Search Results (23,373)

The association between urban green space (UGS) and public health represents a core interdisciplinary issue in ecological city construction and the “Healthy China” strategy. However, the underlying mechanisms, contextual constraints, and regional heterogeneity of the impact of UGS on public health at the provincial macro-scale remain insufficiently understood. Existing research primarily examined the health effects of green space at the resident-level micro-scale, with samples often limited to single cities or local urban clusters. Furthermore, limited attention has been paid to the moderating mechanisms and nonlinear threshold characteristics through which green space affects public health. Using panel data from 30 provinces in China from 2012 to 2023, this study empirically investigates the effects, moderating mechanisms, threshold effects, and regional heterogeneity of UGS on public health through two-way fixed-effects ordinary least squares (OLS) regression, moderation models, and threshold regression models. UGS The results show the following: (1) From 2012 to 2023, the overall levels of provincial UGS and public health in China increased, exhibiting a spatial distribution pattern characterized by higher levels in the east and lower levels in the west. (2) The development level of UGSs is significantly and positively associated with provincial public health in China; (3) The health-promoting effects of UGSs exhibit both moderation and threshold characteristics. Environmental regulation, traffic accessibility, and population aging exert positive moderating effects, whereas the level of urbanization exhibits a nonlinear threshold relationship. (4) Regarding regional heterogeneity, the health benefits of green spaces are more pronounced in urban functional zones and coastal regions, whereas key ecological functional zones and major grain-producing areas demonstrate synergies between ecological protection and health improvement. Accordingly, China should strengthen the coordinated advancement of UGS planning and public health protection, optimize the spatial layout of age-friendly green space, improve the supporting environmental regulation systems, and thereby promote the coordinated development of residents’ health and well-being and ecological environmental quality. Full article

Policies for cultural heritage protection have increasingly shifted toward the integrated conservation and development of historical cultural landscapes. In vernacular settlements located in the southwestern border regions of China, some cultural landscape remains that were once widespread are gradually disappearing. Nevertheless, these landscapes continue to be recognized, valued, and maintained by local ethnic communities. Understanding how place-based perceptions are formed, how hidden cultural landscapes can be identified, and how their cultural significance can be interpreted is therefore of considerable importance. Drawing on landscape perception theory, this study develops an analytical framework that integrates landscape structure interpretation, oral history analysis, and local ethnic group perception. The archaeological remains of the “Ancient Temple” in Baiya City, located within the Zhaozhou intermontane basin (“Bazi”) in Dali, are selected as a case study. Through field investigations, oral history interviews, and Semantic Differential (SD) scale questionnaires, perception factors are examined across four dimensions—environment, ritual, construction, and psychology—to systematically analyze the elements shaping spatial perception. The results reveal that, although local ethnic groups exhibit relatively low levels of perception regarding the architectural form of the ancient temple, they maintain strong psychological and emotional attachments to ritual pathways, ruin landscapes, and related cultural elements. The remains of the “Ancient Temple” constitute an implicit cultural landscape that plays a significant role in shaping local cultural identity and sense of place. At the same time, it reflects the community’s capacity for self-organization and the latent mechanisms underlying the reconstruction of cultural space. Based on these findings, strategies for cultural landscape regeneration should emphasize the preservation of indigenous spatial order, the revitalization of local ritual practices, and the strengthening of ethnic psychological identity. This study contributes to a deeper understanding of the social functions and cultural significance of implicit cultural landscapes in contemporary urban and rural development and provides practical references for their conservation and regeneration. Full article

