Search Results (18,576)

Improving the cultivated land use eco-efficiency (CLUE) is crucial to achieving sustainable land use and the green transformation of agriculture. This study is based on the data from 353 prefecture-level cities in China from 2013 to 2021. The slacks-based measurement (SBM)-undesirable model, the social network analysis (SNA), and the fuzzy set qualitative comparative analysis (fsQCA) are adopted to measure and analyze the spatial patterns, network characteristics, and multiple driving pathways of inefficiency in the cultivated land use eco-efficiency in prefecture-level administrative units. Results show the following: (1) From 2013 to 2021, CLUE in the study areas shows spatial heterogeneity, with most efficiency values at a moderate level and showing a fluctuating downward trend over time. (2) The nine major agricultural regions have formed a complex association network, with the overall network connectivity being weak but efficiency relatively high. The hierarchical structure is gradually flattening, and inter-regional cooperation is increasing. (3) There are significant differences in influence, control, and accessibility within individual networks, and the collaborative network is developing into a “multi-core-hierarchical” structure. (4) The formation of inefficiency involves multiple concurrent mechanisms. Four typical inefficiency paths were identified, with significant heterogeneity across different agricultural regions. In the future, differentiated land use and ecological protection policies should be implemented based on the spatial network characteristics and inefficiency driving pathways of each agricultural region to promote the coordinated improvement of CLUE. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

We review the structure of pseudoscalar mesons within an algebraic model formulated in the light-front framework. The approach provides a unified description of leading-twist parton distribution amplitudes, light-front wave functions, generalized parton distributions, parton distribution functions, elastic electromagnetic form factors, charge radii, and impact-parameter space distributions, all obtained from the same underlying Bethe–Salpeter wave-function representation. The analysis covers light mesons $(\pi ,K)$, the mixed $\eta$–${\eta }^{\prime }$ system, heavy–light states $(D,D_s,B,B_s,B_c)$, and heavy quarkonia $({\eta }_c,{\eta }_b)$, thereby enabling a systematic study of quark-mass effects, flavor-symmetry breaking, and the transition from emergent hadronic mass to heavy-quark dynamics. Where available, results are compared with experimental measurements, functional methods such as lattice-QCD calculations and Dyson–Schwinger Equation formalism, and other phenomenological approaches. The algebraic model thus offers a transparent, symmetry-preserving, and analytically tractable framework for connecting the longitudinal, transverse-momentum, and spatial structure of pseudoscalar mesons across all quark-mass regimes. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

Podophyllum hexandrum Royle (1834) (Himalayan Mayapple), a key Himalayan medicinal plant and source of podophyllotoxin for anticancer drugs, is declining due to overharvesting, habitat loss, and climate change. This study, conducted from May to September 2024 across nine populations in Swat, Pakistan, assessed its ethnobotanical importance and conservation status. A total of 331 participants (270 individual surveys + 61 group discussions) were included. Using ethnobotanical surveys, IUCN-CMP threat frameworks, and spatial analysis, results showed high cultural value (Use Value = 0.63–0.92) and strong consensus for rheumatism (ICF = 0.91) and fever (ICF = 0.89). Fidelity levels were 94% for rheumatism and 88% for fever. Only 35% of respondents demonstrated conservation awareness. Overharvesting was the main threat, followed by habitat degradation and climate change. The species showed restricted distribution (EOO = 4250 km²; AOO = 295 km²), high fragmentation (0.68), and a 35% population decline over 10 years. It is assessed as Endangered (EN B1ab (iii, v)). This study provides the first integrated ethnobotanical–GIS assessment of P. hexandrum in the Hindu Kush–Himalaya region of Pakistan, offering measurable conservation baselines and community perception data previously unavailable. Findings align with global medicinal plant decline trends and support integration with CBD, SDGs (3 and 15), and potential CITES listing. Urgent conservation actions are required, including community-based management, habitat restoration, sustainable harvesting, ex situ conservation, and policy enforcement. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. In this study, AER is operationalized as a composite index capturing resistance and recovery capacities across pressure, state, and response dimensions. Using 2012–2023 provincial panel data from China (31 provinces × 12 years = 372 observations), we measure AER via the entropy method and identify policy effects using a staggered multi-timepoint difference-in-differences (DID) model. We find that policy-based staple crop insurance significantly increases AER by approximately 2.5 percentage points, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major staple-crop producing areas, and regions with higher natural risks. Robustness is confirmed via event study, alternative weighting schemes (PCA and equal weighting), and placebo tests. This study provides reliable causal evidence for the resilience-enhancing effect of agricultural insurance and clarifies its internal transmission mechanisms, offering empirical support for the optimization of agricultural risk governance policies. Limitations include the use of provincial-level aggregate data and the lack of analysis of spatial spillover effects between regions. Our findings suggest that differentiated policy implementation can support more sustainable and targeted agricultural risk governance. Full article

