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In this study, dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [18F]GE180 and the amino acid [18F]FET were acquired and normalized to healthy background (tumor-to-background ratio, TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

Based on panel data spanning from 2008 to 2021, this study employs the super-efficiency SBM-DEA model to quantify the efficiency of regional environmental governance in China. Through the utilization of Social Network Analysis, we investigate the spatial correlation characteristics within the network of environmental governance efficiency. Furthermore, a spatial econometric model is employed to conduct an empirical analysis of the key factors influencing regional environmental governance efficiency. The findings reveal noticeable spatial disparities in regional environmental governance efficiency across China, with an overall efficiency that remains in the low-efficiency stage. The spatial correlation of the network gradually increases and transitions from polarization to convergence. Moreover, population size, economic development level, and fiscal autonomy demonstrate negative effects on regional environmental governance efficiency. Conversely, factors such as industrial structure upgrading, investment in industrial pollution control, local government competition, and the intensity of environmental regulation have a positive impact on regional environmental governance efficiency. Consequently, in order to enhance regional environmental governance efficiency, it is imperative for the government to consider not only economic and social factors but also focus on government behavior factors.

The trade-offs and synergies reveal the profit and loss relationship between ecosystem services, which is of great significance to the sustainable development of natural resources. The ecosystem services in Jiangxi Province, such as net primary productivity (NPP), soil conservation (SC) and water yield (WY) during 2000–2020, were estimated in this study. The correlation coefficient was adopted to analyze the trade-offs and synergies between the three ecosystem services by static space correlation and dynamic space correlation from such perspectives as Watershed, county and grid. Moreover, the influence of the three ecosystem services and the relations between them were explored from four aspects: landform, NDVI, accumulated temperature and precipitation. The results showed that the ecological environment quality in Jiangxi Province was improved and that the distribution of ecosystem services had significant regional characteristics. In the static analysis, ecosystem services at all scales were remarkably synergistic, and synergies weakened rapidly and even turned into trade-offs as the scale decreased. In the dynamic analysis, ecosystem services at all scales were mainly synergistic; the proportion of significant samples was much lower than that in the static analysis, the degree of trade-offs/synergies decreased with the decrease in scale, and the decrease was smaller than that in the static analysis. The major constraints for SC were landform and NDVI. The main constraint for WY was precipitation, and that for NPP was NDVI. Affected by various factors, NPP and SC were stably synergistic, NPP and WY were in a stable trade-off relationship, and the relationship between SC and WY was unstable. The trade-offs and synergies changed with factors and zoning.

Population growth is a global issue that contributes to the changes in the distribution and concentration of population. Population growth affects the sustainable development of an area from both a social and spatial point of view. To relate the global problem to a local issue, this research investigates one of the Malaysian government policies addressed as the New Economic Policy (NEP) because the policy may be linked to long-term spatial demographic changes in Peninsular Malaysia, particularly in the distribution of people. Back in 1970, the policy was implemented after an unwanted incident on 13 May 1969. Its goals were to eradicate poverty regardless of race and to restructure society by eliminating the identification of race with economic functions. To measure the successfulness of the policy, two indicators that were derived from the goals are the long-term spatial changes of both racial and occupational segregation. The magnitude for both segregations was calculated using the Entropy Index (H). The values were then carried forward to evaluate the relationship between these two variables. The final analysis was conducted using the Local Bivariate Relationships application of a Geographic Information System (GIS) tool. The outputs then reflect the two sustainable goals that are, (i) reduced inequalities, and (ii) sustainable cities and communities in Peninsular Malaysia.

Spatial dependence plays a key role in all phenomena involving the geographic space, such as the social processes associated with transport and land use. Nevertheless, spatial dependence in multinomial discrete models has not received the same level of attention as have the other kinds of correlations in the discrete modeling literature, mainly due to the complexity of its treatment. This paper aims at offering a brief discussion on the different kinds of spatial correlation affecting multinomial discrete models and the different ways in which spatial correlation has been addressed in the discrete modeling literature. Furthermore, the paper offers a discussion on the advantages and limitations of the different approaches to treat spatial correlation and it also proposes a compromise solution among complexity, computational costs, and realism that can be useful in some specific situations.

