Search Results (3,048)

Accurate diameter identification of buried pipelines remains challenging in complex municipal environments because ground-penetrating radar (GPR) echoes are often weak and hyperbolic reflections are frequently incomplete. To address this problem, this study proposes a diameter identification method that integrates five-point hyperbolic A-scan features with burial depth information. Faster R-CNN is used to locate the hyperbolic region and remove redundant background information. HRNet is then employed to detect five keypoints, including the hyperbola vertex, the left and right diffraction midpoints, and the left and right tail points, from which the corresponding A-scan signals are extracted as lightweight features. The burial depth of the pipeline is subsequently estimated using LSTM and introduced as prior information. Finally, a Curve-Net regression network fuses the five key A-scans and the burial depth feature for diameter regression. Experimental results show that the proposed method achieves an MAE of 0.0159 m, an RMSE of 0.0202 m, and an R2 of 0.8354 on the test set. In field validation, the maximum relative error is 8.50%, with an absolute error of only 1.7 cm. These results demonstrate that the proposed method can effectively improve the accuracy of concealed pipeline diameter identification in small and medium-sized, shallow-buried municipal pipeline scenarios, and exhibits promising engineering application value. Full article

The integration of AI vision-based sensing and human motion analysis has grown to be a key element of intelligent perception systems, which now allow for automated interpretation of human movement, activity patterns, and complex visual behaviors. However, accurate functional movement assessment from monocular aerial and ground-view videos remains challenging due to low spatial resolution, background clutter, occlusions, and large variations in body posture, limiting the reliability of AI-assisted future healthcare applications. This study presents a multi-level framework that integrates asymmetric deep feature representation with transformer-based architecture and graph-driven optimization for robust vision-based human movement analysis. First, a Heavy Attention Transformer is employed to enhance image quality and emphasize clinically relevant anatomical and motion patterns by suppressing background interference. Panoptic segmentation and Real-Time Detection Transformer V2 are then used for subject localization, followed by skeletal keypoint extraction using YOLOv8. The proposed framework adopts an asymmetric dual-stream feature extraction strategy, where global contextual information is captured through Bag of Visual Words, Video Swin Transformer, Video Masked Autoencoder, and TimeSformer, while local biomechanical motion dynamics are modeled using DiffPose, PoseFormer, and Spatial–Temporal Graph Convolutional Networks. The key contribution lies in the asymmetric feature design that preserves the distinct information structures of visual context and skeletal dynamics. To reduce feature redundancy and select discriminative clinical representations, the Slime Mould Algorithm is utilized as a metaheuristic optimizer. The optimized features are subsequently classified using a Graph Attention Network for automated functional movement assessment. Experimental evaluation on the UAV-Human and UCF-ARG benchmark datasets achieved an accuracy of 82.50% and 78.20%, respectively. The proposed framework illustrates the potential of asymmetry-aware AI-enabled vision sensing to perform strong human movement analysis in complex viewpoints and lays the groundwork for future healthcare-related applications such as remote human movement evaluation and rehabilitation monitoring. Full article

Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the structural diversity of non-dominated feature subsets. To address these issues, this paper proposes PGS-MODE-FS, a prior-guided and structure-aware multi-objective differential evolution method for high-dimensional feature selection. Specifically, feature–class relevance and feature redundancy are integrated into a unified feature importance measure to guide the generation of candidate solutions with different sparsity levels. The same feature-priority information is further used to construct a Top-k activated subspace, in which individuals are assigned to multiple islands according to their structural differences on informative features, thereby promoting diverse evolutionary search. Experiments on 17 public and biomedical datasets, including parameter analysis, comparative experiments, and ablation studies, demonstrate that PGS-MODE-FS achieves competitive performance in solution-set quality, classification accuracy, feature reduction, and computational efficiency. Further diversity analysis shows that the proposed multi-island mechanism effectively preserves structural diversity during evolution. Full article

