Search Results (896)

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems. Full article

Soil moisture prediction is essential for precision irrigation in water-limited agricultural systems. This study presents a deep learning-driven irrigation framework for winter wheat, integrating a novel Informer–UNet model with a Comprehensive Irrigation Index for adaptive water management. The Informer–UNet combines ProbSparse self-attention mechanisms with UNet’s multi-scale feature fusion, enabling simultaneous prediction of soil moisture at 27 monitoring points across three depths, 10, 30, and 50 cm, while quantifying prediction uncertainty through Monte Carlo Dropout. A Comprehensive Irrigation Index incorporating moisture deviation, spatial variance, and confidence interval width was developed, with weights optimized via genetic algorithm. Field experiments were conducted in Chengdu, China, over two winter wheat growing seasons. The Informer–UNet achieved superior prediction accuracy, R² greater than 0.98, RMSE less than 0.65, compared to LSTM, Transformer, and standard Informer models, with the fastest convergence and lowest validation loss. The proposed DeepIndexIrr strategy maintained soil moisture within the target range, 55% to 75%, for over 81% of the irrigation period, reducing water consumption by 38.2% compared to fixed-threshold control and 19.2% compared to expert manual scheduling. These results demonstrate that integrating spatially distributed deep learning predictions with uncertainty-informed decision rules offers a promising approach for sustainable precision irrigation. Full article

Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead to feature ambiguity and unstable detection performance in real-world maritime environments. To address these challenges, this paper proposes a lightweight one-stage ship detection framework designed for robust real-time perception under degraded maritime sensing conditions. The proposed method incorporates an Adaptive Expert Selection Attention (AESA) mechanism to perform adaptive feature selection and background suppression under visually degraded conditions, together with a Geometry-Aware MultiScale Fusion (GAMF) module that enables orientation-aware aggregation of contextual information for elongated ship targets near complex sea–sky boundaries. In addition, a geometry-aware bounding box regression refinement is introduced to improve localization consistency in image space. Extensive experiments conducted on a unified real-world maritime benchmark demonstrate that the proposed framework consistently outperforms the baseline YOLO11n model by approximately 2–5 percentage points in terms of mAP@0.5 and mAP@0.5:0.95, while maintaining moderate computational complexity and real-time inference capability. These results indicate that the proposed method provides a practical and deployment-oriented perception solution for maritime automation applications, including onboard electro-optical sensing and coastal surveillance. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article

This study examines the tourism value chain of the Cartagena de Indias–Santa Cruz de Mompox river corridor in Colombia. The objective is to analyze how the corridor’s territorial configuration, prioritized nodes, and inventory of attractions contribute to strengthening the sustainable integration of destinations. The research is based on three questions: (RQ1) How is the corridor’s territorial configuration structured and refined? (RQ2) Which locations should be prioritized according to the multi-criteria evaluation? (RQ3) How do the attractions and industry trends influence opportunities for strengthening the sustainable value chain? A case study design combined document review, mapping, field validation, expert consultation, multi-criteria scoring, and stakeholder surveys. The findings reveal a spatially continuous but functionally uneven system. Central nodes, such as Cartagena and Mompox, show greater integration of attractions and services, while intermediate municipalities show untapped potential, limited by insufficient promotion and training. While infrastructure and basic services are positively assessed, governance coordination and marketing remain critically deficient. Trend analysis indicates high viability for heritage and nature tourism, while eco-innovation and well-being require gradual institutional and capacity development. This study provides a replicable framework that integrates territorial mapping, prioritization matrices, and attraction-based value chain analysis for sustainable tourism in corridors. Full article

