Search Results (1,317)

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

In harsh marine environments, vessel energy storage systems (VESS) face elevated thermal runaway (TR) risk, yet practical early warning remains difficult because early voltage differences between TR high-risk cells and normal cells are often weak, warning thresholds vary across operating segments, and decisions relying on a single feature are prone to false or missed warnings. To overcome these difficulties, this study develops a four-part early warning strategy for TR high-risk cells in VESS. First, the original cell voltages are denoised through multiscale jump plus mode decomposition and Spearman correlation guided mode reconstruction to suppress irrelevant interference. Second, an improved Sigmoid nonlinear mapping is introduced to enhance subtle inter-cell voltage deviations and improve early separability. Third, sparse representation is used to construct a cell deviation score, and an adaptive threshold is employed to perform primary abnormal-cell screening under varying segment conditions. Finally, multidimensional mutual information value derived from voltage, temperature, and their rates of change is incorporated into a joint assessment methodology to further verify the abnormal state of flagged cells. Validation on 18 independent real operation cases comprising 2483 discharge segments shows that, across the evaluated TR high-risk cases, the shortest confirmed warning lead time achieved by the proposed strategy was 14 days. The proposed strategy also reduced false and missed warnings, outperformed the compared benchmark methods overall, and retained computational feasibility for onboard application in VESS. Full article

The precise segmentation of small-sized defects on wood surfaces is critical for the quality grading of glued laminated timber (GLT). Existing semantic segmentation models face core bottlenecks in this context: high miss rates, blurred boundary localization, and excessive size measurement errors. To address these issues, this paper proposes an improved Defect-Mask2Former model that integrates an Attention-Guided Pyramid Enhancement (AGPE) module and a Defect Boundary Calibration and Correction (DBCC) module. Through synergistic optimization, the model achieved pixel-level precise segmentation. To support model training and validation, a custom image acquisition device was designed, and the PlankDefSeg dataset was constructed, comprising 3500 pixel-level annotated images covering five defect types across six industrial wood species. Experimental results demonstrate that on the PlankDefSeg dataset, Defect-Mask2Former achieved a mean Intersection over Union (mIoU) of 85.34% for small-sized defects, a 17.84% improvement over the baseline Mask2Former. The miss rate was reduced from 20.78% to 5.83%, and the size measurement error was only 2.86%, strictly meeting the ≤3% accuracy requirement of the GB/T26899-2022 standard. The model achieved an inference speed of 27.6 FPS, satisfying real-time detection needs. By integrating the model into the GLT grading workflow, a grading accuracy of 94.3% was achieved, and the processing time per timber was reduced from 30 s to 1.5 s, a 20-fold efficiency improvement. This study provides reliable technical support for intelligent GLT quality grading and offers a reference solution for other industrial surface defect segmentation tasks. Full article

Multilevel threshold image segmentation is a key task in image processing, yet it faces challenges such as low search efficiency in high-dimensional spaces, difficulty in balancing segmentation accuracy and stability, and insufficient adaptability to complex scenes. Existing solutions mainly include traditional thresholding methods and metaheuristic optimization-based schemes, but they still face limitations in high-dimensional and complex segmentation tasks. The standard Seagull Optimization Algorithm (SOA) suffers from shortcomings including a single exploration mechanism, weak local exploitation capability, and a tendency for population diversity to deteriorate, making it difficult to meet the demands of high-dimensional optimization. To address these issues, this paper proposes a multi-strategy fused improved Seagull Optimization Algorithm (MFISOA), which integrates three strategies: adaptive cooperative foraging, differential evolution-driven exploitation, and centroid opposition-based boundary control. These strategies jointly construct a collaborative optimization framework with dynamic resource allocation, fine local search, and population diversity maintenance, thereby improving global exploration efficiency, local exploitation accuracy, and population stability. To evaluate the optimization performance of MFISOA, numerical simulation experiments were conducted on the CEC2017 and CEC2022 benchmark test suites, and comparisons were made with nine other mainstream advanced algorithms. The results show that MFISOA outperforms the competing algorithms in terms of optimization accuracy, convergence speed, and operational stability. Its superiority is further verified by the Wilcoxon rank-sum test and the Friedman test, with statistical significance (p < 0.05). In the multilevel threshold image segmentation task, using the Otsu criterion as the objective function, MFISOA was tested on nine benchmark images under 4-, 6-, 8-, and 10-threshold segmentation scenarios. The results indicate that MFISOA achieves better performance on metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Feature Similarity Index (FSIM), enabling more accurate characterization of image grayscale distribution features and producing higher-quality segmentation results. This study provides an efficient and reliable approach for numerical optimization and multilevel threshold image segmentation. Full article