Student engagement within Learning Management Systems (LMSs) provides valuable behavioural data for understanding and predicting learning outcomes. However, predicting students’ forum consumption behaviour remains challenging because LMS datasets may contain redundant engagement indicators that increase model complexity. This study proposes a hybrid predictive modelling framework that integrates Ant Colony Optimisation (ACO) with three ensemble regression algorithms—Random Forest (RF), Gradient Boosting (GB), and Stacking—to predict forum consumption behaviour using LMS-derived engagement indicators. Guided by Educational Data Mining (EDM) and Social Learning Theory (SLT), behavioural, cognitive, and social engagement dimensions were operationalised using LMS indicators, with Freq_Forum_Consume serving as the target variable. ACO was employed as a wrapper-based feature-selection technique to identify informative predictors before model training. The performance of the ACO–ensemble models was compared with corresponding baseline models using the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results show that ACO reduced the predictor space from nine to six variables for GB and to seven variables for both Stacking and RF, while maintaining or improving predictive performance. ACO-GB achieved the strongest overall performance (R² = 0.8332, MAE = 55.3173, RMSE = 71.4150). Consistent results across multiple ACO parameter configurations further demonstrated parameter consistency within the tested search settings. The selected predictors represented behavioural, cognitive, and social engagement dimensions, highlighting their complementary contribution to predicting forum consumption behaviour. The proposed framework provides a more parsimonious and interpretable approach to LMS-based learning analytics while retaining predictive performance. Full article

Unsupervised feature selection is essential for high-dimensional data analysis, where irrelevant and redundant variables may obscure the intrinsic data structure, reduce interpretability, and degrade downstream learning performance. The key challenge is to identify informative features without label supervision while suppressing redundant selections under nonlinear dependencies. To address this issue, this paper proposes an unsupervised Hilbert–Schmidt Independence Criterion (HSIC)–Elastic Net (ENet) feature selection framework, termed U-HSIC-ENet. The proposed method reformulates unlabeled feature weighting as a self-supervised kernel alignment problem. Specifically, a target kernel is constructed directly from unlabeled data to encode global sample relationships, while each feature is represented by a centered and Frobenius-normalized feature-induced kernel. Feature relevance is then measured by centered kernel alignment (CKA) in a reproducing kernel Hilbert space (RKHS). The main novelty of U-HSIC-ENet lies in a unified relevance–redundancy–stability formulation. Feature–target alignment is used to estimate nonlinear relevance, whereas pairwise similarities between feature-induced kernels are used to characterize inter-feature redundancy in the same kernel alignment space. On this basis, an explicit off-diagonal redundancy penalty is incorporated into a nonnegative Elastic Net-type objective, which strengthens the suppression of co-selected similar features while preserving sparse and stable feature weighting. The resulting quadratic formulation clarifies how relevance promotion, redundancy control, sparsity, and numerical stabilization are coupled within a single optimization framework. Experiments on eight benchmark datasets under a fixed-budget evaluation protocol show that U-HSIC-ENet achieves the strongest average performance on Normalized Mutual Information (NMI), the Adjusted Rand Index (ARI), and clustering accuracy (ACC) compared with representative graph-, spectral-, and HSIC-based baselines. The advantage is the most pronounced on NMI, suggesting that the self-supervised target kernel and CKA-based relevance modeling are effective in preserving the clustering-relevant nonlinear structure. Friedman tests and Wilcoxon signed-rank tests with Holm correction provide statistical support for the observed improvements. Subsampling-based stability evaluation reveals a trade-off between clustering effectiveness and selection reproducibility: several baselines achieve higher stability scores despite the stronger average clustering performance of U-HSIC-ENet. These results indicate that the proposed framework is effective for unsupervised nonlinear feature weighting when relevance estimation, redundancy control, and stability are considered jointly. Full article

