Search Results (748)

This study investigates how participants conceptualise sustainability and sustainability citizenship, as well as how these conceptualisations relate to perceived agency. Drawing on two open-ended prompts, it analyses participants’ visions of a sustainable future and the roles they would like to play within it. The dataset was based on 1714 coded response segments from 164 participants. Methodologically, the study combines qualitative content analysis, independent human-AI double coding, manual validation, inter-rater reliability assessment, and residual-based co-occurrence analysis within a qualitatively grounded mixed-methods design. The results show that sustainability is predominantly framed in civic, symbolic, and ecological terms, whereas strategic competence and professionally articulated agency remain less visible. Sustainability meanings and role conceptions also vary systematically across disciplinary contexts. In addition, the analyses reveal patterned gaps between participants’ future visions and their self-attributed roles in sustainability transformations. The study contributes empirical insights into sustainability meaning-making and perceived agency and shows how LLM-assisted coding can be embedded in a transparent mixed-methods workflow. For sustainability education, the findings underline the importance of strengthening strategic and systemic dimensions of competence and linking civic engagement more closely to professional pathways of action. Full article

Microplastics represent an emerging threat to aquatic ecosystems, human health, and coastal aesthetics, with increasing concern about their accumulation on beaches due to ocean currents, wave action, and accidental spills. Despite their environmental impact, current methods for detecting and quantifying microplastics remain largely manual, time-consuming, and spatially limited. In this study, we propose a deep learning-based approach for the semantic segmentation of microplastics on sandy beaches, enabling pixel-level localization of small particles under real-world conditions. Twelve segmentation models were evaluated, including U-Net and its variants (Attention U-Net, ResUNet), as well as state-of-the-art architectures such as LinkNet, PAN, PSPNet, and YOLOv11 with segmentation heads. Models were trained and tested on augmented data patches, and their performance was assessed using Intersection over Union (IoU) and Dice coefficient metrics. LinkNet achieved the best performance with a Dice coefficient of 80% and an IoU of 72.6% on the test set, showing superior capability in segmenting microplastics even in the presence of visual clutter such as debris or sand variation. Qualitative results support the quantitative findings, highlighting the robustness of the model in complex scenes. Full article

Hammer throw is a complex discipline requiring strength, refined technique, and precise inter-segmental coordination. We developed an IoT-enabled system with inertial measurement units (IMUs) to provide objective, real-time analytics for coaches and athletes. IMUs were mounted on the hip, knee, and ankle to capture tri-axial acceleration and angular velocity during the throwing action. Data were streamed wirelessly and processed to extract rotation rate profiles, joint coordination metrics, and temporal events (winds, turns, and release). Two collegiate athletes performed 10 throws, and the results were compared with video-based analysis. The IMU system captured finer-grained variations in angular velocity and acceleration during rapid rotation phases and achieved an accuracy of 93.5% in classifying higher- and lower-quality throws using cross-validated models. The system developed enables quantitative feedback and continuous progress tracking in training. The feasibility of IMU + IoT integration for hammer throw performance analysis provides a foundation for AI-assisted, on-field decision support. Full article

Long-term sports assessment is a challenging task in video understanding, since it requires judging subtle movement variations over minutes and evaluating action–music coordination. However, in many sporting events the background music is only weakly related to the performed movements, and the cues that matter for synchrony are often temporal and structural, such as small phase or tempo deviations that occur around decisive moments, rather than semantic correspondences between audio content and action categories. Prior approaches typically rely on implicit cross-modal fusion over dense sequences to learn such weak associations, which can smooth out near-miss misalignment and become brittle under tempo or phase shifts. To address this issue, we propose BEATSCORE, a beat-guided audio–visual learning framework that explicitly models action–music alignment at the beat level and performs event-centric sparse grading for long videos. In our framework, we first convert audio and motion into beat-synchronous tokens, enabling direct comparison on a unified rhythmic timeline. We then introduce a beat-level contrastive objective with near-offset hard negatives to sharpen sensitivity to misalignment. To handle the sparsity of decisive moments, we further design an event proposal and grading module that scores a small set of key segments and aggregates them via learnable multiple-instance pooling into a final assessment score. We evaluate BEATSCORE on public long-term sports benchmarks to demonstrate improved accuracy with competitive efficiency. Full article