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article

Longan is an important economic fruit in tropical and subtropical regions, whose harvesting primarily relies on manual labor. Automated longan harvesting is key to improving the industry’s economic benefits but faces core challenges: mature pericarp is highly similar in color to fruiting mother branches, plus dense branches and severe leaf occlusion, leading to difficult cluster detection and fruiting branch segmentation. Herein, we propose a picking point localization method named PLYS-Longan integrating three customized core modules: Dynamic Convolution, Convolutional Gated Linear Unit (CGLU), and Dynamic Hyperbolic Tangent Activation (DYT) are introduced into YOLongan module to enhance the model’s ability to detect longan clusters. For SELongan module, Depthwise Over-parameterized Convolution (DO-Conv) and Ultra-light Subspace Attention (ULSA) are adopted to improve main branch segmentation precision. The PCLongan module then performs morphological erosion on the segmentation masks and calculates centroids to precisely determine the picking points. Experimental results show that the improved model achieves a mAP@50 of 90.1% (3.3% higher than baseline model) in object detection and a mIoU of 77.24% (1.75% improvement) in semantic segmentation, outperforming the various model significantly. This study provides an efficient and robust solution for longan picking point localization, laying a solid foundation for subsequent automated harvesting. Full article

With the ongoing advancement of urbanization, urban water distribution networks (WDNs) are increasingly challenged by asset aging, corrosion, and pipe bursts, which collectively threaten the safe and reliable operation of urban systems. Consequently, rigorous risk assessment of urban WDNs has become essential. It enables the identification of high-risk segments and hotspots, and provides an evidence base for maintenance prioritization and network optimization. In this study, research progress on risk assessment and failure analysis of urban WDNs over the past 25 years was systematically reviewed. Mainstream approaches, including indicator-based scoring, statistical modeling, and machine learning (ML), were emphasized, and their fundamental principles, methodological characteristics, applicable contexts, and reported practical performance were comprehensively summarized. Indicator-based scoring methods are valued for their transparent structure and ease of implementation, and have been widely adopted in engineering applications. Statistical methods leverage historical records to develop failure models with explicit probabilistic interpretability. ML methods can capture complex nonlinear relationships and show strong predictive capability in data-rich settings. Nevertheless, prevailing approaches continue to face persistent limitations, including incomplete and heterogeneous data, constrained model transferability across systems, and substantial computational demands. Building on these findings, this study highlights future research priorities in enhancing multidimensional models, developing interoperable data-sharing platforms, and conducting life-cycle-oriented risk assessment, with the goal of supporting intelligent and sustainable management of urban WDNs. Full article

