Geographic Coordinates Research Articles

ON-BOARD LOG AND COORDINATE TRANSFORMATION FOR DETECTED OBJECTS ON THE SURFACE OF WATER

Context. The relevance of the work is to the demand for UAV technologies with the integration of artificial intelligence in today’s conditions. Objective. The goal of the work is to develop a minimum working version of the UAV explorer and software for controlling the UAV data. Method. The proposed mathematical description, which calculates the coordinates of the object, based on the dimensions of the original image from the camera, the dimensions of the image with which the neural network works, the angle of the field of view of the camera, the position of the UAV and the angles of roll, pitch and yaw, allows you to transfer the coordinates of the object, of the found NN, in the image to the geographical coordinates, thereby moving away from the rigid reference to the coordinates of the UAV. Results. The problem of systematization of objects detected during the mission on the surface of water bodies was solved bycreating a flight log, organizing interaction with a neural network, applying post-processing of recognized objects, mathematically transforming the coordinates of objects for display and visualization into geographic coordinates, thereby move away from the rigid reference to the coordinates of the UAV. Conclusions. A workable logbook generation and storage system has been created, which takes into account the peculiarities of information presentation in the logbook, and ensures effective interaction of the components of the created information system within the proposed hardware and software complex, which allows organizing the process of researching water bodies using the SITL environment from the flight controller developers.

Design Of A Hybrid Power Generation System in Cipatujah Tasikmalya Regency

The escalating daily demand for electricity, propelled by technological advancements and the pervasive use of electronic devices, accentuates the imperative for a dependable and sustainable power generation system. Renewable energy sources emerge as a viable solution to address the electricity needs in Indonesia, particularly in remote areas where access to the main grid is challenging. Tasikmalaya Regency, situated in West Java Province and defined by its geographical coordinates between 7°02' 29" - 7°49' 08" South Latitude and 107°54' 10" - 108°25' 52" East Longitude, presents a promising setting for the implementation of such sustainable energy solutions. The coastal area of Tasikmalaya, specifically Cipatujah Subdistrict, exhibits substantial renewable energy potential, including wind, solar radiation, and ocean waves. This research endeavors to design an efficient hybrid power generation system in Cipatujah Subdistrict using HomerPro software. The methodology involves crafting various models to determine the most economically efficient generation strategy. The study utilizes electricity consumption data sourced from the Karangnunggal Tasikmalaya Substation, situated in the southern coastal region of Tasikmalaya, particularly in Cipatujah Subdistrict, with a daily consumption of 20.6917 MW. By addressing the specific energy needs of the region, the research aims to make a positive contribution to the development of renewable energy and, concurrently, serve as a guiding framework for similar system designs in locations sharing comparable characteristics. This multifaceted approach underscores the significance of tailoring sustainable energy solutions to the unique requirements of diverse regions, fostering a broader transition towards a reliable and environmentally conscious energy future.

Subsistence strategies and climate change as key factors in the vicinity of the Great Wall in northern China from 500 BCE

Whether, and in what ways, the association between changes in climate and the shifting of imperial frontiers (i.e. the Great Wall) could be mediated by the social responses of major ethnic groups is a question that remains unanswered. Ulanqab is a region in Inner Mongolia associated with a long history of Great Wall construction from the Warring States to the Ming dynasties (~500 BCE–1644 CE) and the Great Wall is often regarded as the boundary of the Central Plains dynasty. This paper uses Ulanqab as a case study to investigate the historical inter-relationship between changes in climate and subsistence strategies on the one hand, and the positioning of the Great Wall on the other. We determined the precise geographical coordinates of the Great Wall in this region during each dynastic period of Chinese history, and compared its location to (1) the empire’s boundaries in each dynasty, (2) evidence that is available on climate change from high-resolution paleo-climate reconstructions and (3) subsistence strategies (whether agriculture, pastoralism, or nomadism) that people adopted as recorded in historical documents. Geo-referenced information from associated archaeological sites was also used to determine the extent of habitation in different areas. We found that during the period of agriculturalist empires (the Han and Ming dynasties), the Great Wall was a frontier between farming and animal husbandry, and was located to the south of the Daqing Mountains. In contrast, during the period of pastoralist empires (the Zhao Kingdom [of the Warring States], Northern Wei and Jurched Jin periods), the Great Wall was the frontier between animal husbandry and nomadism, and was located to the north of the Daqing Mountains. These mountains (42.5°N) seem to have been the northern limit of human settlements that were based on animal husbandry and farming. We observed that, when precipitation and temperature increased, this resulted in an augmentation of agricultural production that most likely facilitated the northward expansion of the agriculturalist peoples. A decrease in precipitation, on the other hand, resulted in the shrinkage of pastures and food, triggering a southward expansion that was characteristic of the pastoralist peoples. This study attempts to provide new insights into the interrelationships among changes in climate, human societies and geopolitics in Chinese history.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction

