Search Results (242)

Multi-radar systems can significantly improve tracking robustness and accuracy, but practical deployments are challenged by asynchronous sensing timestamps across distributed platforms and by limited communication bandwidth. This paper proposes a communication-efficient asynchronous track fusion framework based on state and covariance projection. Each radar performs local Kalman filtering and transmits only a compact track message consisting of the posterior state estimate, the associated error covariance, and a timestamp. At the fusion center, a causal reference time is chosen as the latest received timestamp, and all tracks are projected to this common time using a hybrid constant-acceleration (CA)/constant-velocity (CV) motion model with appropriately discretized process noise, followed by information-form (inverse-covariance) fusion. Under standard linear-Gaussian assumptions, the fusion rule is minimum mean square error (MMSE)-optimal when the projected estimation errors are approximately independent. We also analyze the computational complexity and the communication payload of the proposed procedure. Monte Carlo simulations with five heterogeneous radars and random inter-radar time offsets up to 37.5 ms over 100 runs show that the proposed fusion reduces the steady-state range root mean square error (RMSE) by about 66% and the radial-velocity RMSE by about 31% relative to the average single-radar tracker, while maintaining statistical consistency as verified by the normalized estimation error squared (NEES). These results indicate that projection-based track fusion provides an effective accuracy–communication trade-off for asynchronous multi-radar tracking. Full article

Gait recognition methods based on silhouette templates, such as the Gait Energy Image (GEI), achieve high accuracy under controlled conditions but often degrade when appearance varies due to viewpoint, clothing, or carried objects. In contrast, skeleton-based approaches provide interpretable motion cues but remain sensitive to pose-estimation noise. This work proposes two compact 2D skeletal descriptors—Gait Skeleton Images (GSIs)—that encode 3D joint trajectories into line-based and joint-based static templates compatible with standard 2D CNN architectures. A unified processing pipeline is introduced, including skeletal topology normalization, rigid view alignment, orthographic projection, and pixel-level rendering. Core design factors are analyzed on the GRIDDS dataset, where depth-based 3D coordinates provide stable ground truth for evaluating structural choices and rendering parameters. An extensive evaluation is then conducted on the widely used CASIA-B dataset, using 3D coordinates estimated via human pose estimation, to assess robustness under viewpoint, clothing, and carrying covariates. Results show that although GEIs achieve the highest same-view accuracy, GSI variants exhibit reduced degradation under appearance changes and demonstrate greater stability under severe cross-view conditions. These findings indicate that compact skeletal templates can complement appearance-based descriptors and may benefit further from continued advances in 3D human pose estimation. Full article

Nonprobability sampling has been increasingly used in epidemiologic research, yet direct inference based on such samples is subject to selection bias. Current adjustment methods commonly rely on a reference probability-based survey sample that shares a set of covariates with the nonprobability sample. However, these common covariates are often limited and may bias estimates in the presence of population heterogeneity. Existing methods generally assume population homogeneity in models and fail to address such heterogeneity adequately. To overcome this limitation, we propose the Nonprobability Heterogeneity-adjusted Doubly Robust (NHDR) method, a novel inference framework that explicitly accounts for population heterogeneity during selection bias adjustment. NHDR proceeds in three stages: (1) identifying latent subpopulations via finite mixture modeling; (2) incorporating the resulting latent-class structure as a grouping variable into mixed-effects models for both the propensity score and outcome projection; and (3) constructing a doubly robust estimator that integrates these adjusted models. The key methodological contribution of NHDR is its formal integration of latent-class-based population structure into a doubly robust estimation framework, which enables more reliable inference under heterogeneous population settings. Simulation studies demonstrate that the proposed method control the coverage probabilities well in most scenarios. Under heterogeneous conditions, NHDR consistently outperforms existing methods achieving an average reduction in relative bias of approximately 1.8–4.5% and a corresponding decrease in mean squared error of about 5.1–15.5 compared to the benchmark method. We illustrate the practical utility of NHDR by applying it to estimate nine health indicators using data from the Health Monitoring SMS Survey in Guangzhou, China, with the seventh Guangdong Health Service Survey serving as the reference sample. Full article

