Search Results (207)

Under the macro background of the rural revitalization strategy and urban-rural integrated development, rural settlements are undergoing a profound transformation from physical morphology to functional connotation. However, existing studies mainly focus on the expansion of single land elements, lacking long-term quantitative monitoring of the coupling relationship between rural development and policy texts. Taking Fuzhou City as a case study, this research selects long-term Global Human Settlement Layer (GHSL) and Night-Time Light (NTL) data from 1990 to 2025, combined with policy text quantification methods. Based on rural development units, the Coupling Coordination Degree Model (CCDM), Macro-Micro Matching Index (MMI), and gravity center migration analysis are employed to systematically reveal the spatiotemporal evolution characteristics and driving mechanisms of rural settlement morphology under policy institutional changes. The research results indicate that: (1) Fuzhou’s rural settlements exhibit relatively stable gravity centers of construction land, while the gravity center of economic vitality has significantly shifted toward the southeastern coastal area under policy guidance; (2) The coupling coordination degree of rural human–land relationships has generally increased, but with significant spatial heterogeneity, forming a pattern of high-quality coordination in coastal areas and low-efficiency lag in mountainous regions; (3) The shift in policy orientation from scale expansion to functional enhancement has driven economic factors to concentrate in key policy areas ahead of physical spatial expansion. The analytical framework combining remote sensing monitoring and policy quantification constructed in this study reveals the precedence of factor flow and the lag of physical space driven by policies, providing a scientific basis for the differentiated governance of rural areas in coastal mountainous cities. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

The process of manually scoring drawings for the Goodenough–Harris Draw-a-Person (DAP) test is time-consuming and labor-intensive. It is also prone to inconsistencies due to subjective interpretation. Keeping these drawbacks in mind, this study aims to introduce a hybrid model of automated analysis and scoring of DAP test results using a combination of deep learning and rule-based reasoning. The proposed model has two different modules: one for predicting ten visual anatomical features of drawings using a convolutional neural network (CNN), and another set of six rules for representing geometric and spatial relationships. The output of the CNN is converted to binary using thresholding and then concatenated with the results of heuristic rules to obtain a final set of sixteen features. The proposed model was also evaluated using five-fold cross-validation methods and a separate hold-out test set containing 948 labeled drawings. The evaluation using the five-fold cross-validation approach shows that the proposed approach maintains consistent performance with high average F1-scores for all primary anatomical features above 0.90. On the other hand, the evaluation using the hold-out test set revealed that the proposed approach achieved a high macro-average accuracy of 91.78% for all sixteen features. This implies that the proposed approach has a high degree of generalization capability for the problem domain. The proposed approach achieves almost-perfect scores for structurally prominent anatomical features such as the head, limbs, trunk-related relationships, and all heuristic-based features. Nevertheless, the proposed approach performs poorly for less visually distinguishable anatomical features such as the ears (average F1-scores ≈ 0.09–0.12) and the neck (average F1-scores ≈ 0.75). The evaluation results show that the proposed approach is efficient in approximating expert-level scoring with a considerable reduction in human effort. Nevertheless, some limitations exist in the proposed approach. First, the proposed approach is less robust for subtle anatomical features. Second, the proposed approach relies on heuristic thresholds for feature extraction. Third, the proposed approach equally weighs all sixteen features; however, this may not exactly match the actual DAP scoring system. Full article

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

This study explores the coupling relationship between wetland ecotourism and resource–environmental carrying capacity in the Dongting Lake region. By constructing a comprehensive index system and utilizing a coupling coordination degree model, we analyzed the temporal and spatial evolution characteristics across 24 districts and counties from 2014 to 2022. The results indicate the following: (1) The quality of both ecotourism and environmental carrying capacity has steadily improved, though significant regional disparities remain. (2) The coupling coordination degree exhibits a “high in the center, low in the periphery” spatial pattern, showing a positive correlation between ecotourism levels and environmental capacity. (3) The region comprises three development types: balanced coordination, well-matched, and lagging. These findings provide a scientific basis for optimizing ecotourism pathways and achieving high-quality regional sustainable development. Full article

This paper investigates the performance of a multi-user multiple-input single-output (MU-MISO) broadcast channel under the practical constraints of imperfect, delayed, and quantized channel state information at the transmitter (CSIT). Conventional interference alignment (IA) strategies—classified into spatial (SIA), temporal (TIA), and message-domain (MIA) techniques— typically designed for specific, idealized CSI regimes and often rely on successive interference cancellation (SIC) at the receiver. However, the iterative structure of SIC is highly susceptible to error propagation, particularly under CSI uncertainty and high-order modulation. We propose Deep-STMIA, a novel end-to-end deep learning framework that jointly optimizes interference management across the space, time, and message domains. Using a neural network-based autoencoder architecture with structural message-domain regularization, Deep-STMIA learns to mitigate the catastrophic effects of error propagation and adapts to a continuum of CSIT conditions. Simulation results demonstrate that Deep-STMIA matches the performance of degrees-of-freedom (DoF) optimal benchmarks in extreme CSI regimes and significantly outperforms state-of-the-art baselines, such as rate-splitting multiple access (RSMA), in practical imperfect CSIT scenarios. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article

