Search Results (788)

Frequent failures in wind turbines underscore the critical need for accurate and efficient online monitoring and early warning systems to detect abnormal conditions. Given the complexity of monitoring numerous components individually, subsystem-level monitoring emerges as a practical and effective alternative. Among all subsystems, the gearbox is particularly critical due to its high failure rate and prolonged downtime. However, achieving both efficiency and accuracy in gearbox condition monitoring remains a significant challenge. To tackle this issue, we present a novel adaptive condition monitoring method specifically for wind turbine gearbox. The approach begins with adaptive feature selection based on correlation analysis, through which a quantitative indicator is defined. With the utilization the selected features, graph-based data representations are constructed, and a self-supervised contrastive residual graph neural network is developed for effective data mining. For online monitoring, a health index is derived using distance metrics in a multidimensional feature space, and statistical process control is employed to determine failure thresholds. This framework enables real-time condition tracking and early warning of potential faults. Validation using SCADA data and maintenance records from two wind farms demonstrates that the proposed method can issue early warnings of abnormalities 30 to 40 h in advance, with anomaly detection accuracy and F1 score both exceeding 90%. This highlights its effectiveness, practicality, and strong potential for real-world deployment in wind turbine monitoring applications. Full article

Exploring homogeneous dense groups is one of the important issues in social network structure measurement. k-core decomposition and core member filtering are common methods to uncover homogeneous dense groups in a network. However, existing methods of k-core decomposition struggle to support in-depth exploration of homogeneous dense groups. To address this issue, we store social networks in a graph database, taking advantage of its characteristics such as property indexes and batch queries. Based on this storage, we propose a k-core decomposition algorithm to improve the efficiency of homogeneous dense group detection. Subsequently, we introduce a core member filtering algorithm for identifying core members, a key exploration goal of this study. In experiments, we verify the efficiency of the k-core decomposition algorithm. Finally, we conduct an in-depth analysis of the characteristics of k-cores and their core members, yielding several important conclusions. For example, the relationship between the core number and the number of nodes obeys the power law distribution. In addition, we find that despite the strong connection of the core members, they do not play an important role in the information spreading of social networks. Full article

Vortex-induced vibration (VIV) is a kind of abnormal vibration which needs to be automatically identified and warned in real time to guarantee the operational safety of a bridge. However, the existing VIV identification methods only focus on identification and have limitations in visualizing identification results, which causes difficulty for bridge governors in other fields to quickly confirm the identification results. This paper proposes an automatic VIV identification, warning, and visualization method. First, a recurrence plot is introduced to analyze the signal to extract the characteristics of the vibration signal in a time domain. Then, a feature index defined as recurrence cycle smoothness is proposed to quantify the stability of the vibration signal, based on which the VIV can be automatically identified. An automatic VIV identification and multi-level warning process is finally established based on the severity of the vibration amplitude. The proposed method is validated through a suspension bridge with serious VIVs. The result indicates that the proposed method can automatically identify the VIV correctly without any manual intervention and can visualize the identification results using a graph, providing a good tool to quickly confirm the VIV identification results. The multi-level warning can successfully warn the serious VIV and provide possible early warning for large amplitude VIV. Full article

Multi-agent systems (MAS) typically model interaction topologies using directed or undirected graphs when analyzing consensus convergence rates. However, as system complexity increases, purely directed or undirected networks may be insufficient to capture interaction heterogeneity. This paper adopts hybrid networks as interaction topology to investigate strategies for improving consensus convergence rates. We propose the Hermitian Kirchhoff index, a novel metric based on resistance distance, to quantify the consensus convergence rates and establish its theoretical justification. We then examine how adding or removing edges/arcs affects the Hermitian Kirchhoff index, employing first-order eigenvalue perturbation analysis to relate these changes to algebraic connectivity and its associated eigenvectors. Numerical simulations corroborate the theoretical findings and demonstrate the effectiveness of the proposed approach. Full article

Technological advancements have enabled users to digitize and store an unlimited number of multimedia documents, including images and videos. However, the heterogeneous nature of multimedia content poses significant challenges in efficient indexing and retrieval. Traditional approaches primarily focus on visual features, often neglecting the semantic context, which limits retrieval efficiency. This paper proposes a hybrid deep learning and knowledge graph approach for intelligent image indexing and retrieval. By integrating deep learning models such as EfficientNet and Vision Transformer (ViT) with structured knowledge graphs, the proposed framework enhances semantic understanding and retrieval performance. The methodology incorporates feature extraction, concept classification, and hierarchical knowledge graph structuring to facilitate effective multimedia retrieval. Experimental results on benchmark datasets, including TRECVID, Corel, and MSCOCO, demonstrate significant improvements in precision, robustness, and query expansion techniques. The findings highlight the potential of combining deep learning with knowledge graphs to bridge the semantic gap and optimize multimedia indexing and retrieval. Full article

