Search Results (1,422)

Graph anomaly detection (GAD) is crucial for safeguarding the integrity and security of complex systems, such as social networks and financial transactions. Despite the advances made by Graph Neural Networks (GNNs) in the field of GAD, existing methods still exhibit limitations in capturing subtle structural anomaly patterns: they typically over-rely on reconstruction error, struggle to fully exploit structural similarity among nodes, and fail to effectively integrate attribute and structural information. To tackle these challenges, this paper proposes a structure-aware dual-path attention graph node anomaly detection method (DPGAD). DPGAD employs wavelet diffusion to extract network neighborhood features for each node while incorporating a dual attention mechanism to simultaneously capture attribute and structural similarities, thereby obtaining richer feature details. An adaptive gating mechanism is then introduced to dynamically adjust the fusion of attribute features and structural features. This allows the model to focus on the most relevant features for anomaly detection, enhancing its robustness and antinoise capability. Our experimental evaluation across multiple real-world datasets demonstrates that DPGAD consistently surpasses existing methods, achieving average improvements of 9.06% in AUC and 11% in F1-score. Especially in scenarios where structural similarity is crucial, DPGAD has a performance advantage of more than 20% compared with the most advanced methods. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

Accurate monitoring of snow and ice cover is essential for climate research and disaster management, but conventional remote sensing methods often struggle in complex terrain and fog-contaminated conditions. To address the challenges of high-resolution UAV-based snow and ice segmentation—including visual similarity, fragmented spatial distributions, and terrain shadow interference—we introduce IceSnow-Net, a U-Net-based architecture enhanced with three key components: (1) a ResNet50 backbone with atrous convolutions to expand the receptive field, (2) an Atrous Spatial Pyramid Pooling (ASPP) module for multi-scale context aggregation, and (3) an auxiliary path loss for deep supervision to enhance boundary delineation and training stability. The model was trained and validated on UAV-captured orthoimagery from Ganzi Prefecture, Sichuan, China. The experimental results demonstrate that IceSnow-Net achieved excellent performance compared to other models, attaining a mean Intersection over Union (mIoU) of 98.74%, while delivering 27% higher computational efficiency than U-Mamba. Ablation studies further validated the individual contributions of each module. Overall, IceSnow-Net provides an effective and accurate solution for cryosphere monitoring in topographically complex environments using UAV imagery. Full article

Rapid socio-economic development has made energy application and environmental issues increasingly prominent. Hydrogen energy, clean, eco-friendly, and highly synergistic with renewable energy, has become a global research focus. This study, using the EnergyPLAN model that includes the electricity, transportation, and industrial sectors, takes Jinan City as the research object and explores how hydrogen penetration changes affect the decarbonization path of the urban integrated energy system under four scenarios. It evaluates the four hydrogen scenarios with the entropy weight method and technique, placing them in an order of preference according to their similarity to the ideal solution, considering comprehensive indicators like cost, carbon emissions, and sustainability. Results show the China Hydrogen Alliance potential scenario has better CO₂ emission reduction potential and unit emission reduction cost, reducing them by 7.98% and 29.39%, respectively. In a comprehensive evaluation, it ranks first with a score of 0.5961, meaning it is closest to the ideal scenario when cost, environmental, and sustainability indicators are comprehensively considered. The Climate Response Pioneer scenario follows with 0.4039, indicating that higher hydrogen penetration in terminal energy is not necessarily the most ideal solution. Instead, appropriate hydrogen penetration scenarios should be selected based on the actual situation of different energy systems. Full article

STEM education is constantly seeking innovative methods to enhance student learning. Virtual Reality technology can represent a critical tool for effectively teaching complex engineering subjects. This study evaluates an original Virtual Reality software application, entitled Submarine Simulator, which is developed specifically to support competencies in hydrodynamics within an Underwater Engineering course at MINES Paris—PSL. Our application uniquely integrates a customized physics engine explicitly designed for realistic underwater simulation, significantly improving user comprehension through accurate real-time representation of hydrodynamic forces. The study involved a homogeneous group of 26 fourth-year engineering students, all specializing in engineering and sharing similar academic backgrounds in robotics, electronics, programming, and computer vision. This uniform cohort, primarily aged 22–28, enrolled in the same 3-month course, was intentionally chosen to minimize variations in skills, prior knowledge, and learning pace. Through a combination of quantitative assessments and Confirmatory Factor Analysis, we find that Virtual Reality affordances significantly predict user flow state (path coefficient: 0.811) which then predicts user engagement and satisfaction (path coefficient: 0.765). These findings show the substantial educational potential of tailored Virtual Reality experiences in STEM, particularly in engineering, and highlight directions for further methodological refinement. Full article

