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

Addressing the contemporary challenges of mitigating and adapting to climate change, conserving biodiversity, and ensuring human well-being constitutes a central focus for humanity. These challenges, though often treated in isolation, are intricately interconnected, sharing common drivers. Current and future research emphasizes the pursuit of an integrated approach that minimizes trade-offs and fosters synergies among the Sustainable Development Goals (SDGs). The nexus between ecosystem service value (ESV) and the social cost of carbon (SCC) is pivotal in informing national and governmental decisions regarding human well-being and climate change mitigation and adaptation, respectively. This study employs the Zhoushan Archipelago as a case study, exploring ESV and SCC dynamics from 2010 to 2020. An in-depth analysis of spatial development characteristics and the temporal and spatial correlation between ESV and SCC is conducted. The findings reveal an increase in Zhoushan Archipelago's land area from 2010 to 2015, stabilizing thereafter, with construction land exhibiting the most notable proportional growth. Carbon emissions were observed during the study period, with forests identified as the primary carbon sink, followed by mudflat and waterbody, while construction land contributed significantly to carbon emissions. Notably, the SCC in Zhoushan Archipelago increased by 2452%, contrasting with a modest 1.5% decrease in ESV during the study period. Global spatial correlation analysis demonstrated a significant positive correlation between ESV and SCC in Zhoushan Archipelago throughout the study period, validated by passing the P-value test. However, local spatial correlation analysis revealed a specific nature of the global spatial correlation between ESV and SCC in the island area. This nuanced understanding enhances our comprehension of the intricacies surrounding ESV and SCC dynamics, contributing valuable insights to the broader discourse on sustainable regional development, economic decarbonization, and human well-being improvement.

Panax ginseng C. A. Meyer (P. ginseng), has been widely used in traditional Chinese medicine (TCM). It contains a number of chemical components and possesses a variety of pharmacological activities. Alzheimer's disease (AD) is neurodegenerative disease that creates a huge burden on the lives and health of individuals. In recent years, studies have indicated that the chemical components of P. ginseng, especially ginsenosides, play a pronounced positive role in the prevention and treatment of neurological diseases. This review aimed to summarize currently studies on chemical components and the mechanisms action in AD intervention treatment of P. ginseng, especially ginsenosides. In this review, the components of P. ginseng and their respective active effects were first introduced. Then the key molecular mechanisms and signaling pathways of P. ginseng were introduced, and its different active ingredients in the prevention and treatment of Alzheimer's disease-related pathogenesis were also summarized from the pathogenesis of AD Aβ generation and aggregation, hyperphosphorylation of tau protein，oxidant stress，neuroinflammation，mitochondrial damage，disorder of neurotransmitter and gut microbiota. Signaling pathway networks related to the action of ginseng active ingredients was constructed, which could serve as a therapeutic target for Alzheimer's disease. In addition to a detailed report on P. ginseng in improving Alzheimer's disease-related pathogenesis, the application of the current technology, spatial metabonomics in AD therapeutics and diagnostics were later discussed. Spatial metabonomics could be applied to investigate the multi-target intervention of P. ginseng on Alzheimer's disease. Research perspectives for the study of P. ginseng in the treatment of Alzheimer's disease were provided for further studies.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

This study’s primary goal was to conduct an analysis on the flood propensity of the Tabua (Ribeira Brava) drainage basin’s main watercourse. In addition to that, this study also recommends two different methodologies in order to mitigate flood’s impacts, namely by dimensioning a detention basin and adjusting the riverbed roughness coefficient. Regarding the study on the flood propensity, it was necessary to resort to geomorphological data, which was obtained when characterizing the watershed; that data was crucial to determine the expected peak flow rate, according to the Gumbel Distribution methodology and considering a 100 years return period, and to perform necessary tasks in the SIG ArcGIS software. Lastly, it was also analyzed the drainage capacity of this drainage basin’s river mouth, in order to conclude whether it would have the capacity to drain the total volume of rainwater if an extreme flood event was to happen. Indeed, the main results point out that this watershed’s river mouth doesn’t have the necessary drainage capacity to cope with an extreme event, for the return period that was considered. As a consequence of that, the two mitigation measures aforementioned were developed considering Tabua (Ribeira Brava) drainage basin’s specific features: the sizing of the detention basin was estimated through the Dutch Method and the Simplified Triangular Hydrograph Method, while the adjustment of the roughness coefficient was considered a valid solution to enhance the drainage capacity of this river mouth.

This study seeks to measure the degree of agglomeration of educational quality in Colombia, based on the non-socialization of the population that exhibits low educational quality, with the population that exhibits high educational quality, and thus determine how such agglomeration affects the phenomenon of academic segregation. To this end, we perform a spatial analysis of the educational quality in Colombia and of variables that may influence to the phenomenon of educational agglomeration. The level of agglomeration in educational quality in Colombia is demonstrated by the calculation of the Moran’s Index, in which a result of 0.62 was obtained. High educational quality is concentrated in the Andean region, while low educational quality is agglomerated in the periphery of the country, in areas such as the Pacific region. A spatial regression model was carried out to measure the dependence of municipalities on their neighbors, and to determine the main socio-economic factors affecting the phenomenon of educational agglomeration in Colombia, finding that living conditions, unsatisfied basic needs and fiscal transparency have all an impact on the educational quality of the municipalities. It is also found that the number of homicides in the municipalities does not seem to have a significant relationship with education.

Large terrestrial carnivores play a crucial role in top-down control within terrestrial ecosystems, maintaining ecosystem stability and biodiversity. However, intense interspecific competition often arises among sympatric large carnivores, leading to population reductions or even extinctions. Spatial partitioning through divergent habitat selection helps mitigate such competition. In Xinlong County, Sichuan Province, we used 293 infrared cameras for monitoring from September to May 2016 and March to October 2022. By employing the Generalized Linear Model (GLM) and the Maximum Entropy Model (MaxEnt), we developed an ensemble model predicting the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). We analyzed the main environmental factors influencing habitat selection and the fragmentation of suitable habitats. We found that suitable habitat distribution differed significantly between them. Both species preferred areas with gentle slopes close to settlements. While leopards' habitat selection primarily depended on the distance from settlements, the slope was predominant for wolves. Suitable habitats displayed aggregation, yet wolves exhibited higher fragmentation and more complex patch shapes, indicating greater susceptibility to human activities. These results suggest that sympatric large carnivores, such as leopards and wolves, can reduce spatial competition intensity and promote spatial partitioning by selecting divergent suitable habitats, thereby facilitating species coexistence.

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.

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.

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and protection of the small-scale farmers’ practices, all issues integral to food security and environmental health. Remote sensing technologies can assist precision agriculture to effectively address these complex problems, by providing farmers with a high-resolution lens. The use of vegetation indices (VIs) is an essential component of remote sensing, which combine the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. Currently a wide array of VIs is available that could be used to provide a classification and an evaluation of the state and health of crops. However precisely this high number leads to difficulties in selecting the best VI and combination of VIs for a specific objective. Without a thorough documentation and analysis of appropriate VIs, users might be confronted with difficulties in using remote sensing data or even with a very low accuracy of the results. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the most important features related to the effective use of VIs for the discrimination and monitoring of the most important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities (from farmers, researchers up to institutions dealing with the centralization and monitoring of agricultural crops).

