Search Results (1,136)

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

Parkinson’s disease is a progressive neurodegenerative disorder characterized by motor impairments that significantly affect handwriting and fine motor control. Recent advances in artificial intelligence have enabled the non-invasive analysis of handwritten patterns as reliable digital biomarkers for early Parkinson’s disease detection. However, existing deep-learning approaches often struggle with diagnostic uncertainty and lack interpretability, limiting their clinical reliability and practical adoption. Moreover, models trained on single datasets frequently exhibit poor generalization across heterogeneous handwriting sources. This study uses two image-based handwriting datasets and one CSV-based HandPD feature dataset, including the Parkinson’s Augmented Handwriting Dataset, Parkinson’s Drawings Dataset, and HandPD Spiral/Meander feature records. A Transformer-based architecture is employed to learn global motor patterns from handwriting images, followed by a fuzzy-logic-based decision layer to handle uncertainty and improve robustness. The novelty of this work lies in integrating Transformer-driven deep feature learning with fuzzy clinical reasoning, supported by an AIC-based handcrafted feature analysis for interpretability. The model performance is evaluated using accuracy, precision, recall, F1-score, MCC, and AUC metrics. The experimental results demonstrate that the proposed Transformer–Fuzzy framework consistently outperforms CNN and Transformer-only baselines, achieving superior classification performance and robust generalization across all datasets, thereby establishing its effectiveness for reliable and interpretable Parkinson’s disease screening. Full article

Soil water availability, salinity dynamics, and nitrate transport are key factors controlling agricultural sustainability in arid environments characterized by limited water resources and high evaporative demand. This study evaluated the combined effects of soil texture, nitrate–nitrogen application, and sawdust mulching on soil water retention, evaporation losses, salinity redistribution, and nitrate movement in loamy sand and sandy clay loam soils under controlled greenhouse conditions. Results showed that soil texture was the dominant control on hydrochemical behavior, with sandy clay loam exhibiting higher water retention and lower drainage than loamy sand. Sawdust mulching significantly improved soil water conservation by reducing evaporation and stabilizing moisture distribution, while the 4 cm mulch treatment achieved the highest overall CSI performance. Evaporation strongly governed salinity accumulation in surface layers, whereas mulching reduced salt build-up and promoted a more uniform salinity profile. Nitrate transport closely followed water fluxes, resulting in higher leaching in loamy sand and greater retention in sandy clay loam. Increasing nitrogen application enhanced nitrate mobility and leaching in both soils. A Composite Sustainability Index (CSI) was developed to integrate soil water conservation, evaporation reduction, salinity control, and nitrate retention into a unified metric. Sensitivity analysis demonstrated that treatment rankings were largely unaffected by alternative weighting schemes, confirming the robustness of the CSI framework. The CSI identified mulch application, particularly the 4 cm mulch treatment, as the most effective management option based on overall sustainability performance. The CSI framework provides an integrated decision-support tool for evaluating coupled water–salt–nitrate interactions and improving water use efficiency and salinity management in arid agricultural systems. This study offers a novel integrated CSI-based framework for simultaneously quantifying hydrological and hydrochemical soil responses under mulch management in arid environments. Full article

Background/Objectives: Heart failure (HF) classification based on left ventricular ejection fraction (LVEF) provides an incomplete representation of disease complexity, as it does not fully integrate functional impairment, structural remodeling, and clinical severity within a unified framework. Although N-terminal pro-B-type natriuretic peptide (NT-proBNP) is widely used for diagnosis and risk stratification, prior studies have primarily evaluated its role in isolation or within individual HF phenotypes, leaving its phenotype-specific distribution and integrative capacity across the HF spectrum insufficiently defined. This study aimed to address this gap by systematically evaluating NT-proBNP across HF phenotypes and assessing its potential as an integrative biomarker linking ventricular dysfunction, structural remodeling, and clinical severity. Methods: A cross-sectional study was conducted including 125 participants, comprising 65 clinically stable HF patients and 60 age- and sex-matched controls. HF patients were stratified according to LVEF into HF with reduced EF (HFrEF) (n = 28), (HFmrEF) (n = 20), and HF with preserved EF (HFpEF) (n = 17). Serum NT-proBNP concentrations were measured using a standardized electrochemiluminescence immunoassay. Clinical and echocardiographic parameters, including LVEF, left ventricular end diastolic diameter (LVEDD), left atrial diameter (LAD), and New York Heart Association (NYHA) functional class, were recorded and analyzed. Results: NT-proBNP levels were significantly higher in HF patients compared with controls (1845 ± 620 vs. 95.7 ± 40.5 pg/mL; p < 0.001) and demonstrated a clear stepwise increase across phenotypes (HFrEF: 2850.6 ± 710.4; HFmrEF: 1620.8 ± 480.2; HFpEF: 920.9 ± 310.3 pg/mL; p < 0.001). NT-proBNP showed a strong inverse correlation with LVEF (r = −0.68, p < 0.001) and significant positive correlations with LVEDD (r = 0.61, p < 0.001) and LAD (r = 0.57, p < 0.001). Higher levels were associated with more advanced NYHA functional class (III–IV vs. II: 2510 ± 680 vs. 980 ± 340 pg/mL; p < 0.001). ROC analysis demonstrated robust discriminatory performance across HF phenotypes, with the highest accuracy observed in HFrEF. Conclusions: NT-proBNP exhibits a phenotype-dependent gradient and consistently reflects ventricular dysfunction, adverse structural remodeling, and clinical severity across the HF spectrum. These findings support its role as an integrative biomarker that captures the multidimensional nature of HF, with potential implications for phenotype-based risk stratification and more precise clinical decision making. Full article

