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Few strategies have been developed to effectively delineate management zones (MZs) in crop-pasture rotation (CPR) systems that accommodate site-specific management for multiple crops using a single map. This study aimed to propose and evaluate several feature selection approaches that account for multiple crops in CPR systems and propose a framework for MZ delineation in CPR systems that results in a single MZ map. The feature selection approaches were based on the spatial correlation between attributes (soil, crops, and terrain attributes) and yield variables (grain and pasture yield, spatial trend of yield, and yield temporal stability). This study was conducted in an area with an integrated crop-livestock system, featuring the CPR of soybean and pasture. The results showed that the approach based on yield temporal stability was the most effective for selecting relevant attributes used in the MZ delineation in CPR systems, resulting in greater differentiation among MZs. A higher number of MZs was needed (four zones), emphasizing the importance of carefully selecting the number based on variance reduction and yield differences to ensure that the final MZ map reflects the variability across all crops and guides their integrated management. The proposed framework is one of the first to use yield temporal stability for feature selection specifically aimed at delineating MZs in CPR systems. This approach improves the ability to select significant attributes used in the MZs delineation process, providing a better solution for improving input use efficiency and maximizing grain and pasture yield in integrated farming systems. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems are vital carbon sinks that can effectively restrain the rise in CO2 in the atmosphere. How ecosystem carbon storage (CS) in semi-arid watershed areas with slow urbanization is affected by comprehensive factors of the environment and land use, along with its temporal and spatial changes has still not been fully explored. Notably, there is a paucity of research on the temporal and spatial changes and development trends of CS in the rapid deformation belt of slopes from the eastern margin of the Qinghai–Tibet Plateau to the Loess Plateau. Taking Bailong River Basin (BRB) as an example, this study combined GeoSOS-FLUS, the InVEST model, and localized "social–economic–nature" scenario to simulate the long-term dynamic evolution of CS. The aim was to study how topographic factors and land use change, and their interactions impact carbon sinks and gradient effects in steep-slope areas, and then find out the relationship between carbon sinks and topographic factors to explore strategies to improve regional carbon sink capacity. The results showed that the following: (1) CS in BRB increased year by year, with a total increase of 558 tons (3.19%), and showed significant spatial heterogeneity, mainly due to the conversion of woodland and arable land; (2) except for land use type, the relationship between CS and topographic gradient is inverted U-shaped, showing a complex spatial response; and (3) it is estimated that by 2050, under the arable land protection and natural development scenarios, CS will decrease by 0.07% and 0.005%, respectively, encroachment on undeveloped mountain areas, while the ecological protection scenario gives priority to protecting the carbon sinks of woodland and grassland, and CS will increase by 0.37%. This study supports the implementation of targeted ecological protection measures through topographic gradient zoning, provides a reference for policy makers in similar topographic regions to effectively manage the spatial heterogeneity of CS, and helps further strengthen global and regional climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

In the current tide of technological revolution and industrial transformation, digitalization and greening have surfaced as two prominent trends, serving as the inherent requisites and impelling forces of new productive forces. Augmenting carbon emission efficiency is a crucial pathway for attaining green development. Digital inclusive finance, as an innovative financial paradigm, is a significant determinant influencing carbon emission efficiency and the progression of green development. By harnessing panel data from 108 cities within the Yangtze River Economic Belt spanning from 2011 to 2021, this study deploys the super-efficiency SBM model to gauge carbon emission efficiency. Additionally, it employs fixed effects and mediating mechanism models to empirically scrutinize the impact of digital inclusive finance on carbon emission efficiency within urban agglomerations in the Yangtze River Economic Belt. The study further probes its spatial effects through a spatial Durbin model. The research findings disclose that digital inclusive finance can substantially augment carbon emission efficiency in regional cities along the Yangtze River Economic Belt, manifesting notable regional correlations. This enhancement is accomplished by propelling industrial structure upgrades and augmenting scientific and technological capabilities. To enhance carbon emission efficiency in the Yangtze River Economic Belt, efforts should be centered around advancing digital inclusive finance, expediting industrial structural transformation, and fortifying scientific and technological development. [ABSTRACT FROM AUTHOR]

