Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN—Facebook and Foursquare) and on the awareness of the use of voluntary geographic information generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) & Inverse Distance Weighting (IDW) Interpolation.

Abstract: Our goal is to create point-of-care (POC) strategies that accelerate decision making, increase efficiency, improve outcomes, and enhance standards of care in island communities faced with global warming, rising oceans, population migration, and intensifying weather disasters. We assessed needs in the Bantayan Archipelago and mainland Cebu Province, Visayas Islands, Philippines, to map POC diagnostics, rescue times, and spatial care paths. Significant deficiencies were lack of cardiac troponin testing for rapid diagnosis of acute myocardial infarction, absence of blood gas and pH testing for support of critically ill patients, and geographic gaps prolonging patient transfers and delaying treatment. Strengths comprised primary care that can be facilitated by POC testing, logical inter-island transfers for which decision making and triage could be accelerated with onboard diagnostic testing, and healthcare small-world networks amenable to POC advances, such as pre-hospital testing, that avoid overloading emergency rooms. Healthcare resources must be distributed to archipelago islands, not concentrated in large metropolitan areas inaccessible for emergency interventions. We conclude that a point-of-need focus will help improve public health, decrease disparities in mortality among rural islanders versus urban dwellers, and pave the way for heightened resilience in anticipation of the adverse impact of global warming on vulnerable coastal areas.

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].

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.

The present study examines inner and outer suburban ring attainment outcomes among racial and ethnic groups residing in the nation’s metropolitan areas. The main objective is to evaluate the extent to which the relationship between racial and ethnic group’s socioeconomic status characteristics and residence between inner and outer suburban rings conforms to the tenets of the spatial assimilation model. Using micro-level data from the 5-year 2012-2016 American Community Survey, the author calculates multinomial logistic regression models to determine the effects of SES and other relevant predictors on residence within the nation’s metropolitan area’s suburban inner and outer rings. The results both confirm and contradict the main tenets of the spatial assimilation model. To the extent that income, education, and homeownership are positively related to residence in both suburban rings, the findings also suggest that access to inner and outer rings is hierarchically stratified by race and ethnicity.

Drylands are arid and semiarid ecosystems, where the lack of rains imposes harsh conditions for the survival of organisms. These ecosystems are also susceptible to degradation and desertification, and their conservation depends on the understanding of the ecological functioning of vegetation and soil. In drylands, the vegetation is spatially structured as a mosaic of patches (vegetation) and interpatches (bare soil), a consequence of plant-plant interactions (facilitation and competition). Empirical data and modeling approaches reinforce the role of ecological facilitation for the maintenance of all organisms in drylands. However, the actual range of facilitation is still poorly known. Here, we explored data of meso- and micro-arthropods found in soil as bioindicators to infer the range of facilitation provided by plants to soil. We regarded data of abundances and species densities (independent samples) collected in random patches and bare soil places as dependent variables. Data of patch size and distances between bare soil and patches were arranged in a single shuffle, producing a 1-d coordinate system centered at the border of the patch. Discrete portions of this system are taken to calculate averages and variances of abundance and species density, and we investigated how soil communities variate across the patch border. We employ techniques of signal analysis to reduce the data noise and obtain a smooth and continuous behavior, which allowed us to fit a logistic function. Our findings indicate that soil communities suddenly change from simple patterns to numerous and diverse communities in bare soil regions, meaning that the influence of vegetation on soil goes beyond the patch border. We interpreted the variations in fauna as a consequence of the positive influence provided by plants (or its lack) on surrounding bare soil. We observe a fast decaying of fauna quantities at 0.35 m outside the patch border, a threshold that reveals the mean range of facilitation provided by plants. However, the changes in soil communities outside the patches seem not necessarily related to the efficiency of soil processes mediated by arthropods, which seem to be more active inside large patches. Concluding, we found a minimum patch-size (radius ~ 0.5 m) able to maintain high diverse communities in soil, and an average distance of vegetation influence along the patch border (halo between 0.35 and 0.50 m). This information can be interpreted in terms of facilitation provided by plants to soil conditions, which seems to differ from the quantitative functionality of soil processes. Our findings contribute to the general understanding of the ecological functioning of drylands, as well as to better plan conservation actions.

Detailed kinetics for a 3-wavelength photopolymerization confinement (PC) system is presented for both numerical solutions and analytic formulas. The dynamic profiles are simulated for oxygen, free radical, and conversion for various situations of: blue-light only, 2-light (red and UV), and 3-light (red, blue, UV). An effective PC requires two conditions: (i) a strong N-inhibition for uncured regime with a low conversion (triggered by the UV-light); and (ii) a weak S-inhibition (oxygen-induced) for high conversion under the blue-light or blue and red-light initiation. Good PC candidates are governed by collective factors of: (i) the double ratio of light-intensity and initiator-concentration, (ii) monomers rate-constant; and (iii) effective absorption constants at specific wavelength and initiators. A new reverse feature for the role of N-inhibition on the blue-conversion is found. Higher oxygen concentration leads to a lower conversion, which could be enhanced by reducing the S-inhibition via a red or blue-light pre-irradiation, having a pre-irradiation time TP=200 s for red-light only, and reduced to 150 s, when both red and blue-light. System under UV-only leads a conversion lower than that of blue-only. However, conversion could be improved by the dual-light (blue and UV), and further enhanced by the pre-irradiation of red-light. The two competing factors, N-inhibition and S-inhibition, could be independently and selectively tailored to achieve: (a) high conversion of blue-light (without UV-light), enhanced by red-light pre-irradiation for minimal S-inhibition; and (b) efficient PC initiated by UV-light produced N-inhibition for reduced confinement thickness and for high print speed.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

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.

The need for a relatively quick solution to the problem of providing highways with roadside service facilities necessitates the development of a series of appropriate standard projects. To increase the efficiency of these series, it is advisable to carry out the interconnection of space-planning solutions based on a particular module. Taking into account the variety of planning and landscape characteristics of the sites for the placement of objects of the mainline service, it seems advisable to choose as a module not a square or rectangular, but a triangular configuration, which allows in most cases to harmoniously block the modules. The proposed roof module in the form of a "regular" triangle facing the tetrahedron has a structural basis in the form of a single-tier rod spatial plate. The principal space-planning solutions of all four dozen objects from the approved nomenclature of the mainline service performed in the process of analyzing the possibilities show the real possibility of solving the development tasks on the basis of this system. The use of the proposed modular system makes it possible to successfully solve a number of tasks to reduce the harmful impact on the environment and effectively use renewable energy sources.

Localizing the source of a signal is often as important as deciphering the signal’s message. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. Comparing these strategies across modalities and model systems allows a broader understanding of the general principles shaping spatial processing. In this review we focus on the electrosensory system of knifefish and provide an overview of our current understanding of spatial processing in this system, in particular, localization of conspecific signals. We argue that many mechanisms observed in other sensory systems, such as the visual or auditory systems, have comparable implementations in the electrosensory system. Our review therefore describes a field of research with unique opportunities to provide new insights into the principles underlying spatial processing.