Accurate point cloud registration is a fundamental prerequisite for reality-based 3D reconstruction and large-scale spatial modeling. Despite significant international progress, reliable registration in architectural and urban scenes remains challenging due to geometric intricacies arising from repetitive and strongly symmetric structures and photometric variability caused by illumination inconsistencies. Conventional ICP-based and color-augmented methods often suffer from local convergence and color drift, limiting their robustness in large-scale real-world applications. To address these challenges, we propose Hybrid Adaptive Residual Optimization (HARO), a unified framework that organically integrates geometric cues with hue-robust color features. Specifically, RGB data are transformed into a decoupled HSV representation with histogram-matched hue correction applied in overlapping regions, enabling illumination-invariant color modeling. Furthermore, a novel adaptive residual kernel dynamically balances geometric and chromatic constraints, ensuring stable convergence even in structurally complex or partially overlapping scenes. Extensive experiments conducted on diverse real-world datasets, including Subway, Railway, urban, and Office environments, demonstrate that HARO consistently achieves sub-degree rotational accuracy (0.11°) and negligible translation errors relative to the scene scale. These results indicate that HARO provides an effective and generalizable solution for large-scale point cloud registration, successfully bridging geometric complexity and photometric variability in reality-based reconstruction tasks. Full article

Light carrying orbital angular momentum (OAM) has emerged as a promising tool for manipulating light–matter interactions, providing an additional degree of freedom to explore chiral-optical phenomena at the nanoscale. When such vortex beams interact with chiral metamaterials, a unique phenomenon of optical asymmetry known as vortical dichroism (VD) arises. Nevertheless, most existing chiral metamaterials exhibit limited VD responses, and the underlying physical mechanisms are yet to be fully clarified. In this work, we propose three-dimensional spiral metamaterials that achieve gigantic VD effect. This pronounced VD effect originates from the intrinsic coupling between the spiral structure and the chirality inherent to optical vortices, which leads to strongly asymmetric scattering intensities for left- and right-handed OAM beams of opposite topological charges. Numerical simulations confirm a remarkable VD value of 0.69. Further analysis of electric field distributions reveals that the asymmetric VD response stems from a handedness-dependent excitation of distinct electromagnetic modes. For opposite handedness, spatial mode mismatch results in enhanced scattering. In contrast, matching handedness enables efficient energy coupling into a guided spiral mode, which suppresses scattering. These findings not only deepen the physical understanding of VD mechanisms but also establish a versatile platform for developing advanced chiral photonic devices and enhancing OAM-based light–matter interactions. Full article

The supply–demand relationship of flood regulation services (FRS) plays a vital role in mitigating urban flooding. Yet, existing studies still fall short in the comprehensiveness of FRS indicators, the accuracy of assessment scope, and the fine-scale analysis needed to delineate spatial supply–demand features and precisely identify critical areas. Using Xiamen Island as a case study, we first quantify ecosystem-based FRS supply with the InVEST model and assess socioeconomic FRS demand under the H-E-V framework; second, we perform parcel-level supply–demand matching to identify spatial patterns and typologies; then, we diagnose FRS status via the coupling–coordination degree model (CCDM); and finally, we delineate flood-risk hotspots through priority-intervention grading. The results indicate that (1) higher FRS supply clusters in the south, southwest, and northeast, whereas demand is markedly higher in the central–northern sector, yielding an overall pattern of “pronounced mismatch in the central and north, with relatively sufficient supply along the periphery.” (2) Low supply–high demand zones exhibit the highest flood risk and contain higher proportions of industrial, transportation, and residential land. (3) These low supply–high demand zones are further subdivided into five priority-intervention levels, for which we propose tiered, differentiated risk-management strategies. Collectively, the findings clarify supply–demand mechanisms and mismatch characteristics, providing decision support for urban flood safety and sustainable development. Full article

As the core area of the Silk Road Economic Belt, Xinjiang still faces problems such as unbalanced development in the process of urban–rural integration, accompanied by the increasingly prominent imbalance between population flow and land resource allocation in county-level towns. Specifically, clarifying the impact of urban–rural integration development on the human–land matching relationship in Xinjiang’s county-level towns is the key to promoting coordinated regional development. This study constructs a spatial matching model and a multinomial logistic regression model to analyze the human–land relationship and the influencing factors of urban–rural integration in 83 county-level towns in Xinjiang from 2010 to 2023. The research results show that (1) from 2010 to 2023, there were significant differences in the spatial matching degree between the total amount and increase in urban population and urban land in Xinjiang’s county-level towns; the number of counties with a relatively high matching level was generally larger in northern Xinjiang than in southern Xinjiang, and the overall spatial matching degree was at a relatively low level. (2) The proportion of counties with sustained population growth and sustained land growth was the highest, reaching 49.40% and 26.51%, respectively. Counties in southern Xinjiang were mainly of the sustained-population-growth type, while counties in northern Xinjiang had more types and were scattered, and were mainly of the land-growth type as a whole. (3) Factors such as the proportion of ethnic minority population, the comparison of industrial output value, and the number of medical beds per capita had a significant impact on the spatial matching level of urban population and land in most types of counties. The types of counties in southern Xinjiang were mainly affected by factors such as the ethnic population structure and medical conditions, while the counties in northern Xinjiang were mostly affected by factors such as the level of industrial coordination and urban spatial expansion. It is suggested to implement differentiated spatial governance and enhance coordination between southern and northern Xinjiang, thereby improving the level of human–land matching and promoting the integrated development of urban and rural areas. Full article