Achieving accurate daily runoff predictions in data-scarce regions is challenging. This paper presents an improved anticipated learning machine (IALM) based on nonlinear dynamics. The IALM leverages both the spatial information among runoff stations and the temporal information within runoff sequences, achieving precise runoff prediction. First, temporal information is extracted through a convolutional neural network (CNN). Second, spatiotemporal information is fused through an artificial neural network (ANN). Third, the mapping between the nondelay and delay attractors is simulated by an anticipated learning neural network. Using daily runoff sequences from 107 stations across the United States as input, the performance of the IALM is evaluated at eight stations for training sample sizes ranging from 300 to 70. Compared with the ALM, autoregressive neural network (ARNN), and three deep learning models (self-attentive long short-term memory [SA-LSTM], temporal convolutional network (TCN), and long- and short-term time-series network [LSTnet]), the IALM maintains high predictive accuracy and stability as the number of training samples decreases. The IALM method consistently captures runoff trends, accurately predicts runoff peak points, and overcomes challenges associated with time lag and low accuracy under small sample conditions. Moreover, in comparison to the ALM, the IALM significantly enhances the predictive accuracy, achieving R and Nash–Sutcliffe efficiency (NSE) indices of 0.9 for multiple stations. At the USGS 01013500 station, the NSE value of the IALM improves by 127.71–252.93% when using only 100 training samples, while the NSE value of the LSTnet decreases to approximately 0 under the same conditions. When 70 training samples are used, the IALM achieves an R improvement of 21.17–47.66% relative to the ALM and the three deep learning models. Compared with the ARNN at eight sites, the IALM maintains superior prediction performance at each site, while the ARNN performs well at only a few sites and average at most sites, with unstable prediction performance. The results show that IALM, based on nonlinear dynamics, effectively incorporates and exploits spatiotemporal information from short-term high-dimensional data, compensating for limited sample lengths. Therefore, IALM provides a novel methodology for runoff prediction in data-scarce regions. [ABSTRACT FROM AUTHOR]

In 2013, China launched a cooperative initiative to build the "Silk Road Economic Belt" and the "21st Century Maritime Silk Road". Based on the data of import and export trade between 57 countries along the "Belt and Road" and China from 2010 to 2021, this paper conducts research using the spatial Durbin model. The results show that the double fixed-effects model is optimal. The 57 countries along the "Belt and Road" have significant positive spatial spillover effects on trade with China, among which economic development, educational development, and transportation accessibility have significantly promoted trade between the countries along the "Belt and Road" and China. [ABSTRACT FROM AUTHOR]

As the largest carbon-emitting industry in China, the power industry has huge potential for carbon emission reductions. It is vital to study the spatial correlation of carbon emission efficiency in the power industry (CEEP) from a system perspective to understand the interaction mechanisms of CEEP in different provinces. This study applies the SBM-undesirable model to measure the CEEP in China, and a modified Gravity model and social network analysis (SNA) method are applied to analyze the interaction mechanism of the CEEP from a system perspective. Finally, the influencing factors of the CEEP's spatial correlation are investigated using the quadratic allocation procedure (QAP) method. The results show that (1) the national CEEP is gradually increasing, while the CEEP gap between provinces is widening; (2) the overall network size shows an increasing trend, but the hierarchical structure is somewhat fixed; (3) the central province of a network has a high degree of consistency with the geographically central province, but the spatial spillover effect of the central node provinces on the peripheral provinces is not sufficient; and (4) differences in geographic proximity, energy intensity, and technical level of power generation significantly affect the formation of spatially correlated networks in the CEEP. [ABSTRACT FROM AUTHOR]

The sensor‐based dam health monitoring (DHM) systems of concrete‐faced rockfill dam (CFRD) are easily affected by environmental factors, which inevitably causes sensor fault, and the measured value of its effect quantities is nonlinear and unstable. The application of machine learning in the preprocessing of dam safety monitoring data is very extensive, mainly including two parts: gross error elimination and missing data completion. In this paper, support vector regression (SVR), a typical machine learning algorithm, is chosen to accomplish these two tasks, while suggesting possible optimizations in different situations of hydraulic monitoring, including optimization of parameters in SVR using the population algorithm sparrow search algorithm (SSA); optimization of the pattern of gross error discriminant using the minimum covariance determinant (MCD) algorithm; and the hierarchical clustering on principal components (HCPC) algorithm to optimize the selection method of spatial measurement points when completing a segment of missing data. The results show that the optimized SVR method has greater accuracy in both gross error elimination and the completion of individual missing data or a segment of missing data for DHM systems, which is applicable to measured data of CFRD. These optimization methods can also be extended to other engineering applications. [ABSTRACT FROM AUTHOR]