Symmetry and asymmetry appear together in aero-engine prognostics and health management. A monitoring system should preserve a common decision structure across operating scenarios, while sensor evidence becomes asymmetric under changing operating conditions and fault modes. This study formulates the National Aeronautics and Space Administration (NASA) Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan benchmark as a four-level health-state assessment problem using remaining useful life (RUL) thresholds of 100, 50 and 15 cycles, and proposes a neural evidential reasoning (ER) framework with decision-structure symmetry. FD001–FD004 share the same health-state space, threshold map, reliability-discounted ER operator and final argmax rule, whereas feature selection, evidence transformation and reliability parameters are estimated independently for each subset. Sliding statistical descriptors and training-fold-only minimum-redundancy maximum-relevance (mRMR) selection retain six compact sensor-derived features. FD001 and FD003 therefore use six inputs; FD002 and FD004 additionally retain the same three operating-setting variables and use nine inputs. Boundary soft labels and auxiliary ordinal/RUL supervision exploit ordered degradation information near adjacent-state boundaries. Evaluation uses engine-disjoint validation, three random seeds, identical subset-specific inputs for every comparator, ensemble/neural/ordinal baselines, component ablation and calibration/error-detection analyses. Across the four subsets, the proposed model achieves the highest mean three-metric average (Avg3), 0.664 (standard deviation 0.007), with Accuracy 0.816 (0.006), Macro-F1 0.582 (0.010) and Balanced Accuracy 0.594 (0.005). It is statistically comparable to histogram gradient boosting, random forest and a plain multilayer perceptron, and significantly exceeds the tested compact-input cumulative ordinal, Feature Transformer and correlation-graph attention controls after Holm correction. Fixed-probe diagnostics identify the five-cycle window as the best overall short-history setting. The mRMR top-6 interface reduces the sensor-feature dimension by 97.1% while retaining 98.2% and 97.0% of full-pool Avg3 on FD002 and FD004, respectively. Unknown mass ranks erroneous predictions above correct predictions on every subset (area under the receiver operating characteristic curve (ROC-AUC) 0.749–0.810), including the highest value on the most heterogeneous FD004 subset. These results support a compact and auditable evidence interface that combines competitive classification with source-wise reliability and uncertainty information. Full article

Classification accuracy in retinal optical coherence tomography (OCT) alone does not establish whether a model is reliable or whether to refer to a specialist. To address this, we propose DAVis-Net, a VGG16-based architecture that is equipped with two Convolutional Block Attention Modules (CBAMs), an auxiliary deep-supervision head, and an integrated reliability framework that includes Monte Carlo Dropout uncertainty estimation, model calibration, split conformal prediction, and quantitative multi-method attribution analysis. DAVis-Net has achieved a cross-validated accuracy of 98.02% ± 0.12% on the four classes of OCT (CNV, DME, DRUSEN, NORMAL), statistically significantly higher than the VGG16 baseline in a matched-fold paired comparison (accuracy: p = 0.023; macro-F1: p = 0.004), with the highest gain on the hardest class (DRUSEN F1 +4.57 pp). Joint correlation analysis showed strong redundancy between predictive entropy and conformal set size (r = 0.67–0.78) across all classes, and a near zero linear correlation between both of these and a geometric proxy for spatial attention placement (|r| < 0.08), suggesting that, as captured by this central-region localization proxy, distributional uncertainty and spatial attention placement may reflect largely distinct reliability dimensions. A composite Trust/Refer triage rule achieved an accuracy of 99.76% on the 73.9% of cases it retained. All reported figures are internal estimates derived from a single dataset at the image-level, and multi-center validation is still required. The results show that the multi-dimensional reliability assessment is a more informative characterization of a medical image classifier than accuracy alone. Full article

