Search Results (9,628)

Macau’s aging communities face growing challenges in meeting the needs of older residents due to rising population density and extremely limited land resources. The concentration of outdated residential buildings—home to a substantial older adult population—exacerbates issues related to age-associated physical decline. For seniors who prefer familiar environments, the spatial constraints inherent in these densely built urban areas increasingly conflict with their specific gerontological needs, indicating the urgent need for urban renewal. This study employs a multi-methodological framework to examine aging populations in Macau’s high-density urban contexts. In Phase I, questionnaire surveys combined with SPSS 26.0-based cluster analysis are employed to (1) stratify older adults according to walking behavior patterns; (2) identify subgroup-specific needs and (3) establish key demographic correlates. Based on the socio-ecological framework, Phase II implements spatial analytics through ArcGIS demarcation of pedestrian catchment areas. This phase further integrates point-of-interest (POI) distribution analysis with space syntax-derived axial map evaluations to formulate typological mobility guidelines for different age cohorts. This study outlines the community walking space requirements of older adults in Macau and explores the influence of high-density community spaces on older adults. A practical evaluation method is proposed to assess age-friendly features of urban pathways, identifying the key environmental factors and their respective impacts. These preliminary findings may inform basic planning principles and adaptive design approaches for older adult-oriented pedestrian spaces. Full article

Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature of TDOA, providing the basis of deinterleaving. Then, a SSC (Sorting Skipping Clustering)-DBSCAN algorithm is proposed to achieve TDOA clustering by pre-sorting and traversing key points, which reduces the computational complexity. The TDOA mapping algorithm is further proposed to separate pulses and eliminate Cross-Pulse TDOAs simultaneously based on a one-time clustering result, which can significantly decrease the false alarm rate while avoiding clustering TDOA repeatedly. Simulation results show that the proposed method is capable of deinterleaving pulses of various PRI modulation modes and the performance remains excellent under multiple parameter settings. The running time and the false alarm rate have been reduced by at least 66% and 17%, respectively, compared with the existing methods. Full article

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes a comprehensive evaluation method based on a self-organizing map (SOM). Using multi-source remote sensing data, the method classifies and analyzes candidate landing zones by combining scientific purposes (such as hydrogen abundance, iron oxide abundance, gravity anomalies, water ice distance analysis, and geological features) and engineering constraints (such as Sun visibility, Earth visibility, slope, and roughness). Through automatic clustering, the SOM model finds the important regions. Subsequently, it integrates with a supervised learning model, a random forest, to determine the feature importance weights in more detail. The results from the research indicate the following: the areas suitable for landing account for 9.05%, 5.95%, and 5.08% in the engineering, scientific, and synthesized perspectives, respectively. In the weighting analysis of the comprehensive data, the weights of Earth visibility, hydrogen abundance, kilometer-scale roughness, and slope data all account for more than 10%, and these are thought to be the four most important factors in the automated site selection process. Furthermore, the kilometer-scale roughness data are more important in the comprehensive weighting, which is in line with the finding that the kilometer-scale roughness data represent both surface roughness from an engineering perspective and bedrock geology from a scientific one. In this study, a local examination of typical impact craters is performed, and it is confirmed that all 10 possible landing sites suggested by earlier authors are within the appropriate landing range. The findings demonstrate that the SOM-model-based analysis approach can successfully assess lunar South Pole landing areas while taking multiple constraints into account, uncovering spatial distribution features of the region, and offering a rationale for choosing desired landing locations. Full article

As climate change accelerates and urbanization intensifies, mitigating the Urban Heat Island (UHI) effect has become crucial for sustainable urban planning. This study evaluated the role of four key urban indicators—buildings, greenery, streets, and pedestrian paths—in reducing air temperature and improving energy efficiency within the Kartal District of Istanbul. To ensure accurate and data-driven results, multiple advanced software tools were integrated throughout the research process. QGIS, Google Earth, and OpenStreetMap were used to generate high-resolution land use/land cover (LULC) maps, while Meteoblue climate data and the Global Heat Island Map provided essential climatic parameters. The InVEST Urban Cooling Model was employed to simulate temperature reduction effects, and eQuest energy simulation software assessed the impact of building modifications on energy consumption. The study tested multiple UHI mitigation scenarios, including green roofs, increased street tree cover, grass-covered pedestrian paths, and high-albedo pavement, comparing their individual and combined effects. The results indicated that integrating all strategies achieved the most significant cooling impact, reducing air temperatures by 1.14 °C and improving energy efficiency by 61%. Among the individual interventions, green roofs provided the highest building energy savings (28% reduction), while grass-covered pedestrian paths homogenized the district-wide temperature distribution. These findings underscore the importance of combining GIS-based spatial analysis, climate modeling, and energy simulation tools to develop reliable, scalable, and effective urban heat mitigation strategies. Future urban planning should prioritize a multi-software approach to enhance sustainability, optimize energy efficiency, and improve urban resilience. Full article