Existing CO₂ leakage risk assessment frameworks for CO₂ capture, geological storage and utilization (CCUS) projects face limitations due to subjective biases and poor adaptability to long-term scale sequestration. This study proposed a dynamic risk assessment method for CO₂ leakage based on a timeliness analysis of different leakage paths and accurate time-dependent numerical simulations, and it was applied to the first CO₂ enhanced gas recovery (CCUS-EGR) pilot project of China in the Maokou carbonate gas reservoir in the Wolonghe gas field. A 3D geological model of the Maokou gas reservoir was first developed and validated. The CO₂ leakage risk under different scenarios including wellbore failure, caprock fracturing, and new fracture activation were evaluated. The dynamic CO₂ leakage risk of the CCUS-EGR project was then quantified using the developed method and numerical simulations. The results revealed that the CO₂ leakage risk was observed to be the most pronounced when the caprock integrity was damaged by faults or geologic activities. This was followed by leakage caused by wellbore failures. However, fracture activation in the reservoir plays a neglected role in CO₂ leakage. The CO₂ leakage risk and critical risk factors dynamically change with time. In the short term (at 5 years), the project has a low risk of CO₂ leakage, and well stability and existing faults are the major risk factors. In the long term (at 30 years), special attention should be paid to the high permeable area due to its high CO₂ leakage risk. Factors affecting the spatial distribution of CO₂, such as the reservoir permeability and porosity, alternately dominate the leakage risk. This study established a method bridging gaps in the ability to accurately predict long-term CO₂ leakage risks and provides a valuable reference for the security implementation of other similar CCUS-EGR projects. Full article

To improve path planning performance for mobile robots in complex environments, this study proposes a hybrid method combining an improved jump point search (JPS) algorithm with the dynamic window approach (DWA). In global planning, a quadrant pruning strategy guided by the target direction and a sine-enhanced heuristic function reduces the search space and accelerates planning. Natural jump points are retained for path continuity, and the path is smoothed using cubic B-spline curves. In local planning, DWA is enhanced by incorporating a target orientation factor, a safety distance penalty, and a normalization mechanism into the cost function. An adaptive weighting strategy dynamically balances goal-directed motion and obstacle avoidance. Simulation experiments in static and complex environments with unknown and dynamic obstacles demonstrate the method’s effectiveness. Compared to the standard approach, the improved JPS reduces search time by 36.7% and node expansions by 60.9%, with similar path lengths. When integrated with DWA, the robot adapts effectively to changing obstacles, ensuring safe and efficient navigation. The proposed method significantly enhances the real-time performance and safety of path planning in dynamic and uncertain environments. Full article

This research proposes a green belt as a strategic response to urban expansion in Lomas del Paraíso, Villa María del Triunfo, Lima. Uncontrolled urban growth threatens the local ecosystem, exacerbates the lack of public spaces, and limits employment opportunities. The study employs an integrated methodology that includes urban, community, and especially environmental analysis. This involved the collection of climatic data, and the identification of local flora and fauna, supported by digital tools such as Google Earth, AutoCAD 2023, Revit, and 3D Sun-Path. The proposal incorporates urban, environmental, technological, and community-based design strategies grounded in permaculture principles, circular economy frameworks, and the Sustainable Development Goals (SDGs). These approaches emphasize the symbiotic relationship between the community and the Lomas ecosystem. The feasibility and potential impact of the proposed green belt were compared with similar case studies, such as Medellín’s metropolitan green belt (Jardín Circunvalar) and the Arví Ecotourism Park. These benchmarks highlight the relevance of community involvement and user awareness in ecological preservation. In conclusion, implementing a green belt in Lomas del Paraíso would not only curb unregulated urban sprawl but also enhance community–nature connectivity and promote sustainable development through integrated environmental, social, and urban strategies. Full article

Background: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. Methods: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. Results: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. Conclusions: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery. Full article

This study explored gender-specific nuances in the applicability of Social Cognitive Theory (SCT) to predict physical activity behavior. This study aimed to determine whether similar or different prediction patterns emerge for men and women, particularly emphasizing the tenability of the SCT model’s theoretical assumptions across gender. Six hundred fifty-four participants (58.1% women, 41.1% men) completed two validated questionnaires at separate time points (t1 = social cognitive and demographic variables; t2 = physical activity behavior). We employed a multigroup Structural Equation Model (SEM) to examine the validity of the theoretical assumptions and the influence of gender. The results suggest that SCT’s theoretical assumptions hold true for men and women, indicated by a highly satisfactory fit of the SEM despite the variance explained being small (R²_women = 11.9%, R²_men = 7.3%). However, the importance of the specific theoretical paths and the underlying mechanisms of action might differ between genders, and the interplay of the social and cognitive variables to predict physical activity may vary significantly for men and women. The use of SCT can be recommended for explaining and predicting physical activity behavior, although gender-specific differences in the underlying theoretical relationships should be taken into consideration when designing interventions or when being used to explain physical activity behavior. Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

