It is found in this paper that ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. (1) After ilr transformation, RPCA is used for processing. It can be seen from the double-plot diagram that the first principal component and the second principal component transform with the element sequence transformation, and the unique principal component cannot be calculated; (2) the score and the load are transformed into the CLR space through the U matrix. According to the formula CLR=ALR·U, the score result can correspond to the original order of elements. However, the load result obtained by the use of this formula cannot correspond to the original order of elements, and the result can only be obtained through the formula” CLR=UT·ALR”. In order to obtain the best element assemblage of porphyry copper deposit, this paper adopted the mineral assemblage of discovered deposits in Gangdese metallogenic belt for statistical analysis, and obtained that the element assemblage of porphyry copper deposit was Cu, Mo, Au, Ag, W, Bi. Then, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and C-A fractal filtering is applied to identify the anomaly and background. In order to facilitate comparison, the different minerals and ore deposit types have been analyzed, the results show that (1) in view of the porphyry copper deposits, recognition effect of combination elements are better than which single element, (2) skarn type copper deposit that it has nothing to do with the porphyry had a high degree of difference, but skarn type copper deposit related to the porphyry had a low degree of difference, (3) This method has a certain advantage over the single element method in porphyry gold deposits, and it can reduce the level of anomalies. Such an advantage is that in the aspect of anomaly evaluation of porphyry copper deposits, the level is reduced at the initial stage of evaluation to reduce the investment in such anomalies, (4) This method has limited ability to distinguish porphyry copper deposit from porphyry molybdenum deposit.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Accurately glacier mapping is crucial for understanding climate change impacts, but existing efforts may be biased due to overlooking spatial autocorrelation during map validation. To address this, we compared several widely used machine learning algorithms as gradient boosting machines (GBM), k-nearest neighbor (KNN) and random forest (RF) with parametric logistic regression (GLM) and an unsupervised remote sensing-based method (NDSI) for mapping Peru's glacier regions in a thoughtful experimental setup. Spatial and non-spatial cross-validation methods were used to evaluate model’s performance and compared with a fully independent test set. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results when compared to independent test set, emphasizing the need to consider spatial autocorrelation when using machine learning for glacier mapping. K-nearest neighbors (KNN) was the overall best model across regions consistently demonstrating the highest performance followed by logistic regression (LR) and gradient boosting machines (GBM). Our novel validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

This paper reviews recent literature on Spatial Data Infrastructure (SDI) for remote sensing, aiming to provide a comprehensive analysis of the state-of-the-art practices, challenges, and future directions. It also tries to find out if current projects and initiatives involving the exploitation of satellite remote sensing are still framed in the SDI concepts.

Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China's provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taked 30 provinces, autonomous regions and municipalities directly under the central government in China from 2011 to 2020 as the research object, the employment quality evaluation system was constructed from six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data was expressed as a three-order high-dimensional tensor spatio-temporal data form, and the employment quality of China's provinces was measured from the spatio-temporal perspective by using principal tensor analysis, and the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time-space dual fixed effect model of the spatial Dubin model was selected to analyze the direction and magnitude of the influence factors of employment quality on itself and its neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high and the employment quality varies greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China's provinces, while the industrial structure had a significant negative effect on the development of employment quality.

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

At the beginning of 2019, the petroleum crisis impacted many economies dependent on this industry. The Mexican government started programs to identify points and government officials involved in the gasoline stealing from PEMEX (Petróleos Mexicanos), the country’s leading government petroleum company. The programs consisted of supervising and monitoring the Mexican country network of gasoline ducts to detect points where gasoline was being stolen. Consequently, large urban regions faced a lack and shortage of gasoline. This situation generated several reactions in social media and many open data in news media. Although the government provided open data about stealing gasoline locations related to crimes, it did not analyze the collected data to identify patterns, insights, and the spatio-temporal characterization of this phenomenon. This paper presents a study to deal with the regional semantics described in the social media locations of gasoline stealing. Thus, a framework to discover the trends that emerge from social media and how it is correlated with the government’s open data is also presented—the proposed methodology used machine learning techniques based on linguistic and semantic technologies. The analysis was applied to a dataset of 24,317 geo-referenced tweets. The obtained results reflected the Mexican thinking opinion regarding discovered social topics, polarization maps, and regional insights. According to discovered trends, there were long fuel lines between 1.5 and 5 kilometers (on average) at fuel stations in different Mexican states.

PrEP uptake in the Netherlands is growing but remains at suboptimal levels. Hence, the analysis of hurdles is paramount. Given the initial focus of PrEP provision among men-who-have-sex-with-men (MSM) via a demonstration project that was launched in June 2015, AmPrEP in Amsterdam, and pharmacies in the main urban areas (so called “Randstad”, entailing Amsterdam, Utrecht, Leiden, The Hague and Rotterdam), investigating regional differences is necessary. This study seeks to unravel regional differences jointly with psycho-social determinants of PrEP uptake. This cross-sectional study included 3,232 HIV-negative Dutch MSM recruited via the EMIS survey in late 2017. Prevalence and standardized prevalence ratio (SPR) of PrEP awareness, intention and uptake were measured on a regional level (Randstad vs. the rest of the country). Multilevel logistic modelling was conducted to identify the association of PrEP uptake with PrEP awareness and intention, sociodemographic, psycho-social determinants, and random effects from regional differences. MSM from the Randstad used more PrEP (SPR=1.4 vs. 0.7) compared to the rest of the country, but there were minor differences for awareness and intention. The regional distinction was estimated to explain 4.6% of the PrEP use variance. We observed a greater influence from PrEP intention (OR=4.5, 95%CI 2.0-10.1), while there was limited influence from the awareness of PrEP (OR=0.4, 95%CI 0.04-4.4). Lower education (OR=0.4, 95%CI 0.2-0.9) was negatively associated with PrEP uptake, however, no significant difference was found between middle and high education (OR=1.2, 95%CI 0.7-2.0). We showed that regional differences – MSM in non-urban regions – and other psycho-social determinants account for lower PrEP uptake. Based on these findings, more fine-tuned PrEP access with a focus on non-urban regions can be implemented, and tailored campaigns increasing intention/use can be conducted among target populations.

To reduce solar power production invariance, it is critical to study varying patterns of power production in the concerned region. Analyzing the patterns of past power production trends can help simulate power production scenarios for future. The current study area is around Amsterdam, located in Netherlands. PVoutput.org website is used to mine 6 months of solar power production data for 120 stations around Amsterdam city. FME Workbench software is used to actively fetch the data from the mentioned website and manage in a MySQL database. Solar attenuation maps created using ArcGIS, helped to graphically visualize the variations in solar power production at different times and locations. Further, spatial autocorrelation is checked between the stations using semi-variograms in geostatistical tool of ArcMap. This feature allows to check whether the stations located close to each other are more correlated to each other rather than stations which are far apart. The statistical data analysis of power production can aid solar power production companies to better interpolate and predict solar power in advance for the concerned study region.

Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

Understanding the spatial distribution pattern and evolution characteristics of the elderly population in urban areas is of great significance for the development of urban planning and the implementation of public management policies in the context of rapid aging. Accurately identifying the spatial distribution and evolution characteristics of the elderly population in the city requires a comprehensive analysis of multiple indicators and large-scale data. Taking Wuhan city as an example, this article measures the spatial distribution characteristics and evolution trend of the elderly population from 2000 to 2020 at the street/township level, based on the fifth, sixth, and seventh census data, using methods such as kernel density hotspot detection, spatial clustering analysis, and standard deviation ellipse analysis. The results show that: ① there are significant differences in the aging spatial pattern between the central area and the suburban areas of Wuhan; ② overall, Wuhan's aging rate shows a typical "core-periphery" growth mode in space, while the density of the elderly population has significant spatial aggregation characteristics and shows an evolution trend of "centralized concentration, peripheral outliers, axial development, and near-field growth"; ③the center of gravity of the elderly population remains relatively stable over time.