Karst rocky desertification (KRD) is a process where strong anthropogenic disturbances and exposure of carbonate bedrock occurs in fragile karst ecosystems. The fractional cover of rocky outcrops is a key indicator and mechanistic driver of KRD and can be accurately assessed using remote sensing technology. Nevertheless, rugged karst terrain relief can cause shadow effects on satellite imagery and combine with high heterogeneity of karst landscapes to prevent fractional cover retrievals. In this study, we explored the feasibility of applying multispectral high spatial resolution ALOS imagery for fractional cover extraction of rocky outcrops. We selected the dimidiate pixel model (DPM), which has been applied in previous studies, and spectral mixture analysis (SMA; including simple endmember spectral mixture analysis (SESMA) and multiple endmember spectral mixture analysis (MESMA)) to explore the feasibility of using remote images for KRD monitoring and improve accuracy for estimating fractions. Results from MESMA achieved high overall accuracy (76.4%) in monitoring percentage of rocky outcrop fraction in the study area. SESMA appears to underestimate percentage of rocky outcrop likely because the development of KRD was driven by complex factors (soil erosion, dissolution and anthropogenic disturbances). This results in spectral reflectance of rocky outcrop being variable in different settings. Predicted exposed bedrock coverage using SESMA and MESMA was similar in sun-lit and shaded areas although predictions from SESMA were smaller than reference data. DPM underestimated the fractional cover of rocky outcrops on south-facing slopes and overestimated it in shaded areas. Furthermore, SESMA and MESMA effectively reduced topographic effects. We conclude that it is better to extract percentage of rocky outcrop using MESMA in the karst region of southwestern China. Remote sensing is emerging as a feasible method to extract surface condition information in heterogeneous and rugged landscapes.

This research deals with risk assessment for human life, man-made infrastructure, and agriculture in a landslide-prone area using GIS-based spatial methods. The study area landslide inventory map was prepared based on previous landslide information, aerial photograph analysis, and several field observations. A total of 550 landslides have been included with 182 debris flow and 368 soil slides. All included landslides were classified into two groups by random selection; half were used for model calibration and the rest were used for cross-validation. In the analysis, fourteen causative factors were vastly used, such as aspect, slope, curvature, elevation, topographic wetness index, forest timber diameter, forest type, forest crown density, forest age, land-use, geology, soil drainage, soil depth, and soil texture. Moreover, to identify the interaction between occurred landslides and causative factors, the affected pixels were divided into different sub-classes using a frequency ratio method. Based on the total dataset, three landslide susceptibility maps were constructed using Bayesian prediction, likelihood ratio, and fuzzy set method. By evaluating cross-validation and success rate curve, model susceptibility results were plotted with a receiver operating characteristic (ROC) curve and the area under the curve (AUC) was estimated. In addition, for risk assessment, each social data layer such as agriculture, house, industry, business, road, river, population intensity, monetary value, and vulnerability level was added based on the local standard and incident time and was converted into US dollars. During the analysis, each method hazard map was used with a specific group of thematic data layers. Subsequently, for preparing the probability table, study area total pixels and predictive landslide affected pixels were considered. Matching with the affected pixels, a standard of 5000 pixels was selected to run the final evaluation. Based on the results, the agricultural field showed the highest vulnerability and estimated risk of US $ 16.3 million. Further, the man-made infrastructure map showed a risk of US $ 31.3 million. The total estimated population casualties were 6.77, which was relatively similar to the published data.

Stream chemistry effectively integrates watershed ecosystem processes on both spatial and temporal scales. Streams of coastal areas integrate a more homogeneous, flat topography wherein there can be interactions between the stream and the body of water into which it drains, especially where tidal fluxes occur. The present study assessed water quality of Thompson Bayou, which comes to the campus of the University of West Florida in a wetland after flowing through 4 km of commercial and private property with associated impacts on water quality. Sampling was carried out for one year at eight discrete sites along Thompson Bayou from the UWF campus to the Escambia River. We used a portable field meter to measure temperature, pH, dissolved O2 (DO), and specific conductivity (SC). Except for temperature, all variables exhibited a spatial pattern of significant variation with distance, with consistent increases in DO and SC as the stream approached the river. These variables also exhibited a temporal pattern of significant seasonal variation, including—and especially—temperature. Data suggest that spatial and temporal patterns of water quality of Thompson Bayou are determined by (1) processing of water by the wetland, (2) interactions of the stream channel with upland forest stands, and (3) the tidal hydrology of the Escambia River.

Ethiopia has been known for its recurrent droughts and poverty due to extreme climate change and variability affecting agricultural production. This study was, therefore, conducted to analyze the seasonal and inter-annual variability in rainfall and temperature v over the past three decades in western Tigray. The study consisted of 34 years of temperature and rainfall data from nine stations in two administrative zones in the western part of Tigray. The climate data was obtained from the National Aeronautic Space Administration (NASA) from 1983 to 2016. Total rainfall, simple daily intensity index, Rx1day and Rx5day, and maximum and minimum temperature, tropical nights, diurnal temperature range, cold spell and warm spell duration indicator values, cold days and cold nights, warm days, and warm nights were analyzed. RClimDex software was employed to analyze the trends and quality control of the extreme rainfall and temperature indices; in addition, the Mann-Kendall test was also used to detect the changes and variability of the variables. The trend analysis showed that there was significant (p<0.05) variability in extreme temperatures, with maximum and minimum temperatures varying from 0.04 to 0.051 0C, and 0.022 to 0.031 0C, respectively. The results also revealed that there was a negative trend in extreme rainfall, with no uniform pattern. The highest and smallest reduction in the annual rainfall was 14.6 mm and 8.4 mm, which were observed at Maygaba and Adigoshu, respectively. More than 60% of the total rainfall was received in July and August throughout the study areas, with the maximum monthly rainfall of 294.2 mm observed at Adiremets. Finally, this study demonstrated that areas in the northwestern and western zones of Tigray were significantly affected by extreme rainfall and temperature variability, which subsequently affected crop yield, natural resources, food security, and the livelihood of the community during the last three decades.