To improve semantic segmentation performance for complex urban remote sensing images with multi-scale object distribution, class similarity, and small object omission, this paper proposes MFPI-Net, an encoder–decoder-based semantic segmentation network. It includes four core modules: a Swin Transformer backbone encoder, a diverse dilation rates attention shuffle decoder (DDRASD), a multi-scale convolutional feature enhancement module (MCFEM), and a cross-path residual fusion module (CPRFM). The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. The DDRASD’s diverse dilation rates attention (DDRA) block combines convolutions with diverse dilation rates and channel-coordinate attention to enhance multi-scale contextual awareness, while Shuffle Block improves resolution via pixel rearrangement and avoids checkerboard artifacts. The MCFEM enhances local feature modeling through parallel multi-kernel convolutions, forming a complementary relationship with the Swin Transformer’s global perception capability. The CPRFM employs multi-branch convolutions and a residual multiplication–addition fusion mechanism to enhance interactions among multi-source features, thereby improving the recognition of small objects and similar categories. Experiments on the ISPRS Vaihingen and Potsdam datasets show that MFPI-Net outperforms mainstream methods, achieving 82.57% and 88.49% mIoU, validating its superior segmentation performance in urban remote sensing. Full article

The value realization of ecological products is an important part of rural and agricultural development. As a significant force for protecting traditional agricultural systems and promoting rural revitalization, agricultural heritage systems (AHSs) have formed diverse value realization paths of ecological products in the process of dynamic protection and adaptive management. Through theoretical research, this article analyzed the characteristics of ecological products in AHS sites (EPAHSSs) and summarized the framework of value realization paths of EPAHSSs. Then, the Wannian Rice Culture System in China was selected as a case for conducting empirical research. The results showed that EPAHSSs exhibit obvious uniqueness in terms of climate environment, germplasm resources, farming and breeding models, and cultural heritage. The value realization paths of EPAHSSs mainly include industrial development support, such as the extension of agricultural industrial chains and the development of tourism, as well as fiscal transfer payments. The case analysis results indicated that Wannian County contains a rich variety of ecological products and developed a value realization pathway mainly based on the integration of industries and supplemented by fiscal transfer payments during the process of protection and development. However, further optimization is needed to promote the development of tourism and other paths. This study not only contributes to the sustainable development of the Wannian Rice Culture System, but the proposed framework is also applicable to other heritage systems and similar regions. Full article

Hyaluronic acid (HA) is a key extracellular matrix component of vertebrates, where it mediates cell adhesion, immune regulation, and tissue remodeling through its interaction with specific receptors. Although HA has been detected in a few invertebrate species, the lack of fundamental components of the molecular HA pathway poses relevant objections about its functional role in these species. Mining genomic and transcriptomic data, we considered the conservation of the gene locus encoding for the extracellular link protein (XLINK) in marine mussels as well as its expression patterns. Structural and phylogenetic analyses were undertaken to evaluate possible similarities with vertebrate orthologs and to infer the origin of this gene in invertebrates. Biochemical analysis was used to quantify HA in tissues of Mytilus galloprovincialis. As a result, we confirm that the mussel can produce HA (up to 1.02 ng/mg in mantle) and that its genome encodes two XLINK gene loci. These loci are conserved in Mytilidae species and show a complex evolutionary path. Mussel XLINK genes appeared to be expressed during developmental stages in three mussel species, ranking in the top 100 expressed genes in M. trossulus at 17 h post-fertilization. In conclusion, the presence of HA and an active gene with the potential to bind HA suggests that mussels have the potential to synthesize and use HA and are among the few invertebrates encoding this gene. Full article

Green hydrogen, produced via renewable-powered electrolysis, offers a promising path toward deep decarbonisation in energy systems. This study investigates the major technological, infrastructural, and economic challenges facing green hydrogen production in Jordan—a resource-constrained yet renewable-rich country. Key barriers were identified through a structured survey of 52 national stakeholders, including water scarcity, low electrolysis efficiency, limited grid compatibility, and underdeveloped transport infrastructure. Respondents emphasised that overcoming these challenges requires investment in smart grid technologies, seawater desalination, advanced electrolysers, and policy instruments such as subsidies and public–private partnerships. These findings are consistent with global assessments, which recognise similar structural and financial obstacles in scaling up green hydrogen across emerging economies. Despite the constraints, over 50% of surveyed stakeholders expressed optimism about Jordan’s potential to develop a competitive green hydrogen sector, especially for industrial and power generation uses. This paper provides empirical, context-specific insights into the conditions required to scale green hydrogen in developing economies. It proposes an integrated roadmap focusing on infrastructure modernisation, targeted financial mechanisms, and enabling policy frameworks. Full article