This study focused on the sensitivity analysis of Bayesian nonparametric spatial models which combine the strengths of spatially structured random effects and the Dirichlet mixture to account for the unobserved heterogeneity of crash counts. Various evaluation criteria were employed to compare the performance of models with varying spatial weight matrices and precision parameters (alpha). The results demonstrate that there exists strong correlation among the posterior number of clusters, alpha, the fraction of variation explained by the spatial random effect, and different evaluation criteria. Even though the increased upper bound value of alpha does not necessarily lead to the enhanced model performance, the models with the full flexibility to choose the desirable amount of clustering tend to perform better than those with limited flexibility due to smaller allowable mass components. Compared with the precision parameter, no obvious trend is illustrated for the different evaluation criteria along the varying spatial weight matrices. [ABSTRACT FROM AUTHOR]

The modeling and analysis of sea clutter are of great significance in radar target detection studies in marine environments. Sea clutter typically exhibits non-Gaussian characteristics and spatiotemporal correlations, posing challenges for modeling, especially when generating simulation data of continuous correlated non-Gaussian random processes. This paper proposes a novel method for sea clutter modeling. First, feature description functions are constructed to individually characterize the amplitude, temporal, and spatial correlations of sea clutter, allowing for an accurate depiction of its characteristics with fewer parameters. Subsequently, simulation data are generated based on these feature description functions, satisfying the amplitude distribution, temporal correlation, and spatial correlation characteristics of sea clutter. Additionally, complex signal forms are introduced in the underlying signal processing to generate texture and speckle components of sea clutter, enhancing the alignment of simulation data with actual data. Through comparison with measured sea clutter data, the proposed method has been shown to accurately simulate complex sea clutter with real-world characteristics. [ABSTRACT FROM AUTHOR]

For the risk analysis of spatially distributed structures, the joint prediction of ground motion intensities at multiple sites is required. Therefore, many researchers have come up with spatial correlation models for seismic intensity measures (IMs). The spatial correlation model requires site-specific non-ergodic ground motions. One of the chosen approaches for correlation studies is physics-based simulations (PBS), which account for the complexities related to earthquake source, path and site. The present study evaluates the efficiency of the artificial neural network-based broadband response spectra generator (BBANN) of Sharma et al. (2023) in terms of residual analysis and spatial correlation. The Turkey earthquakes of 6th February 2023 are taken as the case study. The model-predicted values corresponding to a short period are compared with the recorded values for the events. We found that the model was able to capture ground motion trends without any bias in residuals. Also, in the spatial correlation scale, the model predictions are comparable with recorded values. The results highlighted the efficiency of the BBANN model in effectively capturing the spatial pattern of ground motion intensity measures. The study draws attention to the ability of PBS to generate non-ergodic ground motion and, hence, can be useful in seismic hazard and risk frameworks. [ABSTRACT FROM AUTHOR]

Spatial epidemiology often involves the analysis of spatial count data with an unusually high proportion of zero observations. While Bayesian hierarchical models perform very well for zero‐inflated data in many situations, a smooth response surface is usually required for the Bayesian methods to converge. However, for infectious disease data with excessive zeros, a Wombling issue with large spatial variation could make the Bayesian methods infeasible. To address this issue, we develop estimating equations associated with disease mapping by including over‐dispersion and spatial noises in a spatial zero‐inflated Poisson model. Asymptotic properties are derived for the parameter estimates. Simulations and data analysis are used to assess and illustrate the proposed method. [ABSTRACT FROM AUTHOR]

