Search Results (5,803)

This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization. Full article

Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

Background/Objectives: Social rejection during the latency stage is a significant risk factor for children’s emotional and social development. Whereas earlier research focused primarily on individual characteristics of rejected children, contemporary perspectives emphasize peer-group processes, including norm formation, hierarchies, and social status structures. Although autonomy has been widely examined as an individual developmental construct, less attention has been given to its possible collective expression within the classroom peer group. This study aimed to explore how children understand and experience group autonomy and to clarify its role in social status and peer rejection. Methods: Twelve classroom-based focus groups were conducted with 140 fifth-grade children from five public elementary schools in Israel. Discussions were initiated using a projective narrative describing ambiguous peer exclusion. Data were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Coding was conducted independently by two researchers and refined through iterative comparison and reflexive procedures. Results: Three themes emerged: (1) a shared longing for classroom-based group autonomy and collective decision-making; (2) group autonomy as an implicit hierarchical criterion shaping social status, whereby reduced reliance on adults and alignment with peer norms were associated with higher status, while adult dependence was linked to marginalization; and (3) an ambivalent structure of autonomy, as children valued peer independence yet expressed fear of adult punishment and responsibility. Conclusions: Findings suggest that during the latency stage autonomy shifts toward a collectively organized peer-group process. Recognizing group autonomy as a developmental dimension may deepen understanding of social status and peer rejection within classroom contexts. Full article

Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant attention in medical image analysis. This comprehensive review examines the current state of the art in diffusion models for medical image segmentation, covering theoretical foundations, methodological innovations, computational efficiency strategies, and clinical applications. We analyze recent advances in latent diffusion frameworks, transformer-based architectures, and ambiguous segmentation modeling while addressing the practical challenges of implementing these models in clinical environments. The review encompasses applications across multiple medical imaging modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and X-ray imaging, providing insights into performance achievements and identifying future research directions. Through systematic analysis of publications mostly from 2019 to 2025, we demonstrate that diffusion models have achieved remarkable progress in addressing fundamental challenges including data scarcity, inter-observer variability, and uncertainty quantification. Notable achievements include inference time being reduced from 91.23 s to 0.34 s for echocardiogram segmentation (LDSeg, Echo dataset), DSC scores up to 0.96 for knee cartilage MRI segmentation, and a +13.87% DSC improvement over baseline methods for breast ultrasound segmentation. This review serves as a comprehensive resource for researchers and clinicians interested in leveraging diffusion models for medical image segmentation, providing a roadmap for future research and clinical translation. Full article

Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model’s sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera. Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework. Full article

Historically, the sustainable development of education bears the mission of advancing the sustainable development of human society, and the transformation of universities is a crucial link in the sustainable development of higher education. This paper addresses the top-down, government-led transformation of local undergraduate universities, a process currently hampered by ambiguous objectives, insufficient internal motivation, and a mismatch in supporting systems, resources, and institutional culture. To analyze and optimize this process, we establish an asymmetric evolutionary game model involving the local government, local universities, and teachers. By integrating optimization theory, this study determines the optimal equilibrium conditions for the game system. We then use numerical simulations to depict the system’s evolutionary paths under various transformation scenarios. Furthermore, we have analyzed the key influencing factors for promoting university transformation and development, which form the basis for proposing targeted policy recommendations. Full article

Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box annotations for six complex behaviors across diverse habitat scenes. Leveraging this data, we propose AviaTAD-LGH, a real-time multi-task framework that incorporates auxiliary motion supervision into a dual-pathway 3D backbone to enhance feature discriminability. A critical bottleneck in such multi-task settings is the negative transfer caused by conflicting optimization objectives. To resolve this, we present Lightweight Gradient Harmonization (LGH), a plug-and-play optimization strategy that dynamically modulates task weights based on the cosine similarity of gradient directions. This mechanism effectively aligns optimization trajectories without introducing inference latency. Extensive experiments demonstrate that AviaTAD-LGH achieves a state-of-the-art mAP of 68.60%, surpassing strong public baselines by 7.44% and improving upon the single-task baseline by 2.80%, with significant gains observed on ambiguous dynamic classes. The proposed pipeline enables efficient, scalable ecological monitoring suitable for edge deployment. Full article

