Search Results (9,424)

The urban built-up land green use efficiency (UBLGUE) of the Central Plains Urban Agglomeration (CPUA) is greatly affected by the Central China Rise policy. However, studies on how socioeconomic factors affect UBLGUE in underdeveloped urban agglomerations are lacking, and little empirical research has placed particular emphasis on the Central China Rise policy. Based on the statistical data of 2003–2020, this study explores the dynamic spatial–temporal characteristics and determines the influencing mechanism of UBLGUE in the CPUA via the super-SBM–DEA method and panel regression model. The empirical results indicate the following: The average UBLGUE in the prefecture cities of the CPUA presents a significant fluctuating trend from 2003 to 2020. The UBLGUE of the CPUA is characterized by spatial imbalance. Over the period of Central China Rise, the main factors influencing the spatial–temporal differentiation of the UBLGUE in the CPUA are the economic development, industrial structure, environmental regulation intensity, and energy consumption intensity. UBLGUE has strong economic attributes and is positively promoted by economic development. In contrast, the industrial structure, environmental regulation intensity, and energy consumption intensity significantly hinder the UBLGUE. Energy consumption intensity has the strongest negative effect on UBLGUE. Finally, corresponding policy recommendations are proposed to promote UBLGUE based on the conclusions obtained. Full article

Total nitrogen (TN) and total phosphorus (TP) are essential indicators for assessing water quality. This study systematically analyzes the spatial and temporal distribution of TN and TP in China’s surface waters and examines the influence of natural factors and human activities on their concentrations. Utilizing data from 1387 monitoring sites (2020–2021) and employing K-means clustering and geographically weighted regression (GWR), we found that the national average concentrations were 3.89 mg/L for TN and 0.096 mg/L for TP. Spatially, higher TN and TP levels were observed in northern regions, coastal areas, and plains compared with southern, inland, and mountainous areas. Notably, TN concentrations reached up to 29.49 mg/L in the Haihe River basin and related plains, while TP peaked at 0.497 mg/L in the southeastern Shandong and northern Jiangsu coastal zones. Temporally, TN levels were approximately 50% higher in winter than summer, whereas TP levels were about 40% higher in summer. Key influencing factors included rainfall, elevation, fertilizer use, and population density, with spatial heterogeneity observed. Rainfall was the primary factor for TN change and the secondary factor for TP change. Soil type positively correlates with TN and TP changes, affecting non-point source pollution. Human activities such as land use, fertilizer application and population density had a significant effect on the nitrogen and phosphorus concentrations, while woodland had a significant impact on the improvement of water quality. The geographically weighted regression analysis showed spatial heterogeneity in the effects of each factor on TN and TP concentrations, and the best fit was at the watershed scale. The findings highlight the need for enhanced control of agricultural runoff, improved sewage treatment, and region-specific management strategies to inform effective water environment policies in China. Full article

In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning. Full article

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security. Full article

The accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these biases by employing visible reflectance data from the Himawari-9/AHI satellite and RTTOV (TOVS radiation transfer) simulations derived from CMA-MESO model outputs. The time-shift method was applied to analyze two precipitation events—20 October 2023 and 30 April 2024—in order to assess its impact on precipitation forecasts. The results indicate the following: (1) the time-shift method improved cloud simulations, necessitating a 30 min advance for Case 1 and a 3.5 h delay for Case 2; (2) time-shifting reduced the standard deviation of observation-minus-background (OMB) bias in certain regions and enhanced spatial uniformity; (3) the threat score (TS) demonstrated an improvement in forecast accuracy, particularly in cases exhibiting significant movement patterns. The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts. Full article

To assess the carbon balance of terrestrial ecosystems, it is crucial to consider the net primary productivity (NPP) of vegetation. Understanding the response of NPP in Xinjiang’s vegetation to climate factors and human activities is essential for ecosystem management, the Belt and Road Initiative, and achieving carbon neutrality goals. Based on the CASA model, this study uses meteorological data, DEM data, and land cover data, employing trend analysis and partial derivative analysis methods to investigate the temporal trends and spatial distribution of NPP in Xinjiang from 2000 to 2020. Additionally, it quantifies the contributions of climate factors and human activities to NPP fluctuations. The key findings are: (1) The average annual NPP is 101.52 gC/m², with an upward trend, showing an overall growth rate of 0.447 gC/m²/yr. Spatially, NPP is higher in northern Xinjiang than in the south, and in mountainous areas compared to basins. (2) Over 21 years, climate factors contributed an average of 1.054 gC/m²/yr, while human activities contributed 0.239 gC/m²/yr to NPP changes. Among climate factors, temperature, precipitation, and sunshine duration contributed 0.003, 0.169, and 0.588 gC/m²/yr, respectively, all showing positive effects on NPP. (3) Forests have the highest average NPP at 443.96 gC/m², with an annual growth rate of 2.69 gC/m²/yr. When forest is converted to cropland, the net loss in NPP is −1.94 gC/m², and the loss is even greater in conversion to grassland, reaching −17.33 gC/m². (4) The changes in NPP are driven by both climate factors and human activities. NPP increased in 77.25% of the area, while it decreased in 22.69%. Climate factors have a greater positive impact than human activities. Full article

