Geographic Coordinates Research Articles

Mongolian accessions of crested wheatgrass (Agropyron cristatum (L.) Beauv.) in the VIR collection

Recently, the interest in forage crops with resistance to adverse environmental factors in changing climate conditions has grown significantly among researchers. Among these crops is wheatgrass, diploid specimens of which are included as the initial genetic material in breeding and genetic programs. The first and only one wild specimen of crested wheatgrass was collected in 1922 by V.E. Pisarev, a close associate of N.I. Vavilov, during the Mongolian expedition of the State Institute of Experimental Agronomy. Two more wheatgrass specimens collected by A.I. Ivanov, the Leading Researcher of the Department of Forage Crops of VIR, arrived in 1966.It was only in 1986-1987 that the VIR collection was replenished with a rather large number of diploid wild-growing Mongolian specimens of crested wheatgrass, distinguished by traits valuable for breeding, such as short vegetation period, salt-, drought-, and winter hardiness, resistance to diseases and pests.In the summer of 1986 and from the end of summer until the autumn of 1987, scientists from the Plant Resources Team of the Joint Soviet-Mongolian Complex Biological Expedition (JSMKBE) examined the flora of several aimags of Mongolia: Selenge, Bulgan and Central aimags in 1986; Arkhangai, Bayankhongor, Bulgan, East Gobi, Selenge, Uvs, Arkhangai, Khentii, Central and South Gobi aimags in 1987.The objective of the Plant Resources Team was to broaden the gene pool of crop wild relatives (CWR). Seed samples of mid- and late-ripening perennial wild forage grasses resistant to limiting environmental factors, were collected for replenishing plant collections of the USSR and Mongolia, study, and further use by breeding centers of both countries. During the expedition, the institute’s collection was replenished with samples of forage grasses, including 30 of crested wheatgrass, one of Mongolian wheatgrass, Michno's wheatgrass and Ericksson crested wheatgrass. The geographical coordinates and collection sites of the samples were recorded, and the ecological and a geographical classification of the Mongolian populations of crested wheatgrass was created.

Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework.

Environmental monitoring and disaster mitigation are critical applications of underwater acoustic sensor networks (UASNs). However, UASNs face significant challenges, including high latency, limited bandwidth, and energy constraints. This study introduces an Internet of Things (IoT)-driven location-aware framework (ILAF) designed to enhance UASN performance by utilizing non-GPS geographic coordinates for determining the location of sensor and sink nodes, identifying their neighbors based on coordinates and transmission range, and optimizing node placement and routing without the need for GPS modems. The framework is compared with several state-of-the-art protocols, including Bald Eagle Search inspired optimized energy efficient routing protocol (BES-OEERP) and IoT-enabled depth-based routing technique (IDBR), demonstrating superior performance. Specifically, ILAF achieved a packet delivery ratio (PDR) of 99%, which outperforms energy-efficient region-based source distributed routing algorithm (EERSDRA) (98%) and energy-efficient geo-opportunistic routing protocols (EEGORP) (96%). Additionally, ILAF reduced energy consumption by 20% compared to these existing protocols. These improvements result in a more energy-efficient network with fewer dead nodes (12 after 1,000 rounds) and higher throughput (5.7 kbps at 1,000 rounds), making ILAF suitable for real-time underwater applications. Future research will explore integrating lightweight IoT protocols like Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to enhance the framework's performance and reliability further.

A global modern pollen and spore dataset

Here we present a new global compilation of modern pollen and spore data for use in testing and evaluating palaeoclimate reconstruction methods. The resulting dataset contains 21,503 pollen assemblages from all continents excluding Antarctica. All pollen assemblages have geographical coordinates listed, and the majority have an elevation measurement. Taxonomic nomenclature has been standardised.

