Natural Geographical Factors Research Articles

The spatiotemporal distribution and environmental suitability evaluation of Neolithic settlements in the Western Guanzhong Basin of China

Illustrating the relationship between past settlement changes and human-land could help us to understand the complex interactions among climate, human activities, and the environment. Based on archeological literature and fieldworks, this paper combines dating techniques and GIS analysis to explore the spatial and temporal distribution characteristics and human diffusion of Neolithic settlements in the Western Guanzhong Basin (WGB), and to quantitatively analyzes the relationship between human activities and natural geographic factors by index model. The results show that: (1) There was a continuity of Neolithic cultures in the WGB and the absolute chronological sequence of human activities ranges from 7500 to 3900 a BP; (2) The distribution of settlements tends to be on river terraces and Loess tableland, and with the evolution of Neolithic cultures, human activities shifted from near-water to far-water distributions and expanded to areas of high gradient. The center of activity of the settlement spreads from a single nucleus (the Wei River) to multiple nuclei (the Wei River, the Qian Rivers, and the Qishui River) and back to a single nucleus on the northern bank of the Wei River in the area of present Qishan-Fufeng, laying the foundation for the development and strength of the Western Zhou Dynasty Cultures in this area; (3) The suitability level of the natural geographic environment in the WGB gradually decreased from the Wei River to the north and south, and settlements were mainly distributed in the highly and moderately suitable zones; (4) It was found that as early as the Neolithic period, mankind had already grasped certain laws of nature, and the spatial distribution of settlements reflected mankind’s awareness of disaster avoidance and “compromise and reconciliation.”

SDG: A global large-scale airport perception disparity cognition modeling method based on deep learning and geographic knowledge

Global airport perception levels vary due to natural geographical factors and economic development disparities. Understanding these differences is crucial for assessing regional airport development and its correlation with geographical patterns. However, there are limited methods available to effectively comprehend these disparities. To address this issue, this paper proposes a Salience, Disturbance, and Geographic-knowledge (SDG) approach for the cognitive analysis of global large-scale airport perception differences. Salience is assessed using a two-class deep learning model to evaluate the prominence of known airports. Disturbance is evaluated using an object detection model to measure background interference in large-scale airport perception. Geographic-knowledge analysis considers the correlation between regional airports and their surrounding geographic environment. The results rank perception difficulties for 17 regions worldwide, with Tajikistan exhibiting the highest difficulty at 0.922, while the Jiangsu–Zhejiang–Shanghai region in China has the lowest at 0.102. We also performed correlation analyses to validate the effectiveness of our model. To our knowledge, this paper pioneers the cognitive analysis of target perception difficulty differences across multiple global regions.

Spatial Distribution Characteristics and Driving Factors of Little Giant Enterprises in China’s Megacity Clusters Based on Random Forest and MGWR

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness.

Evaluation of Ecological Environment Quality and Analysis of Influencing Factors in Wuhan City Based on RSEI

It is crucial to assess the quality of ecological environments in urban areas and investigate the driving forces that would affect urban ecological environments. Utilizing the GEE platform, RSEI was computed by us for Wuhan from 1990 to 2020. Employing geodetector tools and the PLS-SEM approach, driving factors for ecological environment quality in Wuhan were discussed. The overall trend of ecological environment quality in Wuhan was to decline at first and then rise from 1990 to 2020 spatial aggregation characteristics of RSEI were significant; moreover, land use, location, population density, and GDP were included as the main influence factors causing spatial differentiation of RSEI; each influence factor’s effect was also different. Over the past three decades, a fluctuating decline has been exhibited by ecological environment quality in Wuhan. Central urban areas have poor ecological environment quality, while southern and northern distant urban zones have superior ecological environment quality. Clustering is shown to be significant spatially by both. The main influencers of ecological quality in Wuhan are human geographic factors, while natural geographic factors have comparatively minor impacts.