The period from 2019 to 2021 witnessed an enduring and severe drought that impacted southeast South America. Initially characterized as a meteorological drought, its influence later extended across the entire hydrological cycle. This paper explores the application of the S-TRACK method to monitor and characterize the 2019/21 drought in Southern Brazil (SB) and Southeast Brazil (SEB). Monthly precipitation data from reanalyses and satellites, alongside the Standardized Precipitation Index (SPI) for drought quantification, are utilized. Precipitation datasets, validated against rain gauge records, highlight ERA-5 and CHIRPS as having the highest correlation in both SB and SEB, with PERSIANN exhibiting a moderate correlation. The utilization of the S-TRACK method for spatial drought tracking revealed the temporal dynamics of the drought events, with three significant episodes occurring in 2020/2021. Initially, the onset of the drought was observed in the SB region, characterized by intensities ranging from moderate to extreme. Subsequently, the drought extended northward, reaching its maximum intensity at lower latitudes within the SEB region before either dissipating or experiencing a reduction in both its spatial extent and intensity. The prolonged presence of drought-affected areas in the same region over an extended period contributed significantly to the exacerbation of drought severity, particularly during the March to May trimester.

There has been a rapid increase in the use of Marine Spatial Planning (MSP) throughout the world recently, partly due to the continued loss of marine biodiversity and habitat. The sustainability of marine resources is threatened in all regions of the world by major events such as climate change, marine pollution, overfishing, illegal, unreported and unregulated fishing both on the high seas and in country waters. Here, we present a comprehensive review and analysis of how economic information has been applied and used to inform decisions about MSP in British Columbia (BC), Canada and other similar jurisdictions. This focus for the paper was selected because important gaps remain in the literature in terms of incorporating economic questions into MSP. We first present different definitions of MSP, and then we extract useful lessons from MSP regimes with well–tested decision support tools, and use this to guide MSP implementation in BC. We finally present and discuss case studies from Australia, South Africa and Belgium. Our review suggests that applying economic information to support the design and implementation of MSPs would lead to better decisions. This in turn would foster livelihoods, attract finance, increase buy–in and advance United Nations Sustainable Development Goal 14: Life Below Water, thereby achieving Infinity Fish, i.e., ensuring that ocean benefits flow to humanity forever.

Electric energy is one of the driving forces in every country. The supply of electrical energy continues to present various challenges, such as high costs associated with procuring raw materials for generation, sparsely populated areas that are not connected to the main grid, and the need for infrastructure to support generation and network delivery. These challenges have contributed to the growing adoption of renewable energy sources, particularly solar photovoltaics (PV).In this study, we have developed a practical method to assess the spatial PV potential in a selected urban area. The methodology combines data collected from an unmanned aerial vehicle (UAV) photogrammetry, Geographic Information System (GIS), and the energy output from three remote photovoltaic systems installed at different locations. The results obtained from this approach not only provide the energy generated per square meter, per year (kWh m-2 yr-1), but also provides a base to calculate the potential distance that electric cars could travel based on the energy generated. To illustrate the application of this method, two practical cases were selected: Senglea, Malta, and Munxar, Gozo. These examples highlight the versatility and effectiveness of our approach for evaluating and harnessing solar energy through photovoltaic panels in different contexts.

Flanders (Belgium) is ageing at a rapid pace. Elderly people will continue to live at home in large numbers, partly because this is encouraged by the government. However, many homes are not adapted to reduced mobility. For example, it is not possible to circulate with a wheelchair. But also the living environment poses challenges for the elderly. Flanders is known for a far-reaching spread of housing and facilities, with the result that daily facilities can often only be reached by car. As a result of this spatial fragmentation, the elderly themselves often live for care providers in unreachable places. This ensures, among other things, that the in-home help travels several car kilometers. And research shows that so-called informal care is not self-evident either. Because what about the elderly who don't have children living nearby? Or with the residents who only see their next neighbor hundreds of meters away, who is also often a little mobile peer? The (untouched) appropriateness of the home, the presence or absence of facilities or neighbors, all of which belongs to what we can call the spatial capital of an environment. If this is not present, an autonomous life for a less mobile elderly person is not possible. The argument is that without sufficient spatial capital, the policy options are to allow the elderly to live in their homes for as long as possible or caring neighborhoods dead ends.

In this work a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, Reanalysis dataset and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 hours ahead, with mean absolute errors (MAE) between 2cm and 10cm respectively.

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in the last decades, they are predominantly used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environment bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in Sofia city based on CityGML 2.0 standard. The model represents the buildings in LOD1 with a focus on CityGML features of related to the buildings like building part, terrain intersection curve and address. Similar building models of 18 cities provided as open datasets are explored and compared in order to extract good modeling practices. As a result, workflows for generation of 3D building models in LOD1 are elaborated and improvements in the feature modeling are proposed. Two options of building model are examined: modeling of a building as a single solid and modeling of a building with separate building parts. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

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.

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.

Spatial light modulators (SLMs) have been widely used to achieve dynamic control of optical traps. Often, holographic optical tweezers have been presumed to provide nanometer or sub-nanometer positioning accuracy. It is known that some features concerning the digitalized structure of SLMs cause a loss in steering efficiency of the optical trap, but their effect on trap positioning accuracy has been scarcely analyzed. On the one hand, the SLM look-up-table, which we found to depend on laser power, produces positioning deviations when the trap is moved at the micron scale. On the other hand, phase quantization, which makes linear phase gratings become phase staircase profiles, leads to unexpected local errors in the steering angle. We have tracked optically-trapped microspheres with sub-nanometer accuracy to study the effects on trap positioning, which can be as high as 2 nm in certain cases. We have also implemented a correction strategy that enabled the reduction of errors down to 0.3 nm.

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.

We show that an experimentally plausible system consisting of a modulated wire medium hosted in a metal cavity can preserve the longitudinal field profile shaping predicted by Boyd et al. (2018) on the basis of a perfectly periodic wire-only structure. These new frequency domain numerical results are a significant step towards justifying the construction of an experimental apparatus to test the field profile shaping in practise.

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

The potency and challenges of sorghum development in tidal swamplands in Indonesia have yet to be well studied. Our research was the first to evaluate land suitability, economic performance, and strategies for developing sorghum in tidal swamplands in Central Kalimantan. The assessment methods for the potency and utilization of sorghum used the land suitability evaluation method, gross margin and profit analysis, break-even, and competitive analysis. As a tool for decision-making, we used SWOT followed by the quantitative strategic planning matrix (QSPM) analysis. The results showed that the potency of arable land suitable for sorghum development was 578,511 ha. Economically, sorghum farming can generate an IDR 12,894,000 per ha with a revenue-cost ratio of 1.72; the break-even price was IDR 2,447 per kg, around 42% lower than the current market price. Sorghum was also more competitive than cassava, sweet potato, and soybeans (Q values of 0.76, 0.58, and 0.61, respectively) and less competitive than maize (Q = 1.33). Based on the QSPM, there were five alternative strategies for developing sorghum in tidal swamplands, i.e., (1) Optimization of productivity, (2) Improving the quality of human resources of farmers, (3) Facilitation of partnership cooperation, (4) Application of site-specific technology, and (5) Optimization of waste utilization. Those strategies show the potential expansion of sorghum planting in the tidal swamplands and the economic value enhancement for the community.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality, particularly the soil properties, is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality and examine the influence of soil properties on mortality rates. Multicycle Forest Inventory and Analysis data were compiled to capture white oak plots across the eastern US. White oak mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Soil variables were analyzed to assess soil properties. Spatial analysis techniques, including geostatistics and regression modeling, were used to analyze the relationship between white oak mortality and soil characteristics. Results found clustered spatial patterns of white oak mortality across a broad scale depicting the significant effects of coarser soil textures, nutrient-deficient sites, and extreme soil moisture levels. Our findings demonstrated the importance of soil properties in shaping the spatial pattern of the white oak mortality rate. This idea can inform forest management practices for the conservation of white oak populations. Future research is needed for comprehensive soil assessment including biotic and abiotic factors for forest management strategies at a broader scale aimed at mitigating white oak mortality.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality, particularly the soil properties, is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality and examine the influence of soil properties on mortality rates. Multicycle Forest Inventory and Analysis data were compiled to capture all white oaks across the eastern US. White oak tree mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Soil variables were analyzed to assess soil properties. Spatial analysis techniques, including geostatistics and regression modeling, were used to analyze the relationship between white oak mortality and soil characteristics. Results found clustered spatial patterns of white oak mortality across a broad scale depicting the significant effects of coarser soil textures (p = 0.00), nutrient-deficient sites, and extreme soil moisture levels (p <0.1). Our findings demonstrated the importance of soil properties in shaping the spatial pattern of white oak mortality rate. This idea can inform forest management practices for the conservation of white oak populations. Future research is needed for comprehensive soil assessment including biotic and abiotic factors for forest management strategies at a broader scale aimed at mitigating white oak mortality.

