Search Results (120)

In this paper, voxel-based Asymptotic Homogenization (AH) is employed to calculate the thermal expansion and thermal conductivity characteristics of lattice materials that have a Representative Volume Element (RVE) with non-orthogonal periodic bases. The non-orthogonal RVE of the cellular lattice is discretized using voxel elements (iso-parametric hexahedral element, on a cartesian grid). A homogenization framework is developed in python that uses a fast-nearest neighbor algorithm to approximate the (non-orthogonal) periodic boundary conditions of the discretized RVE. Validation studies are performed where results of the homogenized Thermal Expansion Coefficient (TEC) and thermal conduction performed in this paper are compared with results generated by commercially available software. These included comparison with the results for (a) bi-material unidirectional composite with orthogonal RVE cell envelope; (b) bi-material hexagon lattice with orthogonal cell envelope; (c) bi-material hexagon lattice with non-orthogonal cell envelope; and (d) bi-material square lattice. A novel approach of visualizing the contribution of each voxel towards the individual terms within the homogenized thermal conductivity matrix is presented, which is necessary to mitigate any potential errors arising from the numerical model. Additionally, the effect of the thermal expansion and thermal conductivity for bi-material hexagon lattice (orthogonal and non-orthogonal RVE cell envelope) are presented for varying internal cell angles and all permutations of material assignments for a relative density of 0.3. It is found that when comparing the non-orthogonal RVE with the Orthogonal RVE as a reference model, the numerical error due to approximating the periodic boundary condition for the non-orthogonal bi-material hexagon is generally less than 2% as the numerical error is pseudo-cyclically dependent on the discretization along the cartesian axis. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. 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

Multistatic sonar networks (MSNs) have emerged as a powerful approach for enhancing underwater surveillance capabilities. Different from monostatic sonar systems which use collocated sources and receivers, MSNs consist of spatially distributed and independent sources and receivers. In this work, we address the problem of determining the optimal route for a mobile multistatic active sonar source to maximize area coverage, assuming all receiver locations are known in advance. For this purpose, we first develop a Mixed Integer Linear Program (MILP) formulation that determines the route for a single source within a field discretized using a hexagonal grid structure. Next, we propose an Ant Colony Optimization (ACO) heuristic to efficiently solve large problem instances. We perform a series of numerical experiments and compare the performance of the exact MILP solution with that of the proposed ACO heuristic. Full article

Range expansions driven by global warming are increasingly documented, particularly in birds and insects. The House Bunting, a species native to North Africa, has recently established the first confirmed breeding population in mainland Europe in Algeciras, southern Spain. This study presents the results of the first systematic survey of this population, conducted in December 2024. Using a standardized survey method across a grid of hexagonal sampling units, we recorded a minimum of 18 individuals, including juveniles, indicating both successful reproduction and possible new arrivals. Observations were concentrated in low-rise urban areas, mirroring the species’ preferred habitats in Morocco. The presence of individuals with juvenile plumage in December suggests an extended breeding season, which may facilitate population growth. Given the geographical proximity to North Africa and predicted increases in aridity due to climate change, further expansion into Iberia appears likely. Although no immediate ecological impacts have been detected, the potential for interactions with resident species justifies continued monitoring. This study provides a baseline for assessing the establishment and growth of this population, contributing to a broader understanding of how climate change influences species distributions and the colonization dynamics of expanding bird populations. Full article

Cropland slope structure is a key factor influencing agricultural sustainability and ecological risk, especially in topographically complex regions. This study proposes a novel framework that integrates slope spectrum analysis with H3 hexagonal grid partitioning to examine the spatiotemporal dynamics of cropland slope across China from 1990 to 2023. Using 30 m CLCD land cover data, we derived key indicators, including the T-value, upper slope limit (ULS), peak area proportion (PaP), slope at maximum area (SMA), and cropland slope change index (CSCI). This grid-based, multi-indicator approach enables the fine-scale detection of slope structure transitions. Results show that the average slope of cropland fluctuated at around 4.12°, peaking at 4.18° in 2003, while the ULS remained stable at 17°, with 95% of cropland below this threshold. Regionally, cropland in southwest and northwest China was concentrated on steeper slopes (ULS > 26°, PaP < 10%), whereas flatter areas in north and south China had cropland mainly below 15°. From 1990 to 2023, upslope expansion was evident in south China (CSCI > 10), while downslope shifts aligned with high-slope cropland in the western regions. Geographically weighted regression revealed significant positive correlations between increasing ULS and CSCI and elevated cropland fragmentation and soil erosion in hilly areas. These findings highlight the ecological risks of cropland expansion into steep terrain. The proposed framework offers a spatially explicit perspective of cropland slope evolution and supports targeted strategies for land management and ecological restoration. Full article