Accurate estimation of forage aboveground biomass (AGB) from unmanned aerial vehicle (UAV) imagery is essential for monitoring alpine grassland productivity and supporting sustainable grassland management. However, the biomass of non-palatable species may be included in remote sensing-based estimates when their spatial distribution is not explicitly considered, potentially affecting spatial assessments of forage biomass. This study developed a UAV RGB-based framework for spatially constrained grassland AGB assessment by integrating the extraction of the growing-season non-palatable species Achnatherum splendens, mask-based spatial exclusion, feature optimization, and AGB inversion using field measurements from the northwestern shore of Qinghai Lake. Among tested semantic segmentation models, the Attention U-Net achieved the highest segmentation accuracy and was selected to identify A. splendens, while vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features were optimized using the minimum redundancy maximum relevance (mRMR) algorithm. Random forest regression (RFR), support vector regression (SVR), and partial least squares regression (PLSR) models were subsequently evaluated. The Attention U-Net achieved high segmentation accuracy (PA = 89.98%, mIoU = 88.89%, and F1 = 93.88%). Feature optimization improved the performance of all regression models, with SVR providing the highest estimation accuracy (R² = 0.72; RMSE = 23.73 g m⁻²). The estimated AGB ranged from 67.88 to 205.85 g m⁻², revealing pronounced spatial heterogeneity. In a typical sample area with high-density A. splendens, excluding pixels classified as A. splendens reduced the estimated AGB by 15.45%, demonstrating the influence of dense A. splendens patches on spatial AGB assessment. These results demonstrate that integrating deep learning-based species masking with feature optimization provides a spatially explicit approach for grassland AGB assessment after excluding areas classified as A. splendens and provides useful spatial information for fine-scale grassland monitoring and management. Full article

Background/Objectives: To determine whether a coagulation-immune index, vaginal microbiome composition, and colposcopic imaging carry non-redundant information about HPV-associated lower genital tract co-infection when integrated within a single learned model. Methods: This retrospective three-center study analyzed 560 women referred for colposcopy: 420 formed the development cohort, and 140 were a withheld external cohort from a third center. All three modalities were sampled within one visit. Participants were classified as HPV-negative, HPV-positive without co-infection, or HPV-positive with co-infection, the latter confirmed by nucleic acid amplification for Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, or Mycoplasma genitalium, with overlapping taxa masked a priori. TIM-Fusion combined a ConvNeXt-Tiny image encoder, a feature-tokenizer transformer for the D-dimer-to-lymphocyte ratio, and a perceptron for centered log-ratio abundances through modality-confidence weighting and bottleneck cross-attention. Results: On the external cohort, TIM-Fusion achieved a macro-AUC of 0.92 (95% CI 0.86 to 0.96) and accuracy of 0.90 across the three categories, exceeding the image-only baseline at 0.81, the tabular-only baseline at 0.76, and the best pairwise combination at 0.86. All thirteen comparisons, differences of 0.03 to 0.16, retained significance under Holm correction. Ablation attributed 0.05 (0.01 to 0.10) to bottleneck fusion, the only component with an interval excluding zero; withholding imaging, microbiome, or the ratio cost 0.13, 0.06, and 0.04. Imaging was therefore the dominant contributor, its removal costing three times as much as either of the other modalities. Entering D-dimer and lymphocyte count separately rather than as a ratio gave 0.89 (0.82 to 0.93), so the ratio form is an estimation convenience at this sample size and not a requirement. Co-infection-class AUC was 0.87 (0.75 to 0.94) on 26 events. In a secondary analysis restricted to the 84 HPV-positive external participants, binary discrimination of co-infection was 0.86 (0.76 to 0.93) with sensitivity 0.85 and specificity 0.91, lower than the one-versus-rest figure, which includes HPV-negative women in the negative class. Conclusions: Colposcopic morphology carries most of the discriminatory signal, while vaginal microbial ecology and coagulation-immune status each add information the others do not. Discrimination of co-infection status itself is more modest than the macro-averaged figure suggests, since within the HPV-positive stratum alone it falls to 0.86 on 26 events. Full article

