Search Results (264)

Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated the spatial and temporal dynamics of PM2.5 in northern Thailand. Specifically, it explored how pollution at one monitoring station influenced concentrations at others and revealed the seasonal structure of PM2.5 transmission using network-based analysis. We developed a Python-based framework to analyze daily PM2.5 data from 2022 to 2023, selecting nine representative stations across eight provinces based on spatial clustering and shape-based criteria. Delaunay triangulation was used to define spatial connections among stations, capturing the region’s irregular geography. Cross-correlation and Granger causality were applied to identify time-lagged relationships between stations for each season. Trophic coherence analysis was used to evaluate the hierarchical structure and seasonal stability of the resulting networks. The analysis revealed seasonal patterns of PM2.5 transmission, with certain stations, particularly in Chiang Mai and Lampang, consistently acting as source nodes. Provinces such as Phayao and Phrae were frequently identified as receptors, especially during the winter and rainy seasons. Trophic coherence varied by season, with the winter network showing the highest coherence, indicating a more hierarchical but less stable structure. The rainy season exhibited the lowest coherence, reflecting greater structural stability. PM2.5 spreads through structured, seasonal pathways in northern Thailand. Network patterns vary significantly across seasons, highlighting the need for adaptive air quality strategies. This framework can help identify influential monitoring stations for early warning and support more targeted, season-specific air quality management strategies in northern Thailand. Full article

Obsidian was a key toolstone for the development of maritime lifeways in the western archipelago of southernmost South America. This area is a fragmented landscape where the major north–south movement of people along the Pacific was only possible by navigation because it is constrained by major biogeographic barriers. Two obsidian sources have been recorded, each one located on the extremes of the archipelago, and each has played a key role in the canoe-adapted societies that used them. As indicated by repeated inductively coupled plasma mass spectrometry analyses, obsidian from Chaitén Volcano to the north was distributed between 38°26′ S and 45°20′ S, and obsidian from Seno Otway to the south was distributed between 50° and 55° S, although it mainly occurred in sites close to the Strait of Magellan and within constrained time periods. This study explores the distribution of these two types of obsidians, their chronology, their frequencies in the archaeological record, the main artifact classes that are represented, and the technological processes in which they were involved. This examination indicates common aspects in the selection of high-quality toolstones for highly mobile maritime groups and discusses the different historical trajectories of two obsidians that appear decoupled across the Holocene. Full article

Innate immune cells appear to have an important implication in the resolution and/or the aggravation of the COVID-19 pathogenesis after infection with SARS-CoV-2. To better appreciate the role of these cells during COVID-19, changes in blood eosinophil, the neutrophil and monocyte count, and levels of surface protein markers have been reported. However, analyses at several timepoints of multiple surface markers on granulocytes and monocytes over a period of one month after a SARS-CoV-2 infection are missing. Therefore, in this study, we performed blood eosinophil, neutrophil, and monocyte phenotyping using a list of surface proteins and flow cytometry during a period of 30 days after the hospitalization of patients with severe SARS-CoV-2 infections. Blood cell counts were reported at seven different timepoints over the 30-day period as well as measures of multiple mediators in serum using a targeted multiplex assay approach. Our results indicate a 95% drop in the blood eosinophil count by D1, with eosinophils displaying a phenotype defined as CD69/CD63/CD125^high and CCR3/CD44^low during the early phases of hospitalization. Conversely, by D7 the neutrophil count increased significantly and displayed an immature, activated, and immunosuppressive phenotype (i.e., 3% of CD10/CD16^low and CD10^lowCD177^high, 6.7% of CD11b^highCD62L^low, and 1.6% of CD16^highCD62L^low), corroborated by enhanced serum proteins that are markers of neutrophil activation. Finally, our results suggest a rapid recruitment of non-classical monocytes leaving CD163/CD64^high and CD32^low monocytes in circulation during the very early phase. In conclusion, our study reveals potential very early roles for eosinophils and monocytes in the pathogenesis of COVID-19 with a likely reprogramming of eosinophils in the bone marrow. The exact roles of the pro-inflammatory neutrophils and the functions of the eosinophils and the monocytes, as well as these innate immune cell types, interplays need to be further investigated. Full article

Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

This study investigates the seasonal and floral phenology, breeding strategies, and floral morphology of Azorina vidalii, an Azorean endemic Campanulaceae with hermaphroditic, protandrous flowers, dichogamy and secondary pollen presentation. Seasonal phenology was recorded in four field populations and floral phenology in a garden population. Reproductive strategies were assessed via controlled hand pollinations in one field population. Floral morphometrics were analysed using 23 floral and five pollen traits from 121 flowers across fourteen populations throughout the Azores archipelago. Non-parametric and parametric tests, discriminant analysis, and reproductive indices were used to infer answers to this study’s goals. Results showed that temperature and humidity influenced vegetative and reproductive phenophases. The male phase was shorter than the female, likely due to pollen dynamics, and some functional overlap suggested incomplete dichogamy. Geographic variation in floral traits indicated morphological differentiation across subarchipelagos, presumably linked to environmental factors or isolation. Reproductive indices suggested a mixed mating system, partial self–incompatibility and signs of inbreeding depression. Fertilisation was absent without pollinators, and spontaneous selfing was excluded due to an absence of pollen–pistil contact during stigma retraction. These findings contribute to understanding the reproductive biology and morphologic variation in A. vidalii. The implications of these findings for the conservation of this insular plant are discussed. Full article

Optimizing sensor node coverage remains a central challenge in wireless sensor networks (WSNs), where premature convergence and suboptimal solutions in traditional optimization methods often lead to coverage gaps and uneven node distribution. To address these issues, this paper presents a novel velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm that integrates dynamic mechanisms to enhance population diversity, guide the search process more effectively, and reduce uncovered areas. The proposed algorithm is evaluated through extensive simulations across multiple WSN deployment scenarios with varying node densities, sensing ranges, and monitoring area sizes. Comparative results demonstrate that the approach consistently outperforms several widely used metaheuristic algorithms, achieving faster convergence, better global exploration, and significantly improved coverage performance. On average, the proposed method yields up to 14.71% higher coverage rates than baseline techniques. These findings underscore the algorithm’s robustness and suitability for efficient and scalable WSN deployments. Full article

In dynamic and low texture environments, traditional point-feature-based visual SLAM (vSLAM) often faces the challenges of poor robustness and low localization accuracy. To this end, this paper proposes a semantic vSLAM approach that fuses point-line features with YOLOv8-seg. First, we designed a high-performance 3D line-segment extraction method that determines the number of points to be sampled for each line-segment in terms of the length of the 2D line-segments extracted from the image, and back-projects these sampled points combined with the depth image to obtain the 3D point set of the line-segments. On this basis, accurate 3D line-segment fitting is realized in combination with the RANSAC algorithm. Subsequently, we introduce Delaunay triangulation to construct the geometric relationships between map points, detect dynamic feature points by matching changes in the topological structure of feature points in adjacent frames, and combine them with the instance labels provided by the YOLOv8-seg to accurately remove dynamic feature points. Finally, a loop-closure detection mechanism that fuses point–line features with instance-level matching is designed to calculate a normalized similarity score by combining the positional similarity of the instances, the scale similarity, and the spatial consistency of the static instances. A series of simulations and experiments demonstrate the superior performance of our method. Full article

While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction. Full article

In order to solve the problems of low efficiency and insufficient accuracy of the traditional manual template method in the forming detection of shell plates, a digital solution based on laser scanning detection system was proposed. By introducing a six-degree-of-freedom robotic arm and a high-precision line laser sensor to build a three-dimensional detection platform, a digital template method framework including data acquisition, point cloud registration, surface reconstruction, and deviation analysis was innovatively constructed. A point cloud non-penetration registration algorithm fused with boundary geometric information was proposed. Based on the improved Delaunay triangulation algorithm, the surface is reconstructed and the digital template is extracted. Experimental verification shows that the method achieves an accuracy of less than 1 mm of error in the detection of outer plates, shortens the single detection time to less than 10 min, and improves the detection efficiency by more than 75% compared with the traditional method. Full article