There is high confidence that climate change has increased the probability of concurrent temperature-precipitation extremes, changed their spatial-temporal variations, and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily weather data (131 years, 1889-2019) at 700 grid cells (1◦ × 1◦) across Australia was obtained to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot-dry (CHD) events based on an ‘And’ hazard scenario (hotter than a threshold ‘And’ drier than a threshold). CHD extremes were defined as years with joint return periods of larger than 25 years. Mann-Kendall nonparametric tests was used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. A general cooling-wetting trend was observed over 1889-1989. Significant increasing trends were detected over 1990-2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). Results showed a significant increase in the association between temperature and precipitation at various temporal scales. The frequency of CHD extremes was mostly stable over 1889-1989, but significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south-east and south-west. Spatial homogeneity (i.e. connectedness) and propagation of extreme events from one grid cell to its neighbouring cells was investigated across Australia. It can be concluded that this connectedness has not significantly changed since 1889.

This paper presents a two-way factor design incorporating both spatial and temporal variation in the prediction of COVID 19 in Africa. In line with this, the impact of COVID-19on the GDP in Africa is well scrutinized. In contrast to the existing works [1–3], this work also extends the two-factor design into the one-way factor design through incorporating covariates into spatial effects. The data rely on the spatial and temporal obtained from WHO datasets [4, 5]. The one-factor design with more covariates is taken into consideration to identify the major potential predictor variables responsible for the deaths and confirmed cases due to COVID 19 in Africa. The MANCOVA considered population density, temperature, humidity; perception, and wind are all considered as co-variates. Simulations show that the two-way analysis of variance has shown that there is a statistically significant difference between the spatial (Fcal= 8.2704, Pvalue= 3.099∗10−6)and temporal (Fcal= 48.7964, Pvalue= 9.147∗10−16) effects. South Africa and Nigeria are highly influencing due to the pandemic where their GDP also relatively mostly declined. A significant economic change is observed before the pandemic and after the outbreak of the pandemic(tcal= 2.9548, Pvalue= 0.01805). COVID 19 negatively influenced the economy of1 most of the African countries. The population density, temperature, and wind are found to be statistically significantly associated with COVID 19 cases and deaths.

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN — Facebook and Foursquare) and on the awareness of the use of Volunteered Geographic Information (VGI) generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) & Inverse Distance Weighting (IDW) Interpolation.

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

(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.

The pandemic outbreak of COVID-19 had begun in March of 2020 on a global scale. This outbreak has originated numerous questions for society and many challenges for public managers. The disease is worrying because it has a high propagation velocity, high lethality levels, and there is no cure. Some groups are considered more vulnerable due to pre-existing disease conditions, age-range, and living conditions. In Latin American countries, people live in different conditions than those who live in countries located in the North hemisphere, such as climatic conditions, less favorable socioeconomic conditions, different educational levels, inequality, precarious urban infrastructure, etc. These factors generate even more concern and uncertainty about the pandemic than in developed countries. This study aimed, although preliminarily, to identify areas of great socio-spatial vulnerability and susceptibility of infection of people over 60 years old of COVID-19 in Brasília, Brazil. In this research, publicly available data and information about the population characteristics and social aspects were used, all connected directly to the census sectors. With the support of Geographic Information System (GIS), a matrix was used to cross-check the data and, thereby, achieve the objective of identifying the most vulnerable sectors for people aged over 60 years old. The results point out to more than 400 census sectors classified as Extremely Vulnerable in Brasília and it should be the object of special attention for public managers to do specific health care actions.

The mechanization of agricultural activities has led to indiscriminate deposition of toxic xenobiotics, including organophosphates in the biomes, and this has led to intoxication characterized with deleterious oxidative and neuronal changes. This study investigated the consequences of oxidative and neurogenic disruptions that follow exposure to two organophosphates, chlorpyrifos (CPF) and dichlorvos (DDVP) on neuro-cognitive performance and anxiety-like behaviors in rats Thirty-two adult male Wistar rats (150 – 170g) were randomly divided into 4 groups, orally exposed to normal saline (NS), DDVP (8.8mg/kg), CPF (14.9mg/kg) and DDVP+CPF for 14 consecutive days. On day 10 of exposures, anxiety-like behaviors and amygdala dependent fear learning were assessed using Open Field and Elevated Plus Maze paradigms respectively, while spatial working memory was assessed on day 14 in the Morris water maze paradigm, following 3 training trials each on days 11, 12 and 13. On day 15, the rats were euthanized, and their brains excised, hippocampus and amygdala removed, 5 of which were homogenized and centrifuged to analyze nitric oxide (NO) metabolites, total reactive oxygen species (ROS), and acetylcholinesterase (AChE) activity, and the other three processed for histology (cresyl violet stain) and proliferative marker (Ki67 immunohistochemistry). Marked (p≤0.05) loss in body weight, AChE depletion, and overproduction of both NO and ROS were observed after repeated exposure to individual and combined doses of CPF and DDVP. Insults from DDVP exposure appeared more severe owing to the observed greater losses in the body weights of exposed rats. There was also a significant (p≤0.05) effect on the cognitive behaviors recorded from the exposed rats, and these deficits were related to the oxidative damage and neurogenic cell loss in the hippocampus and the amygdala of the exposed rats. Taken together, these results provided an insight that oxidative and neurogenic damages are central to the severity of neuro-cognitive dysfunction and increased anxiety-like behaviors that follow organophosphate poisoning.

Shenzhen is China’s top ten clean air city and the cleanest air megacity. Even so, epidemiologic studies have shown ambient air pollution had significant adverse impacts on human health in this less polluted city. In this study, the concentrations of six criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) from 2014 to 2017 were analyzed and compared to thresholds of both national and international air quality standards. The results showed concentrations of all air pollutants were below target values of current national air quality standard, but levels of particulate matter (PM) and O3 were still much higher than the recommended levels by the World Health Organization. Within national air quality standards, the number of over-limit days was rare with few variations between highly polluted and low pollution areas. The air quality improvement was slowing down recently. Our results suggest annual and daily thresholds for PM are too loose for air quality improvement in Shenzhen. Hence, we call for evaluation and establishment of tougher air quality standard.

About 50% of colorectal cancer patients develop liver metastases. Patients with metastatic colorectal cancer have 5-year survival rates below 20% despite new therapeutic regimens. Tumor heterogeneity has been linked with poor treatment response and clinical outcome, but was so far mainly studied via bulk genomic analyses. In this study we performed spatial proteomics via MALDI mass spectrometry imaging on six patient-matched CRC primary tumor and liver metastases to characterize interpatient, intertumor and intratumor hetereogeneity. We found several peptide features that were enriched in vital tumor areas of primary tumors and liver metastasis and tentatively derived from tumor cell specific proteins such as annexin A4 and prelamin A/C. Liver metastases of colorectal cancer showed higher heterogeneity between patients than primary tumors while within patients both entities show similar intratumor heterogeneity sometimes organized in zonal pattern. Together our findings give new insights into the spatial proteomic heterogeneity of primary CRC and patient-matched liver metastases.

