Search Results (2,351)

Understanding soil moisture’s spatiotemporal variations and the factors influencing it is crucial for the restoration and growth of vegetation across the Loess Plateau, particularly in the Ziwuling region. This study employs soil moisture remote sensing data, complemented by information on soil properties, environmental conditions, and topography, to examine soil moisture variability within the Ziwuling region between 2001 and 2020. Using trend analysis, geographic detectors, and multi-scale geographic weighting techniques, this research aims to elucidate the effects of driving factors on soil moisture’s spatiotemporal patterns. The findings indicate the following: (1) Over the study period, the mean soil moisture in the Ziwuling region exhibited a relatively stable declining trend, with an annual decrease of −0.00047 m³/(m³·a). Spatially, higher soil moisture levels were observed in the south-central area, while lower levels occurred in the northern, western, and eastern peripheries. (2) Geoprobe analysis illustrated that the normalized difference vegetation index (NDVI) had the most notable effect on the spatial distribution of soil moisture in the region. As a direct indicator of vegetation cover, NDVI strongly affects soil moisture distribution through ecological and hydrological processes. Following NDVI, average annual potential evapotranspiration and annual precipitation were identified as the next most influential factors. The combined effect of these factors on soil moisture surpassed that of individual factors, with the interaction between NDVI and annual precipitation being particularly pronounced, predominantly controlling the spatial variability of soil moisture in the Ziwuling region. (3) Different factors exhibited varying effects on soil moisture levels. Notably, slope and elevation consistently had negative impacts, whereas variables such as soil texture (loam and sand), land use, temperature, precipitation, NDVI, and slope aspect showed bidirectional influences. This study offers a comprehensive analysis of the spatiotemporal variability of soil moisture and its controlling factors in the Ziwuling region, ultimately offering a scientific basis to support ecological restoration and sustainable development initiatives on the Loess Plateau. Full article

Global warming is attributed to anthropogenic emissions of CO₂ and the contribution from the transport sector is significant. Estimating on-road vehicle CO₂ emission factors is essential for guiding carbon-reduction efforts in transportation. In order to accurately calculate carbon emission factors from vehicles, this study built a multi-scenario model for open, semi-enclosed, and enclosed road environments based on Fick’s second law and the law of conservation of mass. During the model optimization phase, it was found that the model’s applicability domain effectively encompassed most urban roadway scenarios, making it suitable for estimating urban traffic CO₂ emissions. The spatiotemporal heterogeneity analysis of field measurements indicated that this method can effectively distinguish variations in CO₂ emission factors across different road types and time periods. The method proposed in this study offers an effective solution for the real-time monitoring of large-scale on-road vehicle carbon emissions. Full article

Against the backdrop of urban–rural integrated development, special ecological function zones, as spatial carriers with significant regional ecological value and rural development functions, are confronted with a striking conflict between ecological conservation and regional advancement. This contradiction is comprehensively reflected in the interactions among land use functions (LUFs) that differ in nature and intensity. Therefore, exploring the trade-off and synergy (TOS) among regional LUFs is not only of great significance for optimizing territorial spatial patterns and advancing rural revitalization but also provides scientific evidence for the differentiated administration of regional land use. Taking 185 townships in the Funiu Mountain area of China as research units, this study constructs a land use assessment system based on the ‘Production–Living–Ecological’ (PLE) framework, utilizing multi-source datasets from 2000 to 2020. Spearman correlation analysis, geographically weighted regression (GWR), and bivariate local spatial autocorrelation methods are employed to examine the spatio-temporal dynamics of LUFs and the spatial non-stationarity of their TOSs. The findings indicate that, throughout the research period, the production function (PF) displayed a fluctuating declining trend, whereas the living function (LF) and ecological function (EF) demonstrated a fluctuating increasing trend. Notably, EF held an absolute dominant position in the overall structure of LUFs. This is highly consistent with the region’s positioning as a special ecological function zone and also a direct reflection of the effectiveness of continuous ecological construction over the past two decades. Spatially, PF is stronger in southern, eastern, and northern low-altitude townships, correlating with higher levels of economic development; LF is concentrated around townships near county centers; and high EF values are clustered in the central and western areas, showing an opposite spatial pattern to PF and LF. A synergistic relationship is observed between PF and LF, while both PF and LF exhibit trade-offs with EF. The TOSs between different function changes demonstrate significant spatial non-stationarity: linear synergy was the primary type for PF-LF, PF-EF, and LF-EF combinations, but each combination exhibited unique spatial characteristics in terms of non-stationarity. Notably, towns identified as having different types of trade-off relationships in the study of spatial non-stationarity are key areas for township spatial governance and optimization. Through the allocation of regional resources and targeted policy tools, the functional relationships can be adjusted and optimized to attain sustainable land use. Full article