In the Moon–Earth elliptic restricted three-body problem, near-polar and near-circular lunar-type periodic orbits are numerically continued from Keplerian circular orbits using Broyden’s method with line search. The Hamiltonian system, expressed in Cartesian coordinates, is treated via the symplectic scaling method. The radii of the initial Keplerian circular orbits are then scaled and normalized. For cases in which the integer ratios $\{j/k\}$ of the mean motions between the inner and outer orbits are within the range $[9,150]$, some periodic orbits of the elliptic restricted three-body problem are investigated. For the middle-altitude cases with $j/k\in [38,70]$, the perturbations due to $J_2$ and $C_{22}$ are incorporated, and some new near-polar periodic orbits are computed. The orbital dynamics of these near-polar, near-circular periodic orbits are well characterized by the first-order double-averaged system in the Poincaré–Delaunay elements. Linear stability is assessed through characteristic multipliers derived from the fundamental solution matrix of the linear varational system. Stability indices are computed for both the near-polar and planar near-circular periodic orbits across the range $j/k\in [9,50]$. Full article

When two axisymmetric stress waves of the same strength propagate radially at the same velocity, the stress wave wavefronts collide and interact along a specific locus, which is the perpendicular bisector between the two sources. The maximum principal stress occurs along this perpendicular bisector, and the tensile stresses result in crack bridging between the two source points. This symmetric wave propagation behavior allows us to use the Voronoi tessellation diagram and its symmetric dual graph, the Delaunay triangulation, to gain first-order insight into complex wave propagation phenomena for an arbitrary distribution of wave propagation sources. The inherent symmetry of these simultaneous wave propagation mechanics allows us to rapidly visualize and predict the stress wave propagation and interactions, and the resultant crack bridging patterns that arise from random blast sources. The current work is focused on rock blast fracture mechanics, but the visualization scheme can be implemented for any application where waves propagate axisymmetrically and interact. Full article

This study proposes a technique for generating 3D objects from Shapefile-based 2D spatial data and converting them to comply with the CityGML 3.0 standard. In particular, the proposed Wise Interpolated Texture (hereafter referred to as WIT) technique optimizes texture mapping and enhances visual quality. High-resolution Z-values were extracted using DEM data, and computational efficiency was improved by applying the constrained Delaunay triangulation algorithm. This study implemented more realistic visual representations using high-resolution orthorectified imagery (hereafter referred to as orthoimages) TIF files and improved data retrieval speed compared to existing raster methods through vector-based processing techniques. In this research, data weight reduction, parallel processing, and polygon simplification algorithms were applied to optimize the 3D model generation speed. Additionally, the WIT technique minimized discontinuity between textures and improved UV mapping alignment to achieve more natural and uniform textures. Experimental results confirmed that the proposed technique improved texture mapping speed, enhanced rendering quality, and increased large-scale data processing efficiency compared to conventional methods. Nevertheless, limitations still exist in real-time data integration and optimization of large-scale 3D models. Future research should consider dynamic modeling reflecting real-time image data, BIM data integration, and large-scale texture streaming techniques. Full article

Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers the ability to fully capture human understanding of the real world. Based on this, this paper proposes a novel method for building group recognition that integrates both spatial geometric and semantic features. The method effectively identifies building group structures by considering spatial proximity, geometry, and semantic similarity. First, spatial proximity between buildings is defined by constructing a neighborhood graph based on Delaunay triangulation, and the spatial geometric features of each building are extracted. The spatial distance and semantic intensity relationships between Point of Interest (POI) data and buildings are used for semantic feature extraction. Subsequently, a spatial–semantic dual clustering strategy is applied in two stages to aggregate the buildings and generate preliminary grouping results. Finally, the grouping results are refined through an optimal graph segmentation strategy, which ensures both global and local optimization. The proposed method is applied to two areas in Shenzhen City, China, and the experimental results demonstrate that, compared with other methods, it more effectively identifies building groups with coherent spatial, geometric, and semantic features, improving the Adjusted Rand Index (ARI) from 0.589 to 0.701. This approach provides significant support for intelligent map generalization and personalized knowledge services in the era of big data. Full article