Dengue infection is one of the most important vector-borne diseases worldwide and is a significant public health problem in the tropics. Mosquito control continues to be the primary approach to reducing the disease burden and the dengue virus (DENV) spread. Aside from the traditional larviciding and adulticiding interventions, autodissemination using pyriproxyfen-treated (AD-PPF) ovitraps is one of the promising methods to complement existing vector control strategies. Our paper assessed the efficacy of AD-PPF in reducing DENV infection in two barangays in Parañaque City. Using saliva samples from the participants from both the control and intervention sites, we collected the seroprevalence data for three months in each of the two years. Spatial analysis was conducted to determine hotspot areas and identify DENV infection distributions across the trial periods. Results showed that the intervention site was identified as having clustering of DENV infection in Month 0 of Year 1 and shifted to random dispersion of dengue cases at the end of Month 3 in Year 2. The disappearance of the clustering of the intervention site translates to a decrease in the cases of DENV infection relative to the control site. Furthermore, we also identified that DENV infection transmission occurs at a small-scale level that did not go beyond 86 meters. In conclusion, AD-PPF is suggested to be an effective strategy and may be used as an additional vector control approach, albeit in its short-term implementation.

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics and spatial transcriptomics now allow recording the patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multi-scale dynamics of its evolution. Here, we review latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed a burgeoning growth in recent past in terms of mapping heterogeneity within tumor cell types as well as stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor, and a more systematic investigation of implications of heterogeneity for the patient outcomes.

The city of Barcelona, ​​like other cities in the world, suffers strong internal socio-economic inequalities. Numerous works have sought to detect, quantify, characterize and / or map existing intra-urban differences, almost always based on quantitative methodologies. With this contribution, we intend to illuminate the complementary role that qualitative methodologies can play in studies on urban socio-environmental vulnerability. We consider aspects that are not quantifiable but that may be inherent to many such vulnerable spaces, both in the constructed environment and in the social ambit. These questions are considered through selected neighborhoods of Barcelona which have been shown (in prior works, mainly studies of quantitative manufacturing) to possess elements of vulnerability including a high presence of immigrants from less-developed countries, low per capita income, aging populations, or low educational levels. The results reveal the multidimensionality of vulnerability in the neighborhoods analyzed, as well as the essential complementarity among methodologies that detect and support possible public actions aimed at reducing or eliminating intra-urban inequalities.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

The rapid development of the regional economy in China has led to the rise of local ecological risks. It is very important to provide enjoyable ecosystem services to residents while reducing ecological risks. In order to understand spatial relationships between ecosystem services and ecological risks, we took Chongqing as an example in this study to assess the spatial relationship between ecosystem services and ecological risks at the county scale based on the ES-DPSIR system. The main findings include: (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas, (2) the increase of the comprehensive ecological risk index from east to west, ranging from -0.134 to 0.333, (3) the spatial relationship between ecosystem services and ecological risks was prominent, with 52.63% of the districts and counties being imbalanced or mild imbalanced, and (4) the significant differences between development trends of ecosystem services-ecological risks, including 60.53% being imbalanced districts and 30.47% mildly balanced. Overall, it was necessary to improve the relationship between ecosystem services and ecological risks in Chongqing by reducing ecological risks., and these research results could provide effective approaches and technical support for improving regional ecological security and enhancing ecosystem service capabilities.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

The kinetics and modeling of dual-wavelength controlled photopolymerization confinement (PC) are presented and measured data are analyzed by analytic formulas and numerical data. The UV-light initiated inhibition effect is strongly monomer-dependent and different monomers have different C=C bond rate constants and conversion efficacy. Without the UV-light, for a given blue-light intensity, higher initiator concentration (C₁₀) and rate constant (k’) lead to higher conversion, as also predicted by analytic formulas, in which the total conversion rate (R_T) is an increasing function of k’R, which is proportional to k[gB₁C₁]^0.5. However, the coupling factor b₁ plays a different role that higher b₁ leads to higher conversion only in the transient regime; whereas higher b₁ leads lower steady-state conversion. For a fixed initiator concentration C₁₀, higher inhibitor concentration (C₂₀) leads to lower conversion due to stronger inhibition effect. However, same conversion reduction was found for the same H-factor of H₀ = [b₁C₁₀ - b₂C₂₀]. Conversion of blue-only are much higher than that of UV-only and UV-blue combined, in which high C₂₀ results a strong reduction of blue-only-conversion, such that the UV-light serves as the turn-off (trigger) mechanism for the purpose of spatial confirmation within the overlap area of UV and blue light. For example, UV-light controlled methacrylate conversion of a glycidyl dimethacrylate resin formulated with a tertiary amine co-initiator, and butyl nitrite, subject to a continuous exposure of a blue light, but an on-off exposure of a UV-light. Finally, we developed a theoretical new finding for the criterion of a good material/candidate governed by a double ratio of light-intensity and concentration, [I₂₀C₂₀.]/[I₁₀C₁₀].

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

The formula of the Generalized Ambiguity Function (GAF) of passive bistatic SAR system using the non-cooperative illuminators which transmit PSK modulating signals is derived to analyze the spatial resolution of the system. The average GAF is introduced to remove the effect of particular sequence of symbols on resolution because the particular sequence of symbols is usually unpredictable before being received. The influence of the waveform parameters of the PSK modulating signals, such as length of the symbol sequence and roll-off factor, on spatial resolution is investigated by numerical simulation. It is confirmed that the influence of the length of the symbol sequence and roll-off factor is very slight but still exists.

The economy is a complex system, and the interactions between different agents are still not easy to quickly see-through. This complexity should reflect in a spatial dimension; in this way, tracking the tradeoffs opens a new window to the nexus of place and flow. Due to the fact, the economic systems often go through transitions and end up in another state, and this evolution is embedded in cities as the new motor of paradigm shift. To adequately represent and study these dynamics, we aim to develop an integrated method based on network analysis science and geographic economy synthesis to detect a multiscale navigator to track the transition from regional to the local level. This paper seeks to explore the specialization of regional clusters and their innovative behaviour in a particular lagging region, hence unfolding the innovation ecosystem to the smallest granularity then simulating the emergence phase of this complex system. First, our findings reveal that the local scale is relevant to start a bottom-up planning approach on policy implementation. Second, the global challenges could be addressed on a regional scale if we investigate the local complexity to unfold the innovation flow over its complex ecosystem and lead the knowledge as a critical element for inclusive transition, most probably into cities. Finally, the innovation network is an existing fact which can translate as a host for prosperity; In this line of reasoning, we intend to spatialize the track of the innovation flow to achieve transition hotspots and respond adequately to upcoming world concerns.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

Safety of source water streams is an urgent environmental issue, while protections in monsoon controlled subtropical regions face difficulties because of the lack of small scaled observation and analysis in small source water catchments. Basing on continuous weekly water quality data (2014-2017) in Pingqiao River Catchment, the annual average values of TN, NO₃, NH₄ and TP are 3.36, 1.64, 0.28 and 0.02 (mg/L) respectively. During dry, normal and wet seasons, the variability of parameters is over 35%, which indicates an obvious seasonality. Multiple methods are combined in order to assess the water quality and find the driving factors during dry, normal and wet seasons. This study suggests precipitation and fertilization are the mainly seasonal factors, which can make water quality better in wet season than dry season due to the dilution effect. The mechanism between seasonality and compound of nutrients can also be traced by log(TN:TP), and log(NO₃:NH₄). Among six main land use types (forest, tea plantation, cropland (paddy), urban, bare soil and water), the former three ones are influential mostly during dry and wet season. Tea plantation has the largest nutrients discharge amount per area, which is similar to cropland in dry season. By contrary, forest has the double power in reducing nitrogen release in wet and normal seasons. When transformed into paddies, croplands can lower the phosphorus concentration. Conclusions of this study can be used in southeastern China and similar regions on source water protection and agricultural plans.