The natural forest logging ban policy has substantially influenced rural residents’ production activities, daily lives, and income levels. Drawing on panel data from 30 provinces in China, this study examines both the overall effect of the policy on rural households’ income and the internal transmission mechanisms. The policy is regarded as an external shock, and its impact is identified through a multi-period difference-in-differences model combined with a mediation analysis. The results show three main findings: (1) the policy significantly raised rural households’ total income; the structural analysis indicates that its effects are notably positive on wage income and property income; in contrast, the impacts on operating income and transfer income are not statistically significant; (2) mechanism testing found that the policy significantly improved non-agricultural employment and increased ecological protection investment, indicating that the non-agricultural employment and ecological protection investment are important channels for the national natural forest logging ban policy to increase rural residents’ income; (3) heterogeneity analysis shows that the policy effect is more pronounced in areas with a higher distribution of state-owned forest areas, along with the policy effects being more pronounced in non-carbon trading market pilot areas. Therefore, this article proposes policy recommendations for continuously improving the natural forest protection policy system, ensuring effective employment of rural labor, and building coordinated development of forestry systems between regions. Full article

Hydrogen peroxide (H₂O₂) occurs in the environment, including in aquatic environments where mosquitoes might lay eggs. However, little is known about the compound’s impact on mosquitoes. We conducted an experiment to determine the effect of H₂O₂ on Ae. aegypti oviposition behavior and egg hatching using H₂O₂ concentrations similar to those in natural aquatic environments. Oviposition behavior was evaluated by dual-choice and multi-choice bioassays. Gravid Ae. aegypti mosquitoes were placed in cages with containers with different H₂O₂ concentrations (5, 25, 50, and 100 μM). After 72 h, the number of eggs laid was compared between oviposition sites with and without H₂O₂. Additionally, egg hatching was assessed under long-period exposure (48 h) and short-period exposure (2, 4, and 6 h and then in deionized water for up to 48 h). Results showed no significant difference in oviposition preference scores in the multi-choice assay (OAI = −0.135 ± 0.06) (p = 0.138), but a significant difference in the dual-choice assay (0.195 ± 0.01) (p = 0.001). Long-period exposure to H₂O₂ did not significantly affect hatch rates (11.34%) (p = 0.363), but short-period exposure significantly impacted hatch rates (17%) (p = 0.0001), with period of exposure alone playing a significant role (p < 0.0044). Eggs exposed to 100 μM H₂O₂ for 2 h (p = 0.0070) and 4 h (p = 0.0036) had significantly higher hatch rates compared to the control. This study demonstrates that low concentrations of H₂O₂ can influence oviposition site characteristics and egg hatch rates. Combined with other environmental factors, H₂O₂ can shape the reproductive success of Ae. aegypti, offering potential strategies for mosquito control. Full article

This study evaluated the performance of a recirculating aquaculture system (RAS) integrated with macroalgae (Ulva ohnoi) cultivation and sea urchin (Paracentrotus lividus) feeding, in a multi-trophic aquaculture approach. This system aimed to enhance sustainability through water bioremediation by macroalgae and valorization of the algal biomass as echinoderms feed. Over a 180-day trial, biomass production of U. ohnoi remained stable, with daily growth rates ranging from 7.4 to 24.4%. Statistical analyses (PCA and GAM) indicated no significant linear or non-linear relationship between macroalgae growth and environmental parameters (temperature, radiation, photoperiod). A theoretical estimate of nutrient production showed fairly stable values that do not statistically explain biomass production variation, highlighting the species’ adaptability. Sea urchins fed with fresh U. ohnoi showed regular growth, supporting the nutritional suitability of this macroalgae. For fish (Sparus aurata), no significant differences in growth or feed conversion ratio were observed between systems with and without algae. Parasitological monitoring revealed lower parasite loads and egg deposition in tanks in recirculation with U. ohnoi during certain periods, suggesting a potential role of macroalgae in reducing monogenean propagation. These findings underscore the feasibility of integrating Ulva cultivation into RAS, contributing to circular aquaculture models with improved sustainability and resource efficiency. Full article

Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

With the increasing attention to China’s ecological environment protection and the prominence of lake water environment problems, the impact of soil erosion on lake ecosystems has become an important research topic for regional sustainable development. Based on the CiteSpace bibliometric method, this study systematically analyzed 225 research articles on the impact of soil erosion on the water environment of lakes in China in the core collection of Web of Science from 1998 to 2025, aiming to reveal the research hotspots, evolution trends and regional differences in this field. The results show that China occupies a dominant position in this field (209 papers), and the Chinese Academy of Sciences is the core research institution (93 papers). The research hotspots show obvious policy-driven characteristics, which are divided into slow start periods (1998–2007), accelerated growth periods (2008–2015), explosive growth periods (2016–2020) and stable development periods (2021–2025). A keyword cluster analysis identified nine main research directions, including sedimentation effect (#0 cluster), soil loss (#2 cluster) and nitrogen and phosphorus migration (#11 cluster) in the Three Gorges Reservoir area. The study found that the synergistic effects of climate change and human activities (such as land use change) are becoming a new research paradigm, and the Yangtze River Basin, the Loess Plateau and the Yunnan–Guizhou Plateau constitute the three core research areas (accounting for 72.3% of the total literature). Future research should focus on a multi-scale coupling mechanism, a climate resilience assessment and an ecological engineering effectiveness verification to support the precise implementation of lake protection policies in China. This study provides a scientific basis for the comprehensive management of the soil erosion–lake water environment system, and also contributes a Chinese perspective to the sustainable development goals (SDG6 and SDG15) of similar regions in the world. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article