Instance segmentation in high-resolution images is essential for applications such as remote sensing, medical imaging, and precision agriculture, yet remains challenging due to factors such as small object sizes, irregular shapes, and occlusions. Tiling-based approaches, such as Slicing-Aided Hyper Inference (SAHI), alleviate some of these challenges by processing smaller patches but introduce border artifacts and increased computational cost. Overlapping tiles can mitigate certain boundary effects but often result in duplicate detections and boundary inconsistencies, particularly along patch edges. Conventional deduplication techniques, including Non-Maximum Suppression (NMS) and Non-Mask Merging (NMM), rely on Intersection over Union (IoU) thresholds and frequently fail to merge fragmented or adjacent masks with low mutual IoU that nonetheless correspond to the same object. To address deduplication and mask fragmentation, Spatial Mask Merging (SMM) is proposed as a graph clustering approach that integrates pixel-level overlap and boundary distance metrics while using R-tree indexing for efficient candidate retrieval. SMM was evaluated on the iSAID benchmark using standard segmentation metrics, with tile overlap configurations systematically examined to determine the optimal setting for segmentation accuracy. The method achieved a nearly 7% increase in precision, with consistent gains in F1 score and Panoptic Quality over existing approaches. The integration of R-tree indexing facilitated faster candidate retrieval, enabling computational performance improvements over standard merging algorithms alongside the observed accuracy gains. Full article

Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while considering patterns that extend beyond individual vocalizations. This broader view requires unsupervised approaches capable of capturing meaningful structures related to temporal dynamics, frequency content, spatial distribution, and ecological variability. In this study, we present a fully unsupervised framework for analyzing large-scale soundscape data using deep learning. We applied a convolutional autoencoder (Soundscape-Net) to extract acoustic representations from over 60,000 recordings collected across a grid-based sampling design in the Rey Zamuro Reserve in Colombia. These features were initially compared with other audio characterization methods, showing superior performance in multiclass classification, with accuracies of 0.85 for habitat cover identification and 0.89 for time-of-day classification across 13 days. For the unsupervised study, optimized dimensionality reduction methods (Uniform Manifold Approximation and Projection and Pairwise Controlled Manifold Approximation and Projection) were applied to project the learned features, achieving trustworthiness scores above 0.96. Subsequently, clustering was performed using KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), with evaluations based on metrics such as the silhouette, where scores above 0.45 were obtained, thus supporting the robustness of the discovered latent acoustic structures. To interpret and validate the resulting clusters, we combined multiple strategies: spatial mapping through interpolation, analysis of acoustic index variance to understand the cluster structure, and graph-based connectivity analysis to identify ecological relationships between the recording sites. Our results demonstrate that this approach can uncover both local and broad-scale patterns in the soundscape, providing a flexible and interpretable pathway for unsupervised ecological monitoring. Full article

Alpine wetlands, key carbon sinks and biodiversity hubs, remain understudied, especially under climate change pressures. Hence, the present study was conducted to assess the variability in soil enzyme activity (SEA) and the carbon management index (CMI) and to utilize principal component analysis (PCA) to explore the variation and correlation between SEA and CMI as influenced by altitudinal gradients in alpine wetlands. This information is essential for exploring the impacts of soil degradation and guiding restoration efforts. The study was designed in blocks (catchments) with six altitudinal variations (from 2500 to 3155 m a.s.l), equivalent to alpine wetlands from three catchments (Senqunyane, Khubelu and Sani) as follows: Khorong and Tenesolo in Senqunyane; Khamoqana and Khalong-la-Lichelete in Sani; and Lets’eng-la-Likhama and Koting-Sa-ha Ramosetsana in Khubelu. The soil samples were collected in February 2025 (autumn season, i.e., wet season) at depths of 0–15 and 15–30 cm and analyzed for bulk density, texture, pH, electrical conductivity (EC), soil organic carbon (SOC), SEA, and carbon pools, and the CMI was computed following standard procedures. The results demonstrated that the soil was loam to sandy loam and was slightly acidic and non-saline in nature in the 0–15 cm layer across the wetlands. The significant decreases in SEA were 45.33%, 32.20% and 15.11% (p < 0.05) for dehydrogenase, fluorescein di-acetate and β-Galactosidase activities, respectively, in KSHM compared with those in Khorong (lower elevated site). The passive carbon pool (C_PSV) was dominant over the active carbon pool (C_ACT) and contributed 76–79% of the SOC to the total organic carbon, with a higher C_PSV (79%) observed at KSHM. The CMI was also greater (91.05 and 75.88) under KSHM at the 0–15 cm and 15–30 cm soil depths, respectively, than in all the other alpine wetlands, suggesting better carbon management at higher altitudinal gradients and less enzymatic activity. These trends shape climate change outcomes by affecting soil carbon storage, with high-altitude regions serving as significant, though relatively less active, carbon reservoirs. The PCA-Biplot graph revealed a negative correlation between the CMI and SEA, and these variables drove more variation across sites, highlighting a complex interaction influenced by higher altitude with its multiple ecological drivers, such as temperature variation, nutrient dynamics, and shifts in microbial communities. Further studies on metagenomics in alpine soils are needed to uncover altitude-driven microbial adaptations and their role in carbon dynamics. Full article