The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into human-readable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Including soil spatial neighbor information for digital soil mapping

Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

TetraDENSITY 2.0—A Database of Population Density Estimates in Tetrapods

ABSTRACTMotivationPopulation density is a fundamental parameter in ecology and conservation, and taxonomic and geographic patterns of population density have been an important focus of macroecological research. However, population density data are time‐consuming and costly to collect, so their availability is limited. Leveraging decades of research, TetraDENSITY 1.0 was developed as a global repository containing over 18,000 population density estimates for > 2100 terrestrial vertebrate species, aiding researchers in investigating patterns of population density, its intrinsic and extrinsic drivers, and for developing predictive models. Here we present a substantially expanded version of the database, which now includes marine tetrapods and encompasses over 54,300 estimates for 3717 species associated with error estimates and geographical coordinates when available, hence enabling meta‐analytical approaches and better spatial matching of estimates with environmental conditions.Main Types of Variables ContainedPopulation density estimates and associated errors, time and location of data collection, taxonomic information, estimation method.Spatial LocationGlobal.Time Period and Grain1925–2024.Major Taxa and Level of MeasurementAmphibia, Reptilia, Aves and Mammalia. Estimates reported at the population level.Software Format.csv.

Geographic variability of carbon and nitrogen isotope ratios of nonvolant terrestrial small mammals (Rodentia) across 3 Brazilian biomes

Abstract In this study, we investigated landscape variability of the carbon and nitrogen isotopic composition of nonvolant small mammals in the 3 main Brazilian biomes (Amazon, Atlantic Forest, and Cerrado) while also considering the differential spatial distribution of C4 plants in these biomes. We compiled a subset of data on stable carbon and nitrogen isotope ratios from nonvolant small mammals of the order Rodentia and compared the aggregated isotopic variability at the biome level using classical δ13C–δ15N biplot. The concept of isotopic niche width was used to test whether different foraging attributes drive their isotopic composition, while a Bayesian isotopic mixing model was used to estimate the proportion of 3 main food categories available to these small mammals. We also developed isoscapes in the Brazilian biomes, interpolating animals δ13C and δ15N based on the geographic coordinates of each sample and “sourcescapes” considering geographic variation of the dietary sources. The findings of this study advance our understanding of the foraging ecology of small mammals in biodiversity-rich regions of the Neotropics. Classical methodologies such as stomach content confirmed dietary choices revealed by the stable isotopic composition and also highlighted the importance of C3 and C4 plants in the diet of this group of animals, especially in biomes such as the Atlantic Forest and Cerrado, where there is a predominance of highly altered landscapes. These results confirm that replacing the original vegetation with C4 crops has altered the feeding patterns of small mammals, which could lead to critical ecological changes in the trophic structure of these areas. Vertical stratification of the dietary niche and the interaction between biome and foraging lifestyle were also observed. However, in each biome, there was significant intraspecific and interspecific variation caused by preferences for ingestion of plant and animal material, leading to different degrees of omnivory. Finally, the high local variability between individuals and species requires a larger sampling design that may also contribute to improved spatial resolution of the isoscapes.