This paper deals with identifying the accident black spots and the influencing factors causing accidents using factored analysis in the medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the black spots and the factors causing accidents was used for analysis. The most influencing factors driving the accident were identified and ranked based on the repetitive occurrence of accidents in the black spot area. The spearman ranking system obtained the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners to understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

Abstract: Service polycentricism, in the face of the pandemic, took on a new significance because of sanitary requirements. Population concentrations in large service centres face a high risk of infection both at their destination and during the trip. Higher-tier services were largely closed during the threat’s peak. In this paper, the focus of the study has been placed on the urban scale of a large city’s polycentricism as seen through the prism of service concentrations, which, to a degree, are commuter destinations, but primarily act as attractors that are not associated with work—they satisfy higher-tier service needs. To formulate the investigative apparatus used to study the functio-spatial structure of Cracow. The current threat, as well as the high probability of similar situations happening in the future, will probably lead to changes in behaviour patterns of consumers and service providers in reference to siting. Local service centres that are accessible to pedestrians, due to safety-related considerations, can gain significant popularity.

Spatial planning holds a key role in preventing or mitigating the impacts of climate change on both cities and rural areas, taking a forward-thinking and holistic approach to urban and regional development. As such, spatial planning deals with challenges occurring at different scales and across sectors. The international literature points out the need for horizontal and vertical cooperation to tackle climate change impacts. While there is abundant knowledge regarding the challenges related to climate change at different spatial levels, procedural integration into planning frameworks and practice is currently under-researched. This paper presents a novel theoretical framework that integrates various steps towards a holistic, integrative and adaptive climate proofing process. An iterative process was used for conceptual development, based on literature review followed by external feedback meetings and two workshops with the core team of planning experts responsible for exchange across federal states. By specifically addressing the challenges relating to cross-regional and cross-sectoral planning, this novel framework attempts to (i) facilitate a hierarchy of measures, (ii) maximise co-benefits for various adaptation purposes and climate change mitigation and (iii) foster the long-term institutionalisation of integrative processes across sectors, planning areas and policy levels.

Bovine tuberculosis surveillance in Northern Ireland includes Multiple-Locus Variable number tandem repeat Analysis (MLVA) to determine the Mycobacterium bovis genetic type present in both cattle, and the predominant wildlife host, the European badger (Meles meles). These data are informative for investigating clusters of infection and understanding the scale at which interspecific transmission may occur. We utilised a comprehensive dataset of routinely sampled isolates from infected cattle and from badgers killed in road-traffic accidents to investigate the spatial co-location of MLVA types in, and between, the badger and cattle populations. Furthermore, we investigate the hypothesis that the farming enterprise type might explain some variation in this relationship. MLVA types were spatially co-localised in cattle and RTA badger hosts, indicative of a shared epidemic. Dairy herds were more likely to have at least one MLVA type in common with nearby RTA badgers, compared to non-dairy herd types. Marginally more MLVA spatial clustering was observed in non-dairy herds, which may be a consequence relatively more between-herd movements. For the cattle population, local transmission mechanisms such as infection from contiguous herds, infectious wildlife and short-range between-herd cattle movements appear primarily to drive the epidemic: there appears to be a more limited role for long-range movements. Animal management practices are likely the driving force behind this observation, as beef rearing is associated with elevated numbers of animal movements compared to dairy herds.

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.

Boreal forests are one of the largest stores of carbon on Earth, and two-thirds of them are located in Siberia. Despite the fact that these forests have a significant influence on the global climate, they continue to remain understudied. Chernevaya taiga is a unique example of a highly productive Siberian boreal ecosystem. This type of forest is characterized by a series of unique ecological traits, the most notable of which are the gigantism of the perennial herbaceous plants and bushes, complete lack of moss cover on soil surface, and the type of soil it grows on, notable for its particularly high rate of decomposition of vegetative remains and low humic acid content. Abundant rainfall actively washes out nutrients from the top layers of the soil, but its fertility level remains very high. In fact, based on the existing data, it is twice as high as that of fertilized agricultural lands. In some ways the conditions within this type of forest closely resemble those observed in tropical rainforests. Microbiota associated with soil and plants represent an integral part of an interconnected system and contribute significantly to productive processes in soils. Its impacts on the environment require further study, since it could lead to discoveries that will help improve soil fertility without harming the natural environment.

Transportation of goods is as old as human civilizations : past networks and their evolution shed light on long term trends. Transportation impact on climate change is measured as major, as well as the impact on spreading a pandemic. These two reasons motivate the importance of providing relevant and reliable historical geographic datasets of these networks. This paper focuses on reconstructing the railway network in France at its maximal extent, a century ago. The active stations and lines are well documented by the French SNCF, in open public data. However, that information ignores past stations (ante 1980), which represent probably more than what is recorded in public data. Additional open data, individual or collaborative (eg. Wikipedia) are particularly valuable, but they are not always geo-coded, and two more sources are necessary to completing that geo-coding: ancient maps and aerial photography. Therefore, remote sensing and volunteer geographic information are the two pillars of past railway reconstruction. The methods developed are adapted to the extraction of information from these sources: automated parsing of Wikipedia Infoboxes, data extraction from simple tables, even from simple text. That series of sparse procedures can be merged into a comprehensive computer-assisted process. Beyond this, a huge effort in quality control is necessary when merging these data: automated wherever possible, or finally visually controlled by observation of remote sensing information. The main output is a reliable dataset, under ODbl, of more than 9100 stations, which can be combined with the information about the 35000 communes of France, for a large variety of studies. This work demonstrates two thesis: (a) it is possible to reconstruct transport network data from the past, and generic computer assisted methods can be developed; (b) the value of remote sensing and volunteered geo info is considerable (what archeologists already know).

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

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.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

The main scope of the presented paper is an assessment of the potential repellent effect of selected essential oils (EOs) against Alphitobius diaperinus, which can cause economic losses in storages and poultry industry. Due to development of pesticide resistance in A. diaperinus populations, as well as an attempt to limit extensive usage of potentially harmful pesticides in food-related industries, there is a strong need for development of alternative methods of management of A. diaperinus infestations. Because of cost-effectiveness, availability and low vertebrate toxicity EOs are promising agents in pest management. In presented paper four of-the-shelf EOs: mint, vanilla, lemon and citronella (and their mixtures) were tested as a potential repellents. Moreover, novel preference assay providing an extended analysis of preference and the locomotor response was used. The most effective EOs were, respectively: citronella and lemon. EOs mixtures were generally more repellent than single EOs, with lemon and vanilla 1:1 mixture acting as the strongest repellent. Few of tested EOs caused significant alterations in locomotor activity, although direct relation wasn’t observed. In conclusion, EOs can be potentially used as a repellent agents in A. diaperinus management. Additionally, data on locomotor activity may lead to better design of pull-push strategies in pest management.

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

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 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.

Chikungunya virus (CHIKV) and Zika virus (ZIKV) have recently emerged as global infections with consequential disability adjusted life years (DALYs) and economic burden. This study aimed to explore the spatiotemporal heterogeneity in the occurrence of CHIKV and ZIKV outbreaks throughout Barranquilla, Colombia during 2014 and 2016 and explored the potential for clustering. Incidence data were fitted using multiple Bayesian Poisson models based on a suite of explanatory variables as potential risk factors and multiple options for random effects. A best fit model was used to analyse the case incidence risk for both epidemics to identify any risk factors during their epidemics. Neighbourhoods in the northern region of Barranquilla were hotspots for the outbreaks of both CHIKV and ZIKV. Additional hotspots occurred in the south-western and central regions of the CHIKV and ZIKV outbreaks, respectively. Multivariate conditional autoregressive models strongly identified higher socioeconomic strata (SES) and residing in detached houses as risk factors for ZIKV case incidences. These novel findings challenge the belief that these infections are driven by social vulnerability and merits further study both in Barranquilla and throughout the tropical and subtropical regions of the world.