Supercritical CO₂ pipeline transportation is a crucial link in Carbon Capture, Utilization, and Storage (CCUS). Compared with traditional oil and gas pipelines, if a supercritical CO₂ pipeline is shut down for an excessively long time, the phase state of CO₂ may transform into a gas–liquid two-phase state. It is urgently necessary to conduct research on the phase change mechanism and safety control during the restart process of gas–liquid two-phase CO₂ pipelines. Based on a certain planned supercritical carbon dioxide pipeline demonstration project, this paper proposes a new pipeline safety restart scheme that actively seeks the liquefaction of gaseous CO₂ inside the pipeline by injecting liquid-phase CO₂ at the initial station. Through numerical simulation and experimental methods, the co-variation laws of parameters such as temperature, pressure, density, and phase state during the pipeline restart process were revealed. It was found that the pipeline shutdown and restart process could be subdivided into four stages: shutdown stage, liquefaction stage, pressurization stage, and displacement stage. The phase transition line would form a closed curve that is approximately trapezoidal. It is suggested to optimize the restart scheme from aspects such as reducing the restart time, controlling the pressure rise rate, and saving CO₂ consumption. It is proposed that the liquid holdup of CO₂ fluid in the pipe at the initial moment of restart and the mass flow rate of CO₂ injected at the initial station during the restart process are the main controlling factors affecting the evolution of the phase path of pipeline restart. For the demonstration project, the specific critical threshold values are given. The research results can provide a certain theoretical guidance and reference basis for the safe restart method of supercritical CO₂ pipelines. Full article

In this article, we generalize Yano’s concept of a half-symmetric affine connection. With respect to this generalization, we obtain five linearly independent curvature tensors. In the following, we examine which special kinds of affine connections may be the generalized half-symmetric affine connection. At the end of this work, we generalize the term of Killing’s vector given by Yano to affine Killing, conformal Killing, projective Killing, harmonic, and covariant and contravariant analytic vectors. Full article

Coded aperture X-ray computed tomography (CAXCT) measures coded X-ray projections to reconstruct the inner structure of an object. Coded apertures, which determine the point spread function, can be designed to improve the reconstruction quality, but most approaches are computationally expensive, leading to very small images. In this paper, a sparse covariance matrix estimation approach is introduced to minimize the information loss sensed by projections corresponding to large tomographic images. The covariance matrix representing the map of the overlapping information of the projections is obtained by using block matrix multiplication and sparse estimation. A heuristic variant algorithm with a noise factor is presented to search the combinations of D projections leading to maximum non-overlapping information acquisition, where D is the number of unblocking elements on the coded apertures. Numerical experiments with simulated datasets show that the optimization performance of the proposed method is comparable to that of state-of-the-art methods with small images. Further, for the analyzed cases, coded aperture optimization was performed with $512 \times 512$ images by analyzing coefficients smaller than $0.02\%$ in the covariance matrix. Full article

High-dimensional analytical datasets, such as those generated by inductively coupled plasma–mass spectrometry (ICP-MS), require robust computational frameworks for dimensionality reduction, classification, and model validation. This study presents a comparative evaluation of Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms applied to multivariate chemometric data for food origin authentication. The research employs a workflow that integrates Principal Component Analysis (PCA) for feature extraction, followed by supervised classification using LDA and PLS-DA. Model performance and stability were systematically assessed. The dataset comprised 28 apple samples from four geographical regions and was processed with normalization, scaling, and transformation prior to modeling. Each model was validated via leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, balanced accuracy, detection prevalence, p-value, and Cohen’s Kappa. The results demonstrate that, as a linear projection-based classifier, LDA provides higher robustness and interpretability in small and unbalanced datasets. In contrast, PLS-DA, which is optimized for covariance maximization, exhibits higher apparent sensitivity but lower reproducibility under similar conditions. The study also emphasizes the importance of dimensionality reduction strategies, such as PCA-based variable selection versus latent space extraction in PLS-DA, in controlling overfitting and improving model generalizability. The proposed algorithmic workflow provides a reproducible and statistically sound approach for evaluating discriminant methods in chemometric classification. Full article

Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. To overcome these limitations, this study proposes a data-driven optimization framework for sidetrack well placement. It utilizes machine learning techniques trained on a large-scale synthetic dataset generated from field-informed numerical simulations, to establish a robust machine-learning proxy model. Four predictive models—Linear Regression, Polynomial Regression, Random Forest, and a Backpropagation (BP) Neural Network—were systematically compared, among which the Random Forest model achieved the best predictive accuracy. After hyperparameter optimization, a robust prediction model for sidetracking performance was established, achieving a Mean Squared Error (MSE) of 0.0008 (Root Mean Squared Error, RMSE, of 0.0283) and an R² of 0.8059 on the test set. To further optimize well placement, a mathematical model was formulated with the objective of maximizing the production enhancement rate. Three optimization algorithms—the Multi-Level Coordinate Search (MCS), Differential Evolution (DE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES)—were evaluated, with the DE algorithm demonstrating superior performance. By integrating the optimized Random Forest predictor with the DE optimizer, a systematic methodology for sidetrack well placement optimization was developed. A field case study validated the approach, showing significant improvements, including a reduced water cut and an incremental cumulative oil production of 82.7 tons. This research demonstrates the simulation-based feasibility of intelligent sidetrack well placement optimization and provides practical guidance for future sidetracking development strategies. Full article

Cyberbullying is a public health concern in adolescence that requires measures with valid and comparable evidence across subgroups. This study examined the validity and reliability evidence of the European Cyberbullying Intervention Project Questionnaire (ECIP-Q) in Peruvian adolescents. Using an instrumental cross-sectional design, 729 students aged 12–18 years (M_age = 14.6; SD = 1.27) from Lima, Trujillo, and Piura were recruited through non-probabilistic sampling. Items were treated as ordinal; polychoric correlations were estimated (WLSMV, theta parameterization), and a reproducible prevalence-based recoding was applied to mitigate pileups in category 0. Competing CFA and ESEM models were tested for 22- and 19-item specifications, incorporating two residual covariances for “mirror-pair” items. Sex invariance was evaluated at configural, metric, and scalar levels. The two-factor, 19-item ESEM with two residual covariances showed the best fit (χ² = 291.164; df = 130; CFI = 0.982; TLI = 0.976; RMSEA = 0.041 [0.035–0.048]; SRMR = 0.091). Reliability was adequate for cybervictimization (CR = 0.737, ω = 0.888, factor determinacy [fd] = 0.965) and cyberaggression (CR = 0.282, ω = 0.805, fd = 0.938). Cyberbullying dimensions correlated positively with aggression and moral disengagement and weakly with empathy. Regarding sociodemographic variables, cyberbullying was associated with age, grade, and Internet use; moreover, cyberaggression was higher in boys than in girls. Having more friends and better relationships with teachers were negatively associated with cyberbullying, whereas perceiving the school environment as unsafe was positively associated with cyberbullying. Overall, the 19-item ECIP-Q demonstrates acceptable structural validity, reliability, and sex invariance in Peruvian adolescents, supporting its use for screening and monitoring school coexistence. Full article

In the high-pressure environment of the construction industry, employee motivation and productivity are decisive for project success and organizational sustainability. However, performance appraisal (PA) systems tailored to the specific needs of construction workers remain underexplored, particularly in the context of Türkiye. This study aims to evaluate the influence of PA on employee motivation and productivity by employing a quantitative survey of 401 construction workers and analyzing the data through covariance-based structural equation modeling (CB-SEM). A validated questionnaire, adapted from prior studies, was applied to test nine hypotheses concerning the relationships between PA dimensions—purpose of appraisal, appraisal criteria, appraisal practices, and feedback—and workers’ motivation and productivity. The results reveal that four hypotheses were supported: the purpose of PA significantly influences both motivation and productivity, feedback has a strong effect on productivity, and motivation is positively correlated with productivity. Conversely, appraisal criteria and practices did not exhibit statistically significant effects. These findings highlight the differentiated role of appraisal components and emphasize that clear appraisal objectives and constructive feedback mechanisms are key drivers of workforce performance. The study contributes to the construction management literature by addressing an overlooked employee group—construction workers—and provides practical implications for managers seeking to improve appraisal frameworks in labor-intensive sectors. Limitations regarding the cross-sectional design and self-reported data are acknowledged, with recommendations for longitudinal and cross-cultural research. Full article