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policy makers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and regional disparities. The study uncovers intraregional variations, illustrating how different municipalities within Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors, such as unemployment, significantly impact this pattern. The dynamics of the housing market, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. The findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and lack of social capital.

The spread of charging infrastructure (CIS) for battery electric vehicles is crucial for coping with the increasing number of electric vehicles. Therefore, the selection of ideal (fast-) charging locations determines acceptance, utilization and, thus, the economic viability of a single site or the whole charging network. The methodology of the Integrated Model Approach STELLA[1] for site identification of CIS uses proven methods of traffic modeling such as the classic four-step traffic modeling in a new context to enable statements regarding the positioning of CIS. Based on different spatial analyzes and characterizations of urban quarters, traffic generated by individuals is calculated using the FGSV approach of 2010. Because only (electric) motorized individual traffic is of importance for CIS, the share of trips is calculated by differentiating the modal split between various transport groups. One approach is to concretize the modal split share of public transport based on analyzes of different criteria and data sets, e.g. the accessibility of stops. The model approach STELLA, which also combines various extensive data (e.g. transport networks and traffic volumes, settlement structures, vehicle characteristics, power supply data and user requirements), is currently developed for a planning area covering the entire territory of the Federal Republic of Germany. [1] STELLA is the acronym for the German term "STandortfindungsmodell für ELektrische LAdeinfrastruktur”.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

Understanding the vulnerability of areas and the likelihood of specific disasters, particularly earthquakes, is one of the most important issues in Iran. One of the major concerns in Iran is the resilience of rural communities. The present study is devoted to spatial analysis of risk in rural areas and the evaluation of preparedness in the rural districts of the city of Abhar. In particular, this study evaluates the resilience to earthquakes. The research was conducted in two parts in which the first part has used the VIKOR Multiple criteria decision making model as well as the employment of this model in the ArcGis. The second part of the study used field studies, in the form of questionnaires, to evaluate the readiness of the local population against the risks of earthquakes. Four indicators, individual, physical, economic abilities and access, were assessed. The population included rural districts, where statistical samples were villagers. Results of the spatial analysis indicated that 15 villages are in the high-risk areas, 24 villages were in the medium-risk areas and all other villages were in low-risk areas. In terms of readiness of the locals, the results indicated a lack of planning with regards to the four mentioned indicators.

As an important research content in cultural geography, the exploration and analysis of the laws of regional cultural differences has great significance for the discovery of distinctive cultures, protection of regional cultures and in-depth understanding of cultural differences. In recent years, with the "spatial turn" of sociology, scholars are paying more and more attention to the implicit spatial information in social media data and the various social phenomena and laws they reflect. One important aspect is to grasp the social cultural phenomena and its spatial distribution characteristics through the text. Using machine learning methods such as the popular natural language processing (NLP), this paper can not only extract hotspot cultural elements from text data but also accurately detect the spatial interaction pattern of some specific cultures and the characteristics of emotions towards non-native cultures. Taking the 6,128 answers to the question “what are the differences between South and North China that you never know” on the Zhihu Q&A Platform as an example, with the help of NLP, this paper has explored the cultural differences between South and North China in people’s mind. This paper probes into people’s feeling and cognition of the cultural differences between South and North China from three aspects, including spatial interaction patterns of hotspot cultural elements, components of hotspot culture and emotional characteristics under the influence of cultural differences between North and South. The study reveals that 1) people from North and South China have great differences in recognizing each other’s culture. 2) Food culture is the most popular among many cultural differences. 3) People tend to show negative attitude towards the food cultures different from their own. All these findings shed light upon the understanding of regional cultural differences and addressing cultural conflicts. In addition, this paper also provides an effective solution to the study from a macro perspective, which have been difficult for new cultural geography.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

The adapted Posner spatial cue-target paradigm was used to explore the relationship between exogenous temporal attention caused by rhythm and endogenous and exogenous spatial attention. The study was divided into two experiments. In Experiment 1, 33 participants used arrow cues to orient endogenous spatial attention and rhythm to orient exogenous temporal attention. In Experiment 2, with 29 participants, exogenous spatial attention was directed by highlighting the cue box. Exogenous temporal attention was directed by rhythm. Experiment 1 indicated that RTs were faster for attended targets than the unattended. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. RTs were faster for late compared to synchrony. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was not significant. Experiment 2 demonstrated that exogenous spatial attention only exhibited IOR under early four stimulus repetitions. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was significant. The results indicated that rhythmic temporal attention supported foreperiod effects. Exogenous temporal attention and endogenous spatial attention independently affect behavior, while exogenous temporal attention and exogenous spatial attention mutually influenced behavior.

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

Objectives: The present study was conducted to evaluate the indicators of the aging-friendly using GIS software in the eight districts of Ahwaz, southwest Iran in 2022. Materials and methods: this descriptive-analytical study was conducted on 317 older adults by stratified random sampling method. Data collection tools were demographic characteristics and aging-friendly city indicators. GIS methods were used for the spatial analysis and SPSS version 28 software was used to analyze descriptive statistics. Results: The mean age of the participants was 66.21±6.99 years. Most of the participants in this study were male (53.9%), illiterate (34.7%), married (84.9%) and residents of district 7 (22.1%). The findings of the present study showed that all 8 districts follow spatial autocorrelation and cluster pattern in all components of the aging-friendly city (urban open spaces, intra-urban transportation system, public and religious places and buildings, safety and ease of traffic, social participation and communication, social respect, culture-recreation, health and treatment). Conclusion: According to the results, the city of Ahwaz is far from the ideal level among the components of an aging-friendly city; therefore, provincial and city officials should pay more attention to these indicators and take more effective steps to increase their quality.

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and few disease-targeted therapies exist. To identify, focal, spatially restricted alterations in transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of areas of focal myocyte disarray compared to areas of normal tissue, using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and Control tissue, and functional enrichment analysis indicated these genes were primarily involved in interferon production and mitochondrial energetics. Within HCM tissue, differentially expressed genes between areas of mild and moderate disarray were enriched for genes related to mitochondrial energetics (moderate disarray) and response to oxygen/cytokine levels (mild disarray). The comparison between areas of moderate and severe disarray were enriched for genes related to the c-Jun N-terminal kinase (JNK) cascade in severe disarray. Analysis of ligand-receptor pair gene expression revealed that HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling, while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in normal areas from control tissue was also divergent from normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

Target 9.c of the 2015 United Nations (UN) sustainable development goals (SDGs) specifically addresses increasing access to information and communication technology (ICT) resources, and striving for universal access to the internet by 2020. The present study seeks to evaluate the effectiveness of the youth related national strategies implemented in this regard by a select number of countries in the Middle East region. The study does so, by relying on a spatial bivariate copula regression analysis of data on youth respondents from five countries, extracted from the 2018 Program for international students’ assessment (PISA). Focusing specifically on evaluating the availability of ICT resources to the youth population, and also identifying the impact of ICT resources on youth subjective well-being in the region, we find that except for the UAE and Qatar that have above OECD average youth performance on the ICT resource index, youth from the remaining countries reported below OECD level average access to ICT resources. The within region cross-country comparative analysis of ICT resources availability to the youth population at home, also highlighted significant heterogeneity across the five countries, post 2015 SDG adoption by UN country members. Furthermore, looking at the impact of ICT resources on youth well-being, controlling for not only cross-country spatial correlations, and factors such as home educational resources, cultural possessions at home, parental occupation status, youth expected occupation status, economic and socio-cultural status, age, gender, and grade level in school; we found that every standard deviation increase in ICT resources to the youth population in the region raises their self-expressed sense of belonging in school by 1.88% standard deviations. Given the empowering nature of ICT resources to youth, and the potential of both to support national as well as regional economic development initiatives, a concerted effort to ease ICT resources diffusion by member countries in the middle east region could assist not only each country in its own development path, but also the region as a whole to live up to its growth potential by the 2030.