This paper presents a novel Earth-System Stratified Grid (ISEA4H-ESSG) model, designed to address the challenges in multi-layer geoscience data management and analysis. In the realm of geosciences, which encompasses the solid earth, atmosphere, hydrosphere, and biosphere, as well as planetary and space sciences, the effective integration of diverse data sources is crucial. Traditional grids have limitations in three-dimensional spatial modeling, cross-layer data fusion, and dynamic multi-scale analysis. The ISEA4H-ESSG model overcomes these drawbacks by integrating the Icosahedral Snyder Equal-Area Aperture 4 Hexagon Discrete Global Grid System (ISEA4H DGGS) with a degenerative subdivision mechanism. It adheres to six core principles, including stratified spherical coverage, geographic consistency, multi-scale dynamic adaptability, global seamless partitioning, encoding uniqueness and efficiency, and multi-source data compatibility. Through the independent subdivision of spherical and radial layers, this model balances resolution differences and resolves polar-grid distortion and cross-layer data heterogeneity issues. The introduction of a four-dimensional spatiotemporal encoding framework enhances the storage and parallel computing capabilities of massive datasets. Case studies on ionosphere three-dimensional modeling and global atmospheric temperature field formatting demonstrate the high precision and adaptability of the ISEA4H-ESSG model. This research provides a unified spatial data infrastructure for geosciences, facilitating in-depth studies on natural hazards, climate change, and planetary evolution, and offering new perspectives for international partnerships and future Earth-related research. Full article

The identification of the multifunctional combination of production–living–ecological spaces (PLES) in urban agglomerations, particularly in urban cores and peri–urban areas, is a critical issue in the urbanization process. This study, using the Hohhot–Baotou–Ordos–Yulin (HBOY) urban agglomeration, a key node in China’s “Two Horizontals and Three Verticals” urbanization strategy, proposes a hexagonal grid–based PLES quantification framework using POI data. A three–level POI classification system was developed, with functional element weights determined via the Analytic Hierarchy Process and public perception surveys. The framework quantifies PLES within hexagonal grids and analyzes its patterns and functional coupling mechanisms using spatial overlay, Average Nearest Neighbor Index (ANNI), kernel density analysis, and spatial autocorrelation analysis. The following results were obtained. (1) PLES classification accuracy reached 90.83%, confirming the reliability of the method. (2) The HBOY urban agglomeration exhibits a dominant production space (40.84%), balanced living and ecological spaces (29.37% and 29.36%, respectively), and a severe shortage of mixed spaces (0.43%). (3) Production and living spaces show significant clustering ($ANNI$ ≤ 0.581), mixed spaces follow ($ANNI$ = 0.660), and ecological spaces are relatively evenly distributed ($ANNI$ = 0.870). (4) The spatial distribution patterns show that production and living spaces exhibit “core concentration with peripheral dispersion”, ecological spaces show “block concentration with point–like distribution”, and mixed spaces show “point–like dispersion”. (5) Production and living spaces exhibit strong spatial autocorrelation ($Moran’s I$ > 0.7) and the highest spatial correlation ($Bivariate Moran’s I$ = 0.692), while the spatial correlation with ecological spaces is weakest ($Bivariate Moran’s I$ = 0.150). The proposed PLES identification framework, with its efficiency and dynamic updating potential, provides an innovative approach to urban spatial governance under the global Sustainable Development Goals. The findings offer integrated decision–making support for spatial diagnosis and functional regulation in the ecologically vulnerable areas of northwest China’s new urbanization. Full article

Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field. Full article

Stretchability and flexibility are essential characteristics for high-performance electromagnetic interference (EMI) shielding materials in wearable and smart devices. However, achieving these mechanical properties while also maintaining high EMI shielding effectiveness (SE) for shielding materials remains a significant challenge. Here, a stretchable patterned carbon nanotube (CNT) array composite film, reinforced with two-dimensional (2D) nanomaterials (Ti₃C₂T_x and graphene), is fabricated using a straightforward scraping method. The resulting CNT array/Ti₃C₂T_x/graphene composite films possess a periodic grid structure. Specifically, the composite film with a regular hexagonal pattern demonstrates an EMI SE of 36.5 dB in the X-band at a thickness of 350 μm. Additionally, the composite film exhibits excellent stretchability, flexibility, and stability. After undergoing 10,000 stretching cycles, the EMI SE remains stable. Simulation results further indicate that surface reflection is the primary EMI shielding mechanism. This simple scraping method offers a promising approach for developing stretchable and high-performance EMI shielding films, making them well suited for application in flexible devices. Full article