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.

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.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

It was observed that a cluster of ordered genes (Hox1, Hox2, Hox3,…) in the genome are activated in the ontogenetic units (1, 2, 3,…) of an embryo along the Anterior/Posterior axis following the same order of the Hox genes. This Spatial Collinearity (SC) is very strange since it correlates events of very different spatial dimensions. It was later observed in vertebrates, that, in the above ordering, first is Hox1expressed in ontogenetic unit 1, followed later by Hox2 in unit 2, and even later Hox3 in unit 3….This temporal collinearity (TC) is an enigma and even to-day is explored in depth. In 1999 T. Kondo and D. Duboule, after posterior upstream extended DNA excisions , concluded that the Hox cluster behaves ‘as if’ TC disappears. Here the consideration of TC really disappearing is taken face value and its repercussions are analyzed. Furthermore, an experiment is proposed to test TC disappearance. An outcome of this experiment could be the reappearance (partial or total) of TC.

Many parameters and complex boundaries are involved in the spatial arrangement of an under-ground powerhouse for a hydropower station, which requires reference to many relevant cases and specifications. However, in practical applications, retrieving relevant cases or specifications is difficult, and there is a lack of knowledge of cascading logic among design parameters. For this question, a targeted knowledge graph based on knowledge graph management technology is established to support subsequent applications. This paper proposes a new concept of con-structing a knowledge graph for building information modeling (BIM) underground power-houses of hydropower stations. Firstly, the ontology skeleton of hydropower station spatial ar-rangement design, which represents the knowledge organization structure of the knowledge graph, is reconstructed by carefully analyzing the requirements for intelligent modeling of un-derground powerhouses. A large amount of unstructured data is identified based on optical character recognition (OCR) technology and is divided into words to extract correlation knowledge based on THULAC. In the next step, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in a Neo4j knowledge base to build a knowledge graph. Finally, the knowledge graph is serviced to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of spatial arrangement of an underground powerhouse of pumped storage hydropower stations.

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

The development of subterranean non-motorized traffic infrastructure, commonly referred to as the underground pedestrian system (UPS), has become increasingly necessary in densely populated megacities worldwide as a means of advancing the Sustainable Development Goal 11, which aims to promote sustainable cities and communities. To improve the overall spatial performance, it is imperative to decipher the fundamental formation mechanism of sustainable underground pedestrian systems (SUPS) that is simultaneously influenced by spatial morphology and pedestrian behaviors. Thereby, two representative case studies, namely Wujiaochang UPS and Loushanguanlu UPS located in Shanghai, were selected for an in-depth investigation. This study employed correlation and regression analysis to examine the impact of spatial configuration variables and spatial attribute factors on pedestrian flow distributions in distinct SUPSs. The findings indicate that the variables of Betweenness, as measured by both Euclidean and Angular metrics, along with the presence of metro station locations and commercial space connected by UPS, are the three most significant factors influencing pedestrian behaviors in both scenarios. The disclosure has been made that Wujiaochang UPS is seamlessly integrated into a comprehensive three-dimensional pedestrian network both above and below ground. On the other hand, it appears that Loushanguanlu UPS exhibits a greater degree of self-sufficiency as an underground system. This study aims to elucidate the mechanism underlying the development of SUPSs, thus offering effective guidance for the implementation of three-dimensional walking systems in cities that prioritize sustainability.

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;

Uzbekistan, as an emerging national economy, aims to attract foreign direct investment (FDI) to foster the development of its free economic zones (FEZs). While recent policy reforms have enhanced the country's appeal to investors and facilitated the inflow of international capital, challenges persist in attracting investment. This study employs an economic geographical approach to analyze and propose solutions to these obstacles. By examining the spatial patterns of FEZs and FDI through a comprehensive geographical lens, this research utilizes document analysis as an alternative to empirical analysis, considering the broader context rather than focusing on a specific FEZ. The findings reveal that the insufficiency of an innovation-driven environment, logistical systems, energy resource-related issues, among others, negatively impact the inflow of FDI into FEZs. Moreover, the study underscores the significance of economic geography in understanding these factors. Finally, relevant insights and recommendations are provided from an economic geographical perspective.

The Klaten Regency, Central Java Province, Indonesia is an leptospirosis endemic area. This study’s purpose is to spatially describe the abiotic and biotic environmental factors with the Leptospirosis incidence in the Klaten Regency in 2018. This was a descriptive observational with cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environment factors such as poor waste disposal facilities, poor gutter conditions, river <200 meters, and flooding history, namely 35.6%; 41.2%; 54.2%; and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018 with 325 mm of rainfall, average temperature of 270C, average humidity of 82.3%, and altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥3 types of vegetation (79.7%). In conclusion, most respondents' dominant environmental factors were presence of rat nests, ≥3 vegetation types, and river availability <200 meters.

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

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

Soil nitrogen (N) is a common limiting factor where soil N cycling is a key component of agro-ecosystems. Soil N transformation processes are largely mediated by microbes and understanding bacteria involvement in soil N cycling in agricultural systems has both agronomic and environmental importance. This 2-yr field scale study examined the abundances and spatial distributions of total bacterial community (16s RNA), bacterial involved in nitrification (amoA), and de-nitrification (narG, nirK, and nosZ) and soil physicochemical properties of winter wheat (Triticum aestivum L.) – soybean (Glycene max L.) double cropping with 2-3 weeks of spring grazing (WGS) and without grazing (WS) and of tall fescue (Festuca arundinacea (L.) Schreb.) pasture (TF) managed to near natural conditions with similar grazing. The TF had a significantly higher abundance of 16S rRNA, amoA, narG, nirK, and nosZ genes than the WS and WGS, which had similar levels between themselves. Soil organic matter (OM) and soil pH had a stronger effect on the N-cycling bacteria gene abundance. All bacterial gene concentrations and soil pH showed non-random distribution patterns with 141-186 m range autocorrelation. These results indicate that biological N transformation processes are more important in natural agricultural systems and the abundance of N-cycling bacteria can be manipulated by field scale management strategies.