The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Recent experimental studies, however, have started to shed more light on the mechanisms and spatial dynamics of viral spread within hosts. Here, we review these experimental studies as well as the limited number of computational modeling efforts that have begun to integrate spatial considerations for understanding within-host viral spread. We limit our review to influenza virus to highlight key mechanisms affecting spatial aspects of viral spread for pathogens of the respiratory tract. There is considerable empirical evidence for highly spatial within-host spread of influenza virus, yet few computational modeling studies that shed light on possible factors that structure the dynamics of this spatial spread. In existing modeling studies, there is also a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature to provide informed theoretical expectations for what viral and host factors may impact the spatial patterns of within-host viral dynamics and for how spatial spread will affect the genetic composition of within-host viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host influenza virus spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

Associations between blood cancer and genetic predisposition, including both inherited variants and acquired mutations and epimutations, have been well characterized. However, the majority of these variants affect noncoding regions, making their mechanisms difficult to hypothesize and hindering the translation of these insights into patient benefits. Fueled by unprecedented progress in next-generation sequencing and computational integrative analysis, studies have started applying combinations of epigenetic, genome architecture and functional assays to bridge the gap between noncoding variants and blood cancer. These complementary tools have not only allowed us to understand the potential malignant role of these variants but also to differentiate key variants, cell types and conditions from misleading ones. Here, we briefly review recent studies that have provided fundamental insights into our understanding of how noncoding mutations at enhancers predispose and promote blood malignancies in the context of spatial genome architecture.

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

The Asian weaver ant (Oecophylla smaragdina) is a natural enemy, generalist predator of diverse major pest species (i.e. the highly destructive oriental fruit fly Bactrocera dorsalis) in economically strategic agricultural landscapes in Australia and Southeast Asia countries. For effective implementation of the weaver ant for biological control of the invasive bagworms Metisa plana, the dominant pest in the oil palm plantation, its distribution was investigated in Malaysia. From 2018-2022, censuses were first carried out to record Oecophylla colonies presence. Oecophylla colonies’ occupation patterns were monitored on 8 selected plantations. To distinguish between brood and barracks nests, 26 captured nests were evaluated to define the main predictor variable for its practical visual identification. More than 11000 palms with 10821 nests for over 500 colonies were sampled in this study. Results showed that O. smaragdina colonies distribution pattern exhibited three-dimensional ubiquitous occupation dominance. We also found that by observing the colony’s dense population spatial arrangements, it demonstrates them as spreading by waves featuring interconnected irregular geometrical patches shaped for each colony. The height to the ground nest location in palm canopies was the significant visual discriminant factor between Brood and barrack nests. Moreover, polydomous arboreal nesting behavior occupation occurred irrespective of palms or associated host plants sizes (≥ 3 m), throughout the year. However, colony nesting behaviors were polydomous and monodomous on shorter plants (≥ 2 m), while being strictly monodomous on small plants (≤ 1.5 m). Colonies occupied an average 3 to 4 palms yearly beginning from the newly founding phase up to the stable mature phase (3 to 4 years). Furthermore, we found that matured stable colonies (3 ≥ age ≥13) occupied 10-12 palms on average, on 800 m² to 2500 m² area delimited by a minimum average 20 m² of “no ants land” zone between each colony. Our results suggest that a sustained agriculture system in this area would be possible with the presence of matured colonies nests exploitation which could provide an efficient biological control ecosystem service.

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150-250 m respectively in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100 -1,000 m range with a break at (180-360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

We present a new unsupervised method aimed to obtain a partition of a complex urban system in homogenous urban areas, called urban contexts. The area of study is initially partitioned in microzones, homogeneous portion of the urban system, that are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental and socio-economic indicators need to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary to determine the type of urban context. We implement the set of spatial analysis processes necessary to calculate the indicators for microzone and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples - Italy); the reliability of out model is measured by comparing the results with the ones obtained by detailed analysis.

During the 2008 Great Recession, many families with children relied on cash assistance from Temporary Assistance for Needy Families (TANF) program. The present study applied Exploratory Spatial Data Analysis (ESDA) tools to analyze geographically varying spatial clusters of states’ unemployment rates, TANF caseload growth rates, TANF policy choices such as benefit levels and TANF responsiveness rates to the recession. We analyzed 45 contiguous states and Washington D.C. A standardized TANF responsiveness index was developed to compare states’ TANF growth rates relative to their labor market conditions. The western states were found to be very responsive to the recession with ratios greater than one. In contrast, Texas and Arizona, with ratios below 1, were unresponsive to the recession. The presence of strong spatial clusters in unemployment rate and TANF maximum aid were found. In the case of maximum aid, there was a strong presence of Low-Low spatial clusters in Southern States and High-High clusters in Northeastern States. The findings suggest that several neighboring states in the northeast and some in the south had similar levels of financial commitment during the 2008 recessionary as the ones found by earlier research conducted during non-recessionary periods. The findings have implications for future federal actions and for state level collaboration.

The article discusses determinants of living environment in Central and Eastern Europe. As an example, the city of Radzionkow was chosen, with 16 thousand inhabitants, located in the Silesia agglomeration in southern Poland, in the area where hard coal has been mined for almost two hundred years, which largely serves as a source of heating for houses and flats. 360 buildings in 6 groups of 60 buildings were examined in the selected city, which allowed to distinguish 3 different areas in terms of the quality of the living environment depending on the technical condition of buildings, the method of heating and location. To a large extent, determinants are the existing spatial and geophysical conditions of a given location. Many research and reports on the living environment do not take into account the factors mentioned, focusing only on the statistical data of pollution, excluding spatial factors. In the research presented, the author, taking into account location variables, shows differences in the measurements of air pollution in relation to the designated location zones depending on the morphological structure of the building, the degree of its modernization and the types of heating used in buildings.

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

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.

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.

Spatial Data Infrastructures (SDI) support the harvesting, curating, storage, and sharing of data along with providing access to development, analytic, and visualization tools that enable the building of innovative applications to address broad or specific challenges. SDIs can be especially powerful in bringing together data and tools supporting a particular theme – and this paper discusses and demonstrates the value of an SDI focused on Health. Many potential benefits of a Health SDI are proposed, and the case of supporting emergency response efforts is developed in detail. Leveraging a Health SDI, a Health Risk Index was created that provides emergency response personnel (both Emergency Operations Managers and Emergency Medical Responders) key insights into the unique health risks the impacted population faces due to the disaster. In order to establish the Health Risk Index, datasets from multiple national and global sources representing health data and social data that influences health outcomes – typically called social determinants of health – are harvested, merged, and republished to support further efforts at advancing the Health Risk Index. Visualizations of the Health Risk Index at the global, national, and sub-national levels down to the address level are presented along with demonstrations of its use.