In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the YOLOv11 architecture, for the identification of common diseases in the complex feeder road environment. The proposed methodology introduces four key innovations: (1) Switchable Atrous Convolution (SAConv) is introduced into the backbone network to enhance multiscale disease feature extraction under occlusion conditions; (2) Multi-Channel and Spatial Attention (MCSAttention) is constructed in the feature fusion process, and the weight distribution of multiscale diseases is adjusted through adaptive weight redistribution. By adjusting the weight distribution, the model’s sensitivity to subtle disease features is improved. To enhance its ability to discriminate between different disease types, Cross Stage Partial with Parallel Spatial Attention and Channel Adaptive Aggregation (C2PSA_CAA) is constructed at the end of the backbone network. (3) To mitigate category imbalance issues, Weighted Intersection over Union loss (WIoU_loss) is introduced, which helps optimize the bounding box regression process in disease detection and improve the detection of relevant diseases. Based on experimental validation, Rural-YOLO demonstrated superior performance with minimal computational overhead. Only 0.7 M additional parameters is required, and an 8.4% improvement in recall and a 7.8% increase in mAP₅₀ were achieved compared to the initial models. The optimized architecture also reduced the model size by 21%. The test results showed that the proposed model achieved 3.28 M parameters with a computational complexity of 5.0 GFLOPs, meeting the requirements for lightweight deployment scenarios. Cross-validation on multi-scenario public datasets was carried out, and the model’s robustness across diverse road conditions. In the quantitative experiments, the center skeleton method and the maximum internal tangent circle method were used to calculate crack width, and the pixel occupancy ratio method was used to assess the area damage degree of potholes and other diseases. The measurements were converted to actual physical dimensions using a calibrated scale of 0.081:1. Full article

This study aimed to investigate the impact of local canopy environmental characteristics on the regeneration of common tree species in the understory of broadleaved Korean pine forests, thus deepening the understanding of species coexistence and forest growth cycle mechanisms. This study focused on nine tree species found in the Liangshui National Nature Reserve in Heilongjiang Province, China. We stratified trees by height and simulated the LAI distribution of each class using Voronoi polygons. These layers were overlaid to generate an integrated LAI spatial map. All these procedures were integrated into the self-developed R package Broadleaf.Korean.pine.LAI, which was used to calculate individual-level canopy environment indicators, including average local LAI, local LAI standard deviation, canopy percent, vertical distribution tendency degree, local coniferous LAI, and local broadleaf LAI. These indicators were then compared with the average values of uniformly distributed understory sampling points. A principal component analysis (PCA) was conducted to reduce the dimensionality of the local canopy environmental characteristics for both the uniformly distributed points and regeneration habitats of each tree species, resulting in comprehensive canopy environmental characteristics. Wilcoxon rank-sum tests were applied to assess the significance of differences between the regeneration habitats and the understory average, as well as between the regeneration habitats of seedlings and saplings within the same species. Cliff’s delta effect size was used to evaluate the impact of each environmental factor on the transition of regeneration from seedlings to saplings. The results showed that, based on both individual canopy environmental indicators and composite indices derived from principal component analysis, seedlings tended to regenerate in areas with higher canopy coverage, whereas saplings were more commonly established in relatively open habitats. Clear differences exist between the regeneration habitats of coniferous and broadleaf species, with coniferous species tending to regenerate in areas with higher local broadleaf LAIs compared with broadleaf species. The effect size analysis showed that canopy percent, vertical distribution tendency degree, average local LAI, and local coniferous LAI have greater impacts on the transition from seedlings to saplings, while the effect of local broadleaf LAI is relatively small. These findings suggest that strong shade tolerance allows species to establish seedling banks under canopy patches, while interspecific differences in growth response to microhabitats shape their roles in the forest growth cycle. Future research should explore the physiological responses and trait characteristics of tree regeneration under varying canopy patch environments. Long-term monitoring of regeneration processes—including invasion, growth, and mortality—across different canopy patches will help elucidate the mechanisms shaping understory spatial patterns. Full article