The advancement of the intelligent manufacturing industry (IMI) represents the future direction for the world's manufacturing sector, offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency. In this study, we took the industrial robot industry (IRI) as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective, and the modified diamond model (DM) was used to analyze the influencing factors. Results show that: 1) the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar, centered in the southeast. Key investment areas are in the east and south. The spatial distribution of China's IRI covers a multitude of provinces and obtains different scales of investment in different countries (regions). 2) The spatial correlation between foreign investors and China's provincial-level administrative regions (PARs) forms a network, and the network of foreign-invested enterprises is more stable. Different countries (regions) have distinct location preferences in China, with significant spatial differences in correlation degrees. 3) Overall, the interaction of these factors shapes the location decisions and correlation patterns of industrial robot enterprises. This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and suggestions for national IMI planning and relevant industry investors. [ABSTRACT FROM AUTHOR]

Synergistic carbon emission reduction at the urban scale is an inherent requirement for China to realize its carbon emission reduction commitments and promote sustainable and regional synergistic development. Using 16 prefectural cities in Shandong Province as an example, this study defines and quantifies the regional carbon emission reduction capacity (CERC) based on the synergistic development of carbon emission reduction, environmental protection, and economic growth objectives. The spatial network characteristics of the (CERC) and its drivers were analyzed using social network analysis and quadratic assignment procedure regression methods. The results revealed that the spatial correlation of the CERC among prefectural cities has been increasing over the years. Jinan-Zibo-Qingdao is the center of the network, while the spatial linkage strength in the southwest and northeast of Shandong Province is weak. Geospatial distance and scientific development differences have a significant negative effect on the intensity of spatial association, while differences in economic and informatization developments have a considerable positive impact. Environmental regulatory differences and transportation differences are not significant. This study offers a methodological reference for similar studies in other countries or regions. At the same time, the findings provide a scientific basis for the government to rationally allocate urban resources and promote regional synergistic carbon emission reduction. [ABSTRACT FROM AUTHOR]

China actively explores the green development road by promoting the intensive use of urban construction land and low carbon emissions. This research evaluates urban construction land intensity and carbon emission efficiency by using a multifactor comprehensive evaluation method and SBM model and measures the spatial characteristics of between urban construction land intensity and carbon emission efficiency by Moran's I and LISA scattering. The result shows that (1) the average value of urban construction land intensity was constantly improving from 0.11 in 2000 to 0.29 in 2021, (2) the value of carbon emission efficiency generally showed a fluctuating downward from 0.89 in 2000 to 0.66 in 2009 and then a fluctuating upward to 0.84 in 2021, with many provinces located in the Level III and IV carbon emission efficiency intervals, (3) the land development, road density, technology development, and fixed asset investment have a negative impact on carbon emission efficiency, while the greening level and tax have a positive promotion effect, and (4) the correlation and spatial features between urban construction land intensity and carbon emission efficiency in 30 provinces have consistency, as well as heterogeneity in the temporal and spatial development trends. Policy implications are accordingly proposed. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic activities have increased the complexity of hydrology–soil–vegetation interactions in arid-region irrigation areas. Therefore, studies on the spatiotemporal characteristics of these interactions can greatly benefit the sustainable development of arid areas. This study developed a spatially granular dataset of the key hydrology, soil, and vegetation elements for the Hetao Irrigation District (HID) for 2000–2020, recognized as the largest single-port artesian irrigation area in Asia, and explored the interactions between these elements by means of a geodetector, the analytic hierarchy process, and the Pearson correlation coefficient. The key results indicated the following: (1) a declining trend of 0.1–0.15 in the comprehensive influence of hydrology–soil–vegetation interactions; increasing significance of hydrologically driven soil and vegetation evolution, with feedback between soil and vegetation; (2) the maximization of the interactions between soil moisture and precipitation and groundwater, with evapotranspiration as the dominant factor regulating hydrology–vegetation interactions; (3) the interactions between hydrology, soil, and vegetation showing nonlinear synergism; (4) and the spatial distributions of the hydrology–soil–vegetation interactions showing significant band-like patterns with weak coupling between the elements. [ABSTRACT FROM AUTHOR]

Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

This study conducted risk ranking for risk substances present in meat products, exploring the temporal and spatial distribution patterns of these risk items. The entropy weight method was used to calculate the risk values of different risk substances in circulation, production and catering links, and the risk substances were ranked by the 80-20 principle. The descriptive statistical method was used to explore the temporal and spatial distribution patterns of meat product production links. Through global and local correlation analysis, the characteristics of the spatial distribution of high-risk and relatively high-risk substances nationwide were studied. The high-risk substances in the production and circulation links were N-dimethylnitrosamine, benzo[a]pyrene, cadmium, peroxide value, new red, nitrite, arsenic, mercury, coliforms, and total bacteria number, while the relatively high-risk substances in the catering link was the total bacteria number. The highest meat product unqualified rate was 0.21% in 2017, followed by 0.19% in 2020. In various regions, the highest unqualified rate was in Ningxia, at 0.30%. For meat products from all provinces nationwide, the qualified rates for high and relatively high-risk substances only exhibited global spatial correlation for arsenic, allura red, and total bacterial count (P

In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low–carbon transition.

