flood discharges at the Xiangjiaba Hydropower Station. A novel three-dimensional framework for classifying acoustic sources (point, line, and surface) is proposed. By integrating prototype observations with Strouhal-scaled hydraulic model tests, a multi-source sound field model was developed that employs a regression algorithm to quantify the power of individual sound sources based on holistic field measurements. The model achieves prediction accuracy within 1.5 dB when validated against prototype data. The results indicate that the rolling water surface in the stilling basin (surface source) is the dominant contributor to noise. A key quantitative finding is that, under identical discharge conditions, the noise intensity generated by surface spillways is three times greater than that produced by bottom spillways. Overall, this model serves as a critical tool for understanding acoustic characteristics and formulating noise-informed operational strategies. Full article

Water resources are increasingly scarce, with groundwater overexploitation causing major declines in quantity and quality. Effective water accounting is essential for sustainable management, which requires measuring irrigation water use despite limited metering. Traditional modeling approaches suffer from errors when there are narrow spatial coverages. Digital agriculture and remote sensing offer alternatives by enabling large-scale, cost-effective, and near-real-time monitoring. However, issues of accuracy, methodological consistency, and integration with governance frameworks still restrict operational use. This review followed the PRISMA protocol, screening 1485 documents and selecting 79 studies on remote sensing for irrigation water accounting. A structured labeling process classified papers into Technological Readiness, Management Impact, Implementation Barriers, Policy Integration, and Innovation/Gaps. Findings show a strong focus on management benefits and technological innovation, while institutional and policy aspects remain limited. Although many studies addressed multiple themes, governance integration and real-world barriers were often overlooked. Research is concentrated in digitally advanced regions, with limited attention to water-scarce areas in the Global South. The review concludes that although remote sensing improves efficiency and data availability, adoption is challenged by institutional, regulatory, and methodological gaps. Interdisciplinary work, stronger validation, and stakeholder engagement are essential for transitioning these tools into operational components of integrated water management. Full article

Cold pressing is a sustainable oil extraction method that operates without chemical solvents, requires relatively low energy input, and preserves bioactive compounds, making it a recognized green technology in line with circular economy principles. By enabling full utilization of raw materials and valorization of by-products, it supports resource efficiency, waste reduction, and the development of bio-based products. This study provides the first comprehensive mapping of Serbia’s small-scale cold-pressed oil producers, based on data from the Central Register of Food Business Operators, local inspectorates, agricultural fairs, and social media, classified according to NUTS 2024 statistical regions. A total of 55 producers were identified, with over 60% operating as artisanal units (≤15 t/year), typically using screw presses of 20–50 kg/h capacity. Pumpkin seed was the most common raw material (30 producers), followed by sesame (21), hazelnut (20), sunflower (19), and flaxseed (19), while niche oils such as jojoba, argan, and rosehip were produced on a smaller scale. Medium and large facilities (>15 t/year) were concentrated in Vojvodina and central Serbia, focusing on high-volume seeds like sunflower and soybean. Integration of green screw press technologies, zero-kilometer supply chains, and press cake valorization positions this sector as a driver of rural development, biodiversity preservation, and environmental sustainability, providing a strong basis for targeted policy support and process optimization. Full article

Emys trinacris, the Sicilian pond turtle, is a species endemic to the island of Sicily. Despite its global and Italian distribution aligning, E. trinacris is classified as “Data Deficient” by the IUCN Red List, but “Endangered” on the Italian Red List, due to threats from habitat destruction, pollution, invasive species, and the illegal pet trade. To aid conservation efforts, understanding the suitability of the species’ habitat is essential. This study aims to create a habitat suitability map by incorporating bioclimatic variables but also environmental factors related to the species’ preference for wetland habitats. We employed the Maximum Entropy model (MaxEnt), based on 264 georeferenced presence points and 33 climatic, topographic, and habitat-related variables. Our model, with an Area Under the Curve of 0.947 and True Skill Statistic of 0.853, identified key predictors such as winter temperature and summer precipitation, with a notable dependence on wetland vegetation. The resulting suitability map highlights the central-southern regions of Sicily as critical areas for the species, with moderate to high suitability also present in the western coastal areas. However, the map shows a discrepancy between the wide distribution of presence records and the limited high-suitability area. This study also assessed the overlap of suitable habitats with existing Natura 2000 sites, showing satisfactory protection levels, though agricultural reservoirs remain unprotected. Active conservation strategies, including expanding protected areas and improving habitat connectivity, are crucial to ensuring the long-term survival of E. trinacris in Sicily. Full article

