Search Results (7,806)

Real-time processing demands for massive IoT sensor data necessitate reliance on distributed microservice systems within edge clusters. However, pinpointing the root cause of anomalies within these edge microservice clusters poses a critical challenge for intelligent IoT operation and maintenance. To address the issue, a spatio-temporal graph propagation model ST-GraphRCA is proposed for root cause analysis in IoT edge environments. Our approach begins by resolving the fundamental issue of time-series asynchrony across distributed multi-source metrics. A PCA-DTW hybrid feature extraction method is introduced with a dynamic alignment strategy to mitigate the effects of random network delays and data deformation without requiring prior synchronization. Subsequently, ST-GraphRCA constructs a stream-based forward propagation graph based on the flow conservation principle. By integrating dynamic edge weights with node-level input–output anomaly scores, ST-GraphRCA precisely infers fault propagation pathways and identifies potential root cause candidates through causal reasoning. Finally, a topology-constrained high-utility mining algorithm filters these candidates. Using a constraint matrix, the algorithm filters out unreachable service combinations to locate low-frequency and high-risk root causes. Experimental results indicate that ST-GraphRCA achieves an F1-Score of 0.89, outperforming existing methods. In resource-constrained edge scenarios, its average localization time is merely 238.8 ms, representing a six-fold improvement over key benchmarks. Thus, ST-GraphRCA not only provides an efficient anomaly fault tracing solution for large-scale IoT systems but also offers technical support for the intelligent operation and maintenance of distributed microservice systems. Full article

To address the challenges of feature extraction and degradation state identification for railway turnout switch machine power signals over the full life cycle, this paper proposes a multi-dimensional feature-fusion-based degradation state identification method for S700K turnout switch machines. Multi-domain features are first extracted from degradation power signals in the time domain, frequency domain, and time-frequency domain. Subsequently, a Uniform Manifold Approximation and Projection (UMAP)-based feature fusion strategy is employed to construct low-dimensional feature representations that effectively characterize the evolution of the equipment’s operating state, and corresponding degradation performance indicators are established. Based on the fused features, the K-means++ clustering algorithm is applied to divide the performance degradation process of the switch machine into different stages. The clustering results are comprehensively evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and Davies–Bouldin (DB) index, and are compared with those obtained by the fuzzy C-means algorithm and the conventional K-means algorithm. Experimental results demonstrate that the proposed method achieves superior clustering quality and stability in degradation stage partitioning, enabling refined identification of degradation states and providing reliable theoretical support and technical foundations for condition monitoring and maintenance decision-making in intelligent railway turnout operation and maintenance systems. Full article

A large number of vulnerable road users such as pedestrians continue to be injured or killed in road accidents every year, and active safety systems such as automatic emergency braking systems are expected to improve the situation. However, automatic emergency braking systems for pedestrians have been tested in a variety of real-world scenarios. The purpose of this paper is to obtain typical P-AEB test scenarios that can reflect the real and collision scenarios through real pedestrian–vehicle crash data. By using the k-means clustering algorithm based on local outlier detection, the intersection data and the straight-road data are clustered and analyzed separately, with five types of typical P-AEB straight-road test scenarios and seven types of typical P-AEB intersection test scenarios. By comparing with the existing test protocols, the test scenarios proposed in this paper have good coverage and authenticity, and can play a guiding role in the construction of specific P-AEB system test scenarios. Full article