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category “built” in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

Measures in the SARS-CoV-2 pandemic were based on rough ideas regarding transmission routes of pathogens. Quantified models of physical transmission routes are mostly lacking, a gap to be filled. Vaccines and medicines, important, are not studied here. We first survey main routes, from primary production in the alveoli and intestines to emissions, environmental routes, to exposure and alveolar infection. Next, specific routes are modelled, mostly at a preliminary state, open to systematic improvement. Starting from a standardized emitter, modelling results show extreme differences in potential exposure, in a range covering up to 4 orders of magnitude. The outcomes are pathogen-specific, already different between SARS-CoV-2 and influenza. Extreme exposures may result in smaller spaces; with lower ventilation rates; with a high density of emitting persons per m3; who stay there for several hours; and visitors staying more than a few minutes. In spaces where a build-up of concentrations is low, exposures are low, lowest in open air situations. A main conclusion for the next pandemic is that a quantified model can give strong guidance on where measures are primarily due. For SARS-CoV-2, ventilation can be improved short-term. Longer-term, effective ventilation rules and adaptation of buildings may reduce high exposures substantially.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

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.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

Mapping forest composition using multiseasonal optical time series is still challenging. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on ten Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the SVM algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

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.

Green innovation has become the mainstream of the era, and New Urbanization is an inevitable choice in the process of urbanization in China. Focusing on the topics of green innovation and new urbanization, much work has been done to find their factors respectively while the relationship between the two remains to be explored. Hence, in this article, representative indicators of new urbanization and green innovation are selected to study the Yangtze River Delta City Group from the perspective of both the entire urban agglomeration and a single city, in terms of time and space, using the entropy method and the coupling model. The results show that (1). Green innovation promotes the new urbanization development and there is a synergistic effect between the two systems. (2). The level of economic development is an important factor that affects the degree of coupling degree and coordination degree between the two interactions, and its influence is better than the spatial effect. (3). Green innovation and new urbanization have positive spatial autocorrelation and regional agglomeration (there are High-High, Low-Low, and High-Low collections).

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

Bayesian formulations of learning imply that whenever the evidence for a correlation between events in an animal’s habitat is sufficient, the correlation is learned. This implies that regularities can be learnt rapidly, from small numbers of learning examples. This speed of learning gives maximum possible fitness, and no faster learning is possible. There is evidence in many domains that animals and people can learn at nearly Bayesian optimal speeds. These domains include associative conditioning, and the more complex domains of navigation and language. There are computational models of learning which learn at near-Bayesian speeds in complex domains, and which can scale well – to learn thousands of pieces of knowledge (i.e., relations and associations). These are not neural net models. They can be defined in computational terms, as algorithms and data structures at David Marr’s [1] Level Two. Their key data structures are composite feature structures, which are graphs of multiple linked nodes. This leads to the hypothesis that animal learning results not from deep neural nets (which typically require thousands of training exam-ples), but from neural implementations of the Level Two models of fast learning; and that neu-rons provide the facilities needed to implement those models at Marr’s Level Three. The required facilities include feature structures, dynamic binding, one-shot memory for many feature struc-tures, pattern-based associative retrieval, unification and generalization of feature structures. These may be supported by multiplexing of data and metadata in the same neural fibres.

A hemispheric asymmetry is known for the processing of global vs. local visual information. In this study, we investigated the existence of a hemispheric asymmetry for visual processing of low vs. high spatial frequency gratings. Event-related potentials were recorded in a group of healthy right-handed volunteers from 30 scalp sites. Six types of stimuli (1.5, 3 and 6 c/deg gratings) were randomly flashed 180 times in the left and right upper hemi-fields. Stimulus duration was 80 ms and ISI ranged between 850-1000 ms. Participants had to pay attention and respond to targets based on their spatial frequency and location, or to passively look at the stimuli. C1 and P1 visual responses, as well as a later Selection negativity and a P300 components of ERPs were quantified and subjected to repeated-measure ANOVAs. Overall, performance was faster for the RVF, thus suggesting a left hemispheric advantage for attentional selection of local elements. Similarly, the analysis of mean area amplitude of C1 (60-110 ms) sensory response showed a stronger attentional effect (F+L+ vs. F-L+) at left occipital areas, thus suggesting the sensory nature of this hemispheric asymmetry.

Cancer-associated fibroblasts (CAF) are highly prevalent cells in the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). CAFs exhibit a pro-tumor effect in vitro and have been implicated in tumor cell proliferation, metastasis, and treatment resistance. Our objective is to analyze the geospatial distribution of CAFs with proliferating and apoptotic tumor cells in the ccRCC tumor microenvironment and determine associations with survival and systemic treatment. Pre-treatment primary tumor samples were collected from 96 patients with metastatic ccRCC. Three adjacent slices were obtained from 2 tumor-core regions of interest (ROI) per patient, and immunohistochemistry (IHC) staining was performed for αSMA, Ki-67, and caspase-3 to detect CAFs, proliferating cells, and apoptotic cells, respectively. H-scores and cellular density were generated for each marker. ROIs were aligned, and spatial point-patterns were generated, which were then used to perform spatial analyses using a normalized Ripley's K function at a radius of 25μm (nK(25)). The survival analyses used an optimal cut-point method, maximizing the log-rank statistic, to stratify the IHC-derived metrics into high and low groups, and multivariable Cox regression analyses were performed accounting for age and International Metastatic RCC Database Consortium (IMDC) risk category. Survival outcomes included overall survival (OS) from the date of diagnosis, OS from the date of immunotherapy initiation (OS-IT), and OS from the date of targeted therapy initiation (OS-TT). Therapy resistance was defined as progression-free survival (PFS) <6 months, and therapy response was defined as PFS >9 months. CAFs exhibited higher cellular clustering with Ki-67+ cells than with caspase-3+ cells (nK(25): Ki-67 1.19; caspase-3 1.05; P = .04). The median nearest neighbor (NN) distance from CAFs to Ki-67+ cells was shorter compared to caspase-3+ cells (15 μm vs 37μm, respectively; P < .001). Multivariable Cox regression analyses demonstrated that both high Ki-67+ density and H-score were associated with worse OS, OS-IT, and OS-TT. Regarding CAFs, only a high H-score was associated with worse OS, OS-IT, and OS-TT. For caspase-3+, high H-score and density were associated with worse OS and OS-TT. Patients whose tumors were resistant to targeted therapy (TT) had higher Ki-67 density and H-scores than those who had TT response. Overall, this ex vivo geospatial analysis of CAF distribution suggests that close proximity clustering of tumor cells and CAFs potentiates tumor cell proliferation, resulting in worse OS and resistance to TT in metastatic ccRCC.