The histological segmentation of fish gill lesions is a crucial step in environmental biomarker analysis, as morphological alterations in bioindicator species, such as Sciades herzbergii, provide biologically meaningful evidence of exposure to aquatic contaminants. In this context, gill histology enables the assessment of biomarkers; however, manual lesion quantification remains time-consuming, observer-dependent, and challenging to scale for environmental monitoring programs. Moreover, this task remains challenging due to the presence of heterogeneous textures, fragmented lesion boundaries, low-contrast regions, and staining variability. To address these issues, this study proposes a deep learning framework for the semantic segmentation of epithelial lifting (EL) and hyperplasia (HY) in gill histological images. The proposed model combines a ResNet-50 encoder, an ASPP bottleneck for multiscale contextual aggregation, squeeze-and-excitation-based channel recalibration at the bridge, and a nested U-Net++ decoder with deep supervision. The GillHistDB dataset was also developed for this study, comprising 447 RGB histological images and 29,730 annotated lesions, including 16,855 EL and 12,875 HY instances. The proposed method achieved the best overall performance among the evaluated models in the main overlap-based metrics. At the class level, it obtained Dice values of (0.842 ± 0.055) for EL and (0.684 ± 0.190) for HY, with corresponding IoU values of (0.731 ± 0.080) and (0.548 ± 0.196), respectively. For EL, the method also achieved the highest recall (0.848 ± 0.074), while for HY it reached the highest precision (0.653 ± 0.205) and maintained a high recall (0.767 ± 0.139). These results indicate that the proposed architecture provides an effective and robust solution for gill histological lesion segmentation, while GillHistDB establishes a relevant benchmark to support future studies on environmental biomonitoring, histological biomarkers, and the assessment of aquatic pollution. Full article

Crowd-related disasters in dense public spaces unfold into hazardous situations within seconds, repeatedly demonstrating that reactive response alone is insufficient to minimize damage. This reality has intensified the need for monitoring systems that can proactively forecast congestion before it reaches a critical level. Digital twin platforms address this need by providing an operational substrate that represents crowd states on a unified bird’s-eye-view (BEV) grid, on which a predictive module can forecast where congestion will emerge. However, conventional AI-based single-stage dense prediction models are intrinsically ill-suited to this role: although crowd congestion is sparse in both space and time, these models apply uniform high-resolution computation across the entire BEV domain, wasting computation and biasing optimization toward dominant background regions. In this paper, we propose a two-stage coarse-to-fine framework that operates as the predictive module of the digital twin and explicitly exploits the spatio-temporal sparsity of crowd congestion. The first stage, CoarseSTFormer, performs efficient global screening on a low-resolution BEV input to coarsely identify a set of density-critical candidate regions. The second stage, SparseQueryDecoder, selectively reconstructs high-resolution responses only on the identified candidates, rather than uniformly upsampling the entire BEV grid. In simulation environments with up to 20,000 pedestrian agents, the proposed framework matches the strongest dense baseline in reconstruction quality while delivering the most balanced variance profile across grid scales. At inference, it further reduces GPU energy consumption by $1.9\times$ to $5.0\times$ and computational cost (FLOPs) by $3.8\times$ to $54\times$, demonstrating its practicality as a resource-efficient predictive module that satisfies both accuracy and efficiency. Full article

Negative air ions (NAIs) are key ecological indicators of atmospheric cleanliness and forest ecosystem service functions, particularly in the context of forest wellness and ecotourism. However, long-term, high-frequency observations of NAIs across broad spatial scales remain scarce, limiting our understanding of its regional spatiotemporal dynamics and environmental controls. Here, we present a six-year (2018–2023) continuous, hourly monitoring dataset of NAI concentrations from 60 fixed forest sites across Zhejiang Province, a typical subtropical humid region in southeastern China. The provincial mean NAI concentration over the study period was 1672 ions·cm⁻³, with a pronounced “high around the periphery, low in the center” spatial pattern, with the mountainous southwestern areas consistently showing the highest concentrations and the central Jinqu Basin the lowest. On diurnal scales, NAIs exhibited a bimodal pattern with primary peaks at 7:00 and secondary peaks at 16:00, rather than a simple daytime–nighttime dichotomy. Seasonal dynamics showed significantly higher NAI in summer than in autumn and winter; however, the summer–winter difference was only ~25%, much smaller than the ratios reported for temperate regions. Interannually, NAI concentrations increased from 2018 to 2023 (average annual increase of 158 ions·cm⁻³), peaking during the 2020–2022 period, when anthropogenic emissions were substantially reduced. Using linear mixed-effects models, we identified relative humidity as the dominant positive driver of NAI variability, followed by wind speed as a negative modulator, and precipitation playing a minor role. These findings reveal the multi-scale spatiotemporal dynamics of NAIs in subtropical forests and underscore the overriding control of humidity over ion persistence. Our study provides a robust regional benchmark for background NAI levels in humid subtropical climates and offers direct scientific support for forest-based health resource planning and air quality assessment. Full article

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R² = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes. Full article