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies. Full article

Smart home technologies are increasingly integrated into residential environments jointly inhabited by older adults and young children. However, existing research remains largely ageing-centered and insufficiently addresses the governance challenges arising from generational asymmetries in vulnerability, spatial agency, and authority within shared domestic space. Rather than merely complicating design, these asymmetries fundamentally reshape how safety, autonomy, access, and surveillance are structured in everyday residential practice. This study reconceptualizes senior–child intergenerational co-living as a governance-oriented socio-technical system in which generational asymmetry functions as a structuring principle of design prioritization. An expert-based decision framework integrating interdisciplinary focus groups and the Analytic Hierarchy Process was developed to evaluate five design dimensions and thirty indicators. The findings reveal a differentiated priority structure in which intelligent safety, accessibility, and risk governance together with spatial integration and technological accessibility constitute the foundational architecture of inclusive intergenerational housing, while interaction-oriented functions receive comparatively lower weights. By embedding generational asymmetry within a formal hierarchical evaluation model, this study extends smart housing scholarship beyond ageing-centered optimization and provides a structured decision-support logic for inclusive multi-generational residential design aligned with the objectives of the United Nations Sustainable Development Goals (SDGs), particularly those promoting inclusive communities and health equity. Full article

Municipal solid waste incineration (MSWI) is a critical technology for advancing urban sustainability, contributing to improved environmental quality, optimized energy structures, and the circular economy. However, the realization of these sustainability benefits is contingent upon the stable, efficient, and low-emission operation of the incineration process. This operational stability is directly governed by several key variables, such as furnace temperature, main steam flow rate, flue gas oxygen content, and burnout point temperature. The inherent complexity of controlling these interconnected variables necessitates the development of an accurate multi-variable prediction model to ensure both energy recovery efficiency and environmental compliance, which are core pillars of sustainable waste management. Existing studies have often addressed these key controlled variables in isolation, lacking a unified modeling framework. Furthermore, they have not adequately considered how dimensional differences among these variables impact the performance evaluation of predictive models, a critical oversight for ensuring holistic process sustainability. To address these gaps and support the intelligent operation of sustainable waste-to-energy systems, this study proposes a novel multi-controlled variable modeling method based on shared features and an improved fuzzy neural network. Our integrated approach begins by calculating the Pearson correlation coefficient between each manipulated variable and each controlled variable—selected based on expert knowledge—to assess the distinguishability of operating conditions within the current dataset. Subsequently, a correlation threshold, informed by expert knowledge, is applied to identify shared features that influence multiple controlled variables simultaneously. Finally, we enhance the fuzzy neural network by redefining its evaluation criterion to accommodate variable dimensional differences, leading to the development of a robust multi-controlled variable prediction model. This model is designed to provide a more comprehensive and accurate basis for process control, directly contributing to improved energy efficiency and reduced environmental impact. The effectiveness of our proposed model is validated using operational data from an actual MSWI plant, demonstrating its potential to support more sustainable and economically viable waste-to-energy operations. Full article

Background: Stereotactic brain biopsy is the gold standard for the pathological diagnosis of malignant brain tumors. However, conventional procedures rely heavily on manual path planning and unintuitive navigation, which significantly increase the risk of severe complications and impose an additional cognitive burden on surgeons. Methods: We propose an augmented reality-based navigation system that synergizes multi-objective path planning with hybrid registration. Preoperatively, the system utilizes a constrained multi-objective optimization (MOO) model derived from clinical criteria to automatically calculate and visualize optimal biopsy paths within a three-dimensional anatomical environment. Intraoperatively, the system performs rapid initial alignment using quick response (QR) codes, followed by precise refinement through anatomical landmarks. This process ultimately enables the highly accurate, real-time overlay of the surgical path and anatomical models onto the patient’s operative field. Results: An expert study across four common brain tumor locations demonstrated that the MOO model significantly outperformed manual methods in satisfying safety criteria. The hybrid registration reduced the mean fiducial registration error (FRE) from 4.19 ± 1.11 mm to 2.37 ± 0.91 mm (p < 0.001), with a mean target registration error (TRE) of 2.34 ± 0.71 mm and a mean clinical setup time of 2.63 ± 0.36 min. Conclusions: This system assists stereotactic brain biopsy through automated path planning and immersive augmented reality-based guidance, highlighting its potential to support surgical workflow consistency and procedural safety. Full article