Recently intraductal carcinoma of the prostate (IDCP) has attracted increasing interest owing to its unfavorable prognoses. To effectively identify the IDCP-specific gene expression profile, we took a novel approach of characterizing a typical IDCP case using spatial gene expression analysis. A formalin-fixed, paraffin-embedded sample was subjected to Visium CytAssist Spatial Gene Expression analysis. IDCP within invasive prostate cancer sites was recognized as a distinct cluster separate from other invasive cancer clusters. Highly expressed genes defining the IDCP cluster, such as MUC6, MYO16, NPY, and KLK12, reflected the aggressive nature of high-grade prostate cancer. IDCP sites also showed increased hypoxia markers HIF1A, BNIP3L, PDK1, and POGLUT1; decreased fibroblast markers COL1A2, DCN, and LUM; and decreased immune cell markers CCR5 and FCGR3A. Overall, these findings indicate that the hypoxic tumor microenvironment and reduced accessibility to immune cells, which reflect the pathological characteristics of IDCP, may influence the aggressiveness of high-grade prostate cancer.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using Aerosol Optical Depth (AOD) and several climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using Motor Vehicle Emission Simulator (MOVES), the measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS), and NASA’s Moderate Resolution Spectroradiometer (MODIS). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

Dawn of the omics revolution in biological sciences meant that we seek to understand more and in greater detail the molecular constituents of cells and biological systems. While we have gained significant insights from conventional omics tools, we now seek to understand the spatial dimensions of the data where subcellular localisation may impact on cellular physiology and phenotype. This review paper seeks to address current questions in the new field of image-based spatial proteomics as well as outline future challenges of the field. At first glance, spatial proteomics offers enormous potential to expand our understanding of different cell types in different disease and cell states. But limitations in types of fluorophores and issues with spectral overlap significantly hampers the practical implementation of the technique. On the other hand, while we have super-resolution microscopy techniques such as STED, PALM and STORM able to achieve 10 to 20 nm spatial resolution in single molecule localisation, problems with slow image acquisition limits high temporal resolution tracking of multiple protein targets in live cell imaging. Hence, the field of spatial proteomics is a mix of promises and challenges where we could image, in multi-colour, upwards of 10 well-chosen proteins that could inform on the molecular mechanisms of selected biological processes, but, at present, the method could not tackle larger scale questions. In essence, current implementation of image-based spatial proteomics is useful, but it is unable to fulfil the mission of large-scale projects such as the Human Protein Atlas or Human Cell Atlas. Future challenges in the field includes the development of more fluorophores (especially photoswitchable and photoactivable ones) for single molecule localisation microscopy, as well as seeking to improve temporal resolutions to the sub-millisecond range.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

This paper an approach for analyzing the impact of sea level changes on prehistoric human settlement patterns in the Southwestern Iberian Peninsula. The approach is based on highly qualified and fully georeferenced information sources managed within a spatial database. This allows for a more precise analysis of the distance to the coast and its relation to marine resources from a specific location, areas that may have lost their archaeological potential due to being currently submerged, and the actual distribution of sites as a starting point for territorial analysis. Coastal changes, such as sea level fluctuations over the past 120,000 years, have affected the position of the coastline and influenced human settlement patterns. Through an analysis of the archaeological site locations relative to their paleo-coastlines based on available dating data, this study emphasizes the necessity of adopting a comprehensive approach to comprehend human settlement patterns and their correlation with the dynamic coastal changes. This approach provides valuable insights for formulating strategies for exploiting coastal resources and structuring socio-economic systems in the region.

Despite its apparent lack of physicality, the virtual environment produces real experiences to its users. In its intangible context of digital operation, the virtual setting can be a meeting point for individuals to interact and to gain experiences that have an impact upon their lives. After more than three decades of broad accessibility and use of the internet and various digital platforms, the virtual experience has also prompted to rethink many of the assumptions commonly attributed to physical space. From a practical point, the virtual world has been an extension of the real one as a new site that can host people’s activities without many of the limitations associated with the material world. Given its absence of physical restrictions, the virtual space appears as a boundless one, whose potential of evolution is still unclear. This sense of limitedness has caused to shift our common sense about physical space as well, including architectural perception and the methods and practices applied to design it. In response, this present study focuses on the ways in which elements and concepts of the virtual world may be transferred to physical space and enrich architectural aims and the broader design discourse.

This study contributes to the debate on accessibility of higher education in Chile, focusing on both socioeconomic and geospatial dimensions of access to university study. The central question we address in this paper is the following: Does geography (physical distance and neighborhood effects) play a significant role in determining accessibility of higher education in Chile? We use Heckman probit-type (Heckit) models to adjust for selection in the process of completing the trajectory towards higher education – that is, pre-selection, application to study at university, and ultimately admission (or denial) to a higher education institution. The results shows that the geospatial elements have a significant local effect on the student’s application and access to Chilean universities.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

In order to better realize rural revitalization, this paper analyzes the spatial and temporal char-acteristics and influencing factors of agricultural surface source pollution in the Yangtze River Economic Belt from the three perspectives of government, enterprise and agriculture by using the spatial Durbin model and the dynamic GMM method in the period of 2006-2021, and further re-searches the threshold characteristics of the distortion of the factor market on the agricultural surface source pollution under the different strengths of environmental regulation. The results show that there is a positive spatial correlation between agricultural surface pollution in the Yangtze River Economic Belt, and government environmental regulation, input factor market distortion and labor force transfer all have a significant impact on agricultural surface pollution. Among them, factor market distortion has a significant spatial spillover effect on agricultural surface pollution in the Yangtze River Economic Zone, and has a significant single-threshold ef-fect on environmental regulation. Accordingly, the government should strengthen environmental regulation, continuously improve the agricultural factor market mechanism, and pay attention to the construction of talents to provide support for rural revitalization.

Our study examines how dengue incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales. We used the incidence of dengue in Ibagué, Colombia, from 2013 to 2018, to examine the associations with climate, socioeconomic and demographic factors from the census and satellite imagery at various levels of local spatial aggregation, including Manzanas, Secciones, Sectores, and Comunas. Our findings show a significant effect of spatial variables at finer levels of aggregation, showing varying degrees of correlation with dengue incidence. Temporal variables such as temperature and precipitation displayed consistent patterns across all spatial levels, with notable variations in Relative Risk (RR). Our study employs Geographically Weighted Regression (GWR) to identify relevant socioeconomic and demographic predictors. Then, these predictors were integrated into hierarchical models implemented in Integrated Nested Laplace Approximation (INLA) at each spatial level to assess spatiotemporal interactions. We comprehensively analyzed the three distinct models developed for each level: spatial, temporal, and spatiotemporal. A comparative evaluation of the models reveals that while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling and highlight the potential for targeted public health interventions based on localized risk factor analysis. Notably, the intermediate level Secciones emerged as the most informative, balancing spatial heterogeneity and case distribution density, thereby providing a robust framework for understanding the spatial determinants of dengue.

Green innovation has become an important combination of high-quality economic growth and sustainable development of ecological environment. In this paper, the super-efficiency network SBM model is used to measure the two-stage green innovation efficiency of industrial science and technology R&D and achievement transformation in 30 provinces and cities from 2009 to 2019, and exploratory Data Analysis (ESDA) and spatial econometric model are used to investigate the spatial-temporal evolution characteristics and influencing factors of green innovation efficiency. The results show that: firstly, the overall efficiency of industrial green innovation is low, and the efficiency of scientific research and development and achievement transformation has experienced three stages of "upward-declining-revitalized period". The low efficiency of achievement transformation is an important factor hiding the improvement of the efficiency of industrial green innovation. Secondly, The industrial green innovation efficiency gradually increases from northwest to southeast, forming a centralized "line" and "block" distribution. The high efficiency area is still concentrated in the eastern coastal region, and the balanced development trend is obvious in the central and western regions. Finally, openness has a positive impact on the two-stage green innovation efficiency; Industrial structure and government investment in science and technology have a positive impact on the efficiency of science and technology research and development, but have no significant effect on the efficiency of achievement transformation. Enterprise size has a positive effect on achievement transformation efficiency, but has no significant effect on R&D efficiency. Environmental regulation has a positive impact on R&D efficiency and a negative impact on achievement transformation efficiency.

Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi Polygons; Delaunay Triangulations; Perpendicular Bisectors; Circumcenters; Convex Hulls; Minimal Spanning Trees; Ulam Trees; Pitteway Violations; Circularity; Clark-Evans spatial statistics; Variance to Mean Ratios; Gabriel Graphs; and, Minimal Spanning Trees. Furthermore, biologists have developed a number of empirically related correlations for polygonal tessellations such as: Lewis’s Law (the number of edges of convex polygons are positively correlated with the areas of these polygons): Desch’s Law (the number of edges of convex polygons are positively correlated with the perimeters of these polygons); and Errara’s Law (daughter cell areas should be roughly half that of their parent cells’ areas). We introduce a new Pitteway Law that the number of sides of the convex polygons in a Voronoi tessellation of biological epithelia is proportional to the minimal interior angle of the convex polygons as angles less than 90 degrees result in Pitteway violations of the Delaunay dual of the Voronoi tessellation.

The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Taking the Yimengshan Geopark (YG) as an example, based on the productional–living–ecological space (PLES) perspective, which constructs a land-use spatial conflict identification and intensity diagnosis model (LUCSII) using the landscape ecology index. We apply geographic information system (GIS) and other methods to achieve the spatial pattern of LUSC over the last 20 years, and we use the GMOP–Markov–PLUS model to simulate the evolution of LUSC in the future under various scenarios. From 2000 to 2020, the LUSC values in the YG were mainly stable and controllable, with mild conflict, while the areas of severe conflict were mainly concentrated in the central urban area of Mengyin County and in low and flat terrain areas such as southern Bailin Town. The LUSC in the YG showed a significant positive spatial correlation, and spatial agglomeration is gradually strengthening. The high–high clusters are found in contiguous areas at the junction of Changlu Town, Gaodu Town, and Mengyin Street, as well as in the southern hilly areas. The low–low clusters were concentrated in Yedian Town, Daigu Town inarea north of the study, and areas surrounding Yunmeng Lake Wetland Park. In the next ten years, the ecological priority scenario (EPD) and sustainable development scenario (ESD) will both be reasonable options for easing and controlling LUSC in YG. Local governments and park management bureaus should determine the three lines and three zones based on the needs of social and economic development, particularly the boundary red line for construction land growth, and plan production and living spaces to alleviate land-use conflicts and stabilize the land-use system. Regional ecological security can be maintained, and future deterioration of the park’s ecological environment avoided, by performing well in terms of ecological isolation.

We utilized all Spanish marriage records available at the municipality level from 2005-2007 to model spatial variations in intermarriage. We constructed a spatial regime zero inflated Poisson model and grouped-data probit model, with spatially lagged regressors, to predict the absolute and relative presence of intermarriage between Spaniards and migrants based on structural characteristics of the local marriage markets and their neighboring areas (i.e., relative group size, homogeneity of national origins, and sex ratio indicators). Our models do not assume collapsibility of the marriage market. Instead, they incorporate the local dimension of the marriage market and examine the association between intermarriage and structural variables at the spatial local level. The model also investigates intermarriage variation by size of place. The local characteristics of the marriage markets are robust indicators of both the absolute and relative importance of intermarriage, but their impact varies by size of municipality. The relative size of the migrant community positively impacts intermarriage. The homogeneity of the origins of migrants is negatively related to it. The impact of sex ratios in the migrant and native communities on intermarriage is not uniform across all municipalities and is not always related to more intermarriage.

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for environmental monitoring. In this paper, the percentage of the communication attempts of wireless sensors is assumed as cost. Performance evaluation results show that the proposed spatial tracking approach is low cost and can model the spatial signal over time with the same performance as that of spatial modeling.

Background: Military pilots show high visuo-spatial skills. Previous studies demonstrate that they are better in mental rotating a target, in taking different perspectives, in estimating distances, in travel planning and in topographic memory. Here, we compared navigational cognitive styles between military pilots and people without flight experience. Pilots were expected to be more survey users than non-pilots, showing higher navigational strategies. Method: 106 jet military pilots of Italian Air Force and 92 non-pilots were enrolled in order to investigate group differences in navigational styles. Participants were asked to perform a reduced version of the Spatial Cognitive Style Test – SCST, consisting of six tasks that allow to distinguish individuals in landmark (people orient themselves by using a figurative memory for environmental objects), route (people use an egocentric representation of the space) and survey (people have a map-like representation of the space) users. Results: In line with our hypothesis, military pilots mainly adopt a survey style, whereas non-pilots mainly adopt the route style. In addition, pilots outperformed non pilots in both the 3D-Rotation task and Map Description Task. Conclusion: Military flight expertise influences some aspects of the spatial ability, leading to enhance human navigation. Although, it must be considered that they are a population whose navigational skills were already high at the time of selection at the academy before formal training began.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

The evaluation of spatial planning results, or outcomes, has been rather neglected by scholars and practitioners. The causes of this neglect are linked to the characteristics of the planning systems in use or difficulties in quantifying results. To advance the state of the art of outcome evaluation, this paper focuses on assessing the implementation of national spatial planning objectives in urban landscapes, through the use of an evaluation framework that makes use of spatially explicit information. The framework is built on four dimensions which reflect the main domains of spatial planning: efficient built-up development, conservation of agricultural land, landscape preservation, and human perception. Indicators capable of capturing landscape changes in both time and space are used to verify degree of conformance between adopted objectives and actual development patterns. We make use of spatially explicit data and assess whether and where landscape changes occurred, by integrating the framework into a multi-criteria analysis. In the present study, the framework is tested in two study areas in Switzerland and Romania, and the results are interpreted from the perspective of spatial planning approaches in the two countries. The efficiency and utility of the framework is demonstrated by its ability to provide valuable information facilitating improvement in the performance of planning processes, such as identifying where the implementation of objectives is less effective, and the domains of spatial planning that are affected. Our findings highlight that the distance between objectives and outcomes can be attributed to differences in countries’ spatial planning approaches, particularly regarding landscape preservation and management. Our study provides valuable insights for the integration of time series of spatial data into the evaluation procedure.

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will inform agricultural drought and flood disaster assessment and early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Then, we used ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data showed a high correlation with local soil measurements at nine sites. We then analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations showed an increasing trend. We then investigated whether there were abrupt soil moisture changes at each station. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized by “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mainly distributed in the Yangzhou and Huai'an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mainly located in northern Xuzhou.

The determination of the three-dimensional structure is a key step for the identification of new targets as well as the rational design of bioactive compounds. Herein, we report the synthesis and the structural elucidation of novel tetrameric macrocycles. Measurements were taken in solid and solution states with the help of X-ray scattering and NMR spectroscopy. The investigations made will help to find diverse applications for this new, promising compound class.

This paper characterizes the influence of synoptic-scale air mass conditions on spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003-2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the coastal plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003-2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.

The Schelling model of segregation allows for a general description of residential movements in an environment modeled by a lattice. The key factor is that occupants change positions until they are surrounded by a designated minimum number of similarly labeled residents. An analogy to the Ising model has been made in previous research, primarily due the assumption of state changes being dependent upon the adjacent cell positions. This allows for concepts produced in statistical mechanics to be applied to the Schelling model. Here is presented a methodology to estimate the entropy of the model for different states of the simulation. A Monte Carlo estimate is obtained for the set of macrostates defined as the different aggregate homogeneity satisfaction values across all residents, which allows for the entropy value to be produced for each state. This produces a trace of the estimated entropy value for the states of the lattice configurations to be displayed with each iteration. The results show that the initial random placements of residents have larger entropy values than the final states of the simulation when the overall homogeneity of the residential locality is increased.