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.

High-resolution GPS/GSM dataloggers provide spatial information of the highest quality, which outperform previous tracking methods, such as Argos telemetry or conventional VHF ground-tracking. As a result, this has improved our knowledge of home-range behavior and spatial ecology of many species, including large raptors. In this paper, we use high-resolution GPS/GSM dataloggers to assess the home-range size and the role of sex, season (breeding or non-breeding season), and breeding status (reproductive or non-reproductive individuals) on the space use of Bonelli’s eagle (Aquila fasciata). To this end, 51 territorial individuals (25 females and 26 males) were equipped with GPS/GSM transmitters and were tracked over 7 years (2015–2021) in eastern Spain. Overall, we recorded 4,791,080 fixes that were analyzed through kernel density methods (50%, 75%, and 95% fixed kernels). The average individual home-range size according to the 95%, 75%, and 50% kernels was 54.84 ± 20.78 km², 24.30 ± 10.18 km², and 11.17 ± 4.90 km², respectively. Overall, the home-range size of individuals occupying the same territory was similar, mainly due to the cooperative hunting behavior exhibited by the species. We did not find interannual differences in the home-range size (95% fixed kernel) of the majority of individuals, showing a strong territorial fidelity of the breeding pairs. In general, females’ home-range size was slightly smaller than males’ size due to the decrease in activity in the breeding season as a result of laying, incubation, and chick attendance at nests. No seasonal variation in the 95% kernel was found, but it was found in the 75% and 50% kernels. In regard to the breeding status, higher home-range size was recorded in the non-reproductive individuals. Moreover, we found a low neighbor overlap among the territories (4.18% ± 3.06%), which evidences a high level of intraspecific competition in the Bonelli’s eagle. Finally, this study highlights the advantages of the use of accurate telemetry information to improve our understanding of the spatial ecology of the endangered Bonelli’s eagle, which ultimately will serve to better inform management actions for its conservation.

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.

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.

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

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).

Abstract: Few studies have been conducted in Iraq regarding the association of violence at multiple ecological levels and child mental health outcomes. Therefore, the study objectives were 1) to conduct a nationwide analysis to explore violence at multiple socio-ecological levels and children's mental health outcomes, and 2) to identify the most vulnerable children according to the spatial distribution of Iraqi governorates. This cross-sectional study used the 2018 Iraq Multiple Indicator Cluster Survey (MICS 6), which included 12,358 caregivers who provided complete information regarding their parenting and disciplinary practices regarding their children between the ages of 5 and 14. Logistic regression was employed to examine the factors associated with children’s depression and anxiety. This study showed that 22% of children had depression, while 38% experienced anxiety. Notably, children residing in Iraq's South/Central regions, specifically in areas like Nainawa, Najaf, and Basrah, exhibited notably higher anxiety and depression. The research also showed that living in unsafe neighborhoods, women's acceptance of domestic violence, and the use of severe physical punishment as corporal discipline all contributed to the development of anxiety and depression. This study addresses the scarcity of information on children's mental health outcomes in Iraq at national and governorate levels, emphasizing the need for urgent national-level policy discussions to achieve key Sustainable Development Goals related to ending all forms of violence against children by 2030.

During the last few years, advancement in the area of biochemistry, science of the material world, engineering and computer-aided testing has directed towards advancement of high-throughput tools for the sake of profiling information encoded in a gene. Single-cell RNA-sequencing (scRNA-seq) tools capable to examine the sequence data from each individual cells and it shows within population variety and permit exploring of cell conditions and transformation by extreme resolution, possibly give out cell sub-types or gene expression fluctuations that are shaded in mass sequencing processes, which shows population-averaged evaluations. Yet, the major disadvantage for this tool is the lack of success to pick out location related details of the RNA transcriptome, as this needs tissue detachment and cell isolation. Location based transcript determination is among the advancements in the area of medical biotechnology as this can find out the molecules like RNA dataset in their intact physical placement in tissue segment with spatial context at the scale of a single-cell, which is very advantageous as compare to single-cell sequencing techniques. These approaches give key observation in the areas of biomedical field sub-disciplines like neurology, embryology, carcinoma study, immune cell investigation and histological activities. Generally, this review mainly focused towards single-cell sequencing methods, technology development, its challenges observed, different expression data analysis mechanisms and their applications in different areas, such as in cancer research, microbes, central nervous system, sex organs and immune-biology, intensifying the essentiality of sequencing tools at single-cell level for the characterization of extremely dynamic individual cells.

The main focus of this paper is to propose a comprehensive framework for the cognitive meas-urement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, mak-ing them more liveable.

Data on the temporal and spatial evolution of Sars-Cov-2, local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed data from case notification systems, epidemiological investigation reports, and municipal decrees in a small municipality in northeast Brazil. For spatial analysis, cases and deaths in the urban area were mapped. There were a total of 3,020 cases of COVID-19 from April 2020 to December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5,650.3 cases and 252.6 deaths per 100,000 population, respectively. The index case of Sars-Cov-2 in the city was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave of infection peaked in May 2021, with the highest number of deaths in March 2021. In spatial analysis, the highest density of cases and deaths occurred in the urban area. The municipal government issued 69 decrees on restriction measures, surveillance, and maintenance of social isolation to address the spread of SarsCov-2. The spread of the Sars-Cov-2 pandemic in Itapajé mirrored the behavior observed in large metropolitan regions, from central neighborhoods to the periphery.

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using Geographic Weighted Regression (GWR). The sample period included bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that the built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crashes index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. The GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bikes trips’ effects on the severity index.

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.

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.

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.

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread deployment. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed, which could mitigate environment noise through wavelet transform and extract the amplitude or phase covariance matrix as the feature vector. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. The accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

Urban cycling is a sustainable transport mode that cities currently try to expand. However, cities still do not take advantage of geospatial technologies to understand cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental geo-game. About 20 participants per city recorded bicycle trips during one week and, by aggregating them, we found not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, Participants not only recorded bicycle trips but also segments at lower speeds indicating a deviation from an ideal cycling scenario. We described the potential frictions preventing from bicycle commuting based on the distance to bicycle paths, surrounding infrastructure, and location in the urban area.

The mantis shrimp Rissoides pallidus (Giesbrecht, 1910) is among the rarely reported crustacean species in the Central Mediterranean Sea. In December 2020, during a trawl survey off Mazara del Vallo harbour (Strait of Sicily), two specimens of R. pallidus were captured on coastal detritus and coastal terrigenous mud bottoms at about 132 and 152 m depths. This current note, therefore, updates the existing knowledge of R. pallidus in the Strait of Sicily. Additional information specific to biometrics, bottom types, depth preference, habitats, and geographic distribution of this crusta-cean species are provided.