Localized scleroderma (LSc), or morphea, is an autoimmune connective tissue disease causing inflammation and fibrosis of the skin and underlying tissues. While distinct from systemic sclerosis, its clinical presentation is highly diverse. This review summarizes recent advances in the understanding and management of LSc. Pathophysiological insights have evolved significantly; the somatic mosaicism hypothesis is now supported by the observation of all six of Happle’s classic lesion patterns in LSc. Furthermore, recent single-cell RNA sequencing has elucidated key cellular mechanisms, revealing an IFN-γ-driven pro-fibrotic crosstalk between T cells, dendritic cells, and specific inflammatory fibroblast subpopulations. The discovery of a rare monogenic form of LSc caused by a STAT4 gain-of-function mutation provides a powerful human model, solidifying the critical role of the JAK-STAT pathway. Clinically, LSc is classified into subtypes such as circumscribed, linear, and generalized morphea. Extracutaneous manifestations are common, particularly in juvenile LSc, and are associated with higher disease activity and reduced quality of life, necessitating a multidisciplinary approach. Management is becoming standardized, with methotrexate as the first-line systemic therapy for severe disease. For refractory cases, targeted treatments including abatacept, tocilizumab, and JAK inhibitors are emerging as promising options. In addition, reconstructive therapies like autologous fat grafting are crucial for managing atrophic sequelae. These recent advances are paving the way for more effective, targeted therapies to improve outcomes for patients with this complex disease. Full article

Marinesco–Sjögren syndrome (MSS) is a rare autosomal recessive disorder characterized by cerebellar ataxia, congenital cataracts, developmental delay, hypotonia, and progressive myopathy. Most reported cases are linked to pathogenic variants in SIL1, a gene encoding a co-chaperone essential for protein folding in the endoplasmic reticulum. Here, we present a comprehensive case study of a Turkish pediatric patient diagnosed with MSS, supported by genetic, bioinformatic, and structural modeling analyses. Whole-exome sequencing revealed a homozygous splice-site variant (SIL1 c.453+1G>T), confirmed by Sanger sequencing and segregation analysis. In silico annotation using Genomize, InterVar, Franklin, VarSome, ClinVar, OMIM, and PubMed classified the variant as pathogenic according to ACMG guidelines. Structural modeling by Phyre2 and I-TASSER demonstrated that the variant abolishes the intron 5 donor site, leading to truncation of the wild-type 461-amino-acid protein into a shortened ~189-amino-acid polypeptide. This truncation results in the loss of critical Armadillo (ARM) repeats required for HSPA5 interaction, explaining the observed instability and impaired chaperone function. Clinically, the patient presented with congenital cataracts, ataxia, developmental delay, and progressive muscle weakness, consistent with previously reported MSS cases. Comparison with the literature confirmed that splice-site variants frequently correlate with severe phenotypes, including early-onset ataxia and cataracts. This report highlights the importance of integrating genomic, structural, and clinical data to better understand genotype–phenotype correlations in MSS. Our findings expand the mutational spectrum of SIL1, reinforce the role of splicing defects in disease pathogenesis, and emphasize the necessity of comprehensive molecular diagnostics for rare neurogenetic syndromes. Full article

To identify the main controlling factors of well shutdowns in different production stages of coalbed methane (CBM) horizontal wells, this study investigated the production parameters and pump inspection records of 25 horizontal wells in Huabei Oilfield. This paper first summarizes the types, causes, and impact degrees of well shutdown faults. Then, it conducts an analysis focusing on the four core production stages—water drainage, production increase, stable production, and production reduction—and clarifies that the key fault difference across stages lies in the variation in main fault types. The following results show that: (1) a total of 15 types of shutdown faults occurred during production, which are classified into four categories: coal–sand mixture-related faults, gas intrusion-related faults, supporting equipment faults, and other faults. Coal–sand mixture are the core inducement (accounting for 52%), followed by gas intrusion (accounting for 22%). (2) The impact of faults varies significantly: wellbore blockage, pump sticking, and flexible shaft breakage caused by coal–sand mixture and high current due to gas intrusion have a significant impact on production; environmental protection issues only occur in the water drainage stage and do not affect production; supporting equipment faults have a short handling cycle and minimal impact. (3) Shutdown faults exhibit obvious stage characteristics: In the water drainage stage, faults are mainly caused by environmental protection, power outage, and other factors, while high current due to pump sticking is the core downhole fault; in the production increase and stable production stages, pump sticking and flexible shaft breakage induced by coal–sand mixture are dominant; in the production decline stage, gas intrusion problems intensify, and the proportion of coal–sand mixture -related faults decreases but remains the main inducement. Full article