This paper studies the spatial joint evolution of cooperative behavior and a partially assortative matching institution that protects cooperators. We consider cooperation as characterized by a cultural trait transmitted via an endogenous socialization mechanism a la Bisin and Verdier (2001) and we assume that such trait can diffuse randomly in space due to some spatial noise in the socialization mechanism. Using mathematical techniques from reaction-diffusion equations theory, we show that, under some conditions, an initially localized domain of preferences for cooperation can invade the whole population and characterize the asymptotic speed of diffusion. We consider first thecase with exogenous assortativeness, and then endogeneize the degree of social segmentation in matching, assuming that it is collectively set at each point of time and space by the local community. We show how relatively low cost segmenting institutions can appear in new places thanks to the spatial random diffusion of cooperation, helping a localized cultural cluster of cooperation to invade the whole population. The endogenous assortative matching institution follows a life cycle process : appearing, growing and then disappearing once a culture of cooperation is suffciently established in the local population.

In the context of carbon emissions peak, environmental issues highlight the importance of the green economy, how does economic agglomeration release growth potential and enable the coordinated development of the economy and environment? There are few works of literature to analyze it within the framework of spatial economy. This paper constructs a theoretical model to clarify the influence mechanism of economic agglomeration on green total factor productivity (GTFP), and then uses a dynamic SDM model to test the theoretical hypothesis. This contribution has three main findings. First, there is a "U-shaped" curve relationship between economic agglomeration and GTFP, and the formation mechanism is that economic agglomeration has a threshold effect on the agglomeration externalities such as infrastructure sharing, knowledge spillover, and labor market upgrading. Second, the mismatch of industrial structure is an important reason that the economic agglomeration in this region has not produced an obvious spatial spillover effect on other regions; Relaxing restrictions on the concentration of economic activity to regional centers would contribute to the improvement of GTFP. Third, GTFP has the classic "snowball effect" in the time dimension, but has the obvious "warning effect" in the space and time dimension. Based on this, this paper believes that at the present stage, it is necessary to adapt to the layout of economic geography, promote the rational allocation of human resources in the territorial space, promote the coordination between economic agglomeration and the development goal of green economy, and at the same time, it is necessary to cultivate the cooperative linkage mechanism of green economy development and transformation among cities.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for an improved ecosystem management, based on an accounting of changes and on monitoring of their functioning in time. In Europe, the main legislative driver behind this goal is the European Commission’s Biodiversity Strategy to 2020, on one hand aiming at reducing biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%–1.19% of the study area can be attributed as actual riparian area (considering Strahler’s stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558–69.128 km². Similarly depending on the accounting approach, the potential riparian zones are accounted for about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. In this study, we evaluated the performance of the product in Hunan Province and conducted refined analysis of surface heat resources based on the 4km/1h resolution product over a two-year period. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R=0.893), albeit with a relatively high error level (Bias=-6.295℃; RMSE=8.58℃), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by the terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang-Zhu-Tan urban agglomeration during two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite's detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

As a rapidly growing coastal megacity, Shanghai is continuously threatened with land subsidence issues since 1920s. Land subsidence was controlled in 1960s, however in 1990s, unconscious and dangerous urban underground space (UUS) exploration and tunneling development are causing further land subsidence. It is imperative to study previous relations towards future adaptive and resilient scenario modelling and planning. There are multiple cause-effect factors determined in the urban built environment of Shanghai megacity. This paper presents the current evidence based on the relations of the multifactor of the spectrum. Methods consist of understanding the cause-effect relations and spatiotemporal from the crucial period of 1960-2020. Data are collected secondarily from multiple open sourced databases. The results determine Shanghai are highly influenced by the UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Spatiotemporal pattern has shown a mixed positive-negative impact: population, land subsidence is growing in parallel distribution (positive) with tunneling leakage, construction of tunneling, metro system, UUS development, building price, reconstruction area, GDP growth, land price, arable land decrease and further tunnel settlement in Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results are useful for further adaptive and resilient scenario modelling and spatial planning.

Factors responsible of species distribution were intensively investigated in the past, including response at different spatial scales. The aim of the present research is to review the most relevant factors explaining chironomid species distribution focusing to factors operating at different spatial scales, such as latitude, longitude, altitude, water temperature, dissolved oxygen, nutrients acting at regional, country, water basin extension. Data including chironomid species abundances from different lentic and lotic waters from Italy and other surrounding countries were analyzed with different multivariate methods: partial canonical correspondence analysis and multiple discriminant analysis. Spatial analysis included univariate Moran’I correlograms, multivariate Mantel correlograms and Moran’s Eigenvectors Maps (MEM) calculation. The results confirmed previous conclusions, that is habitat type including different running waters reaches (kryal, krenal, rhthral, potamal) and different lake types is the most significant factor in separating chironomid assemblages and spatial factors act only as indirect influencers..

The main goal and essential parameter of laser light conversion is achieving higher laser brightness. That is the main reason why we take one light source to pump another. For every application the desired laser beam must have the highest available beam quality and highest achievable power. Usually by increasing one parameter, you incur a loss in the other. Here we present a method for delaying the reduction in beam quality by using a spatially structured optical pump for a membrane external cavity laser resonator. A slight increase in brightness is achieved under the same focusing conditions just by changing the pump intensity profile. By using a dynamically changing pump pattern a controllable output laser mode can be achieved.

Geotechnical investigation is an important site characterization process in engineering construction. Excavation pits and boreholes are popular geotechnical investigation methods for accessing subsurface soil and rock characteristics. Although they are accurate, they are limited to the discrete locations where tests are carried out. The demand for 3D representation has been increasing as an alternative for improved visualization of geotechnical investigations. This study developed a procedure for 3D modelling of geotechnical investigations using open-source software. It improves 3D visualization of geotechnical investigations and characterization of accuracy and uncertainty assessment. Presently, most geotechnical investigations do not regularly report uncertainty assessment despite its importance in risk analysis of the investigations. The procedure developed in this study was tested in a proposed site for construction of an Inland Container Depot where it depicted geotechnical soil properties with more than 80% accuracy on holdout samples. It was able to model both horizontal and vertical variations of the soil properties with quantification of uncertainty. More testing and wide applications are recommended.

Sperm gain fertilization ability and forward movement characteristics during epididymal transport. Sperm maturation is inseparable from the processing of the epididymis. Compared with other reproductive organs, relatively little is known about the epididymis. However, with the development of molecular biology and the improvement of single-cell sequence technology, we gradually realized that we should re-examine the importance of the epididymis for sperm mat-uration and for the entire male reproductive system. Recently, epididymis related reports have tended to be more regionalized and more refined. We can understand cell distribution and gene expression from the perspective of a single cell. It has been reported that different regions of the epididymis have different functions and different cell compositions, which may be determined by gene expression. Here, we comprehensively reviewed the epididymal studies. From the ana-tomical structure of the epididymis to the division of its regions, we focused on the cell compo-sition and region-specific genes differentially expressed in the epididymis. The gene expression of the epididymis extends to a series of biological processes of sperm in the epididymal cavity, and the serious effects of epididymal diseases on reproduction are discussed. Therefore, the importance of the epididymis in male reproduction is emphasized. With the maturity of single-cell spatial transcriptomics, it is hoped that the technology can be applied to epididymal research and bring about the spatiotemporal landscape of the epididymis at the single-cell level.

Monitoring is an important component of pest management, to prevent or mitigate outbreaks of native pests, and to check for quarantine organisms. Surveys often rely on trapping, especially when the target species respond to semiochemicals. Many traps are available for this purpose, but they are bulky in most cases, which raises transportation and deployment issues, and they are expensive, which limits the size and accuracy of any network. To overpass these difficulties, entomologists have used recycled material, such as modified plastic bottles, producing cheap and reliable traps but at the cost of recurrent handywork, not necessarily possible for all end-users (e.g., for national plant protection organizations). These bottle-traps have allowed very large surveys which would have been impossible with standard commercial traps, and we illustrate this approach with a few examples. Here we present, under a Creative Commons BY-SA License, the blueprint of a fan-trap, a foldable model, laser-cut from a sheet of polypropylene, that can rapidly be produced in large numbers, and could be transported and deployed in the field with very little efforts. Our first field comparisons show that fan-traps are as efficient as bottle-traps, and we describe two cases where they are being used for monitoring.