This study critically addresses the challenge of selecting optimal insulation materials for contemporary, energy-efficient building envelopes, a decision with profound environmental, structural, and occupational health consequences. The paper responds to the growing demand for sustainable, resilient solutions by comparing wool, a bio-based, regenerative material, and expanded polystyrene (EPS), a synthetic polymer widely implemented in the construction industry, and advanced laboratory testing (thermal conductivity, moisture buffering, freeze–thaw resistance) is discussed in a comprehensive synthesis of the recent literature. Also, field evaluations from European retrofits and pilot projects (UK, Denmark, Finland, Iceland, Norway, Sweden, Germany and France) further contextualize performance outcomes, and life cycle impacts are considered. Recent results reveal that wool insulation achieves a moisture buffering value (MBV) between 1.8 and 2.7 (g/m²) % RH, minimal vapor resistance (mvr = 1–2), and preserves functional and structural integrity through more than 100 freeze–thaw cycles, leading to significant stabilization of the interior microclimate and enhanced durability. In contrast, EPS delivers lower thermal conductivity (0.032–0.037 (W/mK), critical for reducing heating/cooling demand, but exhibits limited vapor permeability (lvp = 60–150 MN·s/(g·m)), increased risk of condensation and mold, and reduced compressive strength (<22% after 30 cycles), especially when ventilation details are inadequate. Hybrid envelope systems leveraging both EPS and wool are demonstrated to optimize energy efficiency (up to 23% seasonal savings) and reduce interior humidity fluctuations, while lifecycle and recycling assessments show wool panels to be markedly superior in carbon footprint reduction and circularity. The stratification of insulation layers incorporating wool for vapor and moisture control, and EPS for pure thermal resistance is emerging as best practice in sustainable retrofit and new-build projects. Recommendations highlight the necessity for rigorous laboratory validation, international standards alignment, and integrated material design for robust hygrothermal comfort and environmental performance. The review also covers wool- and EPS-based hybrid composites, showing how natural fibers can improve key mechanical properties without compromising thermal insulation performance or environmental benefits. Full article

Intelligent Transportation Systems (ITS) play a central role in the development of more efficient, adaptive, and resilient road networks. Traffic control strategies have progressively evolved from traditional approaches toward more intelligent and adaptive frameworks. This paper presents a taxonomy-based perspective on intelligent traffic control strategies for road networks, organizing existing approaches according to three complementary dimensions: control scope, decision-making mechanism, and control architecture. Based on this framework, the paper discusses representative methodologies, including rule-based control, model-based methods, simulation-based optimization, data-driven and artificial intelligence-based methods, and emerging cooperative strategies enabled by connected and automated vehicles (CAVs). The analysis also examines key application domains, such as traffic signal control, ramp metering, CAV-based traffic management, and simulation platforms, highlighting their operational principles, advantages, limitations, and implementation challenges. Particular attention is given to the transition from local and reactive control toward coordinated, predictive, and learning-based traffic management systems. The paper identifies major challenges related to scalability, robustness, interpretability, safety, real-world deployment, and the gap between simulation performance and practical implementation. The proposed taxonomy also supports practical comparison and preliminary selection of context-specific strategies. Future directions point toward integrated and hybrid frameworks combining data-driven adaptability, vehicle–infrastructure cooperation, and digital twin technologies. Full article