Monitoring agricultural production in developing nations is essential for assessing food security. Nevertheless, persistent cloud cover in tropical regions severely limits optical satellite observations, and ground-truth data for classification validation are typically unavailable. This study developed a remote sensing methodology to classify agricultural land in southern Uasin Gishu County, Kenya, using weather-independent Synthetic Aperture Radar (SAR) imagery without requiring in situ training data. We processed 29 Sentinel-1 C-band VH-polarized scenes through the Alaska Satellite Facility’s Radiometric Terrain Correction pipeline. We computed the Coefficient of Variation (CV) across the 2017 time series to quantify temporal backscatter variance. VH polarization was selected over VV because a preliminary analysis showed that VV sensitivity to water surface dynamics confounded the CV algorithm. Preprocessing masks excluded water bodies, urban areas, and edge pixels to reduce classification errors from non-agricultural sources of temporal variability. Unsupervised ISO Cluster classification partitioned the CV raster into land-cover classes, and a Python-based statistical analysis determined optimal threshold values. Active agriculture pixels (n = 581,807) exhibited a mean CV of 0.469 (SD = 0.087), while non-agricultural pixels (n = 623,484) showed a mean CV of 0.274 (SD = 0.049). The optimal classification threshold of 0.357, determined by the intersection of fitted normal distributions, achieved an overall accuracy of 87.5% (Kappa = 0.73) when validated against Sentinel-2 reference imagery. User’s accuracy for agriculture was 96.6%, indicating that pixels classified as agricultural were highly reliable, while omission errors reducing producer’s accuracy to 84.6% were primarily attributable to edge pixels and land cover types where preprocessing masks or threshold placement excluded pixels exhibiting intermediate temporal dynamics. The classification identified approximately 810 km² of actively cultivated land (54% of the southern study area), corresponding to an estimated 69,500 to 162,200 metric tonnes (assuming 30–70% maize fraction) of potential maize production based on FAO yield data. The methodology provides a replicable, cost-effective tool for food security monitoring in cloud-prone regions where ground-truth data are unavailable. Full article

UAV-Assisted Mobile Ad Hoc Networks (UAMANETs) provide flexible communication support in dynamic and infrastructure-limited environments. This paper studies a representative UAMANET architecture in which a subset of UAVs forms stable task clusters with ground nodes while simultaneously acting as relays in an airborne backbone network. To characterize the network capacity under contention-based medium access and multi-hop routing, we introduce Aggregate Multi-hop Information Efficiency (AMIE), a capacity-oriented metric that jointly accounts for MAC-layer contention, multi-hop routing, and end-to-end transmission reliability. Based on an IEEE 802.11p access model, we extend Bianchi’s CSMA/CA analytical framework to UAMANETs, enabling a quantitative characterization of how spectrum resource allocation affects AMIE through link activation probability, transmission interruption, and end-to-end hop count. Building on the derived analytical insights, we further develop a soft centralized resource management framework, in which an existing MSF-PSO algorithm is employed as a numerical solver to optimize resource allocation under implicit MAC-layer coupling constraints. Numerical results demonstrate that, compared with conventional IEEE 802.11p spectrum resource settings, the proposed framework can achieve substantial AMIE improvements under representative network configurations. Full article

Microseismic identification and localization of signals from single-component distributed optical fiber acoustic sensors (DAS) in vertical wells are limited by low signal-to-noise ratio and lack of directional information, making effective signal identification and accurate localization difficult. Improving the detection rate and accuracy of such data events is helpful for analyzing the effect of fracturing. To address this, this paper proposes a method for automatically picking and locating microseismic events based on dual fitting modeling and waveform inversion. First, empirical mode decomposition (EMD) is used to adaptively decompose and reconstruct the original DAS signal to filter out approximately 80% of high-frequency noise (noise above 200 Hz). Second, the classic short-time average/long-time average energy ratio algorithm is used to pick all “event points.” Finally, DBSCAN density clustering and RANSAC robust fitting are combined to perform secondary screening and fitting modeling of the “event points” to obtain the continuous event arrival time distribution along the well section direction, and the spatial location of the seismic source is inverted based on the fitting results. Tested with experimental data from Well XX, the automatic detection rate reached 96%, and the accuracy of machine detection compared with manual judgment reached 95%. Full article