Human Impacts and Larger Geographic Area Explain the Highest Number of Non‐Native Species of Freshwater Fishes Into Brazilian Protected Area

ABSTRACTNon‐native species represent a significant threat to biodiversity, particularly in aquatic environments where they are often introduced for purposes such as fishing or the aquarium trade. This study aimed to evaluate the influence of isolation and the size of federal protected areas (PAs) on the number of non‐native and native freshwater fish species in Brazilian PAs. Data from Brazilian PAs were gathered, including year of creation, buffer of human impacts around the PA, PA categories, coverage by freshwater, tourist visits, sampling sites, geographical coordinates (predictor variables) and information on the number of non‐native and native freshwater fish species (response variables). Linear regression analysis was employed to examine the influence of predictor variables on the number of non‐native and native fish species. A total of 33 different non‐native fish species were identified, with Poecilia reticulata, Hyphessobrycon eques, Hoplias lacerda and Cichla kelberi being the most widespread, occurring in 15, 7, 5 and 4 distinct PAs, respectively. The overall fish biodiversity within PAs encompassed 2670 species, with the National Park of Anavilhanas and the Environmental Protection Area of Ilha e Várzea do Paraná exhibiting the highest species richness. Greater human impact, larger coverage by freshwater and increased sampling effort were positively correlated with the number of recorded non‐native species. Factors such as year of creation, type of PA and tourist visitation did not have a significant effect. In contrast, native species exhibited a higher number of occurrences in PAs with lower human impact, longer histories, larger freshwater coverage areas and more extensive sampling efforts. These findings underscore the necessity for comprehensive management strategies to mitigate the impact of non‐native species on biodiversity within PAs.

Sample Size Comparison in Area-Based Image Matching: Cross-Correlation and Mean Relative Gray Value Difference

Abstract Geospatial information is data about a specific location with geographic coordinates that can be collected, modified, and displayed in real-time. Geospatial data can be collected through field surveys and aerial photogrammetry. Aerial photos and Digital Elevation Models are the raw results of aerial photography. An orthophoto is a photograph that shows an object in its true orthographic position. The production of orthophotos involves a process that produces stereo images through image matching using various methods. In this research, area-based and feature-based image matching are performed. For area-based image matching, sample sizes of 9×9, 15×15, and 33×33 pixels and two correlation equations were used: Cross-correlation and Mean Relative Grey Difference. Image matching results between samples from the right and left photographs were compared based on the resulting correlation values. From the data processing, area-based image matching with a sample size of 33×33 gives the best results that high Cross-Correlation values and low MRGVD values indicate strong image matching accuracy, especially for images with many details and variations. Thus, the combination of cross-correlation and MRGVD metrics provides an effective and comprehensive analysis for image matching.

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

Abstract Droughts may exhibit spatiotemporal heterogeneity at a 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 clustering map for the southern plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatiotemporal 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 the 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. Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 and 0.94. Comparison with an existing cluster map [Drought Risk Atlas (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.

Using a knowledge representation logic to estimate the availability of Imbrasia epimethea (Lepidoptera: Saturniidae), an important edible insect in Subsaharan Africa

Based on monthly abundance patterns, we model the density of Imbrasia epimethea (Drury, 1773), an important edible insect in Africa. Categorical data was collected from various regions in Cameroon, and data analysis techniques were used to infer relationships between environmental variables and the level of insect abundance. Through fuzzy logic modeling, we identified the key environmental factors and rules that influence the density of the insect. To visualize the distribution of I. epimethea across African landscapes, interpolation techniques were used on the study area matrix of geographical coordinates based on the corresponding monthly predictor variables for the most recent available year (2022). The results suggested a clear dynamic across Africa through the different months of the year with potentially overlapping generations with relatively high accuracy (>90 %). A clear relationship between regional climatic conditions and the density of I. epimethea could be established across Africa. The models provide insights into the complex dynamics of insect populations and sheds light on the stability and transferability of our results across different African regions (during stability analysis). This research offers a foundation for further investigations on sustainable food production and the promotion of edible insects as a viable protein source.