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.

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing's six central districts. Subsequent to this, a comprehensive analysis of the network's structural attributes was carried out. A walktrap method rooted in modularity analysis was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies.

Ecologists use the net biotic interactions among plants as a major factor to predict other ecosystem features, such as species diversity, community structure, or plant atmospheric carbon uptake. By adopting this approach, ecologists have built a giant body of theory founded on observational evidence. However, growing evidence points out that this may not be the right approach. The literature addressing the biophysical mechanisms underlying the plant interactions is much scarcer. A rising number of scientists claim the need for a mechanistic understanding of plant interactions due to the limitations that a phenomenological approach raises both in empirical and theoretical studies. Scattered studies have recently taken such a mechanistic approach, but we still lack a general theoretical framework to study mechanistically plant interactions. In this review, we first recapitulate the elementary units of plant interactions, i.e., all the known biophysical processes affected by the presence of an influencing plant and the possible phenotypic responses of plants influenced by those processes. Second, we discuss how a net interaction between two plants emerges from the simultaneous effect of these elementary units. Third, we touch upon the spatial and temporal variability of the net interaction and discuss the links between this variability and the underlying biophysical processes. We conclude by discussing how to integrate these processes into a mechanistic framework for plant interactions that must necessarily focus on the individual scale and explicitly incorporate the spatial structure of the community and environmental factors: the plant interaction models (PIM). A PIM incorporates a pair or few plants interacting with their physical environment so that the biotic interaction is not imposed but emerges from the model. This type of model can provide concise, mechanistic hypotheses to be tested empirically. This review calls for a paradigm shift in the ecology of plant interactions, from the classic species interaction study towards a more mechanistic individual-level approach. It also presents a comprehensive foundation for studying the mechanisms underpinning the net interaction between two plants.

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

Ocean circulation serve as the primary channels for transporting material and energy flows throughout the entire ocean system, which plays a crucial role in shaping Earth’s climate, weather patterns, and marine ecosystems. The ocean movements have far-reaching impacts on both the environment and human life. An effective method for semantically modeling the ocean circulation is urgently required to be established. To achieve a unified description of ocean circulation at the semantic level, this paper introduces the theory and methodology of ocean ontology, which is developed through an analysis of domain knowledge in ocean circulation. We focus on analyzing the concepts, temporal relationships, and spatial relationships of ocean circulation. By defining classes, properties, relationships, instances, and constraint conditions within the logical structure of an ontology, it is feasible to formalize the expression of conceptual elements and their relationships. Additionally, semantic inference rules are established to finalize the construction of the ocean circulation ontology. The effectiveness of ontology construction has been verified through practical examples. Furthermore, a specialized knowledge base framework has been developed upon the ontology description of ocean circulation. Some examples of knowledge base queries have been articulated and verified. The results demonstrate that this ontology can effectively represent the relevant knowledge in the domain of ocean circulation and provide a meaningful strategy for investigating semantic integration and knowledge sharing in this field.

Optimal use of Hierarchical Bayesian Model (HBM) assembled aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM2.5 fused surfaces and four respiratory-cardiovascular hospital events in 12 km2 grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included 5 grids (lag grids 04; 720 km2) and the smallest HOSA contained 2 grids (lag grids 01; 288 km2). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM2.5 concentration levels, population density, and poverty percent than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory-cardiovascular chronic diseases in persons residing in rural areas was discussed

Currently, synthetic diamonds are finding new applications. Quantum devices can be created based on diamond plates with the desired concentration of defects in the structure of the crystal lattice. NV color centers can be used in quantum applications by irradiating plates with high-energy particles, followed by annealing at temperatures above 800°C to develop the spin-optical properties of the centers Diamond plates are subjected to electron irradiation and much less often to proton irradiation. As a result, synthetic diamond plates with NV centers ob-tained by proton irradiation are less studied, although proton irradiation makes it possible to obtain a layered structure of NV centers. In this work, single crystal diamond plates irradiated with protons were studied using polarization microscopy, IR spectroscopy and ODMR spectros-copy. It is shown that when creating NV centers, the distribution and concentration of C – defects in the sample, as well as mechanical stresses before and after irradiation (with subsequent an-nealing), can differ significantly from each other.

An imperative challenge emerges from the demand to construct a scientific method to recent agricultural and rural policies throughout the world. The objective of the present study is to conduct an ex-post quantitative evaluation of the Comprehensive Rural Village Development Project, a representative rural development project operated by the Ministry of Agriculture, Food and Rural Affairs, a central government agency in South Korea. The primary purpose of the project is to ensure sustainable rural society. This study found a moderate but significant positive impact of the policy in enhancing the standard of living in rural areas. The present study concludes with suggesting some policy implications and future directions of policy evaluation studies.

In this study, based on frost formation data provided by Harbin Meteorological Bureau, combined with geographical factors, temperature and population density, linear fitting, Mann-Kendall mutation test, pettitt method and sliding T test were used to analyze the spatio-temporal variation characteristics of frost date in Harbin Municipality and the effects of geographical factors, temperature and population density on frost regularity. The results showed that (1) The earliest year of FSD appeared was 1966 and 1967, on the 255th, i.e., August 18th, and the latest was 283 days, i.e., October 10th, 2006. The earliest year in which the FED appeared was April 24th of 2015, the 114th day of that year, and the latest was April 21st of 1974, the 141st day of that year. The year with the most frost was 2012, with 161 days, and the year with the shortest was 1966, with 123 days. (2) Throughout the study period, FSD increased by 7.8 days at a rate of -1.27d/10a, FED increased by 10.9 days at a rate of 1.77d/10a, and FFS increased by 18.9 days at a rate of 3.05d/10a. The tendency rate of FSD and FFS at each site in Harbin showed an increasing trend. For FED, the tendency rate of some sites showed an increasing trend. In general, FSD showed a delayed trend, FED showed an advanced trend, and FFS showed a prolonged trend. Using the method of unary linear regression, FSD of each site showed an increasing trend, FFS showed an increasing trend, and FED showed a decreasing trend in general. (3) Throughout the study period, FSD was mutated in 2000, and the arrival time of it in the study area averaged the 265th, i.e., September 22nd, and after that, the arrival time of it changed to the 272nd, i.e., September 29th of that year. FED was mutated in 2006, and the arrival time of it in the study area averaged 128th, i.e., April 8th. After the mutation, the arrival time of FED changed to 121st, i.e., April 1st of that year. FFS was mutated in 2004. Before the mutation, with an average arrival time of 137days in the study area .But after that, the time changed to 150 days.(4) FSD and FFS in Harbin Municipality were negatively correlated with latitude and positively correlated with temperature, while FED was positively correlated with latitude and negatively correlated with temperature. The FSD, FED and FFS in the central part of Harbin Municipality were the earliest, the latest and the longest, respectively, so the Pearson correlation coefficient method and the influence of longitude could not be reflected in the multiple regression. Several factors were judged by grey correlation analysis method. The longitude is significantly related to FSD, FED and FFS.