While paresthesia-based spinal cord stimulation (SCS) has been proven effective to treat chronic neuropathic pain, initial benefits may lead to the development of “Failed SCS Syndrome’ (FSCSS) defined as decrease over time related to Loss of Efficacy (LoE) with or without Loss of Coverage (LoC). Development of technologies associating new paresthesia-free stimulation waveforms and implanted pulse generator adapters provide opportunities to manage patients with LoE. The main goal of our study was to investigate salvage procedures, through neu-rostimulation adapters, in patients already implanted with SCS and experienced LoE. We retro-spectively analyzed a cohort of patients who were offered new SCS programs/waveforms through an implanted adapter between 2018 and 2021. Patients were evaluated before, and at 1, 3, 6 and 12-month follow-up. Outcomes included pain intensity rating with Visual Analog Scale (VAS), pain/coverage mappings and stimulation preferences. Last follow-up evaluations (N=27) showed significant improvement in VAS (p = 0.0001), ODI (p = 0.021) and Quality of Life (p=0.023). In the 11/27 patients with LoC, SCS efficacy on pain intensity (36.89%) was accompa-nied by paresthesia coverage recovery (55.57%) and pain surface decrease (47.01%). At 12-month follow-up, 81.3% preferred to keep tonic stimulation in their waveform portfolio. SCS conver-sion using adapters appears promising as a salvage solution, with an emphasis on paresthesia recapturing enabled by spatial retargeting. In light of these results, adapters could be integrated to SCS rescue algorithms or should be considered in SCS rescue.

The military areas in Sardinia are around 234 km2, which constitutes 59.97% of the national surface affected by military easements. This situation is due to its geographic centrality in the Mediterranean. This contribution evaluates the performance of the Local Coastline Plan (LCP) and the Site Management Plan of Community Interest (SCI) in conditions of military constraint. The case study is the Municipality of Villaputzu where an important coastal military easement and the use of the coast for recreational tourism purposes coexist together through specific planning, a consequence of institutional agreements between the Municipal Administration of Villaputzu and the Ministry of Defense. The evaluation of the congruence of the specific objectives of the LCP and the SCI shows how their combined action favors the environmental enhancement of Sardinia, contributing to the formation of ecosystem services, even in particular conditions arising from military easements. These are sites that pass from the status of "anti-commons" to "semi-commons". In fact, the military release process in Sardinia, together with the promiscuous military and civil use, activates unique governance policies of their kind that find a significant field of application in Sardinia to guarantee a sustainable renewal of economic development of the ‘semi-commons’ awaiting to become ‘commons’

Influential Observations, which are outliers in x direction, y direction or both, remain a hitch in classical regression model fitting. Spatial regression model, with peculiar nature of outliers due to their local nature, is not free from the effect of such influential observations. Researchers have adapted some classical regression techniques to the spatial models and yielded satisfactory results. However, masking or/and swamping remain stumbling block to such methods. We obtained the spatial representation of the classical regression measures of diagnostic in general spatial model. Commonly used diagnostic measure in spatial diagnostic, the Cook's distance, is compared to some robust methods, Hi2 (using robust and non-robust measures), and classification based on generalized residuals and diagnostic generalized potentials, ISRs-Posi and ESRs-Posi, with the help of the obtained spatial prediction residuals and the spatial leverage term. Results of simulation and applications to real data have shown the advantage of the ISRs-Posi and ESRs-Posi due to classification of outliers over Cook's distance and non-robust Hsi12, which suffer from masking, and robust Hsi22 which suffer from swamping in general spatial model.

Large-area nanostructuring of glasses using intense laser beam remains a difficult task due to the extreme non-linear absorption of the laser energy by the material. Precise optimization of the process parameters is essential for fabricating nanostructures with large area coverage. In this study, we report the findings on creating high spatial frequency LIPSS (HSFL) on borosilicate glass through direct laser writing, using a femtosecond laser with a wavelength λ = 800 nm, pulse duration τ = 35 fs, and repetition frequency frep = 1 kHz. The orientation of the HSFL was found to be parallel to the electric field vector. We measured the single pulse ablation threshold (Fth=3.87±0.26 J/cm2) and incubation factor (S=0.68±0.03) of Borosilicate glasses for precise control for large area surface structuring. Single-spot experiments indicate that uniform LIPSS formation is limited by melt formation inside the irradiated area for higher fluence and a larger number of irradiated laser pulses. The orientation of the scan axis with the laser beam polarization is found to be significantly influencing the uniformity of the large area processing. We found that the orientation of the scan axis with the laser beam polarization significantly affects the uniformity of large-area processing, with redeposition and melt formation being higher when the scan axis is perpendicular to the laser beam polarization. Large-area processing of the borosilicate glass surface is done by line-by-line scanning over the surface with a scan orientation parallel to the laser beam polarization. The optical characterization reveals that the transmittance and reflectance of the borosilicate glass decreased significantly after processing. Also, the wettability of the surface has been changed from hydrophilic to super hydrophilic after processing. These chemical contamination-free and uniformly distributed structures have potential applications in optics, microfluidics, photovoltaics, and biomaterials.

(1) Aim of the study was to investigate the spatial and temporal characteristics of the airflow created by two commercially available non-contact tonometers, the CorvisST and the Ocular Re-sponse Analyser. (2) The airflow pressure was measured using a MEMS pressure sensor to inves-tigate the spatial and temporal distribution. The airflow from the CorvisST and Ocular Response Analyser were mapped to a 600µm and a 1mm resolution grid, respectively. (3) Central airflow pressure of the CorvisST (96.4 ± 1.4)mmHg was higher than the Ocular Response Analyser (91.7 ± 0.7)mmHg. The duration of the air-puffs also differed, with the CorvisST showing a shorter du-ration (21.483 ± 0.2881)ms than the ORA (23.061 ± 0.1872)ms. The rising edge of the CorvisST airflow pressure profile demonstrated a lower gradient (+8.94mmHg/ms) compared to the Oc-ular Response Analyser (+11.00mmHg/ms). Both had similar decay response edges; CorvisST -11.18mmHg/ms, Ocular Response Analyser -11.65mmHg/ms. (4) The study presents a valid method to investigate physical dimensions of the airflow pressure of non-contact tonometers. Novel findings relating to the magnitude, duration and spatial characteristics of the respective airflow pressures are reported. It is anticipated that this information will better inform clinical studies and theoretical models relating to ocular biomechanics.

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.

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.

Computation of Common Middle Point seismic sections and their subsequent time migration and diffraction imaging provides very important knowledge about the internal structure of 3D heterogeneous geological media and are key elements for successive geological interpretation. Full-scale numerical simulation, that computes all single shot seismograms, provides a full understanding of how the features of the image reflect the properties of the subsurface prototype. Unfortunately, this kind of simulations of 3D seismic surveys for realistic geological media needs huge computer resources, especially for simulation of seismic waves’ propagation through multiscale media like cavernous fractured reservoirs. In order to significantly reduce the query of computer resources we propose to model these 3D seismic cubes directly rather than the shot-by-shot simulation with subsequent CMP stacking. To do that we modify the well known "exploding reflectors principle" for 3D heterogeneous multiscale media by use of the finite-difference technique on the base of grids locally refined in time and space. To be able to simulate realistic models and acquisition we develop scalable parallel software, which needs reasonable computational costs. Numerical results for simulation of Common Middle Points sections and their time migration are presented and discussed.

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.

As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density based hotspot detector proposed in this paper can also be used in the analysis of other type of POI data.

