Search Results (109)

Topology optimization (TO) with the variable density concept has made significant advancements in academic research and engineering applications; yet it still encounters obstacles associated with computer inefficiencies in the filtering process. This work introduces a novel filter implementation method that significantly enhances the optimization process by adapting the k-d tree data structure. The proposed method converts traditional neighborhood search operations into extremely efficient spatial searches while preserving solution accuracy. This method inherently accommodates a comprehensive array of manufacturability constraints, including symmetry, local volume control, periodic patterning, stamping-oriented overhang control, and more, without compromising computational duration. Extensive numerical examples validate the proposed method’s efficiency yielding precise, scalable designs, achieving substantial acceleration relative to conventional methods The method demonstrates specific advantage in large scale optimization challenges and intricate complex geometric restrictions, encompassing unstructured meshes. This study explores a new paradigm for effective constraint integration in topology optimization through advanced data structures, providing extensive applicability in engineering design. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

Substance use is a critical public health issue in the U.S., with Black communities, particularly those with criminal justice contact, disproportionately affected. Chronic exposure to stressors can lead to substance use as a coping strategy. This study used data from 1476 Black adults with criminal justice involvement from the National Survey of American Life to examine how psychosocial stress and sleep disturbances relate to lifetime substance use and to determine if there are any sex differences. Sex-separate generalized linear models for a Poisson distribution with a log-link function estimated prevalence ratios and adjusted prevalence ratios (APRs) for lifetime alcohol abuse, lifetime cigarette, and marijuana use. Independent variables include stressors (family, person, neighborhood, financial, and work-related) and sleep problems, with covariates such as age, SES, and marital status. Lifetime alcohol abuse was associated with family stressors (APR = 2.72) and sleep problems (APR = 3.36) for males, and financial stressors (APR = 2.75) and sleep problems (APR = 2.24) for females. Cigarette use was linked to family stressors (APR = 1.73) for males and work stressors (APR = 1.78) for females. Marijuana use was associated with family stressors (APR = 2.31) and sleep problems (APR = 2.07) for males, and neighborhood stressors (APR = 1.72) for females. Lifetime alcohol abuse, as well as lifetime cigarette and marijuana use, was uniquely associated with various psychosocial stressors among Black adult males and females with criminal justice contact. These findings highlight the role of structural inequities in shaping substance use and support using a Social Determinants of Health framework to address addiction in this population. Full article

The structure and interpersonal interactions of traditional residential communities have also been impacted and recreated as a result of the fast development of urban space and related communities. This study explores the interrelationship between neighborhood social cohesion and the life satisfaction of urban adult residents through the mediating effect of place attachment. A comprehensive theoretical model was constructed to analyze the action mechanism among these variables. Data were collected through an online questionnaire platform (n = 301), and structural equation modeling (PLS-SEM) was employed for analysis. The findings revealed a significant positive relationship between neighborhood social cohesion and residents’ place attachment. Place attachment appeared to play a mediating role between neighborhood social cohesion and life satisfaction, in which place dependence was also a potential effective mediator between the three dimensions of neighborhood social cohesion (neighborliness, sense of community, and neighborhood attractiveness) and life satisfaction. The results suggest that enhancing community cohesion may contribute to urban adult residents’ well-being by strengthening their functional dependence on the community. Full article

Recently, a network based on selective state space models (SSMs), Mamba, has emerged as a research focus in hyperspectral image (HSI) classification due to its linear computational complexity and strong long-range dependency modeling capability. Originally designed for 1D causal sequence modeling, Mamba is challenging for HSI tasks that require simultaneous awareness of spatial and spectral structures. Current Mamba-based HSI classification methods typically convert spatial structures into 1D sequences and employ various scanning patterns to capture spatial dependencies. However, these approaches inevitably disrupt spatial structures, leading to ineffective modeling of complex spatial relationships and increased computational costs due to elongated scanning paths. Moreover, the lack of neighborhood spectral information utilization fails to mitigate the impact of spatial variability on classification performance. To address these limitations, we propose a novel model, Dual-Aware Discriminative Fusion Mamba (DADFMamba), which is simultaneously aware of spatial-spectral structures and adaptively integrates discriminative features. Specifically, we design a Spatial-Structure-Aware Fusion Module (SSAFM) to directly establish spatial neighborhood connectivity in the state space, preserving structural integrity. Then, we introduce a Spectral-Neighbor-Group Fusion Module (SNGFM). It enhances target spectral features by leveraging neighborhood spectral information before partitioning them into multiple spectral groups to explore relations across these groups. Finally, we introduce a Feature Fusion Discriminator (FFD) to discriminate the importance of spatial and spectral features, enabling adaptive feature fusion. Extensive experiments on four benchmark HSI datasets demonstrate that DADFMamba outperforms state-of-the-art deep learning models in classification accuracy while maintaining low computational costs and parameter efficiency. Notably, it achieves superior performance with only 30 training samples per class, highlighting its data efficiency. Our study reveals the great potential of Mamba in HSI classification and provides valuable insights for future research. Full article