Conventional fire monitoring systems frequently exhibit high false alarm rates, delayed response times, and a lack of closed-loop control capabilities, which severely constrain their deployment in complex real-world environments. To address these issues, this paper proposes an embedded fire detection, tracking, and extinguishing system based on multimodal information fusion and a lightweight neural model. The system follows a “Perception–Decision–Execution–Feedback” closed-loop paradigm and is implemented on a heterogeneous cooperative computing architecture comprising OpenMV4 H7 Plus and STM32F103C8T6 microcontrollers. The perception layer implements a decision-level RGB-infrared fusion mechanism that incorporates a pruned, INT8-quantized lightweight FOMO model, enabling real-time fire detection with an inference latency of 210 ms and a model size of merely 1.8 MB under resource-constrained embedded conditions. The decision layer employs a Bayesian inference-based multimodal fusion framework that effectively suppresses spurious fire interference. The vision-only false detection rate is 15.3%. After infrared fusion verification, the system-level false alarm rate is reduced to 2.0% on the interference test set. In the execution layer, a sixth-degree polynomial jet trajectory model was established and combined with an improved PID–PI dual-loop controller to enable dynamic optimization of spray angle and flow rate in real time. Experimental results demonstrate that the proposed system achieves an average fire recognition accuracy of 95.6% with a false alarm rate as low as 1.4%. Furthermore, it realizes an extinguishing accuracy better than ±5 cm within an effective operating range of 10–60 cm and completes the entire perception-to-extinguishing cycle within 8.5 s under illumination conditions ranging from 50 to 100,000 lux. These results demonstrate the excellent real-time capability, robustness, and energy efficiency of the proposed system, providing a practical and scalable solution for autonomous embedded fire-fighting applications in household, industrial, and warehouse environments. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

To enhance drive performance, this paper introduces an advanced speed controller architecture intended for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). This newly developed controller integrates decision tree theory (DTT) with sliding mode theory (SMT). Initially, the regression algorithm from the classification and regression tree (CART) framework is applied to partition the deviation between the actual motor speed and the target command into 10 distinct error zones. These intervals serve as the basis for configuring three critical parameters of a standard exponential reaching law sliding mode controller (ERLSMC): namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain. Following each split, the mean squared error (MSE) of the respective nodes is evaluated to determine the root node. The dataset is recursively bifurcated into dual subsets using the chosen split variables and thresholds, establishing a structured decision pathway through each successive child node. As a result, the sliding mode speed controller receives dynamically optimized modifications for its three key gains in real time during BLDCM operation. In addition, the controller continuously computes an updated sliding mode dynamic trajectory control gain by tracking the derivative of the speed error. Tuning these three operational gains effectively mitigates the transient overshoot typically induced by the conventional exponential reaching law (ERL) across diverse running states. This mechanism ensures that the speed response of the BLDCM drive system dynamically and accurately follows target commands under fluctuating conditions. Advantageously, the introduced control strategy avoids intensive computational routines and eliminates the need for extensive training datasets, ensuring straightforward implementation. To validate this approach, the proposed methodology is applied to the BLDCM drive system using the Matlab/Simulink environment. Its execution is benchmarked against conventional sliding mode controllers (SMCs) configured with three distinct control strategies: the constant speed reaching law (CSRL), the standard ERL, and the extension theory combined with exponential reaching law (ETERL). The resulting simulation data confirms that the proposed adaptive controller delivers superior performance over the alternative three reaching laws regarding both transient command tracking and robustness in load regulation. Full article

In the task planning of heterogeneous unmanned aerial vehicle formations, problems such as dynamic topological instability and sparse Pareto front exist, which affect the robustness of the planning. To address this, this paper proposes a cooperative task planning method based on multi-objective dolphin echolocation optimization driving. Firstly, a differentiated dynamic model of heterogeneous unmanned aerial vehicles covering different configurations such as rotors and fixed wings is constructed, and a dynamic communication topology model is established based on time-varying graph theory to quantify transmission delay and link stability. Then, a multi-objective optimization model is designed with task completion, energy balance, and time cost as the core, Bayesian networks are introduced to construct a dynamic threat field, and risk assessment and real-time response are achieved in complex environments. Based on this, a multi-objective dolphin echo optimization algorithm is adopted to solve the model, and its echo beam focusing search and adaptive weight allocation mechanism are utilized to effectively improve the convergence and distribution of the Pareto solution set. Finally, a “decision execution” hierarchical collaborative control architecture is constructed, utilizing the decision layer to output a global planning scheme and the execution layer to achieve rolling optimization and precise tracking of instructions through distributed model predictive control. The simulation test results show that this method can maintain high task completion, energy balance, and communication stability in different formation sizes and complex environments significantly better than traditional algorithms. When the formation size is between 20 and 60 sorties, the hypervolume (HV) index of this method is superior to that of the comparison method. In cases of sudden obstacles and complex electromagnetic interference scenarios, the average energy consumption of a single unmanned aerial vehicle after applying this method is maintained at 150–250 Wh, and the transmission delay is stable at 50–200 ms. The experimental results verify that this method has good planning robustness and collaborative real-time performance. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article