Lead isotope ratios are frequently used in archaeometric provenance analysis; however, the overlap of isotopic fields within the Andean metallogenic belt complicates reliable provenance determination. This study presents a reproducible fidelity-based kernel method for the unsupervised clustering of Andean lead-isotope data and investigates whether a quantum-mechanical similarity space can reveal geologically significant structures beyond the classical Euclidean partition. A dataset of 1522 measurements of ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb, and ²⁰⁸Pb/²⁰⁴Pb was analyzed using a fidelity-based quantum kernel based on a three-qubit Pauli feature map and compared with classical K-means clustering, Gaussian mixture models, and Ward’s agglomerative clustering under various preprocessing strategies and cluster counts. The optimal quantum kernel setup achieved the highest silhouette score at k = 2. However, because analytical uncertainties were not consistently reported across all the compiled sources, an uncertainty-weighted similarity could not be applied. Geological insights indicate that this binary division separates less radiogenic, arc-related compositions from more radiogenic and thorogenic crustal signatures, a contrast that broadly follows the west-to-east crustal-contamination gradient across the Andes. Conversely, the traditional four-cluster approach provides more detailed subdivisions that align with the previously identified isotopic provinces. The reported separation reflects the geometry of the quantum feature space rather than any hardware-level speed-up, as this work represents only a simulation approach. Overall, these findings support a hierarchical and complementary approach to analyzing Pb isotope origins, in which quantum kernel clustering provides robust large-scale separation and classical clustering enhances regional understanding. Full article

Surface soil grain-size distribution (GSD) is a fundamental terminal record of aeolian processes and land-surface erodibility. However, a macro-scale understanding of GSD spatial heterogeneity and its quantitative coupling with seasonal atmospheric circulation dynamics in Central Asia remains insufficient. Based on an extensive dataset of 325 surface soil samples across Kazakhstan, Uzbekistan, Kyrgyzstan, and Tajikistan, this study systematically investigates the GSD patterns and their climatic drivers. Our results reveal a pronounced spatial gradient: coarse-textured soils dominate the northwestern and eastern desert plains, whereas fine-grained sediments are sequestered in the southeastern mountain-basin systems. We demonstrate that this heterogeneity is rigorously governed by seasonal wind regimes: the Siberian High directs coarse particle entrainment and transport during spring, while the mid-latitude westerlies and local topographic modulation (e.g., the Tian Shan and Pamir barriers) control the fine-grained sorting continuum. Furthermore, the desiccated Aral Sea bed serves as a distinctive anthropogenic dust source, perturbing regional natural sorting patterns. These findings provide critical empirical constraints for dust emission modeling and underscore the sensitivity of Central Asian land surfaces to shifting atmospheric circulation patterns. Full article

Sarcopenia is highly prevalent among stroke patients and is associated with poor functional outcomes; however, differences across stroke stages and age groups remain unclear. This cross-sectional study enrolled 80 stroke patients from a regional teaching hospital in Taiwan, categorized into chronic (n = 40) and post-acute care (PAC) groups (n = 40), and further stratified into younger (40–64 years, n = 44) and older (≥65 years, n = 36) groups. Assessments included body composition, muscle strength, ultrasound-measured muscle thickness, gait speed, calf circumference, sarcopenia screening (SARC-F), nutritional status, and health-related quality of life. No significant differences were observed in muscle mass, muscle strength, or ultrasound-derived muscle thickness between the chronic and PAC groups. However, the PAC group demonstrated poorer functional outcomes and health-related quality of life, including lower gait speed (p = 0.018), and lower EQ-5D index and visual analogue scale scores (p = 0.006 and p = 0.002, respectively). In contrast, the chronic group showed a higher prevalence of sarcopenia (p < 0.001), a higher mean SARC-F scores (p = 0.004), a greater proportion of low appendicular skeletal muscle mass index (ASMMI, p = 0.025), and reduced calf circumference (p < 0.001). Age-stratified analysis revealed that older patients had lower muscle mass and structural parameters, including ASMMI (p < 0.001), fat-free mass (p < 0.001), quadriceps thickness (p < 0.001), and calf circumference (p = 0.002), along with a higher prevalence of sarcopenia (p < 0.001). These findings indicate that stroke stage is more closely associated with functional impairment, whereas aging predominantly affects muscle mass and sarcopenia severity. Full article