Efficient workflow scheduling in cloud–edge environments is severely bottlenecked by long-horizon dependencies and myopic resource fragmentation. This paper proposes the Decoupled Offline Sequence-based (DOS) scheduling framework to address these challenges. By decoupling expert policy learning from runtime deployment, DOS utilizes a multi-dimensional priority-aware linearization strategy to deterministically transform DAG-structured workflows into dependency-consistent sequences. Leveraging offline expert trajectories, we train UDC, a Gated CNN achieving unified sequence modeling via innovative triplet-to-unary encoding, equipped with explicit action masking to distill long-horizon spatio-temporal packing patterns. This mechanism enables rapid feed-forward inference without costly online environment interactions or policy updates. Extensive evaluations on real-world Alibaba cluster workloads demonstrate that DOS not only consistently minimizes average makespan compared to classical heuristics, but also drastically reduces resource-blocked steps under extreme concurrency versus online Actor–Critic experts. Crucially, compared to the Decision Transformer (DT) baseline, the UDC model achieves strictly scale-invariant and significantly lower inference latency, highlighting its robust scalability and practicality for large-scale continuum systems. Full article

Driven by the global energy transition, mechanized construction—characterized by enhanced safety, efficiency, and quality—is becoming the mainstream approach in power grid development. Mechanization assessment serves as a critical tool for guiding and optimizing this process, yet current practices remain largely manual, resulting in inefficiency, time-consuming operations, and a lack of real-time insights, which severely limit its practical utility for dynamic project guidance. To address these challenges, this study proposes a novel framework that integrates semantic technology (i.e., ontology) and large language models (LLMs). The framework first constructs a semantic model of the power grid construction domain using ontology. An LLM is then employed to convert multi-source project data into structured ontological instances. Building on this, mechanization assessment criteria are formalized into machine-executable Semantic Web Rule Language (SWRL) rules, which enable automated reasoning and scoring through an ontological reasoner. Furthermore, the LLM is utilized to generate comprehensive and intelligible assessment reports based on the reasoning outputs. To validate the proposed method, 126 real-world project cases were applied to the system. The results demonstrate a 96% accuracy rate in mechanization assessment outcomes compared to expert evaluations. The approach facilitates an objective, standardized, and dynamic evaluation of construction mechanization levels, providing a foundation for intelligent and scalable management models in power grid construction. Full article

The creation and application of classification category labels are essential tasks for transforming complex information into structured knowledge. Categories are used for summary and reporting purposes and have historically been identified by domain experts based on their past experiences and norms. Our interest lies in the general case where expert-generated category lists require improvement, and unsupervised learning, on its own, struggles to effectively identify categories for multi-class classification of human-generated texts. We hypothesise that including an annotated knowledge graph (KG) in an embedding process will positively impact unsupervised clustering performance. Our goal is to identify clusters that can be labelled and used for classification. We look at unsupervised clustering of Maintenance Work Order (MWO) texts. MWOs capture vital observations about equipment failures in process and heavy industries. The selected KG contains a mapping of equipment types to their inherent function based on the IEC 81346-2 international standard for classification of objects in industrial systems. Performance is assessed by statistical analysis, subject matter experts, and Normalized Mutual Information score. We demonstrate that Word2Vec Bi-LSTM and Sentence-BERT NN embedding methods can leverage equipment inherent function information in the KG to improve failure mode cluster identification for the MWO. Organisations seeking to use AI to automate assignment of a failure mode code to each MWO currently need test sets classified by humans. The results of this work suggest that a semantic layer containing a knowledge graph mapping equipment types to inherent function, and inherent function to failure modes could assist in quality control for automated failure mode classification. Full article