The Amazon coastal zone exhibits remarkable habitat diversity and species richness, with nutrient-rich estuaries playing a crucial role in local food webs and supporting fish and other aquatic organisms. To examine the distribution of fish larvae and juveniles in the Taperaçu Estuary and their relationship with environmental variables, monthly sampling was conducted at two fixed stations in 2008. Samples were collected during flood and ebb spring tides using 500 μm mesh nets. In situ measurements of salinity, temperature, and dissolved oxygen were recorded, while pH and turbidity were determined in the laboratory. Abiotic variables did not differ significantly between tides, but salinity and dissolved oxygen were higher during the dry season. A total of 5175 individuals were identified, representing 17 families and 37 species. The ichthyoplankton community was dominated by Rhinosardinia amazonica, Anchovia clupeoides, Stellifer stellifer, and Microgobius meeki. Stations 1 and 2 showed differing abundance ranges, with higher values at station 1 during the rainy season. Preflexion stages were abundant at both stations, indicating the estuary’s importance as a nursery and development area for several fish species. Multivariate analyses revealed spatial and seasonal structuring of larval assemblages along the estuarine gradient, driven primarily by salinity, temperature, and turbidity. Our results emphasize the role of upper estuary sectors of eastern Amazonia as areas of spawning, larval development, and subsequent juvenile settlement, contributing to the dispersal of fish species throughout the estuary and adjacent coastal environments. The present findings also reinforce the ecological value of the studied Extractive Reserve and other protected areas along the Amazon littoral as essential habitats for larval refuge and development. The need for continued monitoring and preservation of these protected zones is evident. Full article

This study investigated the spatial distribution, seasonal variation, and drivers of surface seawater alkalinity (Alk) in Zhanjiang Bay (ZJB) using high-frequency seasonal sampling in the summers and winters of 2023. Surface Alk ranged from 525.3 to 2213.3 μmol·L⁻¹, with mean values of 1373.1 ± 420.9 μmol·L⁻¹ (summer, n = 28) and 1612.3 ± 343.7 μmol·L⁻¹ (winter, n = 20). Spatially, Alk increased progressively from the estuary to the inner bay and further to the bay mouth, reflecting a typical dilution gradient. Correlation analyses showed that summer Alk was positively correlated with salinity (ρ = 0.706, p < 0.001), indicating that salinity changes associated with conservative mixing were a dominant control, whereas the weaker winter correlation (ρ = 0.473, p < 0.001) suggested that biological processes may play a more important role. Tidal forcing was significantly associated with diurnal Alk variations, particularly in the estuary and inner bay. In the estuary, high Alk occurred during high tide, consistent with tidal mixing; in the inner bay, elevated Alk was observed during low tide, suggesting a possible tidal pumping effect. These findings provide baseline data on Alk dynamics in a subtropical estuarine bay and contribute to understanding the carbonate system and buffering capacity in similar coastal systems. However, because measurements of dissolved inorganic carbon and pCO₂ were unavailable, a quantitative assessment of carbon sink capacity requires further investigation. Full article