As a typical human–nature coupled region, the coordination between the agricultural resource–environment system and regional development in the Fujian-Taiwan region is crucial for sustainable development. However, the underlying mechanisms and scale heterogeneity of this relationship remain unclear. This study employed a comprehensive evaluation approach to assess Agricultural Resource and Environmental Carrying Capacity (ARECC) and Regional Development Level (RDL) in the Fujian-Taiwan region in 2010, 2015, and 2019. A Coupling Coordination Degree (CCD) model was used to quantify the coordination relationship, while a geographical detector was applied to identify influencing factors at multiple scales. The main findings are as follows: (1) ARECC in Fujian increased gradually (average value: 0.046 → 0.052 → 0.075), whereas Taiwan exhibited a decline followed by recovery (average value: 0.449 → 0.408 → 0.491), with overall levels remaining higher than those of Fujian. RDL in Fujian was generally higher than that in Taiwan (average value: 0.260 > 0.212), and the average growth rate of Taiwan’s counties and cities was lower than that of Fujian (10.42% < 16.62%). (2) Overall, Fujian maintained a comparatively balanced relationship between ARECC and RDL, with maladjustment occurring only in Nanping City. In contrast, Taiwan experienced a substantially higher degree of maladjustment, with 40.90% of its counties and cities falling into maladjusted categories. Spatially, CCD in Fujian displayed a gradient decline from Fuzhou toward the southern, northern, and western regions, while high- and low-value areas in Taiwan were interspersed. (3) The coupling coordination mechanism exhibited significant scale heterogeneity, and corresponding differentiated regulation strategies were proposed. These findings contribute to a deeper understanding of the coordination mechanisms between ARECC and RDL in the Fujian–Taiwan region and provide references for promoting cross-regional agricultural collaboration and sustainable development. Full article

The demand for large-scale high-performance components with tailored properties in the aerospace and automotive industries has increased interest in multi-material additive manufacturing (AM). Among AM techniques, the Wire Arc Additive Manufacturing (WAAM) process is preferred for bimetallic fabrication due to high deposition rates, low equipment costs, and efficient material utilization. However, differences in metallurgical and thermal properties between dissimilar alloys can cause heat accumulation, leading to thermal stresses, cracking, and weak interfacial bonds. To the best of the authors’ knowledge, no study has reported the fabrication and characterization of a 17-4PH SS/Inconel 625 joint using the large-scale CMT-WAAM Process. To fill this gap, this study characterizes the microstructure and elemental distribution of the joint using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray Microscopy (XRM) and energy dispersive spectroscopy (EDS). Microstructural analysis revealed a martensitic matrix with retained δ-ferrite in the 17-4PH region, a fully austenitic γ-phase in the Inconel 625 region, and a mixed BCC–FCC transition zone at the interface. EDS results demonstrated a Fe–Ni compositional gradient across the interface. Radiographic inspection confirmed a defect-free build, and XRM results showed a porosity of less than 0.003% only in the 17-4PH region. Tensile testing confirmed joint integrity, with fracture occurring in the Inconel 625 region, and average yield and ultimate tensile strengths of 391 ± 7 MPa and 676 ± 9 MPa, respectively. The simplified Johnson-Cook constitutive model successfully predicted the ultimate tensile strength (UTS), with a prediction error of 9.3% compared to the experimental result. Furthermore, a novel 3D-structured light scanner technique was developed and validated with an extensometer to provide insight into localized strain behavior. Full article