The Super-SBM model was used first to assess the energy efficiency of 30 Chinese provinces from 2012 to 2017. After that, an energy efficiency spatial correlation test was conducted, and finally, the influencing elements of energy efficiency were analyzed using a geographic panel model. The findings show that the amount of regional economic development has a substantial positive impact on energy efficiency, whereas the level of regional urbanization and the severity of environmental restrictions have a considerable negative impact on energy efficiency in China’s provinces. Other regions’ energy structure and technical innovation have a substantial positive spillover effect on the region’s energy efficiency, whereas other regions’ economic development and foreign direct investment have a significant negative spillover effect on the region’s energy efficiency.

Drought, a recurring climatic challenge characterized by water scarcity, significantly affects the growth and functional stability of terrestrial vegetation. This study closely examines how extreme droughts vary and how the vegetation recovery period changes on the basis of multi‐source meteorological and remote satellite data. It also assesses the impacts of specific drought characteristics on vegetation recovery time in Guizhou Province, China. The results indicate that drought events are predominantly observed in the summer and autumn seasons across most of Guizhou Province, with a notable cessation of these dry spells often occurring in September and October. The northern and southwestern regions of Guizhou Province tend to experience the onset of drought conditions at an earlier date than other regions, with a concomitant trend of earlier termination of drought events in the northern locales. Droughts typically last 3–5 months, with mild and moderate droughts being the most common. The spatial distribution of vegetation recovery period from the droughts follows a discernible pattern, with most areas recovering within 2 months after drought ceases, whereas the Guiyang, Zunyi, Qiannan, and Qiandongnan regions need 4 to 6 months to recover in some years. Furthermore, substantial variations in vegetation recovery patterns have been observed among diverse land cover types. The average recovery period of forests ranged from 1.60 to 2.98 months, that of shrublands from 1.76 to 3.21 months, whereas the vegetation in croplands showed a relatively shorter recovery period, with most areas returning to normal levels within approximately 1 month. The recovery characteristics of vegetation were jointly influenced by the features of drought. Drought duration and severity often prolonged the length of the recovery period. This study provides a scientific basis for the formulation of regional drought management and ecological conservation strategies. Assessing characteristics of drought over the last 20 years in Guizhou Province, China. Unraveling the dynamics of vegetation recovery period across various land cover types. Drought duration and severity determine vegetation recovery period.

In studies that produce data with spatial structure, it is common that covariates of interest vary spatially in addition to the error. Because of this, the error and covariate are often correlated. When this occurs, it is difficult to distinguish the covariate effect from residual spatial variation. In an i.i.d. normal error setting, it is well known that this type of correlation produces biased coefficient estimates, but predictions remain unbiased. In a spatial setting, recent studies have shown that coefficient estimates remain biased, but spatial prediction has not been addressed. The purpose of this paper is to provide a more detailed study of coefficient estimation from spatial models when covariate and error are correlated and then begin a formal study regarding spatial prediction. This is carried out by investigating properties of the generalized least squares estimator and the best linear unbiased predictor when a spatial random effect and a covariate are jointly modelled. Under this setup, we demonstrate that the mean squared prediction error is possibly reduced when covariate and error are correlated.

To estimate the phase velocity using the array observations of microtremors, some algorithms for the estimation include constraints on the array shape, such as equilateral triangles or the placement of receivers on a circle, in order to reduce the estimation error of the phase velocity. In the present study, a direct estimation technique is introduced for the phase velocity using records obtained through an array with an arbitrary shape based on a complex coherency function (CCF), where CCF is defined as the normalized cross spectrum of the microtremor records observed simultaneously by two receivers. The particle swarm optimization (PSO) method, one of metaheuristic optimization methods, is applied and optimal values are provided for the phase velocity and other unknown parameters. Approximate representations of the stochastic properties for the unknown variables are analytically derived based on the discrete representation of the CCF, for a case where the arrival directions of microtremors are treated as random variables following a uniform distribution. Furthermore, the validity of the proposed method is confirmed using numerical simulations and actual observation records.

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.