Lumpy skin disease (LSD), a vector-borne viral disease of cattle, re-emerged in Italy in June 2025 after six years of absence in Europe, affecting the island of Sardinia, which had previously been disease-free. The insular setting, the predominance of extensive cattle farming systems, and the rapid implementation of control measures provided a unique opportunity to investigate epidemic dynamics and evaluate vaccination effectiveness under field conditions. This study aimed to describe the epidemiological pattern of the first epidemic season (June–October 2025), estimate key transmission parameters, and assess vaccination effectiveness at the farm level. Confirmed outbreaks consistent with local transmission and notified between 20 June and 26 October 2025 were analyzed to characterize epidemic transmission dynamics, while vaccination effectiveness was assessed over an extended follow-up period through 31 December 2025. The between-farm basic reproduction number (R₀) was estimated from the early exponential growth phase using log-linear regression and doubling time calculations. Spatio-temporal clustering was assessed using Kulldorff’s scan statistic under a Poisson model, accounting for the population at risk. Vaccination effectiveness was evaluated using a time-dependent Cox proportional hazards model with a 21-day post-vaccination lag. A total of 79 outbreaks were confirmed, of which 68 were consistent with local transmission. Affected farms included a total of 3443 cattle, with morbidity, mortality, and case fatality rates of 14.4%, 7.0%, and 31.1%, respectively. The exponential growth phase lasted four weeks, with an estimated growth rate of 0.366 per week and a doubling time of 1.89 weeks. The estimated R₀ ranged from 1.55 to 1.92, depending on the assumed generation time, indicating moderate but sustained transmission. The median apparent spatial spread velocity was 4.8 km/day. Spatio-temporal analysis identified a single highly significant cluster in the central-eastern area, accounting for approximately 27% of outbreaks (RR = 58.06; p < 0.001). Vaccination was associated with a substantial reduction in outbreak risk (HR = 0.18; 95% CI: 0.06–0.51; p = 0.001), corresponding to an estimated effectiveness of approximately 82% at the farm level. The 2025 Sardinian epidemic was characterized by moderate transmissibility and strong spatial clustering during the early phase. Rapid implementation of vaccination was associated with a significant reduction in outbreak risk, even under conditions of high infection pressure. The integration of spatio-temporal analyses and time-dependent modeling proved essential to support evidence-based control strategies in newly affected regions. Full article

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

Radio occultation (RO) has become a key technique for monitoring the ionosphere by deriving electron density (Ne) profiles and total electron content (TEC) from GNSS signals. This study assesses the newly deployed PlanetiQ GNOMES constellation by validating its ionospheric Ne profiles and profile-based TEC against collocated measurements from ionosondes and the COSMIC-2 mission under both quiet and disturbed geomagnetic conditions. Data matching for the statistical validation uses conservative spatial thresholds of less than 1° in latitude and longitude and temporal limits of 30 min for ionosondes and 1 h for COSMIC-2, supported by a dedicated sensitivity analysis, whereas storm-time case studies apply tighter temporal collocation and explicit control of the ray path geometry. Quantitative agreement is evaluated using root mean square error (RMSE), mean and absolute mean differences, correlation coefficients, regression analysis, and normalized percentage differences for key F-layer parameters, including the maximum Ne of the F2 layer (NmF2), the peak height of the F2 layer (hmF2), and the critical frequency of the F2 layer (foF2), along with altitude-dependent Ne profiles. PlanetiQ shows strong consistency with ionosonde profiles, with RMSE ranging from 2.94 × 10⁴ to 2.76 × 10⁵ el/cm³, correlations typically exceeding 0.90, and normalized absolute mean differences often near or below about 10–20%, although lower correlations of about 0.31 and 0.69 are found at Poker Flat and Awase, respectively, reflecting complex local structures and regional variability. Comparisons with COSMIC-2 during quiet conditions yield RMSE values between 7.06 × 10⁴ and 2.16 × 10⁵ el/cm³, correlations from 0.94 to 0.99, and percentage differences that generally remain within a few tens of percent, while storm-time analyses show RMSE between 1.12 × 10⁵ and 3.70 × 10⁵ el/cm³ with correlations from 0.80 to 0.99, confirming robust agreement across a wide range of geophysical conditions. Regression results demonstrate slopes near 1.00 and correlation coefficients above 0.90 for NmF2 and foF2 between PlanetiQ and both ionosondes and COSMIC-2, whereas hmF2 exhibits larger scatter, particularly during geomagnetic disturbances; additional binning by spatial and temporal separation indicates that temporal mismatches generally have a stronger impact on discrepancies than horizontal distance. Overall, the results demonstrate that PlanetiQ ionospheric RO data provide accurate and consistent measurements of key ionospheric parameters, comparable to those from COSMIC-2 and ionosondes, and can reliably complement existing observing systems for monitoring ionospheric variability and space-weather impacts. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article