In this paper, we present a low-cost, low-power millimeter-wave (mmWave) skeletal joint localization system. High-quality point cloud data are generated using the self-developed BHYY_MMW6044 59–64 GHz mmWave radar device. A sliding window mechanism is introduced to extend the single-frame point cloud into multi-frame time-series data, enabling the full utilization of temporal information. This is combined with convolutional neural networks (CNNs) for spatial feature extraction and a bidirectional gated recurrent unit (BiGRU) for temporal modeling. The proposed spatio-temporal information fusion framework for multi-frame point cloud data fully exploits spatio-temporal features, effectively alleviates the sparsity issue of radar point clouds, and significantly enhances the accuracy and robustness of pose estimation. Experimental results demonstrate that the proposed system accurately detects 25 skeletal joints, particularly improving the positioning accuracy of fine joints, such as the wrist, thumb, and fingertip, highlighting its potential for widespread application in human–computer interaction, intelligent monitoring, and motion analysis. Full article

Reducing carbon emissions in crop production not only aligns with the goal of high-quality agricultural development but also contributes to achieving the “dual carbon goals”. Based on panel data from 31 provinces in China between 2010 and 2019, this paper explores the impact of Agricultural Socialized Services on carbon emissions in China’s crop production. Utilizing the classical IPCC carbon emission calculation model and spatial econometrics models, this study analyzes the temporal and spatial distribution characteristics of crop production carbon emissions and their driving factors, with a particular focus on evaluating the role of Agricultural Socialized Services in reducing carbon emissions in crop production. The empirical results reveal a “reverse U-shaped” curve for carbon emissions in crop production from 2010 to 2019, with a peak in 2015. Agricultural Socialized Services significantly reduced carbon emissions in crop production, especially in terms of emissions reductions from fertilizer and pesticide use, although the impact on other carbon sources such as plastic mulch, diesel, and tillage was relatively limited. Furthermore, Agricultural Socialized Services exhibited significant spatial spillover effects, effectively reducing local carbon emissions and generating positive carbon reduction effects in neighboring regions through cross-regional services. Based on these findings, the paper suggests improving the Agricultural Socialized Services system according to regional conditions to fully leverage its positive role in reducing carbon emissions in crop production. It also advocates accelerating the innovation of low-carbon agricultural technologies, encouraging farmers’ participation, and utilizing the organizational advantages of village collectives to jointly promote the development of Agricultural Socialized Services and achieve carbon reduction goals. Full article

This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN), which incorporates the temporal change in transaction prices along with spatial proximity to identify spatial areas with similar transaction prices. It represents an advancement over MDST-DBSCAN for this use case, as it considers the change over time as valuable information rather than a constraint that further splits the clustering groups. The results of the case study in Vienna indicate variations in price growth rates among the submarkets (i.e., contiguous regions with similar prices and price growth rates) that confirm the importance of considering the temporal changes in transaction prices. With respect to the Viennese case study, a lower Moran’s I value was observed for 2022 compared to previous years (2018 to 2021), indicating a higher level of homogeneity in transaction prices. This finding was also supported by the cluster analysis, as less expensive clusters demonstrated higher rates of price increase compared to more expensive clusters. Future research can enhance the algorithm’s usability and broaden its potential use cases to other multidimensional spatiotemporal event data. Full article

This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers in hedging and marketing decisions, particularly in the Texas Gulf region. The models evaluated included ARIMA, basic feedforward neural networks, basic LSTM, bidirectional LSTM, stacked LSTM, CNN LSTM, and 2D convolutional LSTM models. The forecasts were generated for five-, ten-, and fifteen-day periods using historical data spanning 2009 to 2023. The results demonstrated that advanced LSTM architectures outperformed other models across all forecast horizons, particularly during periods of significant price volatility, due to their enhanced ability to capture complex temporal and spatial dependencies. The findings suggest that incorporating advanced LSTM architectures can significantly improve forecasting accuracy, providing a robust tool for producers and market analysts to better navigate price risks. Future research could explore integrating additional contextual variables to enhance model performance further. Full article

Primary and secondary growth of trees are key components of carbon sequestration in forest ecosystems. However, the temporal relationships between primary and secondary growth as well as their responses to environmental variations are still poorly understood. Herein, we continuously measured the intra-annual leaf, shoot and stem growth of Quercus serrata for two years on Lushan Mountain, southeastern China. Our results showed that shoots were ranked as the first organ to initiate, peak and cease growth, rather than leaves and stems. Moreover, the phenological stages of shoot growth were negatively associated with those of leaves and stems, whereas there was a weak positive correlation in phenological events between leaves and stems. These temporal connections in phenological events between primary and secondary growth suggest a prioritized carbon allocation to shoot growth and a high dependence of stem growth on carbon from newly developing leaves. Although stem growth started earlier in response to the warmer spring in 2018 compared to the colder spring in 2017, no significant difference in annual increment was observed between years, which was related to the more severe drought condition during the dry season in 2018. At the intra-annual scale, different organs generally had a consistent growth response to temperature variables but showed a divergent response to vapor pressure deficit. Despite a relatively short observational period and potential bias in spatial representativeness, our data provide nuanced knowledge on seasonal growth dynamics in primary and secondary of broadleaved species, underlining the importance of jointly considering intra-seasonal variabilities of environmental conditions in order to correctly predict tree growth response to climate change in subtropical regions. Full article