Background: Giant cell myocarditis (GCM) is a rare condition with an incompletely understood immune pathogenesis, characterized by inflammatory damage to the myocardium and the presence of multinucleated giant cells on histopathological examination. The frequently fulminant and severe course requires rapid intervention for a correct diagnosis and the initiation of immunosuppressive therapy, which is often life-saving. Materials and methods: This article contains information from observational studies and case reports, systematically collected from prestigious publications such as JACC, NEJN, ESC, JCC, Heliyon, and Cureus found in the PubMed and ClinicalTrials.gov databases. Thus, 25 patients diagnosed with giant cell myocarditis between March 2019 and May 2025 were analyzed, with a focus not only on the initial clinical evolution, mortality incidence, and the need for heart transplantation but also on the incidence of major complications such as cardiogenic shock and malignant rhythm and conduction disorders refractory to drug treatment. These parameters were studied according to certain intrinsic factors that cannot be influenced, such as age at onset, gender, and associated pathology of the patient, as well as extrinsic factors that can be influenced, such as the time of diagnosis and the start of immunosuppressive therapy. The results obtained were compared with those in the literature from previous years, considering the limitations of the current study. Results: The selected patients were 13 women (52%) and 12 men (48%), mostly from the US and Japan, aged between 22 and 76 years, with an average age of 44.92 years. An associated autoimmune pathology was found in 40% of patients in this group, and previous cardiovascular pathology in 28%. Only 8% had a history of GCM. The clinical onset of new-onset heart failure, refractory to usual therapy, with progressive dyspnea as the cardinal symptom was found in 12 patients, representing 48% of cases; palpitations as an expression of rhythm or conduction disorders were found in five patients, representing 20%; precordial discomfort to precordial pain accompanied or not by ST-T segment changes was present in four patients, representing 16%; and general signs and symptoms or those of other organs were present in three (12%) cases. The diagnosis was made by histological examination of the biopsy fragment obtained by endomyocardial biopsy or from the myocardial fragment obtained during the implantation of mechanical cardiovascular support devices and, less frequently, on the explanted heart and at autopsy. In terms of progression, of the 25 patients, four (16%) died, four (16%) required heart transplantation, and 16 (64%) had a severe progression with cardiogenic shock, which required mechanical circulatory support in 11 (44%) cases. The outcome was mainly influenced by the early diagnosis and administration of immunosuppressive medication, but also by the age of the patients and associated chronic diseases. Conclusions: Giant cell myocarditis is a serious condition that, in the absence of rapid diagnosis and appropriate immunosuppressive therapy, has a fulminant, often fatal course. Clinical suspicion of giant cell myocarditis remains important in the initial diagnosis. Raising this suspicion, together with modern and improved paraclinical investigations compared to previous years, has led to faster diagnosis and administration of immunosuppressive therapy in this pathology. Histological examination remains the gold standard for final diagnosis, but it should be noted that it may be non-diagnostic. In the face of a strong suspicion of giant cell myocarditis, the best approach is to start immunosuppressive therapy and monitor the patient’s progress. Immunosuppressive treatment remains decisive in influencing the evolution of this condition, both through prompt administration and through the adaptation of therapeutic regimens to the evolution of patients. A more detailed understanding of the immune-mediated pathogenesis of GCM and the identification of clinical risk factors for unfavorable short- and long-term outcomes may enable earlier risk stratification and the development of more targeted, individualized therapeutic strategies. Full article

Coffee is among the primary agricultural commodities in international trade; however, mapping coffee crops in mountainous regions faces limitations due to high spectral variability and complex canopy structures. This study hypothesized that optimized spectral band combinations focused on the visible spectrum may outperform configurations including near-infrared (NIR) for coffee crop segmentation. This work aimed to evaluate how different spectral band combinations affect the performance of the U-Net for segmenting coffee crops in mountainous regions. Seven PlanetScope images (4 m resolution) from Matas de Minas, Brazil, covering different phenological stages in 2023–2024, were divided into 316 training patches and 25 test patches of 256 × 256 pixels and used to train U-Net models across five spectral band combinations: (B, G, R), (B, G, NIR), (B, R, NIR), (G, R, NIR), and (B, G, R, NIR). The visible spectrum combination (B, G, R) demonstrated superior performance with an overall Accuracy of 0.8669 and, for the Coffee Crops class, an F1-score of 0.8682 and an IoU of 0.7671, outperforming all NIR-inclusive configurations. Visible bands’ sensitivity to pigmentation variations proved more effective in heterogeneous environments, while NIR increased spectral confusion near native vegetation and crop edges. The model overestimated cultivated area by 18.3% due to mixed pixels from 4 m resolution and mountainous terrain. These findings confirm that visible-spectrum bands offer a cost-effective alternative for coffee segmentation, though higher spatial resolution is needed for improved boundary delineation. Full article

This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare. Full article

In robotic fine-grained tasks (e.g., grasping and assembly), precise interaction requires a detailed understanding of object components. While Visual Language Models (VLMs) excel at object-level recognition, they struggle with part-level segmentation (e.g., knife handles), limiting performance in complex scenarios. VLMs face three key challenges: (1) Visual granularity mismatch—object-level features lack part-level details; (2) Semantic hierarchy gaps—parts and objects differ significantly in semantics; (3) Cross-modal bias—CLIP’s text–image alignment favors global over local features. To address these, we propose a one-stage VLM-based part segmentation method. First, the Hierarchy-Aware Feature Selection mechanism analyzes Transformer features in different hierarchies to enhance spatial and semantic precision for part segmentation. Second, the Multi-Hierarchy Feature Adapter bridges object-to-part feature granularity via the hierarchical adaptation. Finally, the Hierarchical Multimodal Alignment Module harmonizes classification accuracy and mask integrity via hierarchical alignment of vision–language, mitigating the bias of CLIP’s object-level priori knowledge. Experiments show the proposed method improves part segmentation performance for Zero-Shot, achieving 25.86% on Pascal-Part and 13.09% on ADE20K-Part (gains of +0.81% hIoU and +2.96% hIoU over baseline). This work advances robotic visual perception, with applications in intelligent manufacturing and intelligent service. Full article