For $a\in \mathbb{R}$, the general sum-connectivity index of a graph G is defined as ${\chi }_a\left(G\right)={\sum }_{uv\in E\left(G\right)}{[d_G\left(u\right)+d_G\left(v\right)]}^a$, where $E\left(G\right)$ is the set of edges of G and $d_G\left(u\right)$ and $d_G\left(v\right)$ are the degrees of vertices u and v, respectively. For trees and unicyclic graphs with given order and number of pendant vertices, we present upper bounds on the general sum-connectivity index ${\chi }_a$, where $0<a<1$. We also present the trees and unicyclic graphs that attain the maximum general sum-connectivity index for $0<a<1$. Full article

Surface water resources are under growing pressure from urbanization, industrial activity, and agriculture, making effective monitoring essential for safeguarding ecological integrity and human use. Conventional monitoring methods, which rely on manual sampling and rigid Water Quality Index (WQI) categories, often provide delayed feedback and oversimplify conditions near classification thresholds, limiting their usefulness for timely management. To overcome these shortcomings, we have developed an interactive fuzzy logic-based water quality monitoring interface or dashboard that integrates the WQI developed by Malaysia’s Department of Environment with the National Water Quality Standards (NWQS) Class I–V framework. The interface combines conventional WQI computation with advanced visualization tools such as dynamic gauges, parameter tables, fuzzy membership graphs, scatter plots, heatmaps, and bar charts. Then, triangular membership functions map six key parameters to NWQS classes, providing smoother and more nuanced interpretation compared to rigid thresholds. In addition to that, the dashboard enables clearer communication of trends, supports timely decision-making, and demonstrates adaptability for broader applications since it is implemented on the Replit platform. Finally, evaluation results show that the fuzzy interface improves interpretability by resolving ambiguities in over 15% of cases near class boundaries and facilitates faster assessment of pollution trends compared to conventional reporting. Thus, these contributions highlight the necessity and value of the research on advancing Malaysia’s national water quality monitoring and providing a scalable framework for international contexts. Full article

Cryptography stands out as a scientific methodology for safeguarding communication against unauthorized access. This article proposes a newly formulated graph termed the Index Difference Graph (IDG). The proposed graph model serves as the secret key in the encryption process. Furthermore, we present a new graph-based algorithm, the Index Difference Modular Cryptographic (IDMC) Algorithm, and analyze it using centipede and path graphs. The goal of this graph-based approach is to increase the encryption rate while maintaining computational efficiency. This research investigates different types of index difference graphs and analyzes the time and space complexity of the algorithm. IDMC exhibits a lower collision probability, thereby enhancing encryption security. When employing a graph that admits an Index Difference Graph structure in the cryptographic algorithm, both the sender and receiver must be aware of the graph’s precise structure, as this strengthens the robustness of the cryptographic key. The application of the index difference centipede graph $P_n\odot 2k_1$ in cryptography, examined through the IDMC algorithm, demonstrates exceptionally high brute-force resistance estimated at approximately $2.6\times {10}^{39}$ for smaller instances with $n\le 7$ and escalating to $6.93\times {10}^{163}$ for larger graphs with $n\ge 20$. This resistance underscores the algorithm’s efficiency and cryptographic resilience. Full article

This paper develops a dynamic information-theoretic network framework to quantify systemic risk in China’s maritime–commodity nexus with a focus on the Yangtze River Basin using eight monthly indicators, CCFI, CBCFI, BDI, YRCFI, GAUP, MPCT, CPUS, and ASMC. We resample, impute, standardize, and difference series to achieve stationary time series. Nonlinear interdependencies are estimated via KSG mutual information (MI) within sliding windows; networks are filtered using the Planar Maximally Filtered Graph (PMFG) with bootstrap edge validation (95th percentile) and benchmarked against the MST. Average MI indicates moderate yet heterogeneous dependence (about 0.13–0.17), revealing a container/port core (CCFI–YRCFI–MPCT), a bulk/energy spine (BDI–CPUS), and commodity bridges via GAUP. Dynamic PMFG metrics show a generally resilient but episodically vulnerable structure: density and compactness decline in turbulence. Stress tests demonstrate high redundancy to diffuse link failures (connectivity largely intact until ∼70–80% edge removal) but pronounced sensitivity of diffusion capacity to targeted multi-node outages. Early-warning indicators based on entropy rate and percolation threshold Z-scores flag recurring windows of elevated fragility; change point detection evaluation of both metrics isolates clustered regime shifts (2015–2016, 2018–2019, 2021–2022, and late 2023–2024). A Systemic Importance Index (SII) combining average centrality and removal impact ranks MPCT and CCFI as most critical, followed by BDI, with GAUP/CPUS mid-peripheral and ASMC peripheral. The findings imply that safeguarding port throughput and stabilizing container freight conditions deliver the greatest resilience gains, while monitoring bulk/energy linkages is essential when macro shocks synchronize across markets. Full article