Customer Segment Classification is a machine learning task in marketing analytics that involves assigning customers to predefined categories using features derived from historical transactional data. However, conventional approaches, such as statistical and clustering-based algorithms, may face challenges in fully capturing the nonlinear relationships in customer data, which can lead to limited insights and suboptimal segmentation outcomes. This paper introduces RFM-Net, an approach that integrates Deep Learning with Recency, Frequency, and Monetary (RFM) analysis for customer segment classification. By leveraging RFM features as input and labeled customer segments as output, we designed a specialized Convolutional Neural Network (CNN) model tailored for classification tasks. In the proposed method, labels are generated by a rule-based logic from RFM scores and then used as supervised ground truth. Accordingly, learning an expert-defined mapping is employed to model customer segmentation, rather than discovering a new segmentation structure. The proposed method enables businesses to classify customers into strategically meaningful segments such as Champions, Loyal Customers, At Risk, and Hibernating, thereby facilitating effective and targeted marketing strategies. Unlike traditional CNN architectures, RFM-Net offers a more compact, lightweight, and computationally efficient model with fewer layers and parameters, supporting improved interpretability and reduced risk of overfitting. Experimental results conducted on a real-world dataset demonstrated the effectiveness of RFM-Net with an accuracy of 94.33%. The results of this study showed a relative average increase of 13.17% compared to the results reported in previous studies on the same dataset. The core contribution of this research lies in combining the powerful generalization capabilities of deep learning with the effectiveness of RFM analysis, offering a robust solution for data-driven customer relationship management. Full article

Educational platforms increasingly rely on vector databases to store and retrieve embedding representations of large-scale learning corpora (e.g., lecture notes, assignments, feedback, and student Q&A) for retrieval-augmented generation and analytics. However, directly indexing educational text embeddings raises privacy risks (student identities, sensitive performance signals, and protected attributes) and creates a management challenge: embeddings drift as curricula evolve, access policies change, and new content arrives continuously. This paper studies privacy-preserving intelligent management of educational-corpus vector libraries and proposes a novel, end-to-end algorithmic framework that jointly optimizes (i) privacy leakage control, (ii) retrieval quality, and (iii) operational efficiency under streaming updates. We introduce a hierarchical policy-aware vector lifecycle model, a privacy budget scheduler for adaptive re-embedding and re-indexing, and a secure-aware clustering-and-routing mechanism that supports fast query-time filtering with minimal accuracy loss. The resulting system, PP-EDUVec, enables compliant similarity search across multi-tenant educational data while automatically maintaining index health (freshness, redundancy, and utility) over time. On the EDU-Mix benchmark, PP-EDUVec achieves Recall@10 $=0.835$ while reducing representation leakage (LeakRep) from $0.215$ to $0.136$ ($-36.7\%$) and access-pattern leakage (LeakAP) from $0.398$ to $0.255$ ($-35.9\%$), and lowering mean latency from $42.1$ ms to $33.4$ ms ($-20.7\%$) and weekly maintenance time from $55.0$ to $35.8$ min/week ($-34.9\%$) compared with PostFilter. Full article

Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is proposed. When sparse PU activity is masked by irregular interference bursts, traditional sensing algorithms misclassify weak transmissions as noise, leading to low detection reliability. To resolve this, the first hidden layer employs Discrete Wavelet Sparse Bayesian Kernel Analysis (DW-SBK), integrating Discrete Wavelet Packet Transform (DWPT), Sparse Bayesian Learning (SBL), and Kernel PCA. This restores the true sparse pattern of the spectrum, separates interference from actual PU signals, and enhances detection of weak channels. Additionally, PU signals are fragmented due to cross-scale activity drift, where dynamic bandwidth switching and variable burst durations disrupt temporal continuity. Therefore, the second layer incorporates Gradient Boosted Multi-Head Fuzzy Clustering (GB-MHFC), where Gradient Boosted Decision Trees (GBDT) model nonlinear spectral–temporal patterns, Multi-Head Self-Attention (MHSA) captures long- and short-range temporal dependencies, and Fuzzy C-Means Clustering (FCM) groups feature representations into stable PU activity modes, thereby reducing misclassifications and enhancing robustness under highly dynamic CRN conditions. The proposed method demonstrates superior performance with a maximum detection probability of 0.98, classification accuracy of 98%, lowest sensing error of 5.412%, and the fastest sensing time of 3.65 s. Full article