Background and Objectives: Osteonecrosis of the jaw (ONJ) occurring after infection with SARS-CoV-2 has emerged as an increasingly reported complication in the post-COVID-19 era. Post-COVID-19 osteonecrosis of the jaw (PC-ONJ) has been described in association with both COVID-19-associated mucormycosis (CAM) and non-fungal phenotypes. This narrative review aims to synthesize and critically analyze the available evidence regarding terminology and classification, epidemiology and risk factors, pathophysiological mechanisms, clinical and imaging characteristics, diagnostic challenges, and management strategies relevant to oral and maxillofacial surgery practice. Materials and Methods: An extensive literature search was conducted in the PubMed/MEDLINE, Scopus, Web of Science, ScienceDirect, and Google Scholar databases. The search targeted peer-reviewed publications published between 2020 and 2025, reflecting the post-pandemic emergence of this clinical spectrum. Original studies, systematic and narrative reviews, multicenter case series, consensus guidelines, and well-documented case reports were considered. Results: Available data, largely derived from case reports and small series, demonstrate a predominance of maxillary involvement and frequent association with diabetes mellitus and systemic corticosteroid therapy. Proposed mechanisms include COVID-19-associated endothelial dysfunction, microvascular thrombosis, immune dysregulation, metabolic imbalance, and treatment-related effects. Clinically, patients may present with persistent orofacial pain, tooth mobility, exposed or probeable bone, and frequent sinonasal extension, with symptoms sometimes preceding bone exposure. Diagnostic challenges arise from the overlap with medication-related osteonecrosis of the jaw (MRONJ), osteoradionecrosis (ORN), and chronic osteomyelitis. Imaging is essential for assessing disease extent but remains insufficient for etiologic differentiation, making histopathological examination and targeted microbiological investigations necessary, particularly to exclude invasive fungal infection. Conclusions: Management must be etiology-driven. CAM requires urgent antifungal therapy combined with surgical debridement, whereas non-fungal forms are generally managed with conservative surgery and appropriate antimicrobial stewardship. Standardized diagnostic criteria and prospective multicenter studies are needed to reduce nosological ambiguity and optimize clinical decision-making in this emerging post-viral condition. Full article

Water is a vital resource that requires long-term legal protection to ensure both ecological values and societal benefits. The European Union’s Water Framework Directive (2000/60/EC) is central to this aim, establishing binding requirements for good ecological and chemical status in all water bodies and legally binding environmental quality standards. Sweden has implemented the Directive into national law; however, its application has been characterized by legal ambiguities, particularly regarding the possibility of considering disproportionate costs in environmental measures. This study examines the scope and application of the disproportionate cost criterion within the context of environmental law and hydropower regulation in Sweden. A comparative overview of the criterion’s application in other EU/EEA countries is also provided. Based on a legal approach, the analysis focuses on how these rules affect hydropower, where the goal of renewable energy production often needs to be weighed against the requirement for ecological recovery. The study concludes that applying the disproportionate costs criterion requires transparency and legal certainty to ensure a fair balance between the social benefits of hydropower and the need for long-term protection of the aquatic environments. To avoid differences in how the criterion is applied in different EU Member States, harmonized guidelines are needed. Full article