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. A novel exponential moving average unit (EMAU) enhances feature robustness by adaptively weighting historical states, while morphology-aware augmentation simulates variable road widths and spectral noise. Evaluations on cross-domain datasets demonstrate state-of-the-art performance with 92.16% precision, 80.77% F1-score, and 67.75% IoU (intersection over union), outperforming baseline models by 4.3% in precision and reducing annotation dependency by 94.6%. By reducing per-kilometer operational costs by 78% relative to LiDAR (Light Detection and Ranging) alternatives, OMRoadNet establishes a practical solution for intelligent mining infrastructure mapping, bridging the critical gap between structured urban datasets and unstructured mining environments. Full article

Background: Intestinal metaplasia (IM) is a precancerous gastric condition that requires accurate histopathological diagnosis to enable early intervention and cancer prevention. Traditional evaluation of H&E-stained tissue slides can be labor-intensive and prone to interobserver variability. Recent advances in deep learning, particularly transformer-based models, offer promising tools for improving diagnostic accuracy. Methods: We propose ViSwNeXtNet, a novel patch-wise ensemble framework that integrates three transformer-based architectures—ConvNeXt-Tiny, Swin-Tiny, and ViT-Base—for deep feature extraction. Features from each model (12,288 per model) were concatenated into a 36,864-dimensional vector and refined using iterative neighborhood component analysis (INCA) to select the most discriminative 565 features. A quadratic SVM classifier was trained using these selected features. The model was evaluated on two datasets: (1) a custom-collected dataset consisting of 516 intestinal metaplasia cases and 521 control cases, and (2) the public GasHisSDB dataset, which includes 20,160 normal and 13,124 abnormal H&E-stained image patches of size 160 × 160 pixels. Results: On the collected dataset, the proposed method achieved 94.41% accuracy, 94.63% sensitivity, and 94.40% F1 score. On the GasHisSDB dataset, it reached 99.20% accuracy, 99.39% sensitivity, and 99.16% F1 score, outperforming individual backbone models and demonstrating strong generalizability across datasets. Conclusions: ViSwNeXtNet successfully combines local, regional, and global representations of tissue structure through an ensemble of transformer-based models. The addition of INCA-based feature selection significantly enhances classification performance while reducing dimensionality. These findings suggest the method’s potential for integration into clinical pathology workflows. Future work will focus on multiclass classification, multicenter validation, and integration of explainable AI techniques. Full article

This study addresses the assembly job shop scheduling problem (AJSSP) with parallel machines. In an assembly job shop, product structures are represented through hierarchical tree diagrams, where components and subassemblies are sequentially assembled to form the final product. A mixed-integer linear programming (MILP) model is formulated to minimize the total completion time. A dominance relations-based variable neighborhood search (DR-VNS) is proposed for solving AJSSP with parallel machines. The proposed approach integrates dominance relations among operations in the initialization phase and employs tailored neighborhood structures to address sequencing and assignment challenges, thereby enhancing the generation of neighboring solutions. Experimental studies conducted on test cases of varying scales and complexities demonstrate the effectiveness of the proposed algorithms in solving the AJSSP with parallel machines. Full article

To improve the utilization of port resources and reduce the consumption of resources due to vessel waiting and delays, this paper investigates the Berth Allocation and Quay Crane Assignment Problem (BACAP) in container ports, focusing on the Quay Crane (QC) profile. The objective is to assign berths, berthing times, and QC profiles to vessels arriving at the port within a given planning horizon, thereby extending the traditional BACAP framework. To minimize the sum of idle time costs caused by vessel waiting and delay time costs due to late vessel departures, a mixed-integer linear programming (MILP) model is proposed. Additionally, a variable neighborhood search (VNS) algorithm is designed to solve the model, tailored to the specific characteristics of the problem. The proposed MILP model and VNS algorithm are evaluated using two sets of BACAP instances. The numerical results demonstrate the effectiveness of both the model and the algorithm, showing that VNS efficiently and reliably solves instances of various sizes. Furthermore, each neighborhood structure contributes uniquely to the iterative process. This study also analyzes the impact of different idle and delay costs on BACAP, providing valuable managerial insights. The proposed framework contributes to enhancing operational efficiency and supports sustainable port management. Full article