Background: Imaging-transcriptomic analysis, through the integration of multimodal magnetic resonance imaging (MRI) and transcriptomic data, provides complementary structural, functional, and molecular information that is crucial for the early detection and mechanistic exploration of Alzheimer’s disease (AD). However, effectively extracting features from heterogeneous multimodal data and capturing the associations between microscopic molecular variations and macroscopic brain alterations remain key challenges. Recent advances in deep learning and multimodal integration have enhanced the ability to model nonlinear cross-modal relationships, enabling more accurate identification of imaging-transcriptomic biomarkers and subtypes. Developing robust multimodal frameworks is therefore essential for early AD detection, subtype identification, and advancing precision medicine in neurodegenerative diseases. Methods: In this study, a two-stage method of multimodal Feature Extraction based on Association Analysis and Graph Convolutional Network with Self-Attention and Self-Expression framework (MFEAA-GCNSASE) for early diagnosis of AD and effective identification of subtypes of MCI with different progression to AD is proposed. In the first stage, the MFEAA model is applied to integrate multiple association analysis methods on sMRI, PET, and transcriptomic data to identify key multimodal biomarkers for AD and mild cognitive impairment (MCI). In the second stage, the GCNSASE model enhances classification accuracy between AD and MCI patients through self-attention and self-expression layers. Additionally, unsupervised clustering was performed on MCI samples using top multimodal biomarkers to explore subtype heterogeneity and conversion risk. Reliable MCI subtypes were also identified through a consensus clustering approach. Results: The proposed algorithm integrates sMRI, PET, and transcriptomic data, identifying robust biomarkers including the Left Hippocampus, Left Angular Gyrus, and key genes such as SLC25A5 and GABARAP. To ensure statistical robustness given the extreme class imbalance, we employed a rigorous repeated stratified cross-validation (RSCV) framework. GCNSASE achieved state-of-the-art discrimination performance with mean AUC values ranging from 0.946 to 0.961 across feature subsets (10–50%), significantly outperforming MOGONET (mean AUC: 0.844–0.875, p < 0.001) and conventional machine learning models with tighter 95% confidence intervals, indicating superior stability despite the limited AD sample size. Clustering analysis revealed two distinct MCI subtypes with divergent molecular landscapes: Subtype A was enriched in energy metabolism and cellular maintenance pathways, whereas Subtype B was enriched in neuroinflammatory and aberrant signaling pathways. Notably, the majority of MCI patients who subsequently converted to AD were concentrated in the immune-inflammatory Subtype B. These findings highlight that neuroinflammation coupled with bioenergetic failure constitutes a critical mechanism driving the conversion from MCI to AD. Conclusions: The proposed methods not only provide the key multimodal biomarkers and enhance the accuracy of the classification model for early AD diagnosis but also identify biologically and clinically meaningful MCI subtypes with distinct molecular signatures and conversion risks. Exploring these associated multimodal biomarkers and MCI subtypes is of great significance, as they help elucidate the heterogeneous mechanisms underlying AD onset and progression, enable the identification of high-risk individuals likely to convert to AD, and provide a foundation for targeted therapeutic strategies and individualized clinical management. These findings have important implications for understanding disease heterogeneity, discovering potential intervention targets, and advancing precision medicine in neurodegenerative diseases. Full article