The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement’s technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method’s practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems. Full article

Reliable semantic perception is crucial for service robots operating in complex public indoor environments. However, existing semantic mapping approaches often face the dual challenges of high computational overhead and semantic redundancy in maps. To address these limitations, this paper proposes a low-resource semantic mapping framework based on improved instance segmentation and dynamic constraints from consecutive frames. First, we design the lightweight model MS-YOLO, which adopts MobileNetV4 as its backbone network and incorporates the SHViT neck module, effectively optimizing the balance between detection accuracy and computational cost. Second, we propose a consecutive frame dynamic constraint method that eliminates redundant object annotations through consecutive frame stability verification. Experimental results relating to both fusion and custom datasets demonstrate that compared to YOLOv8n-seg, MS-YOLO achieves improvements in accuracy, recall, and mAP@0.5, while reducing the number of parameters by 11.7% and floating-point operations (FLOPs) by 32.2%. Furthermore, compared to YOLOv11n-seg and YOLOv5n-seg, its FLOPs are reduced by 17.2% and 25.5%, respectively. Finally, the successful deployment and field validation of this system on the Jetson Orin NX platform demonstrate its real-time capability and engineering practicality for edge computing in public indoor service robots. Full article

Environmental awareness is playing an increasingly important role in all segments of the world, with sustainability and recycling being key elements. The aim of the research is to examine the challenges companies face in terms of sustainability when implementing procurement and supply chain management processes related to interior design. The research focused on four main questions: how procurement and supply chain management are reflected in construction processes, what challenges these processes face, and how they can influence the sustainable use of materials in architectural supply chains. The literature review was based on a systematic literature review using the PRISMA screening process and the PEO framework, utilizing the SCOPUS database and processing 70 scientific articles following the selection process. During the research, I also used the Critical Incident Technique (CIT), in which I asked interior designers about their positive and negative experiences with the procurement of sustainable materials and supply chain management processes. The methodology thus provided deeper insight into the decision-making processes of professionals, where sustainability conflicts with economic and operational realities. The qualitative research was supplemented by a questionnaire survey, which aimed to assess sustainability, its prevalence, and professional obstacles. The results of the research show that this topic is a research gap, but the openness of professionals shows a positive trend. Companies face numerous challenges related to new technologies and environmental awareness in order to create or transform well-functioning supply chain management processes. Full article

Load forecasting for Integrated Energy Systems (IESs) is critical to enabling multi-energy coordinated optimization and low-carbon scheduling. Facing multi-load types and multi-site high-dimensional heterogeneous data, there remains a global learning challenge stemming from insufficient representation of spatiotemporal coupling features. In response to the multi-source heterogeneous characteristics of IES loads, this paper designs a Spatiotemporal Topology Encoder that maps load data into a tensorized multi-energy spatiotemporal topological representation via fuzzy classification and multi-scale ranking. In parallel, we construct a MultiScale Hybrid Convolver to extract multi-scale, multi-level global spatiotemporal features of multi-energy load representations. We further develop a Temporal Segmentation Transformer and a Steady-State Exponentially Gated Memory Unit, and design a jointly optimized forecasting model that enforces global dynamic correlations and local, steady-state preservation. Altogether, we propose a multi-scale spatiotemporal fusion and steady-state memory-driven load forecasting method for integrated energy systems (MSTF-SMDN). Extensive experiments on a public real-world dataset from Arizona State University demonstrate the superiority of the proposed approach: compared to the strongest baseline, MSTF-SMDN reduces cooling load RMSE by 16.09%, heating load RMSE by 12.97%, and electric load RMSE by 6.14%, while achieving R² values of 0.99435, 0.98701, and 0.96722, respectively, confirming its feasibility, efficiency, and promising potential for multi-energy load forecasting in IES. Full article