The aim of the present study is to investigate the performance of two carbon fiber-reinforced composite polymers used to manufacture end-use parts via the fused filament fabrication (FFF) method. The materials under investigation were carbon fiber-reinforced Polyamide-6 (PA6-CF15) and carbon fiber-reinforced polyphenylene sulfide (PPS-CF15). To evaluate their mechanical properties and vibrational behavior, specimens were fabricated with four distinct infill patterns: grid, gyroid, triangle and hexagon. In particular, the vibrational behavior of the 3D-printed composites was determined by conducting cyclic compression testing, as well as modal tests. Additionally, the mechanical behavior of the reinforced polymers was determined by conducting both uniaxial tensile and compression tests, as well as three-point bending tests. The results of the mechanical experiments revealed that the grid pattern exhibited the best overall performance, while the gyroid pattern exhibited the greatest strength-to-weight ratio, making it the most durable infill for use with composite filaments. In vibration experiments, PA6-CF15 structures exhibited higher damping ratios than PPS-CF15, indicating superior damping capacity. Among the infill patterns, the hexagon pattern provided the greatest vibration isolation performance. Full article

This paper explores the challenges of detecting wiring anomalies in three-phase, four-wire energy metering devices, especially when large amounts of reactive power compensation are involved. Traditional methods, such as the hexagon phasor diagram technique, perform well under standard loads, but struggle to adapt to new situations, such as over- or under-compensation. To overcome these limitations, this paper proposes a hybrid approach that combines mechanism-based knowledge with data-driven technologies, including backpropagation neural networks (BPNNs). This method improves the accuracy and efficiency of anomaly detection and can better adapt to a dynamic power environment. The result is improved universality of anomaly detection, which helps to achieve safer, more accurate, and more efficient smart grid operation in complex situations. Full article

With the continuous advancement of technology for obtaining geographic spatial data, the accumulated volume of such data has been increasing, thus imposing higher demands on the storage, organization, and management of such data. As a new form of data management, the Discrete Global Grid System (DGGS) provides standardized descriptions and the exchange of geographic information on a global scale, enabling the efficient storage and application of large-scale global spatial data. Constituting a traditional type of GIS spatial data, vector data have advantages such as clear positions, implicit attributes, and suitability for map output. The representation of vector data in the global discrete grid network based on an equal-area projection, such as the hexagonal grid, fundamentally solves problems such as data redundancy, geometric deformation, and data discontinuity that arise when representing multiple vector data in a gridded format. This paper proposes different gridding methods for various types of vector data, and a quantifiable accuracy evaluation system is established from the perspectives of geographical deviation, geometric features, and topological relationships, to evaluate the accuracy of the gridded vector data, covering all types of gridded vector data based on the hexagonal grid. The evaluation method is generally applicable to all hexagonal-grid-based gridded vector data, and can be generalized based on application scenarios, for evaluating the usability of hexagonal grid vector data. Full article

The Discrete Global Grid System (DGGS) provides a foundational framework for the digital Earth, where uniform intercell distances are essential for accurate numerical simulations. However, due to the spherical topology, achieving strictly equidistant spherical grid cells is impractical. Most existing studies have focused on regional scales, which are constrained by data acquisition limitations and render global equidistant optimization algorithms economically infeasible. The equidistant characteristics of cells are influenced by map projections and often exhibit regional variations. In this paper, we analyze these equidistant characteristics and construct an equidistant pattern for an icosahedral hexagonal DGGS. By integrating this pattern into the icosahedral orientation method, we develop a regional-scale equidistant optimization method for DGGS. Experiments on river network extraction in the Yangtze River Basin demonstrate significant improvements: the equidistance of grid cells covering the region increased by over 34.2%, while the accuracy of river network extraction improved by 51.41%. Moreover, this method is extensible to other grid models, enhancing the broader applicability of DGGS. Full article

Understanding the relationship between urban fabrics and sustainability is essential for addressing contemporary urban challenges, as urban fabrics provide critical morphological and socio-economic data that enhance our understanding of the performance and spatial variability of urban systems. This study focuses on Cuenca, spatially divided into a 150 m radius hexagonal grid with 30 sustainability indicators and 18 urban fabric typologies. Using spatial, statistical, and visual analysis, relationships and patterns between sustainability indicators and urban fabric types are explored. The results reveal significant variation in sustainability across different fabric types, with built environment indicators playing a central role. There is marked spatial heterogeneity: inner-core areas exhibit higher sustainability, fringe areas lag behind, and transitional zones are also identified. Spatial clustering reveals that fabric types are homogeneous in terms of sustainability at both the high and low extremes but heterogeneous in mid-range sustainability areas. This quantitative analysis of Cuenca’s urban fabric typologies highlights substantial differences in sustainability and distinct spatial patterns, offering valuable insights for evidence-based urban planning. The open-source data and tools provided facilitate customisation and replication in other urban contexts. Full article