High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article

Vision–language–action (VLA) models have shown strong potential for enabling robots to interpret goals and perform complex manipulation tasks by integrating perception, language, and control. However, existing VLAs rely heavily on large-scale, diverse demonstration datasets, which are difficult and expensive to collect. When trained with limited data, they often overfit to irrelevant visual cues such as background, lighting, or viewpoint, resulting in weak generalization. To overcome this limitation, we propose a simple yet effective object-centric learning framework for VLA. For each sub-task, the framework leverages an instance segmentation foundation model to identify and track task-relevant objects, and trains the policy on both the original RGB scene and two object-focused representations: (i) a masked image emphasizing the target object and (ii) an object-only crop. These multiple visual inputs share the same action supervision, encouraging the policy to attend to the manipulated object rather than the surrounding context. Furthermore, a distance-based chunk alignment mechanism ensures smooth control transitions between consecutive predicted action segments. Experiments conducted in both simulation and real hardware demonstrate that the proposed method achieves robust performance and stable trajectories across various manipulation tasks, validating its practicality and efficiency in training object-aware robotic behaviors. Full article

To explore the stress-bearing characteristics of the “inner tensioned steel ring–segment–surrounding rock” composite structure in TBM (Tunnel Boring Machine) pressurized water conveyance tunnels, a 3D refined finite element model for this composite structure was established, with the Class V surrounding rock section of the TBM pressurized water conveyance tunnel in the Rongjiang-Guanbu water diversion project selected as the research subject. The effects of the internal water pressure, surrounding rock type and tunnel burial depth on the mechanical properties of the composite structures are studied. The findings demonstrate that reinforcing the tunnel structure with an inner tensile steel ring can effectively constrain tunnel deformation, diminish the tensile stress of segments and the extent of tensile zones, and enhance the bearing capacity of the composite structure. Under the effect of internal water pressure, the compressive stress of segments, vertical deformation, joint opening degree, stress of connecting bolts, stress of the inner tension ring, and stress of anchor rods all exhibit a reduction compared to the scenario without internal water pressure. Under the combined action of external water–soil pressure and internal water pressure, variations in surrounding rock types lead to respective increases of 37.16%, 15.75%, and 15.12% in the stress of connecting bolts, segment joint misalignment, and anchor bolt stress. As the tunnel burial depth increases, the stress of connecting bolts and the vertical deformation of segment and the joint misalignment of the pipe segment increase by 140%, 107% and 60.61%, respectively. In addition, under the combined action of external water and soil pressure and internal water pressure, the load-sharing ratios of the surrounding rock, pipe segment, inner tension ring and anchor rod are 34.87%, 34.59%, 21.59% and 8.95%, respectively, and the load-sharing ratio of the inner tensioned ring is 85.80% higher than that observed in the absence of internal water pressure, indicating that internal water pressure effectively enhances the load-sharing performance of the inner tensioned steel ring. In the composite structure, the load-sharing ratio of surrounding rock decreases as the surrounding rock class increases (from Class III to Class V). Under the same load condition, the load-sharing ratio of Class III surrounding rock is 7.14% higher than that of Class V. As the tunnel burial depth increases, the inner tensioned steel ring and anchor rods function more prominently as reserve-bearing components. When the tunnel burial depth reaches 71 m, the load-sharing ratio of the inner tension steel ring and anchor rod increases by 19.91% and 55.72%, respectively, compared with that of the buried depth of 31 m. The research results can provide a theoretical reference for the lining design and late reinforcement measures of similar tunnel projects. 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

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

Arch-suspended structures represent a distinctive form of hybrid suspension system. By combining an arch with a suspended floor system, this structural typology leverages the inherent advantages of both components while mitigating the limitations of each when used independently. This synergy effectively reduces peak internal forces and flexural deformations in structural members. Although widely applied in bridge engineering, research on arch-suspended building structures remains scarce. This paper investigates the construction techniques employed for a large-scale arch-suspended building. The stability of temporary support systems during construction is verified, and the mechanical behavior of critical joints—including the composite slab hanging pillar, arch support, and arch roof—is examined through both experimental testing and numerical simulation. The results demonstrate that a partitioned and segmented construction method is feasible for such complex structures. Structural internal forces and deformations can be effectively controlled by installing tubular temporary supports on both sides of the hanging pillars and lattice temporary supports at the base. Step-by-step unloading of these temporary supports ensures their stability throughout the construction process. Furthermore, the welds in the composite slab hanging pillar effectively transfer tensile forces from the middle plate to the side plates, enabling composite action and collaborative load-bearing among the steel plates. When subjected to loads of 2 times and 4.3 times the design load, localized plasticity was observed in the arch support and arch roof, respectively, while the overall structural integrity remained secure. This study provides a valuable reference for the design and construction of innovative long-span building structures, offering insights that can inform the development and practical application of arch-suspended systems in future architectural projects. Full article