Purple cowpea accumulates abundant anthocyanins in its epidermis, with R2R3-MYB transcription factors serving as potential regulators of anthocyanin accumulation. This study systematically deciphered the genome-wide characteristics of cowpea R2R3-MYB transcription factors, elucidating their critical roles in plant anthocyanin accumulation. Employing a combined strategy of HMMER Hidden Markov Model searches and BLASTP homology alignment, we successfully identified 127 non-redundant VuR2R3-MYB transcription factors. The encoded proteins exhibited remarkable physicochemical diversity: the average length reached 338.8 amino acid residues, with theoretical isoelectric points distributed between 4.79 and 10.91 residues. When performing a phylogenetic analysis with Arabidopsis homologs, 27 distinct subgroups were identified. Among them, the S4–S7 clades showed conserved protein architectures, which might play a role in regulating the phenylpropanoid pathway. An analysis of the gene architecture revealed patterns of intron/exon organization. Specifically, 85 out of 127 loci (66.9%) presented the typical two-intron configuration, whereas 18 genes had no introns. An investigation of the promoters found that, on average, each gene had 52 cis-regulatory elements. These elements were mainly light-responsive motifs and phytohormone-related elements. Chromosomal mapping indicated an uneven distribution of these genes across 11 chromosomes. Duplication analysis further showed 13 tandem repeats and 54 segmentally duplicated pairs. An analysis of evolutionary constraints demonstrated that purifying selection was predominant (Ka/Ks < 0.5) among paralogous pairs. Through comparative transcriptomics of pod color variants, 19 differentially expressed MYB regulators were identified. These included VuR2R3-MYB23 (MYB3 homolog), VuR2R3-MYB95 (MYB4 homolog), VuR2R3-MYB53 (MYB114 homolog), and VuR2R3-MYB92 (MYB5 homolog), which showed a strong correlation with the patterns of anthocyanin accumulation. Our findings are expected to contribute to elucidating the potential regulatory mechanisms through which R2R3-MYB transcription factors mediate anthocyanin biosynthesis and accumulation. Full article

Railway catenary layout drawings (RCLDs) have the characteristics of upper and lower symmetry, a large drawing size, a small size, high similarity among target symbols, and an uneven distribution of symbol categories. These factors make the symbol detection task more complex and challenging. To address the aforementioned challenges, this paper proposes three enhancements to YOLOv8n to improve symbol detection performance and integrates an improved denoising diffusion probabilistic model (IDDPM) to mitigate the imbalance in symbol category distribution. First, the multi-scale dilated attention (MSDA) is introduced in the Neck part to enhance the model’s perception of the global context in complex RCLD scenes, so that it can more effectively capture the symbol information distributed in different scales and backgrounds. Secondly, the receptive field attention convolution (RFAConv) is used in the detection head to replace the standard convolution, to improve the ability to focus on the target symbols in RCLDs and effectively alleviate the occlusion interference between symbols. Finally, the dynamic upsampler (DySample) is used to enhance the clarity and positioning accuracy of the edge area of small target symbols in RCLDs and enhance the detection of small targets. The above design made targeted optimizations to resolve the problems of symbol and background interference, character overlap, and symbol category imbalances in complex scenes in RCLDs, effectively improving the overall detection performance of the model. Compared with the baseline YOLOv8n model, the improved YOLOv8n achieves increases of 2.9% in F1, 1.9% in mAP@0.5, and 1.7% in mAP@0.5:0.95. With the introduction of synthetic data, the recognition of minority-class symbols is further enhanced, leading to additional gains of 4%, 3.8%, and 14% in F1, mAP@0.5, and mAP@0.5:0.95, respectively. These results demonstrate the effectiveness and superiority of the proposed method in improving detection performance. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

The successful application of artificial intelligence (AI) techniques for the quantitative analysis of hybrid medical imaging data such as PET/CT is challenged by the differences in the type of information and image quality between the two modalities. The purpose of this work was to develop color-based, pre-processing methodologies for PET/CT data that could yield a better starting point for subsequent diagnosis and image processing and analysis. Two methods are proposed that are based on the encoding of Hounsfield Units (HU) and Standardized Uptake Values (SUVs) in separate transformed .png files as reversible color information in combination with .png basic information metadata based on DICOM attributes. Linux Ubuntu using Python was used for the implementation and pilot testing of the proposed methodologies on brain ¹⁸F-FDG PET/CT scans acquired with different PET/CT systems. The range of HUs and SUVs was mapped using novel weighted color distribution functions that allowed for a balanced representation of the data and an improved visualization of anatomic and metabolic differences. The pilot application of the proposed mapping codes yielded CT and PET images where it was easier to pinpoint variations in anatomy and metabolic activity and offered a potentially better starting point for the subsequent fully automated quantitative analysis of specific regions of interest or observer evaluation. It should be noted that the output .png files contained all the raw values and may be treated as raw DICOM input data. Full article