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

While the influence of the urban built environment on public emotions has garnered extensive attention, existing studies predominantly focus on temperate climates or warmer seasons. As a result, they rarely extend their scope to winter-specific emotions in cold-region cities, thereby overlooking the complex human–environment emotional interactions under extreme climates. To bridge this seasonal research gap, this study develops an innovative analytical framework integrating Large Language Models (LLMs) with Multiscale Geographically Weighted Regression (MGWR). Drawing on social media data, this framework leverages the powerful zero-shot reasoning capabilities of LLMs to precisely quantify the two-dimensional emotional characteristics of Valence and Arousal. Concurrently, by incorporating the multi-scale spatial modeling strengths of MGWR, it thoroughly investigates the spatial patterns and driving mechanisms of public emotions within the winter context of typical cold-region cities. The results indicate that, first, extreme climates do not lead to urban emotional suppression; instead, frozen rivers transform into vibrant emotional corridors, with the public demonstrating a high degree of thermal-psychological adaptability. Second, by incorporating winter-specific environmental variables, the research reveals a cold-region paradox of emotional valence. Specifically, under snow cover, lower winter Land Surface Temperature (LST) and winter Normalized Difference Vegetation Index (NDVI) paradoxically evoke positive emotions by reconstructing the aesthetic experience of ice-snow landscapes. Furthermore, the impact of urban service facilities on emotional arousal exhibits a significant pattern of diminishing marginal utility. Overall, the LLMs-MGWR framework achieves a closed loop of high-throughput, multi-dimensional semantic decoding and multi-scale spatial interpretation, demonstrating exceptional cross-regional generalizability. Ultimately, this study not only provides a novel paradigm for understanding human–environment interactions in complex environments but also offers transferable planning guidelines for microclimate design, facility decentralization, and the reshaping of winter blue-green infrastructure in global cold-region cities. Full article

High-speed imaging offers a non-intrusive approach for monitoring falling-film flows over horizontal tube bundles, but reflective images are difficult to quantify because grayscale variations are jointly affected by film geometry, interfacial curvature, surface slope, viewing angle, and local highlights. This study proposes an interpretable visual-proxy sensing framework for comparative state assessment of such flows. Isothermal water experiments were conducted on a five-row horizontal tube bundle over Re_Γ = 184 − 960. For each condition, grayscale frames were acquired at fps and analyzed within five fixed row-wise regions of interest. The image sequence was transformed by temporal-median background subtraction, local spatiotemporal mapping, moving-average detrending, and median-absolute-deviation normalization. The resulting normalized map M_n and dynamic renewal field G were used to extract four scalar descriptors: noise-corrected apparent renewal intensity I_R, high-frequency fraction R_HF, spectral peak frequency f_p, and burst-event rate F_B. Results show that M_n and G capture the transition from sparse column flow to more continuous sheet flow and reveal row-dependent activity organization. The descriptors provide complementary information on renewal intensity, frequency composition, dominant time scale, and intermittent events. Zero-response, noise-correction, and sensitivity tests confirm that the framework avoids structured pseudo-waves and maintains stable row-wise comparisons. The method provides a low-calibration visual sensing tool for relative falling-film state assessment. Full article

Grid-connected photovoltaic–battery energy storage–electric vehicle (PV-BESS-EV) charging stations require supervisory energy management that can coordinate tariff response, carbon-intensity signals, peak constraints, storage utilization, and converter-level operability within a transparent evidential framework. This study develops a bounded-reference rule-based supervisory energy management system (RB-SEMS) that preserves lower-level local converter controllers while generating operating modes and saturated reference commands for BESS power, grid exchange, and EV charging limits. A dual-time-scale evaluation framework is established by combining short-time switching/control simulations for dynamic traceability and SOC-sensitive protection with 24 h, 15 min EMS-level energy-balance simulations for cost, carbon, peak, PV utilization, EV service, and storage throughput assessment. Selected daily reference-injection cases are retained as copied-model diagnostic checks rather than as full-day switching-level validation. Under the D4-LSOC condition, RB-SEMS reduces the reported post-startup DC-bus deviation from 46.13 V to 40.60 V and the filtered BESS peak from 269.18 kW to 84.42 kW. In the E1-TOU scenario, E1-TOU-cost reduces daily total cost from 623.57 CNY to 564.05 CNY, lowers peak-period grid import from 183.75 kWh to 126.75 kWh, and increases local PV utilization from 71.13% to 78.71%; E1-PC66 further reduces the maximum 15 min grid import from 77.88 kW to 66.00 kW. Under the prescribed E2-PCC scenario, E2-CP reduces the calculated grid-related CO₂ emissions from 550.29 kg to 500.42 kg, whereas the price-only diagnostic increases them to 572.29 kg. Same-metric PV-SC and MILP comparisons, tested-range sensitivity analysis, and a throughput-based degradation proxy clarify that RB-SEMS is an interpretable supervisory baseline for cost–carbon–peak–cycling trade-off analysis rather than a cost-optimal controller or regionally validated proof of carbon reduction. Full article