High-dimensional unlabeled data often contain complex latent structures that are easily obscured by redundant features, noise, and unreliable neighborhood relationships. Although graph-based learning provides an effective means of preserving sample relationships, most existing methods mainly rely on first-order neighborhoods and therefore fail to fully exploit multi-order dependencies revealed by multi-hop propagation. To address this limitation, we formulate unsupervised representation learning as a graph-guided structure-preserving projection problem and propose Unsupervised Representation Learning with Adaptive Multi-order Structural Graph Fusion (URL-AMGF). The proposed method constructs multi-order graphs to characterize structural relationships at different neighborhood orders and adaptively fuses them into a unified guidance graph. This graph is then integrated into projection matrix learning, enabling graph structure optimization and low-dimensional representation learning to be jointly performed within a unified framework. By integrating local neighborhood information with multi-order structural cues, URL-AMGF learns low-dimensional representations that better reflect the structural relationships among samples. Experiments on multiple benchmark datasets show that URL-AMGF achieves generally competitive clustering performance compared with representative unsupervised dimensionality reduction and graph-based learning methods. These results indicate that adaptive multi-order graph fusion can provide effective structural guidance for structure-preserving unsupervised representation learning. Full article

Truck platooning depends on frequent vehicle-to-vehicle communication to achieve platoon formation, which can create potentially substantial communication and computational burdens. To balance formation performance and communication efficiency, this paper proposes a Proximal Policy Optimization-based Event-Triggered Mechanism (PPO-ETM) for truck platoon formation. Under a predecessor-following topology, each follower uses locally available information and independently determines whether its state should be transmitted. PPO optimizes communication decisions by jointly considering formation errors and transmission costs, while a controller generates acceleration and steering commands. An analytically designed event-triggered filter is further incorporated to guarantee a strictly positive Minimum Inter-Event Time, thereby excluding Zeno behavior. Simulation results show that the proposed framework enables initially dispersed trucks to converge to and maintain the desired formation while avoiding redundant information transmissions. Comparative studies demonstrate that PPO-ETM achieves a favorable balance among formation accuracy, communication efficiency, and learning performance, outperforming other Reinforcement Learning methods. These results indicate that the proposed framework provides a decentralized and scalable solution for communication-constrained truck platooning. Full article

Predictive opportunistic maintenance can improve maintenance coordination in multi-component systems by jointly exploiting health information and shared maintenance opportunities. However, redundancy and component heterogeneity complicate maintenance timing and component selection. Consequently, this paper proposes a reliability-centered predictive opportunistic maintenance framework for k-out-of-n systems with heterogeneous two-stage degrading components. Firstly, a two-stage Wiener process is used to characterize heterogeneous component degradation, with preventive maintenance restricted to defective-stage components. Secondly, future component reliabilities are aggregated according to the k-out-of-n structure to incorporate system redundancy into maintenance triggering. Thirdly, a hierarchical maintenance mechanism distinguishes necessary reliability-restoration actions from opportunistic replacements: failed components are correctively maintained, a minimum-cost necessary preventive-maintenance set is selected when required, and additional high-risk defective components are opportunistically maintained within the same maintenance event. Finally, the state-assessment interval, system maintenance threshold, and opportunistic-maintenance threshold are jointly optimized under a long-run average cost criterion. Numerical results confirm the economic advantage of the proposed policy over the benchmark strategies. Full article

Soil type imposes multilayered environmental filtering via physical structure, chemical properties, and resource availability, shaping microbial community assembly. Using 70 samples (black soil n = 27, meadow soil n = 32, and brown soil n = 11) from a typical black soil region of the Songnen Plain, we compared brown soil (forestland) with black soil and meadow soil (cropland) via 16S rRNA V3–V4 (primers 338F/806R) and fungal (ITS1, primers ITS1F/ITS2) amplicon sequencing and physicochemical analyses. The results showed (1) significantly higher organic carbon in brown soil than in black and meadow soils (p < 0.05); (2) a systematic shift along the r–K strategy axis, with K-strategists (e.g., Mortierellomycota and Acidobacteriota) dominating in brown soil, specializing in recalcitrant organic matter decomposition and carbon sequestration, and r-strategists (e.g., Proteobacteria, Bacteroidota, and Sordariaceae) enriched in black and meadow soils, rapidly utilizing labile carbon and driving carbon release. Redundancy analysis identified total organic carbon and pH as key environmental filters driving this shift. Our findings provide an ecological-strategy perspective for understanding land-use effects on soil microbiomes, informing carbon restoration and sustainable management in black soil regions. Full article