An epidemiological and spatiotemporal analysis of visceral leishmaniasis in West Pokot, Kenya, between 2018 and 2022

BackgroundVisceral leishmaniasis (VL) remains a significant public health concern in West Pokot County, Kenya, where a large outbreak between 2020 and 2022 emphasised the need for improved VL control strategies. However, these measures are partially hampered by limited insight into the geographical distribution of cases and localised outbreaks of the disease. This study aimed to describe the epidemiology and spatiotemporal patterns of VL in West Pokot between 2018 and 2022, in order to map the spread of VL transmission and identify regions that should be prioritised for control interventions.MethodsVL patient demographics and village of residence were retrieved from admission records of Kacheliba Sub-County Hospital in West Pokot, Kenya. The temporal trend in VL admissions between 2018 and 2022 was analysed using seasonal decomposition analysis. To describe the spatial distribution of VL cases, geographic coordinates of villages of residence were collected from pre-established databases, and VL incidence was mapped at the sub-location level. Hotspot analysis was performed per study year to identify villages with high VL incidence, and scan statistics were applied to detect spatiotemporal clusters of VL cases during the study period.ResultsA total of 1948 VL patients were reported between 2018 and 2022. The annual number of cases increased from 245 in 2019 to 598 in 2022, and VL admissions were generally higher at the start of the wet seasons. 70% of the VL cases could be georeferenced, and mapping of VL incidence revealed high case rates in the east of West Pokot during the complete study period. The eastern villages Lotongot and Chepaywat were marked as VL hotspots at a 99% confidence level in all study years. In addition, five significant spatiotemporal clusters were detected in the east and north, suggestive of local VL outbreaks in these regions.ConclusionsThe increase in VL hospital admissions during the study period stresses the need for enhanced VL control and outbreak mitigation in West Pokot. These control measures should be focused on the hotspot regions in the east of the county.

Diagnostic Ratios and Ecological Risk of Non-carcinogenic Polycyclic Aromatic Hydrocarcarbons (ncPAHs) in Egi Crude Oil Communities, Nigeria

The loadings of PAHs, its diagnostic ratios and ncPAHs at three cluster communities in Egi sub district of Niger-Delta with geographic coordinates of latitude 150 32ʹ 50ʺ N and longitude 60 34ʹ 42ʺ E were ascertained using Gas Chromatography (GC-FID, HP 5890 Series). The diagnostic ratios showed that Obagi had an LMW/HMW ratio of 0.2340, Ibewa's was 0.2222, and Obite's was 0.2549. In Obagi, the proportion of Low Molecular Weight (LMW) PAHs compared to High Molecular Weight (HMW) PAHs was 19%, while in Obite it was 18%, and in the Ibewa cluster it was 20%. The sediments of the Obagi, Obite, and Ibewa cluster sites exhibited a significant abundance of PAHs derived from pyrogenic sources, as evidenced by the presence of LMW/HWM ratios below 1. The identified ncPAHs include: Naphthalene, Acenaphthene, Acenaphthylene, Fluorene, Anthracene, Phenanthrene, Fluoranthene, Pyrene and Benzo(ghi)perylene. According to the results obtained the Obite cluster area exhibited the lowest concentrations (1.503mg/Kg) amongst the ncPAHs, while the Obagi study zone demonstrated the highest concentrations (1.5733mg/Kg). Among the nc(PAHs) examined, benzo (g,h,i) perylene exhibited the highest concentrations (2.086mg/Kg) across all locations, whereas acenaphthylene displayed the lowest concentration (0.123mg/kg) across the three cluster areas. However, none of the three study locations had any ncPAHs that are above the maximum permissible limit. Results from the application of risk quotient models (RQNCs and RQMPCs) to assess the ecological risk of ncPAHs as presented indicated that all the sites where these rivers' sediments were tested had RQNCs values for specific ncPAHs below 1, implying that these ncPAHs pose little or no danger to ecosystems. Also, the RQMPC score is less than 1, it means that the contamination from individual ncPAHs chemicals do not necessitate immediate remediation. Because the RQNCs and RQMPCs for a single PAH molecule are both less than 1, the contamination it generates might be considered low risk. Thus, few measures of control or correction would be required. The ecological risk of non-carcinogenic PAHs using RQMPCs and RQNCs indicated a very negligible risk which could be due to factors like heavy flooding that washed away soil surfaces. Application of Detoxification by chemical reaction technology should be used as future remediation method if there is need.

The application of GIS technology in building a multivariate taphonomic profile for improving PMI estimations in Greece.

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context-specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time-consuming and often impractical for large-scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high-resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen-Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS-based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

Dazed and confused: how map projections affect disease map analysis and perception. An echo from GeoVet2019.