Abstract Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Aiming at the problem of insufficient accuracy in wind power prediction caused by the lack of target meteorological forecast data, a wind power prediction method based on spatial correlation and Stacking ensemble learning is proposed in this paper. Firstly, the spatial correlation between the target wind farm and adjacent meteorological stations is analyzed. The delay time is determined based on the extreme points of the correlation coefficient, and a wind speed time-shift dataset is constructed. Secondly, the Stacking ensemble method is used to integrate multiple algorithms to predict the wind power of the target wind farm from multiple data observation perspectives. It leverages the complementary advantages of different algorithms, enhancing overall generalization ability. Additionally, it can effectively balance search time and model performance by adopting the particle swarm optimization algorithm to search for model hyperparameters. Finally, the effectiveness and accuracy of the proposed method have been verified by the measured data from a wind farm in East China. The results show that it can effectively improve the wind power prediction accuracy in the case of missing data by considering the information bias at different locations with the data input and prediction models.

Monitoring the stability of tailings storage facilities (TSFs) is extremely important due to the catastrophic consequences of instability, which pose a threat to both the environment and human life. For this reason, numerous laboratory and field tests are carried out around dams. An extensive database is collected as part of monitoring and field research. The in-depth analysis of available data can help monitor stability and predict disaster hazards. One of the important factors is displacement, including surface displacements—recorded by benchmarks as well as underground displacements—recorded by inclinometers. In this work, methods were developed to help assess the stability of the TSF in terms of surface and underground displacement based on the simulated data from geodetic benchmarks. The context of spatial correlation was investigated using hot spot analysis, which shows areas of greater risk, indicating the places of correlation of large and small displacements. The analysis of displacements versus time allowed us to indicate the growing exponential trend, thanks to which it is possible to forecast the trend of future displacements, as well as their velocity and acceleration, with the coefficient of determination of the trend matching reaching even 0.97. Additionally, the use of a geographically weighted regression model was proposed to predict the risk of shear relative to surface displacements. [ABSTRACT FROM AUTHOR]

Bimolecular recombination of charge carriers in amorphous organic semiconductors is discussed. A common feature of these materials is their spatial correlation of the random energy landscape in which hopping transport of charge carriers occurs. The recombination rate constant for such materials is calculated, including the case of locally ordered materials. It turns out that the spatial correlation of the random landscape causes violation of the Langevin relation connecting the charge carrier mobilities with the recombination rate constant. For different sources of energetic disorder the true rate constant can be either less or greater than the corresponding Langevin value. Promising classes of organic semiconductors are indicated where the recombination rate constant could exceed the Langevin value, leading to a potential increase in the efficiency of light generation in organic light-emitting diodes. Organic semiconductors with low recombination rate constants are promising for the use in solar cells. Features of two-dimensional bimolecular recombination in materials based on oligo- and polythiophenes, in which two-dimensional lamellae are formed, are considered. The formal recombination rate constant becomes being dependent on the charge carrier concentration. The effect of spatially correlated energetic disorder leads to the development of various rate constant dependences on the charge carrier concentration. Analysis of the current–voltage characteristics of organic devices gives the possibility to distinguish between the two-dimensional and three-dimensional recombination. [ABSTRACT FROM AUTHOR]

Green finance is a specialized financial institutional arrangement that has emerged in response to the increasingly severe pressure on resources and the environment. In this paper, we construct a comprehensive indicator system from the perspective of green financial services and adopt an improved entropy method to measure green finance development. Using the panel data of 30 provincial regions in China, we adopt a spatial econometric model to investigate the effect of green finance development on carbon emissions. The results show that the carbon emissions exhibit a positive spatial correlation among the provinces. Meanwhile, green finance development has a significant carbon emission reduction effect. Evidence from China's carbon emissions trading policy further confirms this effect. The relationship we found is consistent with two mechanisms: the technology effect of green technology innovation and the structural effect of industrial structure upgrading. [ABSTRACT FROM AUTHOR]