Multi-rotor unmanned aircraft systems (UAS) are now pervasive, yet quantitative evidence on how and why they fail remains fragmented across heterogeneous national reporting systems. This study analyses 319 multi-rotor UAS occurrences (2016–2022) coded from three official sources: the U.S. SAFECOM system (122), the Australian Transport Safety Bureau database (159) and the U.K. Air Accidents Investigation Branch reports (38). Each occurrence was assigned a primary causal factor from a twelve-factor taxonomy and a flight phase (take-off, en route, landing). Analyses comprised distributional estimation with Wilson confidence intervals, chi-squared association tests with permutation p-values for sparse tables, Cochran–Armitage trend tests, and correspondence analysis. Human factors (23.2%, 95% CI 18.9–28.1) and data-link problems (21.0%, CI 16.9–25.8) dominated, and 74.6% of occurrences arose en route—a phase profile opposite to that of manned aviation. Cause and phase were significantly associated (permutation p < 0.001, Cramér’s V = 0.305): all take-off occurrences were technological, none human-related, and battery failures clustered in landing (42%). Causal profiles differed markedly between reporting systems (p < 0.0001, V = 0.338), cautioning against naive pooling, and data-link problems nearly tripled from 11.9% (2016–17) to 32.9% (2021–22). Findings inform operator training, link redundancy, battery management and reporting standardisation. Full article

The rumen is a complex microbial ecosystem in which anaerobic fermentation determines the availability of energy and protein to ruminant hosts and influences feed efficiency, animal productivity, and environmental emissions. This review integrates current knowledge of host–rumen microbiome interactions with particular emphasis on microbial fermentation, nutrient utilization, feed efficiency, methane (CH₄) production, and sustainable ruminant production. We examine how dietary factors, including forage-to-concentrate ratio, carbohydrate fermentability, protein degradability, and lipid supplementation, alter microbial community structure, hydrogen metabolism, volatile fatty acid production, microbial protein synthesis, and nitrogen utilization. The review further evaluates microbiome-targeted strategies, including 3-nitrooxypropanol, red seaweeds (Asparagopsis spp.), nitrate, direct-fed microbials, and plant-derived bioactive compounds, with emphasis on their effects on fermentation pathways and methanogenesis; these strategies differ substantially in evidence base and mechanistic specificity, with 3-nitrooxypropanol supported by the most consistent mechanistic and in vivo evidence and several plant-derived compounds and direct-fed microbials showing more variable responses. Evidence from microbiome-wide and genome-wide association studies indicates that host genetics contributes to variation in rumen microbial composition and function, with potential consequences for feed efficiency and CH₄ emissions. Host-side determinants of the rumen environment, such as feed intake, digesta passage rate, saliva production, and epithelial and immune function, further shape microbial responses. However, responses to microbiome-targeted interventions remain variable because of microbial functional redundancy, dietary context, adaptation, and host-specific effects. We therefore discuss the major constraints limiting the consistent translation of microbiome research into practical feeding strategies and propose an integrated framework combining functional microbiome indicators, precision nutrition, host–microbiome-informed selection, and real-time monitoring. Understanding and manipulating rumen microbial fermentation through coordinated nutritional and host-based approaches may provide a pathway toward improving ruminant productivity while reducing the environmental footprint of livestock production. Among the strategies reviewed, 3-nitrooxypropanol currently has the strongest and most reproducible evidence base, whereas plant-derived bioactives and several direct-fed microbials remain promising but inconsistent, and validated on-farm microbiome biomarkers remain a major gap. Full article