The coexistence of class imbalance and overlap poses a major challenge in classification and significantly limits model accuracy. Data-level methods alleviate class imbalance by generating samples, but without ensuring their rationality, which may introduce noise. Algorithm-level methods are designed based on the model training process, avoiding noise introduction. However, existing methods often fail to consider the potential multiclass scenarios within overlap regions or design targeted solutions for different overlap patterns. This paper proposes an ensemble imbalanced classification framework via dual-perspective overlapping analysis with multi-resolution metrics. The dataset is divided into multiple resolutions for independent analysis, capturing distributional information from local to global levels. For each independent resolution, overlap is analyzed from the perspectives of “feature overlap” and “instance overlap” to derive more refined overlap scores. Flow model mapping and importance weighting are, respectively, applied to refine overlapping samples according to the two criteria. During testing, classifiers are adaptively selected based on the overlap degree of test samples under different criteria, and predictions across resolutions are integrated for the final decision. Experiments on 39 datasets demonstrate that the proposed method outperforms typical imbalanced classification methods in F-measure and G-mean, with particularly notable gains on 15 severely overlapping datasets. Full article

Background/Objectives: Differentiated thyroid cancer (DTC) represents over 90% of all hyroid malignancies and typically has a favorable prognosis. However, approximately 30% of patients experience recurrence within 10 years after initial treatment. Conventional risk classification frameworks such as the American Thyroid Association (ATA) and AJCC TNM systems rely heavily on pathological interpretation, which may introduce observer variability and incomplete documentation. This study aimed to develop an interpretable machine-learning framework for risk stratification in DTC and to identify major clinical predictors using SHapley Additive exPlanations (SHAP). Methods: A retrospective dataset of 345 patients was obtained from the UCI Machine Learning Repository. Thirteen clinicopathological features were analyzed, including Age, Gender, T, N, M, Hx Radiotherapy, Focality, Adenopathy, Pathology, and Response. Statistical analysis and feature selection (ReliefF and mRMR) were applied to identify the most influential variables. Two modeling scenarios were tested using an optimizable neural network classifier: (1) all 10 core features and (2) reduced features selected from machine learning criteria. SHAP analysis was used to explain model predictions and determine feature impact for each risk category. Results: Reducing the input features from 10 to 6 led to improved performance in the explainable neural network model (AUC = 0.94, accuracy = 92%), confirming that T, N, Response, Age, M, and Hx Radiotherapy were the most informative predictors. SHAP analysis highlighted N and T as the dominant drivers of high-risk classification, while Response enhanced postoperative biological interpretation. Notably, when Response was excluded (Scenario III), the optimizable tree model still achieved strong predictive performance (AUC = 0.93–0.96), demonstrating that accurate preoperative risk estimation can be achieved using only clinical baseline features. Conclusions: The proposed interpretable neural network model effectively stratifies recurrence risk in DTC while reducing dependence on subjective pathological interpretation. SHAP-based feature attribution enhances clinical transparency, supporting integration of explainable machine learning into thyroid cancer follow-up and personalized management. Full article

Breast cancer (BC) is a heterogeneous disease with distinct molecular subtypes that exhibit variable immune responses and metabolic profiles. Recent studies have suggested that immunometabolic pathways play a role in tumor progression and treatment resistance. This study investigates the expression patterns of ALKBH7 and NLRP3 across BC molecular subtypes and explores their relationships with clinicopathological parameters and potential immunometabolic profiles. A total of 118 BC patients were classified into HER2+, TNBC, Luminal A, and Luminal B subtypes. Gene expression levels of ALKBH7 and NLRP3 were analyzed using quantitative real-time PCR (qRT-PCR), and correlations with clinical markers were assessed. ALKBH7 and NLRP3 expression levels varied significantly between subtypes, with the highest expression observed in HER2+ tumors. Strong positive correlations were found between ALKBH7 and NLRP3 in all subtypes, particularly in HER2+ (r = 0.812, p < 0.001). Additionally, NLRP3 correlated with Ki-67 in Luminal B tumors, indicating a link between inflammation and proliferative capacity. These findings suggest that ALKBH7 may function as a dual-role biomarker involved in metabolic adaptation and immune signaling in BC. The strong co-expression of ALKBH7 and NLRP3 suggests a functional association between these molecules that may be critical in shaping the tumor microenvironment. This co-expression set, particularly in aggressive subtypes (HER2+ and TNBC), warrants further mechanistic validation as a potential prognostic marker and a novel therapeutic vulnerability. Full article