Geoeconomic modelling: southwestern trend of vegetable growing in Russia

Relevance. Modern globalization, internationalization, regional competitive differentiation actualize geoeconomic modeling methods for geopolitical planning, management and control. The novelty of the proposed solutions is the proposed concept, based on the translation of the ideas of theoretical mechanics into economics.Methodology. Considering the annual values of statistical indicators of territorial concentrates as a system of geographically distributed objects (subjects of the Russian Federation), it is possible to determine the geographic barycenter. Knowing the values of the geocoordinates of the barycenter, it is possible to determine the tendencies and trend of the barycenter movement as an indicator of the dynamics of the system under consideration through the analysis of the dynamics of these coordinates. Based on Russtat data on the annual gross harvest of agricultural crops (vegetables), population by constituent entities of the Russian Federation for the period 1991-2021, data on the coordinates of the administrative centers of the constituent entities of the Russian Federation, the values of the geographic coordinates of the barycenter of vegetable growing in Russia were calculated.Results. For 2021, the geographic coordinates of the barycenter of vegetable growing in Russia are 50.30° N and 49.49° E with an average regional value with an average regional annual vegetable har-vest of 1722.52 million centners/year and fluctuations in the coordinates of the barycenter in the zone of 3-4 geographic degrees or three hundred territorial kilometers with a pronounced south-west trend Analysis of the dynamics of the barycenter for the period 1991-2021. will allow to reveal a slight shift of the vegetable growing barycenter to the south by 3.5 degrees with an increase in the average regional vegetable harvest by 124.11 million centners per year for each degree (or the all-Russian by more than 9,500 million centners per year for each degree to the south), a slight shift of the vegetable growing barycenter to the west by 4.5 degrees with an increase in the average regional vegetable har-vest by 86.5 million centners per year per degree (or the All-Russian by more than 6,660 million cent-ners per year for each degree to the west). For the period 1991-2021, two opposite trends in the devel-opment of vegetable growing are distinguished. In the period from 1991 to 2000, the movement of the coordinate of the vegetable growing barycenter went in the direction of the northeast of Russia, in the period 2000 to 2021 -- on the contrary, to the southwest.The conclusion is made about the existence of a relationship between the development of vegetable growing and the geopolitics of Russia in the period under review. The obtained method and results can be used in strategic state management of agricultural production in Russia.

Перспективи відновлення розвитку туризму в україні в післявоєнний період на основі стратегії цифрової трансформації

The article identifies priorities for developing digital tourism products in Ukraine’s post-war recovery, emphasizing the need for secure communication between consumers and providers. The prevailing focus on conventional tourism services in current tourism cash flows is set to be complemented by the potential offered by digital infrastructure and virtual tourism, which are identified as promising avenues for innovation and cultural preservation. The restoration of tourism infrastructure should be primarily guided by the prevalence of physical or virtual travel, as indicated by a strategic matrix proposed for assessing recreational potential in de-occupied territories. The issues of post-war zoning of Ukraine are discussed, which typically categorizes areas as unoccupied, liberated, or active conflict zones that are insufficient for local route planning and safety assessments. Recreational cadastres, now essential for ensuring visitor safety, must list suitable areas and facilities with unique identifiers and geographic coordinates mapped digitally. The article proposes a range of tourism types for different territories, emphasizing a strategic approach to recovery predicated on fluctuations in the popularity of diverse tourism products and geographical areas. Key words: digital tourism, post-war reconstruction, zoning of recreational areas, strategy matrix, directions of tourism development.

Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt

Extracting geological resources like hydrocarbon fluids requires significant investments and precise decision-making processes. To optimize the efficiency of the extraction process, researchers and industry experts have explored innovative methodologies, including the prediction of optimal drilling locations. Porosity, a key attribute of reservoir rocks, plays a crucial role in determining fluid storage capacity. Geostatistical techniques, such as kriging, have been widely used for estimating porosity by capturing spatial dependence in sampled point-referenced data. However, the reliance on geographical coordinates for determining spatial distances may present challenges in scenarios with small and widely separated samples. In this paper, we develop a mixture model that combines the covariance generated by geographical space and the covariance generated in an appropriate feature space to enhance estimation accuracy. Developed within the Bayesian framework, our approach utilizes flexible Markov Chain Monte Carlo (MCMC) methods and leverages the Nearest-Neighbor Gaussian Process (NNGP) strategy for scalability. We present a controlled empirical comparison, considering various data generation configurations, to assess the performance of the mixture model in comparison to the marginal models. Applying our models to a three-dimensional reservoir demonstrates its practical applicability and scalability. This research presents a novel approach for improved porosity estimation by integrating spatial and covariate information, offering the potential for optimizing reservoir exploration and extraction activities.

A new record of vibrisseaceous fungus from Hakkari (Türkiye)

This study involved specimens of the genus Vibrissea Fr. growing on submerged wood, rubbish or other submerged organic matter in freshwater habitats collected in Şemdinli (Hakkâri) on 01.05.2015. The Vibrissea specimens, characterised by small and delicate fructification organs in the form of cups or bowls, were identified based on morphological data. In this text, Vibrissea decolorans (Saut.) A. Sánchez & Korf is described and illustrated to be a new record for the Ascomycota of Türkiye. The text provides a brief description of the newly recorded genus including collection date, geographical coordinates, macro- and micromorphological characteristics.

Online Service Function Chain Planning for Satellite-Ground Integrated Networks to Minimize End-to-End (E2E) Delay.

The combination of software-defined networking (SDN) and satellite-ground integrated networks (SGINs) is gaining attention as a key infrastructure for meeting the granular quality-of-service (QoS) demands of next-generation mobile communications. However, due to the unpredictable nature of end-user requests and the limited resource capacity of low Earth orbit (LEO) satellites, improper Virtual Network Function (VNF) deployment can lead to significant increases in end-to-end (E2E) delay. To address this challenge, we propose an online algorithm that jointly deploys VNFs and forms routing paths in an event-driven manner in response to end-user requests. The proposed algorithm selectively deploys only the essential VNFs required for each Service Function Chain (SFC), focusing on minimizing E2E delay-a critical QoS parameter. By defining a minimum-hop region (MHR) based on the geographic coordinates of the routing endpoints, we reduce the search space for candidate base stations, thereby designing paths that minimize propagation delays. VNFs are then deployed along these paths to further reduce E2E delay. Simulations demonstrate that the proposed algorithm closely approximates the global optimum, achieving up to 97% similarity in both E2E delay and CPU power consumption, with an average similarity of approximately 90%.

Initial Report of Inocybe costinitii in Türkiye with Morphological and Molecular Data

Inocybe specimens were collected from Ankara University Beşevler 10. Yıl Campus, (Ankara, Türkiye) on October 19, 2022. As a result, the samples were identified as I. costinitii, a new record for Turkish Inocybe. This study presents a detailed description of this newly recorded species, covering aspects such as its location, observations of its habitat, geographical coordinates, the date of collection, and photographs that highlight its macroscopic and microscopic characteristics. Moreover, the study features the species' drawings and some of its microscopic traits. The study is enhanced by images obtained from a scanning electron microscope (SEM), briefly examining the spore features and discussed briefly.

Spatiotemporal dynamics of rabies virus detected in rabid dogs in Cameroon, 2010–2021