Recurrent event data, which represent the occurrence of repeated incidences, are common in observational studies. Furthermore, collecting possible spatial correlations in health and environmental data is likely to provide more information for risk prediction. This article proposes a comprehensive proportional intensity model considering spatial random effects for recurrent event data using a Bayesian approach. The spatial information for areal data (where the spatial location is known up to a geographic unit such as a county) and georeferenced data (where the location is exactly observed) is examined. A traditional constant baseline intensity function, as well as a flexible piecewise constant baseline intensity function, are both under consideration. To estimate the parameters, a Markov chain Monte Carlo method with the Metropolis–Hastings algorithm and the adaptive Metropolis algorithm are applied. To assess the performance of model fitting, the deviance information criterion and log pseudo marginal likelihood are proposed. Overall, simulation studies demonstrate that the proposed model is significantly better than models that do not consider spatial effects if spatial correlations exist. Finally, our approach is implemented using a dataset related to the recurrence of cardiovascular diseases, which incorporates spatial information. [ABSTRACT FROM AUTHOR]

Clarifying the driving mechanisms of ecosystem service (ES) supply and demand under urbanization is of significant importance for urban ecological planning and management. However, how the balance of ES supply and demand and its driving mechanisms vary with the degree of urbanization has been little studied. In this study, we analyzed the spatiotemporal changes and the correlations between ES supply and demand and the degree of urbanization in the Zhengzhou Metropolitan Area (ZZMA) from 2000 to 2020 and further explored the driving mechanisms behind these changes. The results showed that, (1) between 2000 and 2020, the ZZMA experienced a deficit in comprehensive ES supply and demand, and regions with rapid urbanization development were more likely to trigger imbalances in ES supply and demand; (2) the spatial mismatch between low–high ES supply and demand was primarily distributed in the built-up areas of various cities, while the high–low spatial mismatch was mostly found in forest and grassland areas; (3) the comprehensive urbanization level of the ZZMA was spatially negatively correlated with the ratio of ES supply and demand. Regions with lower ES balance were more susceptible to disturbances caused by urbanization; (4) population density was the key factor influencing the supply and demand of carbon sequestration, oxygen release, water conservation, and food provision services, while the proportions of forest land and construction areas had the greatest influence on the supply and demand of air purification and leisure services. It is important to ensure the ecological status of the northwestern, southwestern, and central mountainous and forested areas; maintain the agricultural status of the main grain-producing areas in the eastern plains; strengthen ecological restoration and green infrastructure in built-up areas; and formulate differentiated management policies to promote the sustainable supply of ES and safeguard the ecological security of the region. [ABSTRACT FROM AUTHOR]

Accurate electricity load forecasting is crucial for efficient energy management, allowing utility companies to optimize resource allocation, reduce operational costs, and ensure a reliable power supply. Various factors, such as weather conditions, economic activities, and demographic changes, significantly influence electricity load. To address these challenges, we designed a deep learning Bidirectional Long Short-Term Memory (BiLTSM-based model) for electricity load forecasting, leveraging weather data as the primary feature and signal decomposition as an additional feature. To select the best weather parameters, we utilize the Principal Component Analysis (PCA) and Correlation Coefficient (CC) to choose the most correlated weather parameters as inputs for the model. Moreover, we introduce spatial correlation to select the best weather locations. Additionally, we perform a signal decomposition technique using the Ensemble Empirical Mode Decomposition (EEMD) to decompose the historical load signal into Intrinsic Mode Functions (IMFs). We also investigate procedures to choose the best IMF to give the best accuracy. As a case study, we investigate the electricity load data in the Jakarta-Banten region of Indonesia, known for its high consumer and industrial activity. The results demonstrate that combining the BiLSTM model and EEMD achieves high prediction accuracy, with a Correlation Coefficient (CC) of 0.956 and a Mean Absolute Percentage Error (MAPE) of 2.824%. [ABSTRACT FROM AUTHOR]