Accurate assessments are vital for the effective conservation of desert steppe ecosystems, which are essential for maintaining biodiversity and ecological balance. Although geostatistical methods are commonly used for spatial modeling, they have limitations in terms of feature extraction and capturing non-linear relationships. This study therefore proposes a novel remote sensing framework that integrates geostatistical methods and machine learning to predict the Shannon–Wiener index in desert steppe. Five models, Kriging interpolation, Random Forest, Support Vector Machine, 3D Convolutional Neural Network and Graph Attention Network, were employed for parameter inversion. The Helmert variance component estimation method was introduced to integrate the model outputs by iteratively evaluating residuals and assigning relative weights, enabling both optimal prediction and model contribution quantification. The ensemble model yielded a high prediction accuracy with an R² of 0.7609. This integration strategy improves the accuracy of index prediction, and enhances the interpretability of the model regarding weight contributions in space. The proposed framework provides a reliable, scalable solution for biodiversity monitoring and supports scientific decision-making for grassland conservation and ecological restoration. Full article

The first Zagreb index of a graph G is defined as the sum of the squares of the degrees of all the vertices in G. The Laplacian spectral radius of a graph G is defined as the largest eigenvalue of the Laplacian matrix of the graph G. In this paper, we first establish inequalities on the first Zagreb index and the Laplacian spectral radius of a graph. Using the ideas of proving the inequalities, we present sufficient conditions involving the first Zagreb index and the Laplacian spectral radius for some Hamiltonian properties of graphs. Full article

Background/Objectives: While some dietary indices have been developed to assess diet quality and chronic disease risk, their comparative effectiveness within the same population remains unclear due to methodological limitations in observational studies. This study employs a causal inference framework to compare nine dietary indices for reducing all-cause and cardiovascular mortality, while investigating inflammatory pathways through multiple mediation analysis. Methods: Using dietary data from 33,881 adults (aged ≥ 20 years, median follow-up 92 months), we applied a causal directed acyclic graph to identify the minimum sufficient adjustment set and implemented generalized propensity score matching to address confounding. Robust Cox proportional hazards regression assessed associations between nine dietary indices—Dietary Inflammatory Index (DII), Composite Dietary Antioxidant Index (CDAI), Healthy Eating Index 2015/2020 (HEI-2015/2020), Alternate Healthy Eating Index (AHEI), Alternate Mediterranean Diet (aMED), Mediterranean Diet Index (MEDI), and Dietary Approaches to Stop Hypertension (DASH/DASHI)—and mortality outcomes. Multiple additive regression trees (MART) algorithm was used for multiple mediation analysis to examine inflammatory markers (PAR, SII, NPR, TyG, LMR, PLR, ELR, CRP) as mechanistic mediators. Results: Among 33,881 participants (mean age 47.07 years, 51.34% women), 4,230 deaths occurred, including 827 cardiovascular deaths. Under the causal inference framework, higher DII scores increased both all-cause (HR: 1.07; 95% CI: 1.02–1.12) and cardiovascular mortality risk (HR: 1.07; 95% CI: 1.04–1.10) by 7%. The aMED demonstrated the strongest protective association, reducing all-cause mortality by 12% (HR: 0.88; 95% CI: 0.80–0.97) and cardiovascular mortality by 11% (HR: 0.89; 95% CI: 0.80–0.98), followed by MEDI with similar magnitude effects. Other healthy dietary indices showed modest 1–3% risk reductions. Multiple mediation analysis revealed that inflammatory markers, particularly neutrophil-to-platelet ratio (NPR) and systemic immune-inflammation index (SII), significantly mediated diet–mortality associations across all indices, with C-reactive protein (CRP) serving as the most frequent mediator. Conclusions: Using causal inference methodology, the Mediterranean dietary pattern (aMED) shows the strongest causal association with reduced mortality risk, with inflammatory pathways serving as key mediating mechanisms. These findings provide robust evidence for prioritizing Mediterranean dietary patterns in public health interventions and clinical practice, while highlighting inflammation as a critical therapeutic target for dietary interventions aimed at reducing mortality risk. Full article