Advancing health equity requires rigorous analysis of how research initiatives incorporate and address structural disparities across populations. In this study, we apply large language models (LLMs) to systematically analyze research projects registered on the All of Us platform, with a focus on identifying patterns and institutional dynamics associated with health equity research. We examine the relationship between projects that explicitly pursue health equity goals and their use of available demographic data, their institutional composition (e.g., single- vs. multi-institutional teams), and the research tier of participating institutions (R1 vs. R2). Using the capabilities of an established LLM, we automate key tasks including the extraction of relevant attributes from unstructured project descriptions, classification of institutional affiliations, and the summarization of project content into standardized keywords from the Unified Medical Language System vocabulary. This LLM-assisted pipeline enabled scalable, replicable analysis of hundreds of projects with minimal manual overhead. Our findings suggest a strong association between the use of demographic data and health equity aims, and indicate nuanced differences in equity-oriented research participation by institution type and collaborative structure. More broadly, our approach demonstrates how LLMs can support equity-focused computational social science by transforming free-text administrative data into analyzable structures, enabling novel insights in public health, team science, and science-of-science studies. Full article

Myelin is essential for efficient neural signaling and can be quantitatively evaluated using the T1-weighted/T2-weighted (T1w/T2w) ratio as a proxy for regional myelin content. Myelin covariance networks (MCNs) reflect correlated myelin patterns across brain regions, enabling the investigation of topological organization. However, a vertex-level map of myelin covariance gradients and their cognitive associations remains underexplored. The objective of this study was to construct and characterize vertex-level MCNs, identify their principal gradients, map their higher-order topological landscape, and determine their associations with cognitive functions and other multimodal cortical features. We conducted a cross-sectional, secondary analysis of publicly available data from the Human Connectome Project (HCP). The dataset included T1w/T2w MRI data from 1096 healthy adult participants (age 22–37). All original data collection and sharing procedures were approved by the Washington University institutional review board. Our procedures involved (1) constructing a vertex-wise MCN from T1w/T2w ratio data; (2) applying gradient analysis to identify principal organizational axes; (3) calculating network connectivity strength; (4) performing cognitive meta-analysis using Neurosynth; and (5) using graphlet analysis to assess higher-order topology. Our results show that the primary myelin gradient (Gradient 1) spans from sensory-motor to association cortices, strongly associates with connectivity strength (r = 0.66), and shows a functional dissociation between affective processing and sensorimotor domains. Furthermore, Gradient 2, as well as the positive and full connectivity strength, showed robust correlations with fractional anisotropy (FA), a DTI metric reflecting white matter microstructure. Our higher-order analysis also revealed that negative and positive myelin covariance connections exhibited distinct topologies. Negative connections were dominated by star-like graphlet structures, while positive connections were dominated by path-like and triangular structures. This systematic vertex-level investigation offers novel insights into the organizational principles of cortical myelin, linking gray matter myelin patterns to white matter integrity, and providing a valuable reference for neuropsychological research and the potential identification of biomarkers for neurological disorders. Full article