Distributed learning (DL), in which multiple nodes in an inner-connected network collaboratively induce a predictive model using their local data and some information communicated across neighboring nodes, has received significant research interest in recent years. Yet, it is challenging to achieve excellent performance in scenarios when training data instances have incomplete features and ambiguous labels. In such cases, it is essential to develop an efficient method to jointly perform the tasks of missing feature imputation and credible label recovery. Considering this, in this article, a distributed partial label missing data classification (dPMDC) algorithm is proposed. In the proposed algorithm, an integrated framework is formulated, which takes the ideas of both generative and discriminative learning into account. Firstly, by exploiting the weakly supervised information of ambiguous labels, a distributed probabilistic information-theoretic imputation method is designed to distributively fill in the missing features. Secondly, based on the imputed feature vectors, the classifier modeled by the random feature map of the ${\chi }^2$ kernel function can be learned. Two iterative steps constitute the dPMDC algorithm, which can be used to handle dispersed, distributed data with partially missing features and ambiguous labels. Experiments on several datasets show the superiority of the suggested algorithm from many viewpoints. Full article

Historical archives hold untapped potential for understanding long-term biodiversity change. This study introduces computational approaches to historical ecology, combining archival research, text analysis, and spatial mapping to reconstruct past biodiversity patterns. Using the 1845 Bavarian Animal Observation Dataset (AOD1845), a comprehensive survey of vertebrate species across 119 districts, we transform 5400 prose records into structured ecological data. Our analyses reveal how species distributions, habitat associations, and human–wildlife interactions were shaped by land use and environmental pressures in pre-industrial Bavaria. Beyond documenting ecological baselines, the study captures early perceptions of habitat loss and species decline. We emphasise the critical role of historical expertise in interpreting archival sources and avoiding anachronisms when integrating historical data with modern biodiversity frameworks. By bridging the humanities and environmental sciences, this work shows how digitised archives and computational methods can open new frontiers for conservation science, restoration ecology, and Anthropocene studies. The findings advocate for the systematic mobilisation of historical datasets to better understand biodiversity change over time. Full article

Traditional pesticide application methods pose systemic threats to sustainable agriculture due to inefficient spraying practices and ecological contamination. Although agricultural drones demonstrate potential to address these challenges, they face critical limitations in energy-constrained complete coverage path planning for field operations. This study proposes a novel BiLG-D3QN algorithm by integrating deep reinforcement learning with Bi-LSTM and Bi-GRU architectures, specifically designed to optimize segmented coverage path planning under payload-dependent energy consumption constraints. The methodology encompasses four components: payload-energy consumption modeling, soybean cultivation area identification using Google Earth Engine-derived spatial distribution data, raster map construction, and enhanced segmented coverage path planning implementation. Through simulation experiments, the BiLG-D3QN algorithm demonstrated superior coverage efficiency, outperforming DDQN by 13.45%, D3QN by 12.27%, Dueling DQN by 14.62%, A-Star by 15.59%, and PPO by 22.15%. Additionally, the algorithm achieved an average redundancy rate of only 2.45%, which is significantly lower than that of DDQN (18.89%), D3QN (17.59%), Dueling DQN (17.59%), A-Star (21.54%), and PPO (25.12%). These results highlight the notable advantages of the BiLG-D3QN algorithm in addressing the challenges of pesticide spraying tasks in agricultural UAV applications. Full article

The regulation of superhelical stress, mediated by the combined action of topoisomerases and fork rotation, is crucial for DNA replication. The conformational changes during DNA replication are still experimentally challenging, mainly due to the rapid kinetics of the replication process. Here, we present the first molecular dynamics simulations of partially replicated circular DNA molecules, with stalled replication forks at both early and late stages of DNA replication. These simulations allowed us to map the distribution of superhelical stress after deproteinization. We propose a five-component model that determines the linking number difference of replication intermediates. At a thermodynamic equilibrium, the contribution of these five components was correlated to the progress of the replication forks. Additionally, we identified four types of segment collision events in replication intermediates, characterized by their geometric properties, including chirality and topological sign. The distribution of these collision events between the early and late stages of DNA replication provides new insights into the coordinated function of topoisomerases, warranting further discussion. Full article