Urbanization induced land use land cover (LULC) changes intensify the urban heat island effects. It magnifies the risk of urban dwellers and sometimes causes the loss of human life, defined as heat-related health risk (HRHR). Hence, urban LULC planning plays a crucial role. Present study analyses the impact of composition and configuration of urban LULC defined as urban landscape metric (ULM) on HRHR in Delhi at the ward level. Firstly, the HRHR is measured by using satellite thermal and other digital data. Then, measured HRHR is validated by conducting a rapid field survey. Thirdly, ULM measured at ward level using Fragstat 4 software. Finally, both, HRHR and ULM linked with bi-variate Moran's I and impacts of ULM are assessed using ordinary least square (OLS) and spatial error (SE) regression. The result indicates the high risk is found as clustered in north-east, central and middle of south-west Delhi. Built-up density intensifies HRHR and abundance of vegetation reduce it; however, it is not similar for all vegetation patches. Larger vegetation patches surrounded by dense built-up might not able to reduce the risk as much as a large vegetation patch could in other regions. Findings can be helpful for heat resilient city planning.

Reductions in water availability and increasing rainfall variability are generating a narrative of growing competition for water in the Mediterranean basin. In this article, we explore the distribution and importance of water resources in the Muga River Basin (Catalonia, Spain) based on key stakeholders’ perceptions. We performed a sociocultural evaluation of the main water ecosystem services in the region through stakeholder interviews and participatory mapping. The basin was generally perceived as a hotspot of ecosystem services, but we detected varying opinions and considerable differences in the perceptions of importance and spatial distribution of water ecosystem services. These discrepancies were linked to the varying levels of stakeholders’ dependence on water. Our findings are important for contributing to correct water planning and management in the river basin, which is a complex water social system marked by conflicts between different stakeholder groups vying for the same resource. This complex situation requires bottom-up strategies to create transparent, participatory decision-making models.

Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are requested to use such a massive amount of data in integrative schemes addressing a large variety of concerns. Here, we provide an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation planners navigate around multiple bio-socioeconomic equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence.

This article attempts to identify factors impacting on the quantity of municipal waste in Polish 2478 communes (LAU-2), taking into account the variability of particular determinants influence depending on their regional diversification. The analysis covers the years 2005-2018. The dependent variable is the volume of municipal waste in kg per capita, whereas the group of determinants include: economic and human development, uncontrolled dumping sites, population density, population at the working age, migration, tourism, urbanization, dwellings and housing, retail sales, entities, education and investments in waste management; The geographically weighted regression with spatial error term (GWR-SEM) was employed in this study. The model enabled not only the specification of the waste production determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual communes. The results proved that the higher the level of education the less waste is generated (in the north-central Poland); the business entities and working-age population are crucial for the waste quantity in communes of eastern Poland; the factors most considerably as to regional range affecting the waste quantity are urban and business development; as to strength–higher education and the share of working age individuals;

(1) Background: tuberculosis (TB) is considered one of the leading causes of death by a single in-fectious agent worldwide. This study aimed to identify hotspots of people diagnosed with tu-berculosis and abusive use of alcohol, tobacco and other drugs in communities through a geo-spatial intelligence application; (2) Methods: ecological study with a spatio-temporal approach. We considered tuberculosis cases diagnosed and registered in the Notifiable Diseases Information System, which presented information on alcoholism, smoking, and other drugs. Spatial Variations in Temporal Trends (SVTT) and scan statistics were applied for the identification of Hotspots; (3) Results: of 29.499 cases of tuberculosis were identified. And when we applied the STTT for al-coholism, three Hotspots were detected, one of which was protective (RR: 0.08 – CI95%: 0.02 – 0.32) and two at risk (RR:1.42 – CI95%: 1.11 – 1.73; RR: 1.39 - CI95%: 1.28 – 1.50). Regarding smoking, two risk clusters were identified (RR: 1.15 - CI95%: 1.01 – 1.30; RR: 1.68 - CI95%: 1.54 – 1.83). For other drugs, a risk cluster was found (RR: 1.13 - CI95%: 0.99 – 1.29) and two protections (RR: 0.70 - CI95%: 0.63 – 0.77; RR: 0.76 – CI95%: 0.65 – 0.89); (4) Conclusions: it was evidenced in the communities a problem of TB and drug addiction. Use disorder perhaps in a person's brain and behavior and leads to an inability to continue their treatment, putting the community at risk for TB.

The reality of people’s lives has shifted from rural to urban areas, where an ever-increasing proportion of the world’s population lives. Providing infrastructure to serve these areas, especially in the Global South, is a key task of sustainable development. A deep understanding of the spatial arrangement and scales of these urban structures and their temporal evolution can help to develop innovative solutions to issues of energy, water, or transportation infrastructures. For this purpose, in this work we study the temporal evolution of urban built-up structures (Global Artificial Impervious Area) and population distributions (Global Human Settlement Population) in four regions of the Global South (Argentina, India, Egypt, and Nigeria). We qualitatively analyze regularity through the pair correlation function and subsequently identify typical scales within the different interurban systems. In doing so, we identify that especially the large settlement objects arrange themselves in a regular way and thus typical scales exist in urban systems. Thus, settlement objects are usually located about 20 to 40 km apart from each other. This information can be used to develop sustainable infrastructure concepts, for example for passenger transport between settlements.

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.

In this paper we analyse spatial variation in Japanese dialectal lexicon by assembling a set of methodologies using theories in variationist linguistics and GIScience, and tools used in historical GIS. Based on historical dialect atlas data, we calculate a linguistic distance matrix across survey localities. The linguistic variation expressed through this distance is contrasted with several measurements, based on spatial distance, utilised to estimate language contact potential across Japan, historically and at present. Further, administrative boundaries are tested for their separation effect. Measuring aggregate association within linguistic variation can contrast previous notions of dialect area formation by detecting continua. Depending on local geographies in spatial subsets, great circle distance, travel distance and travel times explain a similar proportion of the variance in linguistic distance despite the limitations of the latter two. While they explain the majority, two further measurements estimating contact have lower explanatory power: least cost paths modelling contact before the industrial revolution, based on DEM and seafaring, and a linguistic influence index based on settlement hierarchy. Historical domain boundaries and present day prefecture boundaries are found to have a statistically significant effect on dialectal variation. However, the interplay of boundaries and distance is yet to be identified. We claim that a similar methodology can address spatial variation in other digital humanities, given a similar spatial and attribute granularity.

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.

Hox gene collinearity (HGC) is a multiscalar property of many animal phyla particularly important during embryogenesis. It relates events occurring in Hox clusters inside the chromosome DNA and embryonic tissues. These two entities differ in size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC) where the Hox genes are located in the order H1, H2, H3 … along the 3’ to 5’ direction of the DNA sequence. The corresponding embryonic tissues (E1, E2, E3, …) are activated along the Anterior – Posterior axis in the same order. Besides this collinearity a temporal collinearity (TC) has been also observed in many vertebrates. According to TC first is H1 expressed in E1, later is H2 in E2, followed by H3,… Lately doubt has been raised whether TC really exists. A biophysical model (BM) has been formulated and tested in the last twenty years. According to BM, physical forces are created which pull the Hox genes one after the other driving them to a transcription factory domain where they are transcribed. The existing experiments support this BM description. In the present work two equivalent realizations of BM are presented which explain the recent findings on TC as observed in the vertebrates.