Through-plane electronic transport in porous membrane electrode assembly (MEA) electrodes is governed by the three-dimensional (3D) connectivity of the conducting phase. Here, we quantify the role of the spanning-cluster fraction P_∞, defined as the fraction of conducting-phase voxels that belong to the z-spanning connected component in a finite reconstructed volume, on effective conductivity using scanning electron microscopy (SEM)-informed 3D reconstructions of four archetypal morphologies: a granular catalyst layer (CL), labeled CL1; a fibrous gas diffusion layer (GDL), labeled GDL1; an open-cell foam (OCF); and a micro-fibrous non-woven (MFM), labeled MFM1. Each morphology is reconstructed on a 150 × 150 × 150 voxel grid, and z-spanning connectivity is identified with a 26-neighbor flood-fill algorithm. Steady-state conduction is solved by a finite-volume method (FVM) with an imposed potential difference between the z—faces and no-flux lateral boundaries. Although all samples exhibit through-thickness connectivity, the normalized conductivity _σeff/_σbulk varies widely, from ≈0.134 (MFM1) to ≈0.706 (OCF). The corresponding (P_∞, _σeff/_σbulk) pairs are (0.996, ≈ 0.306) for CL1, (0.999, ≈ 0.303) for GDL1, (0.997, ≈ 0.706) for OCF, and (0.901, ≈ 0.134) for MFM1. OCF exhibits the highest response due to vertically coherent channels, whereas MFM1 underperforms due to laminated constrictions; CL1 and GDL1 lie in an intermediate regime with nearly isotropic skeletons. Overall, the results show that while a z-spanning connected component is required for measurable conduction, the magnitude of _σeff is dictated by percolating-skeleton quality (bottlenecks, cross-sectional constrictions, and pathway alignment) rather than phase amount alone. The proposed descriptors therefore enable percolation-aware screening metrics for designing and comparing MEA-relevant GDL and CL microstructures. Full article

Triangular mesh surface representation is widely adopted in geometric design and reverse engineering applications. However, in high-precision Computer Numerical Control (CNC) machining, significant limitations persist in automated Computer-Aided Manufacturing (CAM) tool path generation for such representations. Conventional CAM workflows heavily rely on manual engineering interventions, such as creating drive surfaces or tuning extensive parameters—a dependency that becomes particularly acute for generic free-form models. To address this critical challenge, this paper proposes a novel end-to-end single-step end-milling tool path generation methodology for triangular mesh surfaces in high-precision five-axis CNC machining. The framework includes clustering analysis for optimal workpiece orientation, normal vector distribution analysis to identify shallow and steep regions, Graphics Processing Unit (GPU)-accelerated collision detection for feasible tool orientation domains, and iso-planar tool path generation with Traveling Salesman Problem (TSP) optimization for efficient tool lifting and movement. Experimental validation confirms the framework ensures machining quality and algorithmic robustness. Full article

This study investigated the genetic diversity and population structure of five Hainan indigenous pig breeds (147 individuals from 7 populations representing 5 breeds: 3 Duntou pig subpopulations (DT-DZ, DT-SJ, and DT-SG) and four additional breeds (Wuzhishan, Wenchang, Lingao, and Tunchang)) to address germplasm conservation needs driven by exotic crossbreeding, African swine fever, and inadequate genetic evaluation. After strict quality screening, we used 147 qualified samples for microsatellite genotyping and 104 samples for mtDNA D-loop sequencing. The analyses integrated 17 FAO-recommended microsatellite markers and mtDNA D-loop sequencing. In total, 15 out of 17 loci exhibited high polymorphism (PIC > 0.6), with Wuzhishan pigs exhibiting the highest genetic diversity (He = 0.666, I = 1.279). Pairwise Fst values indicated significant genetic differentiation among all populations (p < 0.05), and AMOVA attributed 87.32% of the genetic variation to within-population differences. Three complementary clustering methods (UPGMA, PCoA, and STRUCTURE with the optimal K value of 2 identified via the ΔK algorithm) divided the populations into two clades, clearly separating the Duntou subpopulations from other breeds. mtDNA D-loop sequencing of 104 individuals yielded a 1175 bp fragment, identifying 12 haplotypes and a high haplotype diversity (Hd = 0.688) low nucleotide diversity (π = 0.00193) pattern; Lingao pigs showed no genetic variation, while Duntou and Wuzhishan pigs had the highest Hd. NJ phylogenetic analysis indicated that Hainan pigs form an independent subclade within Chinese indigenous pigs, closely related to Luchuan pigs. These findings confirm the high overall genetic diversity and distinct population-level divergence in Hainan pigs, with Duntou pigs representing a unique lineage. This work provides a scientific basis for targeted conservation strategies, including prioritizing the conservation of Duntou and Wuzhishan pigs and restoring genetic variation in Lingao pigs. Full article