Surface cracking is a key visible indicator of deterioration in concrete infrastructure and is routinely captured by vision sensors during field inspections. To translate inspection imagery into actionable maintenance information, crack delineation must be accurate at the pixel level and robust to challenging conditions where cracks are slender, discontinuous, low-contrast, and easily confused with joints, stains, texture patterns, and illumination artifacts. This study proposes a lightweight CNN–Mamba hybrid segmentation framework built upon Vm-unet for reliable crack mapping under heterogeneous inspection scenarios and resource-constrained deployment. The framework couples boundary-sensitive convolutional features with long-range state-space representations via a spatially modulated convolution design, refines skip-connection features using reciprocal co-modulation attention to suppress background interference, and enhances cross-scale interactions through a decoder interaction fusion scheme to preserve fine-crack continuity and sharp boundaries. Experiments on a multi-source composite dataset and public benchmarks show consistent improvements over representative CNN-, Transformer-, and Mamba-based baselines. The proposed method achieves 80.11% mIoU and 82.05% Dice on the composite dataset, while maintaining an efficient accuracy–cost trade-off (36.049 GFLOPs, 25.991 M parameters). The resulting crack masks provide a dependable basis for inspection-driven quantitative assessment and maintenance decision support. Full article

Collaborative robots are increasingly deployed in human-shared industrial workspaces, where perception is a key enabler for safe interaction, flexible manipulation, and human-aware task execution. In the context of Industry 5.0, computer vision for cobots must meet not only accuracy requirements but also human-centered constraints such as safety, transparency, robustness, and practical deployability. This paper surveys computer-vision approaches used in collaborative robotics and organizes them through a task-driven taxonomy covering detection, segmentation, tracking, pose estimation, action/gesture recognition, and safety monitoring. Beyond a descriptive literature review, the paper provides a task-driven qualitative analytical perspective that relates families of computer vision methods to key industrial constraints, including occlusion, lighting variability, clutter, domain shift, real-time latency, and annotation cost, and summarizes comparative strengths and failure modes using unified criteria. We further discuss challenges related to data availability and evaluation practices, highlighting gaps in reproducibility, standardized metrics, and real-world validation in shared human–robot environments. Finally, we outline implementation and deployment considerations across common software stacks (e.g., Python-based pipelines and MATLAB-based prototyping), emphasizing ROS2 integration, edge inference, and lifecycle maintenance. The survey concludes with research directions toward robust multimodal perception, explainable human-aware vision, and benchmarkable safety-critical perception for next-generation collaborative robotic systems. Full article

Timely labor market monitoring is essential for policy design and operational planning, yet annual reports can mask turning points and subgroup heterogeneity. This paper develops a reproducible monitoring and prediction framework using administrative statistics from the General Organization for Social Insurance (GOSI) in the Saudi Open Data Portal. We document descriptive patterns in formal participation and insurable wages, including age-group dispersion, stable correlation structure, and explicit handling of an anomalous wage release and limited missing wage entries. We then formulate from non-salary administrative descriptors. Under leakage control, Random Forest models achieve accuracy around 0.71 across releases. Most errors are concentrated between adjacent wage bands, which is consistent with threshold discretization of a continuous wage distribution. To support operational deployment, we add out-of-time validation across releases and probabilistic assessment, showing that predictive skill transfers across updates and that calibration improves the reliability of probability scores for monitoring thresholds. Overall, the results indicate that administrative releases contain persistent actionable signals for wage segmentation without salary-derived inputs, supporting forecasting-oriented surveillance and early-warning dashboards. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

This paper presents a channel segmentation proofreading network for crack counting with imbalanced samples. The network is built by stacking basic blocks called channel segmentation proofreading blocks, which are composed of the Approximate Overlapping Window Transformer and the Counting Proofreading Module. The former is designed to extract sufficient high-level semantic information, enhancing the ability of the network to judge crack quantities. Guided by the calculation results of the self-attention mechanism in the classical Transformer, Approximate Overlapping Window Transformer employs distinct computation steps to obtain the same results. Confining the computation process within overlapping windows, we continuously adjust to obtain the most suitable feature extraction process and internal structure for crack counting. Furthermore, to prevent the misidentification of multiple cracks as a single crack due to incorrect connection predictions of crack regions, the Counting Proofreading Module employs channel separation techniques. Following the concept of splitting positive and negative weights, it constructs positive and negative values with different characteristics, further confirming crack regions. Through the combined action of both components, when trained and tested on the crack counting dataset, our network achieves optimal results across all metrics. Full article