If one considers chemical-biology toolsets that have had the greatest impact on numerous fields of life sciences over the most recent years, proximity-labeling tools, such as APEX, and Bio-ID arguably lead the way. This article reflects upon the current state-of-the-art and discusses key limitations underlying these emerging approaches, in particular, the limited functional knowledge they provide in understanding local proteomes / interactomes. This limitation is directly linked to the use of non-biologically- or non-pharmaceutically-relevant reactive intermediates in the course of covalently labeling the local proteomes. As such, these methods cannot report on specific functions of localized protein players, nor can they scrutinize whether the specific functions of such proteins/interactomes can be directly manipulated by pharmacologically-relevant small-molecule ligands. The latest data hint that precision localized electrophile delivery concept ushers a means to address this limitation with high spatiotemporal resolution, and ultimately, in relevant live animals.

This paper discusses Green Infrastructure, which can be considered a useful tool in the process of ensuring the sustainable development of urban structures in the Carpathian region. It allows for achieving a better quality of the environment of human life and healthy wildlife linkages. The element that supports defining information about the existing state of Green Infrastructure and its resources is the Green Infrastructure fragmentation coefficient based on edge effect calculations, which is the relation between the edge of the patch (circumference) to its surface area [1, 2]. With the use of model analysis of Green Infrastructure, it is possible to implement the provisions of the Carpathian Convention and coordinate planning documents that facilitate the sustainable development of spatial structures. Our study on the state of Green Infrastructure in rural areas of the Polish Carpathian Mountains is a source of knowledge about the quality of this area, its natural environment and fragmentation. Determining the territory’s Green Infrastructure fragmentation coefficient provides an opportunity for higher-precision studies and the detection of threats and integration of GI fragments and addressing proper solutions in conflict areas.

Spatial planning processes generally consider three levels of planning, which are applied to three types of territory: state, county and city. As the coastal areas are of a significant natural, cultural, economic and social value, as well as are characterized by a diverse range of involved society with specific interests and needs, there is a necessity for an innovative and new approach to sustainable development planning in accordance with the modern age of growth, as well as to work with local communities in specific areas. Planning of a small populated area like village territory is more diverse and subject to the wishes and needs of the population. Small territory planning involves a very narrow circle of individuals or communities that identify spatial development needs for the future, including socio-economic, cultural, and environmental and climate change scenarios. In order to assess the development opportunities and needs of the area, it is necessary to monitor the area by regularly updating data. As it is well known, methodically derived data (facts) provide objectivity and transparency. Nowadays, when information about the present and the past is circulating very fast, it is possible to analyze the current situation, to forecast the future using databases, and to show several constructed realities (scenarios) using the geographic information system (GIS). Therefore, it is crucial to explore and find out the local needs-based planning approach to the development of village in coastal areas.

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation at the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species.Menhinick’s and Simpson biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR derived canopy height, effective leaf area index data derived from multi-spectral imagery, and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that, 1.) canopy surface and strata variability may be associated with understory species diversity due to habitat partitioning and radiation attenuation, and that, 2.) such a model can predict both this relationship and biodiversity clustering.The study data yielded significant correlations between predictor and response variables and was used to produce a multiple-linear model comprising canopy relief, texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.

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.

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.

To ensure the security of spatial-temporal Top-k query in two-tiered wireless sensor networks, many schemes have been proposed in the literature in the past decade. However, most of them only consider the scenario where sensor nodes are static, and cannot achieve the security goal for spatial-temporal Top-k query in mobile sensor networks, because the mobility of the sensor nodes will affect the spatial-temporal relationships of the sensory data items generated by the sensor nodes. Although we have proposed some schemes for two-tiered mobile wireless sensor networks (TMWSNs) in our previous work, there is still large room to improve their performances. In this paper, we proposed a novel scheme named STQ-TMWSN for secure fine-grained spatial-temporal Top-k query in TMWSNs based on the virtual-grid construction and the size-order encryption binding. Theoretic analysis shows that STQ-TMWSN can achieve low computation complexity and high security performance. Simulation results indicate that STQ-TMWSN brings much lower communication cost than the state-of-the-art schemes on securing Top-k query in TMWSNs.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

Hox gene collinearity (HGC) is a multiscalar property of many animal phyla particularly important in embryogenesis. It relates entities and events occurring in Hox clusters inside the chromosome DNA and in embryonic tissues. These two entities differ in linear size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC) where the Hox genes are located in the order (Hox1, Hox2, Hox3 …) along the 3’ to 5’ direction of DNA in the genome and a corresponding sequence of ontogenetic units (E1, E2, E3, …) located along the Anterior – Posterior axis of the embyo. Expression of Hox1 occurs in E1. Hox2 in E2, Hox3 in E3… Besides SC, a temporal collinearity (TC) has been also observed in many vertebrates. According to TC first is Hox1 expressed in E1, later is Hox2 expressed in E2, followed by Hox3 in E3,… Lately doubt has been raised whether TC really exists. A biophysical model (BM) was formulated and tested during the last twenty years. According to BM, physical forces are created which pull the Hox genes one after the other driving them to a transcription factory domain where they are transcribed. The existing experiments support this BM description. Symmetry is a physical-mathematical property of Matter that was explored in depth by Noether who formulated a ground-breaking theory that applies to all sizes of Matter. This theory applied to Biology can explain the origin of HGC as applied not only to animals developing along the A/P axis but also to animals with circular symmetry.

This study examines the spatial impact of FDI on the poverty of 44 African countries. In achieving this, the study uses the Driscoll-Kraay fixed effect instrumental variable regression, instrumental variable generalised method of moments estimator (IV-GMM), and the spatial durbin model. The empirical investigation of this study yielded four significant findings: (1) neighbouring countries’ FDI has a positive and significant impact on the incidence and intensity of host country’s poverty. (2) Improved institutional quality in neighbouring countries has a significant impact on FDI-poverty reduction nexus of the host country. (3) the empirical results lend support for a significant spatial spillover of poverty in the region. (4) the marginal effect results indicate that countries within the region are no longer in isolation or independent, i.e., the level of poverty in a particular country is influenced by its determinants in the neighbouring country. This result is robust to the alternative proximity matrix, which is the inverse distance. Since there is spatial interdependence among African countries, we recommend that African governments through the African Union (AU) should not only champion the institutional reform in the region, but also establish a binding mechanism to ensure reform implementation.

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.

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.

The Direction-of-Arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. The DoA methods are employed across a range of orbits, from Low Earth Orbits (LEO) to Geostationary Earth Orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper presents a novel approach for modeling the DoA angle using a closed-form expression, incorporating the boresight angle and satellite and Earth station position data. The method uses the geographic coordinate system in the satellite communication system, precisely the latitude and longitude of the Earth station and altitude parameters of the satellite stations, to calculate the Earth station’s elevation angle and accurately model the DoA angle. Furthermore, this paper performs a comprehensive comparative analysis of various DoA methods to gain deeper insights into the performance of DoA estimation in multi-antenna systems operating under spatially correlated channels. Accordingly, this paper evaluates DoA estimation performance using root-mean-square-error (RMSE) statistics for uplink and downlink conditions through extensive Monte Carlo simulations. The simulation’s effectiveness is validated against the Cramer-Rao Bound (CRB) performance metric for the Additive white Gaussian noise (AWGN) case. (i.e., thermal noise). The simulation results demonstrate improved RMSE performance in satellite systems by incorporating spatial correlation into the system model.

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.