Sea–rail intermodal transport offers high efficiency and environmental benefits, yet its development in China remains limited. Existing studies have mainly assessed the macro-level benefits of sea–rail intermodal transport policies, but rigorous evidence on whether incentive policies work and how their effects differ across policy types remains scarce, which limits evidence-based policy design and efficient allocation between subsidies and capacity expansion. To address this gap, a dual-policy identification framework was established that combines a multi-period difference-in-differences model with event study analysis and used station–month data from China to assess the independent effects, underlying mechanisms, and spatiotemporal heterogeneity of railway freight price subsidies and freight train expansion on container throughput. The results indicate that both policies significantly increased container throughput. Railway freight price subsidies exhibited stronger and more persistent effects with a certain lag, whereas freight train expansion produced rapid but short-lived responses. The impacts of both policies were more pronounced in short-distance transport, but weakened or even turned negative over longer distances. Moreover, the number of participating entities served as a key mediating pathway, while information sharing positively moderates policy impacts. This study makes theoretical contributions to the identification of heterogeneity, mechanism analysis, and spatiotemporal characterization of SRIT incentive policy effects, while offering refined and actionable guidance for SRIT policy optimization. Full article

Promoting sustainable and balanced economic growth remains a key challenge for developing countries. This study empirically investigates the impact of China’s Pilot Free Trade Zone (PFTZ) on regional economic growth from 2010 to 2023, offering important insights into how targeted policy instruments can contribute to sustainable economic growth. Employing a multiperiod difference-in-differences model and a capital–technology–marketization framework, this study finds that PFTZ implementation has a significant and direct influence on promoting provincial economic growth. The growth effects are primarily driven by improved capital flows and enhanced technological innovation. Notably, these positive effects are more pronounced in central and western Chinese provinces and regions with lagging economic development, indicating that PFTZs can serve as effective tools for reducing regional disparities. These findings provide new empirical evidence regarding the regional heterogeneity of PFTZ policy impacts and offer valuable insights into the design, timing, and spatial targeting of PFTZ initiatives in developing countries seeking to support inclusive and sustainable development across the country. Full article

This study presents a new multi-index hierarchical model for flood risk assessment which incorporates three indicator indexes—hazard, vulnerability, and exposure—to develop a five-level risk scale. The methodology is applied to historical data on flood events in The Slovak Republic between 2001 and 2010. The input values are characterized in more detail through the use of weighted values to provide a more balanced overall risk assessment. The original formula used to calculate the risk levels was found to produce results with overly high numerical values, and therefore the multiplication step of the formula was replaced by addition to insure greater simplicity and ease of use. This refined methodology introduces a novel quantitative approach to risk assessment, offering flexibility and variability in the indicator layer. The methodology can be adapted to assess risk at either the macro or micro scale and at more specific periods of time. The resulting risk values offer a nuanced understanding of risk levels across different indexes and underscores the method’s innovation. Full article

Cryo-electron microscopy single-particle analysis (cryo-EM SPA) has advanced three-dimensional protein structure determination, yet resolving intrinsically disordered proteins and regions (IDPs/IDRs) remains challenging due to conformational heterogeneity. This research evaluates cryo-EM’s capacity to map dynamic regions, assesses the adaptability of disorder prediction tools, and explores optimization strategies for dynamic structure prediction. Cryo-EM SPA datasets from 2000 to 2024 were categorized into different periods, forming a database integrating sequence data and disorder indices. Established prediction tools—AlphaFold2 (pLDDT), flDPnn, and IUPred—were evaluated for transferability, while a multi-level CLTC hybrid model (combining CNN, LSTM, Transformer, and CRF architectures) was developed to link local conformational fluctuations with global sequence contexts. Analyses revealed consistent advancements in average resolution and model counts over the past decade, although mapping disordered regions remained technically demanding. Both the adapted AlphaFold pLDDT and the CLTC model demonstrated efficacy in predicting structurally variable and poorly resolved regions. A subset of the cryo-EM missing residues exhibited intermediate conformational features, suggesting classification ambiguities potentially influenced by experimental conditions. These findings systematically outline the evolving capabilities of cryo-EM in resolving dynamic regions, benchmark the adaptability of computational tools, and introduce a hybrid model to enhance prediction accuracy. This study provides a framework for addressing conformational heterogeneity, contributing to methodological advancements in structural biology. Full article