Drones are increasingly becoming a ubiquitous feature of society. They are being used for a multiplicity of applications for military, leisure, economic, and academic purposes. Their application in the latter, especially as social science research tools has seen a sharp uptake in the last decade. This has been possible due, largely, to significant developments in computerization and miniaturization which have culminated in safer, cheaper, lighter, and thus more accessible drones for social scientists. Despite their increasingly widespread use, there has not been an adequate reflection on their use in the spatial social sciences. There is need a deeper reflection on their application in these fields of study. Should the drone even be considered a tool in the toolbox of the social scientist? In which fields is it most relevant? Should it be taught as a course in the universities much in the same way that geographic information system (GIS) became mainstream in geography? What are the ethical implications of its application in the spatial social science? This paper is a brief reflection on these questions. We contend that drones are a neutral tool which can be good and evil. They have actual and potential wide applications in academia but can be a tool through which breaches in ethics can be occasioned given their unique abilities to capture data from vantage perspectives. Researchers therefore need to be circumspect in how they deploy this powerful tool which is increasingly becoming mainstream in the social sciences.

With the rapid development of urbanization and industrialization in China, environmental issues have become a knotty problem, especially issues related to air, water, and solid-waste pollution. These pollutants pose threats to the health of the population and to that of communities and have a vicious influence on the healthcare system. Additionally, pollution also exhibits spill-over effects, which means that pollution in the local region could affect the healthcare services in a neighbouring region. Therefore, it is necessary to explore the relationship between pollution and healthcare. A spatial autocorrelation analysis was conducted and spatial panel econometric models were constructed to explore the characteristics of pollution and healthcare services in China and the relationship between them using data on all 31 provinces over twelve consecutive years (2006-2017). The results showed that the utilization of healthcare services and environmental pollution were not randomly distributed; unsurprisingly, air pollution and solid-waste pollution were mainly found in parts of northern China, while water pollution was highest in southern and coastal China. In addition, environmental pollution exhibited spill-over effects on healthcare services. For example, a 1% increase in solid waste in one specific geographical unit was estimated to increase the inpatient visits per capita in adjacent counties by 0.559%. Specifically, pollution showed different degrees of influence on healthcare services, which means that the impact of environmental pollution on the number of outpatient visits is greater than on the number of inpatient visits. Our results provide the government with evidence for effectively formulating and promulgating policies, especially policies aimed at tackling spill-over effects among different regions.

The sun bear is conservation mammal in Indonesia. The ecological problems as conflict among mammals and human often happen around wildlife, the one of the case is the conflict between sun bear (helarctos malayanus) with human. In Pasaman where around the anthropogenic landscape is bounded by natural forest as sun bear habitat. In this decade, the story has recorded about ±16 incidents. The aim of this research is to geospatial modeling the area of potential conflict between sun bear- human. The method in this research is to use natural logarithmic and regression logistic. The tool is geographical information system and maximum entropy. The result of this research, there has found the distribute energy each variable landscape ecosystem. The statistical model of the potential conflict it has spread on landscape ecosystem. The average value of AUC prediction in this model is at number of 0.91. The variable contribution which affect are forest edge at number of 39.2%, the alternative food (plantation) at number of 31.4%, and forest fragmentation at number of 16.9%.

Urban beggars are social problems that are related to the poverty of citizen. In Denpasar City Bali Province, particularly in West Denpasar District, the beggars have specificity in activities of begging. Studies in this article demonstrate their specificity begging activity which can be evaluated from the spatial movement of beggars based on the place, time and the distance. Through the method of grounded research with qualitative analysis showed that: (1) the beggars have permanent objects to visit, which consist of shops, settlement and places of worship of the Muslim community, (2) begging activities performed every Friday for 6 hours from 08.00 to at 14.00, and (3) beggars not only walking, but also using public transportation to visiting the object that is relatively far from beggars lane. The findings of this study reinforces that begging has become a profession that is conducted in a structured and well-organized.

Climate change is expected to result in rising temperatures, leading to increased occurrences of extreme weather events like heat waves and cold spells. Urban planning responses are crucial for improving the adaptive capacity of cities and communities in dealing with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, the study identifies built-environment factors that contribute to moderate seasonal land surface temperatures (LST). The research focuses on Seoul, South Korea as a case study and calculates seasonal LST values at both the grid level (100m×100m) and street-block level, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for natural reserves. The quadrant analysis reveals spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. The spatial regression analysis demonstrates that higher vegetation density and proximity to water bodies play key roles in moderating LST, leading to cooler summers and warmer winters. Building characteristics have an invariant impact on LST across all seasons, where horizontal expansion contributes to higher LST, while vertical expansion reduces LST. These findings are consistent for both grid- and block-level analyses. The study emphasizes the flexible role of the natural environment in moderating temperatures.

The concept of transformative resilience emerges from complex recent literature and represents a way to interpret the potential opportunities to change in vulnerable territories, where a socio-economic change is required. This article extends the perspective of transformative resilience to assessing of the landscape multi-functionality of inland areas, exploring the potentials to identify a network of synergies among the different municipalities able to trigger a process of territorial resilience. A Spatial Decision Support System (SDSS) for multi-functionality landscape assessment aims to support the local actors to understand local resources and multi-functional values of the Partenio Regional Park (PRP) and surrounding municipalities, in the South of Italy, stimulating their cooperation to the management of environmental and cultural sites and the co-design of new strategies of enhancement.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

Consideration of physical processes in the reference frame K' moving relative to the CMB, provided that clocks synchronization in this frame ensures the independence of time from spatial coordinates, leads to the conclusion about the existence in the K' of spatial anisotropy which should manifest itself in the dependence of physical effects on the speed vector V of the frame relative to the CМB. Some of these effects in the fields of electrodynamics and optics are considered. In particular, it is shown that weak magnetic fields proportional to the vector V should exist in this frame around motionless electric charges. Experimental verification, according to our assessment, is apparently possible.

The migration paths and distribution driving factors of heavy metals in dry and windy area polluted by their production in the North China need a further research. To address this research gap, we collected 675 soil samples, 72 atmospheric deposition samples and 20 water samples around a production area and measured its heavy metal concentrations. Results showed that the Cu, Zn, As and Pb in 0-10 cm soil layer showed a fan-shaped distribution, which was consistent with their atmospheric deposition fluxes. It indicated the distribution patterns of these heavy metals were driven by strong winds in studied area. Although Cr concentrated to the production area in the 0-10 cm soil layer, principal component analysis showed that this migration was through wind as well. The concentration of Cd in the river increased from 0.257 mg/L to 0.460 mg/L along water flowing, and caused the same distribution trend in soil near the river from upstream to downstream. Unlike the above, surface runoff should drive the Cd migration. The concentration of Pb in the river was over threshold of pollution, and also led to an accumulation in the 5-10 cm soil layer. It suggested that the migration of Pb was through both wind and surface runoff. Six studied heavy metals showed different migration behaviors, and specific control strategies for individual heavy metal should be concerned.