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.

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

Human disturbances can have severe environmental impacts on freshwater ecosystems. The main aim of this study was to detect the influences of physiochemical variables, land-cover characteristics, and river connectivity on fish assemblages in Lake Chaohu Basin, China. A cluster analysis of the river connectivity variables identified four groups of sites that characterized by significantly different connectivity gradients in local scale. These four groups of sites showed increasing connectivity from the upper reaches to the lower reaches. At the same time, among the four groups, the values of the environmental variables generally increased from the upper reaches with less human activity towards the lower reaches with more human activities. For instance, some main physiochemical variables (e.g., river width, water depth, nitrate, phosphate) significantly increased among the four groups. In contrast, fish taxa richness and diversity indices were not significantly different among the four connectivity groups. However, fish assemblages showed significant variation among the connectivity groups (p=0.026). In addition, the study determined that upper riparian land uses (e.g., woodland and grassland), flow velocity, and elevation were environmental variables regulating the variance in the fish communities, and for the connectivity variables, only river order and number of branches along a path to the left of the main stem affected the variance in the fish communities. Therefore, new practices aimed at maintaining and even increasing the riparian canopy coverage and the flow velocity of rivers should be integrated into local conservation planning for freshwater ecosystems, especially in the upper reaches of the basin.

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

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

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

Growth benefits of high speed rail (HSR) for enterprises are not automatic, but require a buoyant local economy and a robust strategy. Against above background, main objectives of ex-ante case study have been to investigate local socio-economic and spatial impacts of proposed HSR project around Ahmedabad station area on Mumbai-Ahmedabad HSR corridor, analyze the opportunities and challenges of HSR development and draw policy implications. Qualitative research methodology has been used to elicit the perceptions and perspectives of randomly selected 43 small, medium and large enterprises. Data has been analyzed using descriptive statistics. The study reveals that HSR induced greater human interaction will activate local economy, improve business efficiency and generate employment. Frequent travels on business trips can influence enterprises decision to settle near HSR station for easy access and likely to promote realty development. The proposed HSR project should be developed in synergy with urban dynamics of cities in transition and metropolitan service cities. Poor public transport connectivity is likely to be a major challenge in improving HSR accessibility and enhancing overall impacts of HSR, which can be improved by developing an integrated urban transport system, for which cooperation of all stakeholders and coherence with firms’ strategies is essential.

Urban sprawl propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, substantially alters ecosystem services. Hence, the quantification of urban sprawl is crucial for effective urban planning, and environmental and ecosystem management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive urban sprawl triggered by the doubling of total population over the past three decades. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed spatial metrics to quantify the extent of urban sprawl within a 10km suburban buffer of Chennai. The rate of urban sprawl was quantified using Renyi’s entropy, and the urban extent was predicted for 2027 using land-use and land-cover change modeling. A 70.35% increase in urban areas was observed for the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was ≥ 0.9, exhibiting a two-fold rate of urban sprawl. The spatial metrics values indicate that the existing urban areas of Chennai became denser and the suburban agricultural, forests and barren lands were transformed into fragmented urban settlements. The forecasted urban growth for 2027 predicts a conversion of 13670.33ha (16.57 % of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value of 1.7. Our findings are relevant for urban planning and environmental management in Chennai and provide quantitative measures for addressing the social-ecological consequences of urban sprawl and the protection of ecosystem services.

Land Use /Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key among the past changes is the loss of natural vegetation related to changes in different Land Use/Land Cover (LULC) categories, e.g., water bodies, wetland and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in the Africa continent. Here we applied an open and synergistic framework to update and improve the existing LULC maps at every five years intervals from 1990 to 2020 in West Africa at a finer spatial resolution of 30m. Next, we detected spatial temporal pattern in past and present LULC changes with the intensity analysis framework in the periods of 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 (first period) and 2010–2020 (recent period) than in 2000–2010 (middle). We observed consistent gains in rangeland (shrubland and grassland) in all the periods, which confirms the observed re-greening of rangeland in the West Africa continent. By contrast, forestland areas experienced consistent losses for the entire periods, which indicate deforestation and degradation. As regards cropland, a net loss was observed in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all the periods. We observed net gains in water bodies in 1990–2000 and net losses in the last period. We highlighted the active forestland losses as systematic and hence a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concern and must send an early warning signal to policy makers that the food security of marginal geographic locations is under threat despite the massive expansion of cropland observed in this study area. The intensity analysis framework was vital to identify the settlement category as relevant for LULC change management intervention across time and space in West Africa instead of focusing on dynamics of all the LULC categories that may be irrelevant and hence a cost effective LULC change management approach. Policies to control the expansion of settlements will indirectly regulate the expansion of cropland field because we observed that as settlement encroached on cropland, simultaneously cropland encroached on natural vegetation to meet the food security requirements of the inhabitants.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In-process EBM monitoring for quality assurance purposes has been a popular research area. Electronic imaging has recently been investigated as one of the in-process EBM data collection methods, alongside thermal/ optical imaging techniques. Despite certain capabilities of an electronic imaging system have been investigated, experiments are yet to be carried out to benchmark one of the most important features of any imaging systems – spatial resolution. This article addresses this knowledge gap by: (1) proposing an indicator for the estimation of spatial resolution which includes the Backscattered Electrons (BSE) information depth, (2) estimating the achievable spatial resolution when electronic imaging is carried out inside an Arcam A1 EBM machine, and (3) presenting an experimental method to conduct edge resolution evaluation with the EBM machine. Analyses of experimental results indicated that the spatial resolution was of the order of 0.3mm-0.4mm when electronic imaging was carried out at room temperature. It is believed that by disseminating an analysis and experimental method to estimate and quantify spatial resolution, this study has contributed to the on-going quality assessment research in the field of in-process monitoring of the EBM process.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super-efficient SBM model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing of the industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, the industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: " stationary period", " recession period " and "growth period ". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Mathematician and philosopher, Charles Howard Hinton, posited a plausible correlation between of higher-dimensional spaces, also referred to as ‘hyperspaces’, and the allegorical concept artic-ulated by the Ancient Greek philosopher Plato in his work, Republic, known as the 'Cave.' In Plato's allegory, individuals find themselves situated in an underground 'Cave,' constrained by chains on their legs and neck, perceiving shadows and sound reflections from the ‘real’ world cast on the ‘Cave’ wall as their immediate reality. Hinton extended the interpretation of these 'shadows' through the induction method, asserting that, akin to a 3D object casting a 2D shadow, the 'shadow' of a 4D hyper-object would exhibit one dimension less, manifesting as a 3D object. Building upon this conceptual framework, the authors posit a correlation between the perceived acoustic space of the chained individuals within the 'Cave' and the characteristics of a 4D acoustic space, a proposition substantiated mathematically by scientific inquiry. Furthermore, the authors introduce an interactive art application developed as a methodical approach to exploring the hypothetical 4D acoustic space within Plato's 'Cave,' as perceived by the chained individuals and someone liberated from his constraints navigating through the 'Cave.'