The Málaga coast, in the south of Spain, is a densely populated tourist destination where ports, marinas and coastal protection structures of various typologies (e.g., groins, breakwaters, revetments) and shapes (e.g., “Y”, “L”, etc., shaped groins) have been emplaced. Such structures have modified the long- and cross-shore sediment transport and produced changes in beach morphology and the evolution of nearby areas. To characterize the changes related to shore-normal structures, beach erosion/accretion areas close to coastal anthropic structures were measured using a sequence of aerial orthophotos between 1956 and 2019, and the potential littoral sediment transport for the two main littoral transport directions was determined by means of the CMS (Coastal Modeling System). Available data on wave propagation and coastal sediment transport reflect the complex dynamics of the study area, often characterized by the coexistence of opposing longshore transport directions. Accretion was observed on both sides of ports in all studied periods and on both main coastal orientations. Groins and groups of groins presented mixed results that reflect the heterogeneity of the study area; in certain sectors where the wave regime is bidirectional, changes in shoreline trend were found from one period to another. The study cases described in this paper emphasize the difficulties in finding clear spatial and temporal trends in the artificially induced erosion/accretion patterns recorded along a heavily modified shoreline. Full article

Forest population structure and dynamics represent core research areas in forest ecology, encompassing multiple components such as quantitative analyses of population changes, age structure, life tables, and species dynamics within specific spatial and temporal contexts. These elements provide crucial insights into tree adaptation mechanisms and inform evidence-based strategies for population conservation and management. However, traditional analyses of forest population structure and dynamics face significant challenges due to the absence of specialized analytical software. This limitation not only increases data processing complexity and workload but also elevates the risk of analytical errors. To address these challenges, we developed forestPSD, a novel R package based on established principles of forest population structure and dynamics analysis. This package provides researchers with an efficient and user-friendly tool for analyzing forest population structures and their temporal changes, thereby facilitating advancement in this field. Full article

The Tarim River Basin (TRB) in Northwest China has an extremely fragile ecological environment that is highly sensitive to climate change. Understanding the long-term change dynamics of vegetation coverage in this arid zone is critically important for predicting future trends as well as for improving regional ecological protection and soil and water conservation measures. Based on NDVI data from 2000 to 2022, a temporal and spatial analysis of vegetation coverage in the TRB is carried out using the pixel dichotomy model, Sen trend analysis, the MK test, the Hurst index, and correlation analysis. The results reveal the following: (1) from 2000 to 2022, the vegetation coverage shows a fluctuating increasing trend, with decreases in extremely low and low coverage areas and increases in high and medium coverage areas. Extremely low vegetation coverage accounts for 46.89% of the study area. (2) Throughout the 23-year period, the change trend of vegetation cover essentially remains stable. The proportion of the improved area is greater than that of the degraded area, accounting for 66.49% and 27.93%, respectively, and there is significant fluctuation variation, accounting for 29.99%. Further, there is high variation in vegetation cover as well as high ecological environment vulnerability. The future area of continuous improvement accounts for 31.64%, which is larger than that of continuous degradation (27.17%), and the area of uncertainty accounts for 41.18%, which is strongly random. (3) The distance between land use and the closest river is the main limiting factor of vegetation cover change in the five studied sub-regions of the TRB. The highest explanatory power of the combined factor of land use and precipitation is 0.723. With a correlation Q value above 0.6, the interaction between land use type and natural factors (e.g., temperature, precipitation, evapotranspiration, distance from the river, etc.) is significant. This study is helpful to predict the trend of vegetation change in the TRB, and provides a scientific basis for regional ecological protection, soil and water conservation, and land use planning. Full article

Point-of-interest (POI) recommendation is highly sensitive to temporal factors, including fluctuations in user preferences, variation in user similarity, and decay in the attraction of locations. However, current studies overlook the temporal dynamics of user similarity and the timeliness of POIs, resulting in a disconnect between recommendations and users’ recent preferences. This paper proposes a new framework for dynamic and timely POI recommendation by integrating spatio-temporal influences and social relationships. Dynamic prediction is achieved through an enhanced user-based collaborative filtering approach. A time slot clustering technique was designed based on the statistical check-in features in each time slot. Ratings within the same cluster were shared to address data sparsity. To reflect user similarity drift, we took the time variable as a crucial parameter to dynamically calculate user similarity. Moreover, timely prediction was achieved by integrating the timeliness, popularity, and spatial features of POIs. We introduce a novel method to evaluate the timeliness of POI recommendation, aimed at assessing whether the recommendations align with users’ recent preferences. Comprehensive experiments are performed on Brightkite and Gowalla datasets, with the data divided into workdays and weekends. The experimental results reveal that our algorithm outperforms seven state-of-the-art recommenders in terms of prediction accuracy and system timeliness. Full article