Incidence of COVID-19 has been associated with sociodemographic factors. We investigated variations in SARS-CoV-2 seroprevalence at sub-national levels in the Dominican Republic and assessed potential factors influencing variation in regional-level seroprevalence. Data were collected in a three-stage cross-sectional national serosurvey from June to October 2021. Seroprevalence of antibodies against the SARS-CoV-2 spike protein (anti-S) was estimated and adjusted for selection probability, age, and sex. Multilevel logistic regression was used to estimate the effect of covariates on seropositivity for anti-S and correlates of 80% protection (PT80) against symptomatic infection for ancestral and Delta strains. A total of 6,683 participants from 134 clusters in all 10 regions were enrolled. Anti-S, PT80 for ancestral and Delta strains odds ratio varied across regions, Enriquillo presented significant higher odds for all outcomes compared with Yuma. Compared to unvaccinated, receiving ≥2doses of COVID-19 vaccine was associated with a significantly higher odds of anti-S positivity (OR 85.94, [10.95-674.33]), and PT80 for ancestral (OR 4.78, [2.15-10.62]) and Delta strains (OR 3.08, [1.57-9.65]) nationally, and also for each region. Our results can help inform regional-level public health response, such as strategies to increase vaccination coverage in areas with low population immunity against currently circulating strains.

This study empirically examines the spatial spillover effect of various environmental regulations on green technology innovation using panel data from 284 county-level cities in China between 2007 and 2019. A geographical-economic spatial weight matrix is constructed, and the spatial Durbin model is employed to identify the specific characteristics of this spillover effect. The findings indicate that the spatial spillover effect of green technology innovation primarily occurs through geographical transmission. However, there is no significant spatial autocorrelation when using the economic distance weight matrix. Various types of environmental regulations influence the spatial spillover effect of green technology innovation in distinct ways. Specifically, Market-motivated environmental regulation exhibits a U-shaped relationship with the spatial spillover effect, while Command-controlled environmental regulation demonstrates an inverted U-shaped relationship, suggesting a complementary effect. Additional research shows that the upgrading of industrial structure acts as a mediator between command environmental regulation and the spatial spillover effect of green technology innovation. Government departments should comprehensively coordinate Market-motivated environmental regulation and Command-controlled environmental regulation, accurately assess the intensity of Command-controlled measures, and prevent the migration of green technology innovation elements caused by excessive regulatory measures within enterprises.

Plasmonics is the study of resonant oscillations of free electrons in metals caused by incident electromagnetic radiation. Surface plasmons can focus and steer light on the subwavelength scale. Apart from metals, plasmonic phenomena can be observed in soft matter systems such as electrolytes where resonant charge oscillations can be induced for ions in solution. Due to their larger mass, they are plasmon-active in a lower frequency regime and on a larger wavelength scale. Spatial confinement allows increasingly strong charge interactions and gives rise to nonlocality or spatial dispersion effects. These effects manifest as additional longitudinal propagation modes and are known to cause plasmonic broadening and resonance shifts in metal nanostructures. We derive and discuss the nonlocal optical response of ionic plasmons using a hydrodynamic, two-fluid model in a planar homogeneous three-layer system with electrolyte-dielectric interfaces. Studying such systems enables us to identify and understand plasmonic phenomena in biological and chemical systems.

After industrialization and the baby boom, many houses have been left vacant in many cities worldwide due to changes in the economy, socie-ty, and urban composition. The increase in vacant houses causes social problems, like the collapse of village communities, damage to urban aes-thetics, risk of crime due to vacant houses, and decrease in the value of real estate. Accordingly, policy attempts and studies to reduce and utilize vacant houses are in progress in various countries. In South Korea, the ratio of vacant houses was 6.4% of all houses as of 2021, and in Jeolla-buk-do, it was 11.6%, which is higher than the national average. Jeollabuk-do conducted a fact-finding survey on countermeasures against va-cant houses; 17,732 vacant houses (2.4%) were surveyed. The urbanization, population, and terrain of Jeollabuk-do, consisting of 14 cities and counties, were considered. The ratios, types, grades, and spatial autocorrelations of vacant houses were analyzed after classification into city (fo-cus, small, and medium) and county (plains and mountains) areas. There were significant differences in the averages of the ratios, grades, and spatial autocorrelations between city and county areas. Therefore, policy establishment for vacant house management and countermeasures re-quires consideration of the areas and types of vacant houses.Keywords: : vacant house; spatial autocorrelation; fact-finding survey

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2 and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increase the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects and landscape ecology uncertainty studies.

Tuberculosis (TB), an infectious airborne disease caused by Mycobacterium tuberculosis (Mtb), is a serious public health threat reported as the leading cause of morbidity and mortality worldwide. South Africa is a high TB burden country with TB being the highest infectious disease killer. The study investigated the distribution and clustering of Mtb mutations and spoligotypes in rural Eastern Cape Province. The Mtb isolates included were 1,157 from DR-TB patients and analysed by LPA followed by spoligotyping of 441 isolates, of these, 36 were whole genome sequenced. Distribution of mutations and spoligotypes was done by spatial analysis and clustering analysis was done by Bayesian model-based clustering of allele frequencies at heterozygous sites, using Mclust package in R. The rpoB gene had highest number of mutations. The distribution of rpoB and katG mutations was more prevalent in four health care facilities, inhA mutations were more prevalent in three healthcare facilities and heteroresistant isolates were more prevalent in five healthcare facilities. The Mtb was genetically diverse with Beijing more prevalent and largely distributed. Spatial analysis and mapping of gene mutations and spoligotypes revealed better picture of distribution. Clustering of isolates indicates that there is transmission of mixed infection in this area.

Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.

In this theoretical study we report on molecular electrostatic potential (MEP) of titled molecules confined by repulsive potentials of cylindrical symmetry mimicking a topology. Our calculations show that the spatial restriction significantly changes the picture of MEP of molecules in quantitative and qualitative sense. In particular, the drastic changes of MEP as a function of the strength of spatial confinement are observed for the BrCN molecule. This preliminary study is the first step in the investigations of the behavior of MEP of molecular systems under the orbital compression.

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

This paper uses the spatial analysis software GeoDa as a tool, takes GRP (Gross regional product) of Sichuan Province in 2012 and 2018 as the dependent variable, and takes the city (autonomous prefecture) factor-driven, investment-driven and innovation-driven indicators as the dependent variable to explore the impact of innovation activities on regional economic development and the spatial distribution characteristics of regional economy. Through the comparison of the global correlation and local correlation, this paper explores the crux of the regional economic polarization and unbalanced development, and puts forward some measures to solve the existing economic development problems, such as cultivating and improving the regional industrial dependence, accelerating the regional transportation accessibility and convenience, and constructing the regional collaborative innovation system, So as to achieve the strategic goal of the construction of innovative Province in Sichuan Province.

This research has critically argued that a vigilant combination of flexibility and certainty in spatial planning can bring about the most optimum planning outcomes. Therefore, to reproachfully evaluate the core argument, this research has tried to empirically respond to the research question of which balance of government intervention and market freedom produces the optimal economic, social and spatial outcomes. This research question has been further translated into an associated central hypothesis i.e., a hybrid planning system with an optimal balance between discretionary and regulatory planning approach can bring about the desired economic, social, and spatial outcomes.

Colombia has one of the largest numbers of internally displaced populations in the world and recently entered a period of post-conflict. These socio-political processes and trends have increased the migration of people towards cities and accordingly are affecting the distribution and occurrence of tropical diseases in its urban and peri-urban areas. Studies have suggested that many human phenomena such as urbanization scale according to the size of human populations regardless of cultural context. But other studies show that health epidemics such as malarial and human immunodeficiency virus infections, follow a scale-free distribution in terms of population size and density. We explore these relationships and dynamics in a tropical context using statistical analyses and available geospatial data to identify the scale dynamics between urbanization processes and disease transmission in Colombia. We found that rural populations and certain disease dynamics were described by power-laws that are frequently mentioned in urbanization studies. However, we found that malaria presented higher intensity of infection in human settlements of less than 50,000 individuals, particularly for ethnic indigenous populations. Results indicate that epidemics and urbanization dynamics do indeed follow scales in Colombia; findings that differ from previous epidemiological studies such as those for malarial infection. Additionally, we identified trends showing that malarial infections become endemic in peri-urban areas. Targeting such peri-urban locations and certain demographic groups are key for managing public health issues in the urbanizing tropics.