Rabies is a viral zoonosis that causes an estimated 60,000 human deaths each year, mainly in Africa and Asia. The etiological agent of rabies, the Rabies Lyssavirus or Rabies Virus (RABV) has been characterized in dog populations in Cameroon, in previous studies. However, the dynamics of RABV maintenance and propagation in dogs are still to be documented in Cameroon. This study thus, aimed at investigating the spatial and temporal dynamics of RABV variants in Cameroon. Long genomic sequences of about 4893 nucleotides, encompassing the N, P, M and G genes as well as part of the G-L intergenic region (Ψ), were determined from 56 RABV strains recovered from dog populations in Cameroon from 2010 to 2021. Temporal and spatial dynamics of RABV circulation in Cameroon were investigated by Bayesian analyses with the BEAST 1.10.4 package from extended RABV genomic sequences data combined with their collection dates and the geographical coordinates of their sampling areas. This revealed a genetic evolution rate of 3.14 × 10−4 substitutions/site/year among Africa-1a and Africa-2 clades of RABV from Cameroon. The most recent common ancestor (MRCA) of the studied strains of the Africa-1a lineage was estimated to have emerged between 1880 and 1906 (95 % HPD; mean 1894), while that of the strains of the Africa-2 clade had a slightly later estimated origin between 1907 and 1928 (95 % HPD, mean 1918). Overall, phylogeographic analyses suggested RABV spread in Cameroon between sub-national regions. Our data provides substantial support to previous findings from similar epidemiological settings, indicating human mediated movements of infected dogs between distant cities may be a key factor in the maintenance of the enzootic cycle of rabies among dogs in Cameroon.

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.

Research on position Model of Surface-to-air Missile Defense against Cruise Missile Based on Rule GA

Abstract The deployment of weapon systems prior to the engagement of anti-cruise missiles constitutes the primary research challenge in surface-to-air missile operations. Initially, it is essential to analyze operational requirements and scientifically delineate the guided radar detection area, kill zone, and interception zone based on physical realities. A model should be established to ascertain the fundamental conditions for site deployment, taking into account factors such as position height, shielding angle, and geographic coordinates. In relation to low-altitude approaches by adversarial forces, key considerations include the depth and breadth of firepower from the weapon system in the attack direction, firepower density in that direction, and lethality probability. An Operational Effectiveness model should be developed based on coverage area of firepower, effective interceptions per unit time, and lethality probabilities of weapon systems to evaluate each location’s effectiveness. Finally, leveraging land selection principles and external information coordination mechanisms will facilitate establishing a genetic algorithm-based strategy for deploying surface-to-air missile systems. The validity of this algorithm has been demonstrated through practical examples.

Картографирование историко-религиозных памятников Карельского муниципалитета (Грузия)

In the last 30-year period in Georgia one of the priority directions of thematic cartography is atlas mapping of historical and religious monuments of individual regions of Georgia. The purpose of this article is classification by various features and mapping of historical and religious monuments of the Kareli municipality. In the process of work the materials obtained during the field expedition, as well as the available archive materials, were used. Literary and cartographic sources were also used. In the field conditions the clergy was surveyed about historical and religious monuments in the territory of the studied municipality. The collected records were used to compile the text part of the article and the final map. As a cartographic basis the topographic map of scale 1:200 000 was used, according to which the location of monuments was determined by hypsometric steps. Landscape map of Georgia of scale 1:200 000 was also used in determining the types of landscapes in which the historical and religious monuments of the Kareli municipality are located. In the process of the research the cartographic method of research, the descriptive method and the method of comparative analysis were used. The final result of the work is the map of historical and religious monuments of the Kareli Region on the scale of 1:200 000. The Kareli municipality and especially the valley of the Dzama River located on its territory are considered the richest region in terms of historical and religious monuments of Georgia. It is for this reason that this municipality was chosen as the object of mapping. The compiled map of historical and religious monuments is a part of the complex electronic atlas of the Kareli municipality. In the field conditions the following materials on historical and religious monuments of the Kareli municipality were collected: geographic coordinates, date of foundation of the monument, type and architectural features of the monument, functional purpose of the monument, physical condition of the monument, location of the monument. During the analysis of the compiled map some geographic features of the location of historical and religious monuments by hypsometric steps and by landscapes were revealed. The map of historical and religious monuments of the Kareli municipality can be successfully used for planning new tourist routes on the territory of the municipality and compiling full-fledged tourist guides. To some extent, the map can also be used by the monument protection service for the purposes of monitoring these objects.

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.

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.