Disease maps are integral to spatial epidemiology and public health. The map appearance and analysis of corresponding data may both depend on a map projection used to transform the 3-dimensional world onto a 2-dimensional surface. Map projections necessarily introduce bias - an issue that has not received full attention in the literature. This study aims to demonstrate the impact map projections can have on spatial analysis and disease maps for public health. Case studies applied varying map projections, including the Lambert, Mercator and Robinson projections, to Israel, North Carolina and Southern Ontario as study areas. The effect of projections on various measures, estimates, tests and models was assessed. When the map projection was changed: (i) a distance in Israel increased by 30%; (ii) for Southern Ontario an areal size increased by almost 95%; Moran's I test switched from significant to not; and (iii) a single disease cluster in North Carolina converted into three distinct clusters. Visual bias in disease mapping is unavoidable and should be recognized. Disease maps and spatial analytical inferences, including disease clusters should be reported with their geographic projection. Using geographic coordinates can prevent analytical bias.

GS–CDNet: a remote sensing image cloud detection method with geographic spatial data integration

ABSTRACT In optical remote sensing images, clouds exhibit irregular scales and boundaries that vary with elevation across diverse geographical locations. To accurately capture the diverse visual patterns of clouds, we propose a cloud image segmentation approach named GS-CDNet (Geographic Spatial Data-Cloud Detection Network), which is based on the integration of geospatial data with multifaceted self-attention feature extraction, multi-scale feature aggregation, and boundary clarification techniques.Firstly, we utilize geographical coordinates from optical remote sensing images to extract a raster DEM (Digital Elevation Model) from SRTM3. This process creates a dataset consisting of elevation images, longitude, and latitude maps as geospatial data, enhancing the model’s capability in spatial positioning for cloud detection. Secondly, the proposed method consists of three interconnected modules within the cloud detection network: the Interleaved Self-Attention module(ISAM) utilizes a variety of self-attention mechanisms in an interleaved manner to extract multi-scale feature information.The Bidirectional Multi-Scale Feature Fusion Module(BIMFM) is responsible for integrating features, enabling a more comprehensive contextual understanding. The Boundary Extraction Module(BEM) utilizes a residual structure to generate a boundary cloud mask, effectively addressing the common issue of boundary blurring in multi-scale cloud masks. Finally, we compared and evaluated GS-CDNet with other cloud detection methods and conducted an ablation study on the key components of the method. The validation of generalization performance demonstrates the exceptional performance of the proposed model in cloud mask generation. Geospatial data and the different modules of the method play a significant role in the model.

Hemispheric Asymmetry of Ionospheric Poynting Flux During Geomagnetically Quiet Periods

AbstractUsing a large database containing DMSP F13 observations under geomagnetically quiet conditions from 1998 to 2009, the hemispheric asymmetries of the distribution of the Poynting flux were investigated. Significant hemispheric asymmetries were observed in both altitude‐adjusted corrected geomagnetic (AACGM) and geographic coordinates, and the maximum average flux in the northern hemisphere was greater than that in the southern hemisphere by a factor of three in both coordinates. The distribution of downward Poynting flux hot‐spot (DPFH) between the two hemispheres had longitudinal difference of ∼180°. The Poynting flux's hemispheric asymmetry in AACGM and geographical coordinates at different seasons were investigated. The observed seasonal variations in the Southern Hemisphere (SH) are not as significant as those in the Northern Hemisphere (NH). In addition, the downward net Poynting flux observed during the solstice period is greater than that observed for the equinox period above 60°S.

Identifikasi Lokasi Pemusatan Fasilitas Rumah Sakit Kota di Surakarta

Health is the optimal general state of one's body, mind, and spirit. Someone can do an activity because there is a healthy body so in general everyone always tries to stay healthy. The availability and convenience of health facilities is one of the efforts made by the government to improve the quality of public health. The purpose of this study with the Mean center method and Density estimation analysis with Heatmap, is to gain a better understanding of data distribution patterns. The Mean center method is used to determine the central point of distribution of data which can then be used as a reference to analyze and understand further distribution patterns. From this analysis it can be concluded that the Mean center method can help in identifying the center point or midpoint in the Surakarta area, describing the ideal location in Surakarta City, this method allows geographical representation of the center point using geographical coordinates such as latitude and longtitude which can then provide an easy understanding of the relative location of the center point of the hospital to other hospitals in the region. The results of the Mean center show that the center point of the hospital is located in Kelurahan Kestalan, Kecamatan, Banjarsari Surakarta City. Density estimation results in 3 (three) concentration clusters in Surakarta City. This helps local governments in plans to add health facilities, because of the gap in the northern, central and southern regions.