Against the backdrop of efforts to improve the quality of urban spatial stock, assessments of spatial benefits driven by consumption preferences integrate subjective decision-making and objective environmental factors to provide quantitative evidence for urban planning and public investment. This study constructed a “environment-perception–behavior” analytical framework grounded in SOR (stimulus–organism–response) theory. We combined structural equation modeling with the hedonic pricing method to identify causal pathways and quantify the marginal value of spatial elements. XGBoost was employed to uncover consumption-preference thresholds, Coupling Coordination Degree (CCD) was used to identify spatial supply–demand relationships, and Social Return on Investment (SROI) was applied to evaluate multidimensional urban spatial benefits. The results showed that transportation accessibility, commercial diversity, green-space quality, and cultural ambiance significantly shaped distinct consumption preferences. Central urban areas approached supply saturation in commercial and daily consumption and exhibited diminishing marginal returns, whereas peripheral zones demonstrated greater potential for sports and cultural consumption. Based on these findings, we reveal the underlying logic of spatial benefit distribution and classify the study area into High-efficiency matching zones, transition matching zones, and potential zones. We further propose targeted optimization recommendations that can inform policy on urban spatial functional positioning and social investment and provide evaluation criteria for prioritizing interventions. Full article

We describe the problem of estimating the speaker’s head orientation from the asynchronous multi-channel waveforms observed by microphones distributed in a room. In particular, we address a novel problem of estimating head orientation from sound captured by fewer microphones than the number of distinct head orientations to be distinguished. This is because the head orientation is an important clue indicating the speaker’s intended conversational partners. Head orientation estimation technology is an essential technology within environmental intelligence technology, which uses sensors embedded in rooms to monitor and support people’s activities. We propose a head orientation estimation method that aims to achieve high angular resolution using a small number of microphones. The proposed method achieves high estimation accuracy by using the spatial radiation pattern of the sound source as clues and by integrating information from multiple frequency bands. We conducted an experiment to estimate head orientation with an angular resolution of $15\mathrm{degrees}$ under observation conditions using six microphones. Experimental results showed that higher estimation accuracy was obtained than the conventional method using distributed microphone arrays (Oriented Global Coherence Field method) and the conventional method using distributed microphones (Radiation Pattern Matching method). The proposed method utilizing multiple frequency bands achieved the best performance with a mean absolute error of $10.58\mathrm{degrees}$ in the task of classifying 24 distinct head orientations. Full article

Water and land resources are fundamental to agricultural production, and their spatiotemporal matching and utilization efficiency have a profound impact on agricultural sustainability and food security. Utilizing data from 25 counties in Northern Shaanxi (NS) from 2010 to 2021, this study applied the Gini coefficient, generalized matching coefficient, spatial correlation analysis, and clustering techniques to analyze the spatiotemporal matching patterns of agricultural water and land resources (AWLRs) and propose a functional zoning scheme. The results revealed significant spatial disparities in AWLR matching. The AWLR matching coefficient in NS was below the provincial average and substantially lower than the national average, indicating a generally poor level of matching. From 2010 to 2021, the Gini coefficient of AWLR decreased, reflecting an improvement from a severe mismatch to a generally balanced state. A significant positive spatial correlation was observed between AWLR matching and agricultural development levels. Counties with high AWLR matching but low agricultural development were mainly concentrated in the north, whereas southern counties exhibited both high AWLR matching and high agricultural development. The coordinated development degrees among agricultural development level, agricultural water use benefit, and cultivated land use benefit in the 25 counties were at low or relatively low levels. Furthermore, the K-Means++-AHC clustering algorithm demonstrated better applicability for functional zoning, dividing NS into four distinct agricultural zones: Zone I (good AWLR match but low agricultural development); Zone II (low utilization efficiency and AWLR match); Zone III (water scarcity and high land reclamation, sustaining high agricultural development despite poor AWLR match); and Zone IV (the core agricultural area with comprehensive advantages). The results clarify the matching degree of AWLRs and their utilization potential in NS and the zoning framework will guide targeted interventions to enhance agricultural efficiency in water-scarce regions. Full article