With the development of autonomous driving technology, the application scenarios for mobile robots and unmanned vehicles are gradually expanding from simple structured environments to complex unstructured scenes. In unstructured three-dimensional spaces such as urban environments, traditional two-dimensional map construction and path planning techniques struggle to effectively plan accurate paths. To address this, this paper proposes a method of constructing a map and planning a route based on three-dimensional spatial representation. This method utilizes point cloud semantic segmentation to extract navigable space information from environmental point cloud data and employs voxelization techniques to generate a voxel map. Building on this, the paper combines a variable search neighborhood A* algorithm with a road-edge-detection-based path adjustment to generate optimal paths between two points on the map, ensuring that the paths are both short and capable of effectively avoiding obstacles. Our experimental results in multi-story parking garages show that the proposed method effectively avoids narrow areas that are difficult for vehicles to pass through, increasing the average edge distance of the path by 83.3% and significantly enhancing path safety and feasibility. Full article

Wind energy has emerged as a cornerstone in global efforts to transition to renewable energy, driven by its low environmental impact and significant generation potential. However, the inherent intermittency of wind, influenced by complex and dynamic atmospheric patterns, poses significant challenges for accurate wind speed prediction. Existing approaches, including statistical methods, machine learning, and deep learning, often struggle with limitations such as non-linearity, non-stationarity, computational demands, and the requirement for extensive, high-quality datasets. In response to these challenges, we propose a novel neighborhood preserving cross-dataset data augmentation framework for high-horizon wind speed prediction. The proposed method addresses data variability and dynamic behaviors through three key components: (i) the uniform manifold approximation and projection (UMAP) is employed as a non-linear dimensionality reduction technique to encode local relationships in wind speed time-series data while preserving neighborhood structures, (ii) a localized cross-dataset data augmentation (DA) approach is introduced using UMAP-reduced spaces to enhance data diversity and mitigate variability across datasets, and (iii) recurrent neural networks (RNNs) are trained on the augmented datasets to model temporal dependencies and non-linear patterns effectively. Our framework was evaluated using datasets from diverse geographical locations, including the Argonne Weather Observatory (USA), Chengdu Airport (China), and Beijing Capital International Airport (China). Comparative tests using regression-based measures on RNN, GRU, and LSTM architectures showed that the proposed method was better at improving the accuracy and generalizability of predictions, leading to an average reduction in prediction error. Consequently, our study highlights the potential of integrating advanced dimensionality reduction, data augmentation, and deep learning techniques to address critical challenges in renewable energy forecasting. Full article

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management. Full article

The Vehicle Routing Problem with Time Window (VRPTW) is of crucial importance in modern societies, where the aim is to optimize resource utilization, reduce costs and address constraints such as time and vehicle capacity. Traditional genetic algorithms often face premature convergence and slow speed in solving such problems. This paper proposes an Improved Genetic Ant Colony Optimization (IGA-ACO) algorithm to efficiently solve VRPTW. The algorithm combines the strengths of a genetic algorithm with the Generalized Variable Neighborhood Search (GVNS) and Ant Colony Optimization (ACO), aiming to minimize the total cost and optimize balance. The Solomon insertion heuristic is employed to initialize the population and enhance local search capabilities, while the two-population structure improves global search performance by exchanging the optimal solutions between the two populations, helping to avoid local optima. Experiments on the Solomon benchmark dataset show that the IGA-ACO algorithm matches the Best Known Solution (BKS) in Class C instances, reduces vehicle usage by 24.45% in Class R, with a travel distance deviation of 9.19%, and slightly reduces vehicle usage by 0.19% in Class RC, with a travel distance deviation of 7.05%. These results demonstrate the algorithm’s effectiveness in optimizing vehicle routing, particularly under complex constraints, and its competitive advantage over other methods. Full article

The built physical and social environments are critical drivers of child neural and cognitive development. This study aimed to identify the factor structure and correlates of 29 environmental, education, and socioeconomic indicators of neighborhood resources as measured by the Child Opportunity Index 2.0 (COI 2.0) in a sample of youths aged 9–10 enrolled in the Adolescent Brain Cognitive Development (ABCD) Study. This study used the baseline data of the ABCD Study (n = 9767, ages 9–10). We used structural equation modeling to investigate the factor structure of neighborhood variables (e.g., indicators of neighborhood quality including access to early child education, health insurance, walkability). We externally validated these factors with measures of psychopathology, impulsivity, and behavioral activation and inhibition. Exploratory factor analyses identified four factors: Neighborhood Enrichment, Socioeconomic Attainment, Child Education, and Poverty Level. Socioeconomic Attainment and Child Education were associated with overall reduced impulsivity and the behavioral activation system, whereas increased Poverty Level was associated with increased externalizing symptoms, an increased behavioral activation system, and increased aspects of impulsivity. Distinct dimensions of neighborhood opportunity were differentially associated with aspects of psychopathology, impulsivity, and behavioral approach, suggesting that neighborhood opportunity may have a unique impact on neurodevelopment and cognition. This study can help to inform future public health efforts and policy about improving built and natural environmental structures that may aid in supporting emotional development and downstream behaviors. Full article