Sequence analysis is a robust methodological framework that has gained popularity in various fields, including transportation research. It provides a comprehensive approach to understanding the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to the mobility surveys offers valuable insights into the sequencing and order of travel activities and modes, shedding light on the complex interrelationship between individuals, their travel choices, and the built environment. The Barcelona travel surveys collect detailed data on individuals' travel behavior, such as trip purpose, duration, mode of transportation, and origin-destination pairs. Sequence analysis allows for examining travel behaviors as dynamic processes, unveiling travel patterns' underlying structure and evolution in a day. A data analytics methodological approach is described; it enables the identification of common travel patterns and the exploration of variations across different demographic groups or geographical regions. Sequence analysis reveals insights into the factors influencing mode choice and potential opportunities for sustainable transport interventions. The paper proposes a methodological approach to discover homogeneous travel behavioral segments from diaries included in travel surveys in order to refine transport policies to selected segments by transportation planners and authorities

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

In this paper, we shall introduce the interval-valued spatial error model. Based on the idea of least square method of single-valued case, we give the parameter estimator for interval-valued spatial error model. The theoretical properties of the proposed estimator are proved. Finally, we give the numerical analysis and a real example.

Spatial regression models have garnered significant attention across several disciplines, including functional magnetic resonance imaging analysis, econometrics, home price analysis, and many other domains. The phenomenon of sparsity is often found in nature, when a limited number of factors contribute significantly to the overall variation. Spatial regression models frequently use sparsity to indicate less complex computational and more superficial covariance structures. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach for estimating the vector of regression coefficients in the spatial error model, taking into consideration of the prior knowledge that some coefficients are insignificant and there is multicollinearity among the regressors. It also introduces pretest and shrinkage ridge estimators for spatial error regression models, evaluating their performance compared to traditional maximum likelihood estimators. It also assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

Landscape restoration activities must be conducted through a transdisciplinary process, integrating social, economic, environmental, and governance aspects. Combining visions from the natural and social sciences is a challenge in highly complex territories, where unique ecosystem characteristics, economic processes, stakeholders of diverse nature, and different normativity converge. The harmonization of multiple techniques, such as multicriteria spatial analysis, expert knowledge elicitation, and social mapping, allows for an approach to defining landscape restoration areas in complex regions. This paper employs multiple techniques to define ecosystem restoration areas in a complex Colombian Andes landscape, integrating ecological and social components for sustainable development. We observed that areas of high and very high feasibility for ecological restoration, encompassing 179.5 hectares (4.84% of the study area), are predominantly located near primary forests. Although some areas have low feasibility for conservation processes, they should not be disregarded as they still require protection. Landowners prioritize watershed and soil restoration as the most important landscape restoration activity due to their interest in improving water-related ecosystem services. This proposal enables the identification of areas with higher restoration potential at the property level, facilitating prioritization and investment allocation for future implementation.

This article, inspired by Dr. Jürgen Essletzbichler’s chapter namely “Locating Location Models” in The SAGE Handbook of Economic Geography (2011), delves into the legacy and significance of location theory. By examining its integration of historical analysis and theoretical abstraction, the study highlights the crucial role played by historical and geographical contexts in shaping spatial patterns. It emphasizes the importance of complementing abstract models with historical analysis to gain a deeper understanding of real-world spatial phenomena. Acknowledging the delay in the development of location theory attributed to the dominance of the German historical school, the article recognizes its enduring value and resurgence in the realm of modern evolutionary economic geography. Ultimately, the abstract underscores the contributions of location theory to our comprehension of spatial dynamics and its potential for further advancements in the field of economic geography.

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.

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

Biogeography research is flawed by the poor understanding of microbial distributions due to the lack of a systematic research framework, especially regarding appropriate study units. By combining pure culture and molecular methods, we studied the biogeographic patterns of nematode-trapping fungi by collecting and analysing 2,250 specimens from 228 sites in Yunnan Province, China. We found typical watershed patterns at the species and genetic levels of nematode-trapping fungi. The results showed that microbial biogeography could be better understood by 1) using watersheds as research units, 2) removing the coverup of widespread species, and 3) applying good sampling efforts and strategies. We suggest that watersheds could help unify the understanding of the biogeographic patterns of animals, plants, and microbes and may also help account for the historical and contemporary factors driving species distributions.

The virus SARS-CoV-2 has resulted in numerous modelling approaches arising rapidly to understand the spread of the disease COVID-19 and to plan for future interventions. Herein, we present an SEIR model with a spatial spread component as well as four infectious compartments to account for the variety of symptom levels and transmission rate. The model takes into account the pattern of spatial vulnerability in South Africa through a vulnerability index that is based on socioeconomic and health susceptibility characteristics. Another spatially relevant factor in this context is level of mobility throughout. The thesis of this study is that without the contextual spatial spread modelling, the heterogeneity in COVID-19 prevalence in the South African setting would not be captured. The model is illustrated on South African COVID-19 case counts and hospitalisations.

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.

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.

COVID-19 pandemic event requires a rapid response from various organizations at the international and national levels. One important response is the provision of information sharing facilities and monitoring of the spread of cases around the world, JHU CSSE developed the Dashboard in January 2020 and followed by WHO the same month for the WHO COVID-19 dashboard. Both dashboards have distributed information as expected by the user with their respective pros and cons. JHU CSSE Dashboard provides faster information with good access to mobile device users even though the display and color selection are less attractive. Information on the WHO COVID-19 Dashboard is often late but more data appearances and variations and comparisons between countries can be made. In the Indonesian context, ESSC for COVID-19 Geoportal as Esri Indonesia initiative has been developed with the support of data and information from various parties and developed with the principles of big data management which are fully supported by adequate spatial portal developer software from Esri. Particularly in Indonesia, there is not yet an adequate system to support spatial based decision making at the local level, therefore the development of a GIS dashboard to support provincial and district governments is highly recommended.

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.

Data mining plays a critical role in the sustainable decision making. The k-prototypes algorithm is one of the best-known algorithm for clustering both numeric and categorical data. Despite this, however, clustering a large number of spatial object with mixed numeric and categorical attributes is still inefficient due to its high time complexity. In this paper, we propose an efficient grid-based k-prototypes algorithms, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can remove unnecessary distance calculation. The second proposed algorithm as extensions of the first proposed algorithm utilizes spatial dependence that spatial data tend to be more similar as objects are closer. Each cell has a bitmap index which stores categorical values of all objects in the same cell for each attribute. This bitmap index can improve the performance in case that a categorical data is skewed. Our evaluation experiments showed that proposed algorithms can achieve better performance than the existing pruning technique in the k-prototypes algorithm.