Background/Objectives: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Although chest computed tomography (CT) enables detailed assessment of lung abnormalities, manual interpretation is time-consuming, requires expert expertise, and is prone to diagnostic variability. To address these challenges, this study proposes DE-SAMNet, a hybrid deep learning framework for automated multi-class LC classification from CT scans. Methods: The model integrates two pre-trained convolutional neural networks—DenseNet121 and EfficientNetB0—operating in parallel to extract complementary multi-scale features. A Spatial Attention Module (SAM) is applied to each feature stream to emphasize clinically important regions. Final classification is performed through a compact fusion mechanism involving global average pooling, batch normalization, and a fully connected layer. DE-SAMNet was evaluated on two datasets: a public dataset (IQ-OTH/NCCD) with benign, malignant, and normal cases, and a private clinical dataset including benign, malignant, cystic, and healthy cases. Results: On the public dataset, the model achieved a 99.00% F1-score, 98.41% recall, 99.64% precision, and 99.54% accuracy. On the private dataset, it obtained 95.96% accuracy, 95.99% precision, 96.04% F1-score, and 96.21% recall, outperforming existing approaches. To enhance reliability, explainable AI (XAI) techniques such as Grad-CAM were used to visualize the model’s decision rationale. The resulting heatmaps effectively highlight lesion-specific regions, offering transparency and supporting clinical interpretability. Conclusions: This explainability strengthens trust in automated predictions and demonstrates the clinical potential of the proposed system. Overall, DE-SAMNet delivers a highly accurate and interpretable solution for early LC detection. Full article

Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional usability assessments and consider perceptual, cognitive, emotional, and interaction-related factors. However, existing UX evaluation efforts in XR often rely on isolated instruments or domain-specific studies, lacking a systematic and reusable evaluation methodology. This paper proposes a human-centered methodology for evaluating user experience in extended reality applications, integrating UX dimensions and XR-specific characteristics into a structured and coherent evaluation process. The methodology is grounded in a multi-phase research process that includes a comprehensive literature review, expert consultation, correlation analysis between UX dimensions and XR features, and formal specification of evaluation phases and activities. Based on this process, the proposed methodology supports evaluators in selecting appropriate UX evaluation methods and instruments according to the characteristics and experiential goals of XR applications. The methodology defines a set of UX dimensions tailored to immersive environments, capturing perceptual, cognitive, emotional, and interaction aspects that are critical for the design and evaluation of adaptive and human-centered XR systems. An expert-based validation was conducted to assess the clarity, usefulness, and applicability of the methodology, leading to refinements in its structure and descriptions. The methodology promotes a human-centered approach by considering user perception, emotional impact, and contextual experience across XR modalities. It additionally contributes to the field by offering a reusable process for UX evaluation in XR, supporting more consistent, transparent, and human-centered assessment practices. It also provides a foundation for future empirical studies and the development of evaluation approaches inspired by natural and adaptive human–environment interactions. Full article

The acceleration of economic, technological, geopolitical and environmental processes has significantly increased the exposure of national economies to interconnected financial and non-financial risks. While global financial risks and corporate risks have been extensively analyzed, significant national risks—such as fiscal sustainability, debt vulnerability, systemic inefficiency and investment uncertainty—are often treated fragmentarily or descriptively within conventional sovereign risk frameworks. This article offers a comparative analytical approach to identifying national financial and non-financial risks by examining the degree of convergence and divergence between risks identified in four different sources: national expert scientific studies, World Economic Forum global risk assessments, strategic development documents of Bulgaria and national media coverage. Using expert data, structured content analysis, a modified media visibility index, and nonparametric statistical tests for linked binary data, the study identifies risks that are consistently recognized across sources and therefore pose an increased threat to financial stability, as well as risks that remain systematically underestimated despite their potential fiscal and macroeconomic consequences. The results show that cross-source comparison significantly improves the detection of national risks and reveals blind spots in fiscal planning, investment, and social policy. This article contributes to the literature on the management of risks with a direct negative financial effect or with an indirect financial impact on the national economy by positioning national risk identification within a governance-oriented, multi-source analytical framework. Full article