In challenging environments, there often exist problems of false alarms and missed detections in real-time kinematic (RTK) ambiguity resolution, which significantly reduce the reliability and availability of position information. To address these issues, a machine-learning method is proposed to conduct a correctness check on RTK ambiguity fixing, aiming to reduce the occurrences of false alarms and missed detections. The inter-system differential RTK model is adopted. Compared with the traditional RTK model, this model can provide an effective feature, namely the differential inter-system biases (DISB), to improve the accuracy of machine-learning classification. This is because when the RTK ambiguity is correctly fixed, the DISB usually appears as a stable constant. In addition to DISB, features that are strongly related to ambiguity fixing, such as the ratio value, DOP value, and residuals, are also comprehensively utilized. This method is verified by an open-source, large-scale, and diverse GNSS/SINS dataset—SmartPNT-POS. The experimental results show that, compared with the traditional method of relying solely on the empirical ratio value for ambiguity fixing verification, the missed detection probability of this method is reduced by 2%, the false-alarm probability is decreased by 29%, and the positioning accuracy is improved by approximately 7%. Moreover, compared with other features, the DISB feature provides the highest contribution rate in the machine-learning classification model. Full article

The rapid integration of artificial intelligence (AI) into digital platforms has raised critical questions about how AI’s ethical declarations influence this sector. This study adopts a mixed-methods approach. First, a descriptive content analysis examined 54 declarations, including 45 national declarations across Africa, Asia, Europe, and the Americas, and 9 from major global actors (MGAs) such as the OECD, G7, and the EU. Ethical principle frequency was examined, and a benchmarking index was developed to compare “dominant principles” cited in over 50% of regional declarations with those cited in over 50% of MGA declarations. The analysis reveals universal adoption of societal well-being, fairness, accountability, and privacy (100%), while transparency and security show regional variation (75%). Second, a semi-systematic literature review following PRISMA guidelines identified four opportunities (e.g., global participation) and seven limitations (e.g., lack of standard frameworks, definitional ambiguities, implementation challenges, and legal enforcement difficulties). The implications of these limitations for digital platforms are then examined, leading to the identification of two dimensions for responsible platform governance: assessment mechanisms (e.g., UNESCO’s Ethical Impact Assessment) and governance implementation structures. The study further distinguishes three tiers of enforceability: declarative, procedural, and institutionalized ethics, bridging normative declarations and operational practice in platform governance. Full article

While return migrant workers (RMWs) are increasingly viewed as key to rural development, their specific impact on grain production remains ambiguous. Clarifying this role is critical to manage the dual nature of their reintegration—leveraging valuable resources and knowledge while addressing complex reintegration challenges—to ensure national food security and advance agricultural modernization. Drawing on data from the 2018 China Labor-force Dynamics Survey (CLDS), this study explicitly tests the hypothesis that migration experience significantly reduces the likelihood that RMW households engage in grain production. The empirical results from probit models support this hypothesis, and this finding is robust across multiple specifications. Further analysis shows that migration experience significantly reduces land cultivation scales—especially among larger producers—and increases land abandonment. Additionally, it inhibits technology adoption or invest in agricultural technology. These results suggest that migration experience may weaken, rather than enhance, RMWs’ commitment to grain production, challenging the policy expectation that they can lead agricultural transformation. The study calls for more nuanced policy interventions that account for the structural constraints facing RMW households and their limited contribution to large-scale, efficient grain farming. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

This paper proposes an end-effector (EE) driven whole-body trajectory tracking control algorithm for wheeled mobile manipulators based on linear and angular motion decomposition. Instead of solving a high-dimensional optimization problem across all degrees of freedom, the proposed method formulates the control objective directly in the EE space and decomposes the required motion into planar linear, vertical, and angular components. To address redundancy between the mobile base and the manipulator under non-holonomic constraints, a control authority switching strategy with a radial blending function is introduced. This approach eliminates ambiguity in control allocation while preventing abrupt switching near workspace boundaries. The kinematic controller guarantees exponential convergence of position and orientation errors without requiring a full dynamic model. Numerical simulations demonstrate stable tracking performance in three-dimensional space. Compared with a quadratic programming-based whole-body controller, the proposed method achieves comparable or faster error convergence while reducing computational burden by more than 13 times on average. These results indicate that the proposed EE-driven framework provides a computationally efficient and practically deployable solution for real-time mobile manipulator control. Full article