Industrial quality control is a critical component of modern manufacturing, as defects can lead to significant economic losses and safety risks. Traditional inspection methods, largely reliant on human operators or rule-based systems, often suffer from inconsistency, limited scalability, and reduced accuracy in complex environments. Recent advances in artificial intelligence (AI), particularly in deep learning and computer vision, have enabled automated defect detection and classification with unprecedented performance. This paper provides a comprehensive review of AI-based image processing techniques for industrial quality control, covering classification, detection, and segmentation approaches. Key applications across manufacturing sectors are discussed, alongside current challenges such as data scarcity, real-time implementation, and model generalisation. Furthermore, this paper explores emerging trends toward autonomous inspection systems, integrating real-time analytics, edge computing, and intelligent decision making. The insights presented aim to guide future research toward robust, scalable, and fully automated quality control solutions in smart manufacturing environments. Full article

This study presents a combined numerical–statistical framework based on the Extended Finite Element Method (XFEM) and the Taguchi optimization method to assess the fracture behavior of pressurized pipelines containing external longitudinal cracks. XFEM is employed to evaluate the local fracture response without remeshing, while the Taguchi method is used to quantify the influence of key parameters and identify an optimal configuration with a limited number of simulations. The control parameters considered are internal pressure, initial crack length, and wall thickness, and the evaluated mechanical responses include circumferential stress, the J-integral, and the stress intensity factor. The optimization follows the “smaller-the-better” criterion to minimize stress concentration, fracture-driving forces, and the risk of structural failure. Results indicate that internal pressure predominantly affects circumferential stress and the stress intensity factor, whereas wall thickness has the greatest influence on the J-integral. The optimal parameter combination is determined through signal-to-noise ratio analysis and validated using the delta method, confirming the robustness of the selected configuration. A confirmation simulation performed with XFEM demonstrates a consistent reduction in all fracture-related mechanical responses, highlighting the effectiveness of the proposed approach. It should be noted that the present study is limited to the static fracture assessment of external cracks and does not address fatigue crack growth or fatigue life prediction. Overall, the proposed methodology provides a decision-support tool for pipeline integrity management by integrating numerical fracture mechanics analysis with robust design optimization, thereby contributing to safer operation and improved structural reliability. Full article

Background: Chatbots are becoming increasingly valuable in clinical settings, offering rapid access to medical information, aiding documentation, and improving perioperative patient education. Their adaptability makes them promising tools for personalized perioperative risk stratification (PRS) and anesthesia planning, but their definitive role remains uncertain. We aimed to evaluate chatbot performance in PRS compared to standard clinical judgment and to assess the certainty of the evidence supporting their use. Methods: This systematic review (PROSPERO ID: CRD42025642357) followed PRISMA extended and PRISMA-S guidelines. The population was defined according to the PICO framework: we included adult surgical patients undergoing anesthesia assessment (P), evaluated with LLM-based chatbots for perioperative risk stratification and anesthesia planning (I), compared with traditional clinician assessment (C), and extracted performance metrics (O). Comprehensive searches of PubMed, MEDLINE, Scopus, Embase, Google Scholar, Open Gray, ClinicalTrials.gov, WHO ICTRP, and Cochrane Library Central were conducted through January 2026. Risk of bias and study quality were assessed using PROBAST-AI, RoB-2, and ROBINS-I. Certainty of the evidence was assessed using GRADE system. A random-effects meta-analysis of pooled chatbot accuracy was performed, with subgroup analyses by ASA status and perioperative risk stratification. A sensitivity analysis was performed with a leave-one-out exclusion test. Results: Eleven studies published between 2023 and January 2026 were included (N = 227,059 patients). Five prospective cohorts, two large retrospective cohorts, one randomized non-inferiority trial, and three non-clinical or mixed-methods studies were found. Meta-analysis showed that the pooled accuracy of LLM-based chatbots for AI–clinician concordance in perioperative risk stratification and ASA classification was 0.90 [95% CI: 0.42–0.99; 95% prediction interval 0.03–1.00]. Subgroup analyses indicated that the ASA status prediction subgroup reached a pooled accuracy of 0.91 (95% CI: 0.46 to 0.99), whereas the exploratory perioperative risk stratification subgroup showed an accuracy of 0.73 (95% CI: 0.10 to 0.98). Performance decreased with increasing patient complexity. Evidence is limited by small sample sizes, extreme sample size skew toward a single center, geographic bias, inconsistent outcome definitions and performance metrics, and incomplete reporting of adverse events. Most studies lacked prospective trial registration or robust control for confounding, and publication bias cannot be excluded. Conclusions: LLM-based chatbots show promising performance in routine perioperative risk stratification but remain unreliable in complex cases, with potential safety concerns. Given the overall very low GRADE certainty of evidence, these tools should be used as clinician-supervised decision support aids for routine ASA assessment, and should not be relied upon for autonomous use in complex cases or for general perioperative risk stratification. Other: This research received no external funding. PROSPERO ID: CRD42025642357. Full article