The Three Gorges Reservoir (TGR) in China is one of the world’s largest hydropower projects. Interval inflow, originating from ungauged areas between the upstream gauging control stations (Zhutuo, Beibei, Wulong) and the TGR dam site, is a critical component of total reservoir inflow, but its hydrological characteristics have not been fully clarified. The accurate estimation and prediction of interval inflow are essential for reservoir safety and flood control operations. Using daily hydrological data from 2009 to 2017, we propose an integrated analytical framework combining (i) flow travel time estimation using cross-correlation analysis, (ii) multi-scale statistical characterization, and (iii) K-means clustering with bootstrap validation and algorithm comparison. This framework systematically identified hydrological regimes of interval inflow and their associated flood control risks. The key findings are as follows. (1) The optimal flow travel time from the upstream gauging stations to the dam site is 1 day (correlation coefficient $\rho =0.9809,p<0.001$), and it remains stable across different flow regimes. (2) The interval inflow exhibited a highly right-skewed distribution (mean 1279 m³/s, standard deviation 1651 m³/s) and contributed on average 10.1% to the total inflow. The contribution ratio exhibited an inverted U-shaped relationship with increasing total inflow, peaking at 11.4% when the total inflow ($Q$) was 13,014 m³/s. The quartile thresholds were 5788 m³/s, 9575 m³/s, and 16,869 m³/s (corresponding to Q1, Q2, and Q3, respectively), and the 10th and 90th percentiles (P10 and P90) were 4865 m³/s and 24,625 m³/s, respectively. (3) Five distinct hydrological patterns (C1–C5) were successfully identified, among which Cluster C4 (5.7% of days) was defined as the high-impact pattern based on reservoir operational criteria, with a mean $I$ of 6425 m³/s, a mean $R$ of 27.8% (up to 44% in extreme events), a mean flood duration of 5.8 days, a mean flood volume of 36.1 × 10⁸ m³, and a flashiness index of 1.48. (4) C4 is predominantly triggered by localized heavy rainfall, and its flashy nature implies a substantially shorter forecast lead time compared with mainstream-dominated floods, posing major challenges to real-time reservoir operations. This study demonstrates that interval inflow risk is pattern-dependent and that the proposed framework provides a scientific basis for developing pattern-specific reservoir operation strategies. The proposed framework is transferable to other large river-type reservoirs facing similar ungauged interval inflow challenges. Full article

To address the periodic impacts and amplitude-modulated high-frequency resonance phenomena caused by bearing faults in rotating machinery, this paper proposes a detection method. The core innovation lies in: firstly, constructing a composite fault feature index (CFFI) that integrates normalized kurtosis and fuzzy entropy, which synchronously quantifies the fault impact intensity and periodic structure, and serves as an optimization objective; secondly, definining a spectral energy retention rate (SERR) that includes both the full spectrum and characteristic frequency bands to evaluate the denoising effect and fault feature retention, respectively. Based on this, the method adaptively determines the Variational Mode Decomposition (VMD) parameters through the Triangular Topology Aggregation Optimizer (TTAO), and uses Dynamic Weighted Center Clustering (DWCC) to screen key IMFs containing fault-envelope information. On the IMS bearing dataset, the SERR of the reconstructed signal is 0.21356, which is higher than the actual collected signal value of 0.22465, with a relative error of 4.9%, indicating a higher reconstruction accuracy. These quantitative results indicate that CFFI-guided optimization enhances impulsive and periodic fault components while maintaining stable feature-band retention. This approach is suitable for real-world equipment monitoring and exhibits strong engineering applicability. Full article