Modeling of the Variability of D‐Region Ionospheric Electron Density During Solar Cycle‐24

AbstractSolar cycle variation of earth's atmosphere, particularly the ionosphere is of particular interest in the field of space science and space weather studies. In this article, we present the outcome of our detailed quantitative study on solar cycle variation of lower ionospheric properties using numerical investigation. First, we seek to model and compare the collective D‐region ionization rates (’s) due to the individual contributions from the ionizing sources, namely, (a) solar extreme ultraviolet (EUV) radiation including the Lyman‐ irradiation and (b) solar X‐ray irradiation throughout the solar cycle (C24). Then, we compute the electron density profiles using the ionization rates. We report significant solar cycle variation in ionization rate and electron density profiles for the entire span of C24. We use the sunspot number (SPN) profile to substantiate the finer details of profile during C24. For the segments of the D‐region above two different geographic coordinates, we report that profiles show a consistent “dual peak” nature. It is very similar to the SPN profile during C24. As a next‐order validation, we compare our modeled profiles with their IRI‐2020 counterpart (s). Their overall trends are found to be in agreement. Finally, we discuss the response of the D‐region in terms of due to C24. The work lays the foundation for our upcoming studies on D‐region response to solar cycle variation with Very Low Frequency (VLF) observation.

‘I Still Struggle to Ensure that I am Truly Listening’: Understanding Gender, Sexuality, and Sexual Violence During the Holocaust. A Conversation

ABSTRACT While perspectives on gendered and sexual experiences, practices, and norms during the Holocaust have significantly changed in recent decades, questions remain about how to understand and represent these topics, particularly gender-based and sexual violence. In this conversation, six Holocaust scholars, from varied backgrounds and representing different generations, respond to questions posed by the special issue editors, Dorota Glowacka and Regina Mühlhäuser. They discuss the current state of debates about gender, sexuality, and sexual violence during the Holocaust. Reflecting on the material conditions and distinguishable geographical and ideological coordinates in which they have conducted their work, they recount ideas and scholarly currents that have helped them generate their specific modes of understanding. They consider the challenges and obstacles to knowledge production they have faced at various stages of their work, revealing how ingrained assumptions about gender and sexuality circumscribe research questions, methods of inquiry, and the ways in which their findings are interpreted, presented, and received. While they address the impact of changing social sensibilities and perceptions of gender and sexuality on their work and point to the emergence of new themes and areas of study, they also draw attention to the subjects that remain unexamined, socially sanctioned, or excluded from rigorous examination.

Trust enhanced POI recommendation with collaborative learning

With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the social connections among users. Nowadays, lots of trust-enhanced approaches have fused the trust relationships of users together with other auxiliary information to provide more accurate recommendations. However, in the traditional trust-aware approaches, the embedding processes of the information on different graphs with different properties (e.g., user-user graph is an isomorphic graph, user-POI graph is a heterogeneous graph) are independent of each other and different embedding information is directly fused together without guidance, which limits their performance. More effective information fusion strategies are needed to improve the performance of trust-enhanced recommendation. To this end, we propose a Trust Enhanced POI recommendation approach with Collaborative Learning (TECL) to merge geographic information and social influence. Our proposed model integrates two modules, a GAT-based graph autoencoder as trust relationships embedding module and a multi-layer deep neural network as a user-POI graph learning module. By applying collaborative learning strategy, these two modules can interact with each other. The trust embedding module can guide the selection of user’s potential features, and in turn the user-POI graph learning module enhances the embedding process of trust relationships. Different information is fused through the two-way interaction of information, instead of travelling in one direction. Extensive experiments are conducted using real-world datasets, and results illustrate that our suggested approach outperforms state-of-the-art methods.