Background: Periprosthetic joint infections (PJIs) and fracture-related infections (FRIs) are severe complications in orthopedic and trauma surgery. This study aimed to evaluate patient-reported health-related quality of life (HRQoL) in patients treated for PJI and FRI across multiple centers in Germany and Switzerland. Methods: A retrospective cohort study was conducted in nine hospitals based on a project of the Section “Musculoskeletal Infections” of the German Society of Orthopaedics and Traumatology. Patients treated in 2021 were included to ensure a minimum 12-month follow-up. Diagnoses were verified using EBJIS and FRI consensus criteria. HRQoL was assessed via telephone interviews using the EQ-5D questionnaire and a visual analog scale (VAS). Reinfection rates and additional treatments were also recorded. Generalized estimating equations (GEEs) with age and sex as covariates and clustering on center were used to compare groups, with p-values adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR). Results: In total, 384 patients were included (197 PJI, 187 FRI). Compared with the German reference population, both groups reported markedly reduced HRQoL across all EQ-5D domains. After adjustment, PJI patients had higher odds of reporting problems in self-care (OR 1.69, 95% CI 1.13–2.54, FDR-p = 0.033), usual activities (OR 1.68, 95% CI 1.14–2.47, FDR-p = 0.033), and pain/discomfort (OR 2.35, 95% CI 1.31–4.21, FDR-p = 0.033) compared with FRI patients. VAS scores were similar between groups (PJI: 52.8, FRI: 55.5; p = 0.489). Reinfection was associated with significantly worse outcomes: in FRI, usual activities were more impaired (OR 2.41, 95% CI 1.56–3.72, FDR-p = 0.0004); in PJI, reinfection was linked to worse mobility (OR 2.14, 95% CI 1.55–2.95, FDR-p < 0.001), self-care (OR 3.70, 95% CI 2.49–5.49, FDR-p < 0.001), and usual activities (OR 3.92, 95% CI 2.76–5.57, FDR-p < 0.001). Conclusion: This multicenter study highlights the burden of PJI and FRI on patient-reported outcomes with a significant reduction in quality of life compared to the standard population. PJI patients, in particular, experienced greater impairments in mobility, self-care, and usual activities. Reinfection was associated with poorer outcomes, underscoring the importance of patient-centered rehabilitation in managing musculoskeletal infections. Full article

Raptors, being at top of the food chain, serve as important models to study the impact of changing climate, as they are more vulnerable due to their unique ecology. They are vulnerable to extinction, with 52% species declining population and 18% are threatened globally. The effect of climate change on raptors is poorly studied in the Eastern Himalayan region. The present study offers a complete investigation of climate change effects on the raptors in the northeast region of the Eastern Himalayas, employing ensemble species distribution modeling. The future predictions were employed to model the climate change across two socioeconomic pathways (SSP) i.e. SSP245 and SSP585 for the periods 2041–2060 and 2061–2080. Specifically, five algorithms were employed for the ensemble model, viz. boosted regression tree (BRT), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and random forest (RF). The study highlights worrying results, as only 10.5% area of the NE region is presently suitable for Falco severus, 11.4% for the critically endangered Gyps tenuirostris, and a mere 6.9% area is presently suitable for the endangered Haliaeetus leucoryphus. The most influential covariates were precipitation of the driest quarter, precipitation of the wettest month, and temperature seasonality. Future projection revealed reduction of 33–41% in suitable habitats for F. severus, G. tenuirostris is expected to lose 53–96% of its suitable habitats, and H. leucoryphus has lost nearly 94–99% of its suitable habitats. Such decline indicates apparent habitat fragmentation, with shrinking habitat patches. Full article

Student exam pass prediction (EPP) is a key task in educational assessment and can help teachers identify students’ learning obstacles in a timely manner and optimize teaching strategies. However, existing EPP models, although capable of providing quantitative analysis, suffer from issues such as complex algorithms, poor interpretability, and unstable accuracy. Moreover, the evaluation process is opaque, making it difficult for teachers to understand the basis for scoring. To address this, this paper proposes an approximate belief rule base (ABRB-a) student examination passing prediction method based on adaptive reference point selection using symmetry. Firstly, a random forest method based on cross-validation is adopted, introducing intelligent preprocessing and adaptive tuning to achieve precise screening of multi-attribute features. Secondly, reference points are automatically generated through hierarchical clustering algorithms, overcoming the limitations of traditional methods that rely on prior expert knowledge. By organically combining IF-THEN rules with evidential reasoning (ER), a traceable decision-making chain is constructed. Finally, a projection covariance matrix adaptive evolution strategy (P-CMA-ES-M) with Mahalanobis distance constraints is introduced, significantly improving the stability and accuracy of parameter optimization. Through experimental analysis, the ABRB-a model demonstrates significant advantages over existing models in terms of accuracy and interpretability. Full article