INVESTMENT CLIMATE IN UKRAINE: SITUATION, PROBLEMSAND POSSIBLE WAYS OF IMPROVEMENT

The article presents the results of a study of the state, problems and possible ways to improve the investment climate in Ukraine in the new extreme conditions of doing business. Improving and stabilizing the investment climate in Ukraine during martial law is necessary to ensure economic development, attract international resources, rebuild infrastructure, strengthen the international image and protect national business. The purpose of the article is to study the state and problems of the formation of the country's investment climate in conditions of full-scale military operations, analysis of the main factors affecting the level of investment attractiveness of enterprises in conditions of uncertainty, study of the main ways and directions of improvement of the investment climate in Ukraine.As a result of the research, it was established that the investment climate should be defined as a set of political, economic, legal, social-cultural, scientific-technological, demographic, infrastructural and natural-geographical factors that form the degree of investment attractiveness and determine the level of investment risk. A model of the formation of the investment climate in Ukraine was built, taking into account the main factors of the internal and external environment of enterprises and organizations under martial law. The main threats and changes in the macro-environment of the country under the influence of the war are structured. It was found that there was not a single field of activity of economic entities that was not affected by the war. One of the main ways to improve the investment climate in Ukraine today is the insurance of military and political risks, which is an extremely important aspect for protecting capital investments and ensuring business development. It is the stabilization of the investment climate that will prevent the outflow of capital abroad and preserve resources for further development. In addition to large-scale infrastructure reconstruction projects, investments can be aimed at supporting small and medium-sized businesses, which are an important source of innovation and jobs.

Research on Traditional Village Spatial Differentiation from the Perspective of Cultural Routes: A Case Study of 338 Villages in the Miao Frontier Corridor

The traditional villages in the Miao Frontier Corridor are the products of migrations, social interactions, and transportation, as well as production interchanges between the central plains and the frontiers of China in ancient times. They have made significant contributions to local multicultural inheritance and regional social development. However, with the increasing pressure of heritage conservation and sustainable development, there is growing attention on how traditional villages can tap into their cultural continuity and distinctiveness. This study introduces the concept of cultural routes, with the aim of integrating traditional villages of different ethnicities, regions, and characteristics from the perspective of diversity. It analyzes their spatial differentiation characteristics and the factors influencing them, providing basic support for the overall protection of traditional villages with special characteristics. Following this idea, 338 remaining traditional villages in the Miao Frontier Corridor were selected as the research objects. With the help of 91-satellite maps and a geographic information system (GIS), a cultural and geographic database of the traditional villages in the Miao Frontier Corridor was constructed to objectively explore the roles of the traditional villages’ natural geography, historical, and humanistic elements in the spatial categorization on a large scale. This study shows that the spatial distribution of the traditional villages in the Miao Frontier Corridor is uneven, exhibiting a cluster structure with of a “single primary nucleus with multiple secondary nuclei”. The spatial differentiation of traditional villages exhibits a similar clustering pattern based on individual natural geographic factors, such as elevation, mountain undulation, slope, and water systems. Additionally, there is discernible regularity concerning historical and humanistic factors, such as ethnicity type, age of village establishment, and the presence of guard stations. Further exploring the micro-spatial level, the natural geographical environment serves as the structural foundation of traditional village space, while the historical and humanistic environment fosters multiple differentiations in traditional village space in terms of influencing factors. Together, these factors jointly influence the spatial differentiation of traditional villages. This study enriches the dynamic aspects of linear cultural heritage preservation and also provides new insights into the specialized development within the overall protection of traditional villages.

Exploring and Predicting Landscape Changes and Their Driving Forces within the Mulan River Basin in China from the Perspective of Production–Living–Ecological Space

With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km2 of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km2 by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km2. Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin.

Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China

Investigating the spatial distribution and adaptation mechanisms of traditional villages is crucial for their development and human well-being. However, relatively few studies have focused on the interaction and comprehensive influence of historical-cultural, natural-geographic and socio-economic factors on traditional villages in Jiangnan region. Employing geographically weighted regression (GWR) and Geodetector methods, we developed an integrated framework. We found that (1) Traditional villages exhibit a “one-core, multiple-periphery” clustering pattern (nearest neighbor index <1), with significant high-high and low-low local spatial clustering characteristics and preferring gentle and relatively Complex terrains to adapt to the need to avoid warfare. (2) Urbanization negatively affects traditional village density patterns, while natural-geographic complexity has a positive impact. Their interaction nonlinearly amplifies this effect, with the urbanization-GDP interplay being particularly significant (q = 0.765). (3) Factors exhibit significant spatial heterogeneity in their impact on traditional village density, with the explanatory power increasing by 20% (GWR/R2 = 0.65). The urbanization rate and slope are the most critical socio-economic and natural-geographic factors, respectively (local regression coefficient: 23.31 and 11.58). Our study transcends administrative boundaries, revealing how historical-cultural, natural-geographical, and socio-economic factors collectively influence the distribution of traditional villages. This not only provides a new theoretical foundation for the conservation and development of traditional villages but also provides a new methodology basis for research on traditional villages.

Spatio-Temporal distribution characteristics and driving factors of traditional villages in the Yellow River Basin.

Currently, research on traditional villages mainly focuses on the current development status and evolutionary trends in specific regions, with relatively limited studies from a macroscopic and holistic perspective on the spatiotemporal evolution of traditional villages. Therefore, this study selects traditional villages in the Yellow River Basin (YRB) as the research object. By analyzing the spatiotemporal distribution characteristics and driving factors of traditional villages (TVs) in the basin, it aims to further promote high-quality development in the YRB and protect traditional cultural resources. Based on data from 892 village points of the first to sixth batches of TVs in the YRB, ArcGIS 10.8 spatial analysis techniques were employed to analyze the overall spatial pattern of TVs in the YRB. The results indicate: (1) In the basin, TVs are more numerous in the east than the west and more in the south than the north, forming clusters and contiguous distributions, with dense areas primarily in the upstream regions dominated by Qinghai Province and the midstream areas along the Shanxi-Shaanxi coast. (2) The number and scale of TVs in the basin generally exhibit an increasing trend, with imbalanced provincial distribution. More recent years show a more balanced distribution of villages and proportions, with a higher number of villages in the mountainous and plateau regions of the basin. (3) The layout center of TVs within the basin evolves with each batch, showing a migration pattern from north to south, back to north, and finally east to west. (4) The interaction of natural and social factors plays a synergistic role in driving the spatiotemporal distribution pattern of TVs. Among these, natural geographical factors are the primary factors. TVs are more commonly found in regions with low altitude sunny slopes, mild climate, abundant precipitation, proximity to ancient roads and rivers, gentle slopes, and soil predominantly comprising loess, brown earth, and alluvial soils. The cultural environment is a secondary factor, with TVs often located in areas with larger populations, developed economies, and rich cultural heritage.

Analysis of the Spatial Distribution Characteristics and Influencing Factors of Traditional Mosque Architecture in the Hehuang Area (China)

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China’s Hehuang region has significant positive implications for the overall protection and development of the region’s architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a “central clustering, peripheral dispersal” distribution pattern, forming a spatial structure of “two cores, one belt, multiple points”, with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22 °C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors.

Spatio-Temporal Changes and Driving Forces Analysis of Urban Open Spaces in Shanghai between 1980 and 2020: An Integrated Geospatial Approach

Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai.

Spatiotemporal Evolution and Mechanisms of Habitat Quality in Nature Reserve Land: A Case Study of 18 Nature Reserves in Hubei Province

The contribution of biodiversity to the global economy, human survival, and welfare has been significantly increasing. However, nature reserves have long been subject to a sequence of ecological environmental issues caused by human activities. Therefore, quantitatively assessing the spatiotemporal evolution characteristics of habitat quality due to land use changes and exploring the mechanisms of potential influencing factors can provide a scientific basis for the stable and sustainable development of natural ecosystems. This study aims to analyze 18 nature reserves in Hubei Province to identify the spatiotemporal evolution of habitat quality within these reserves and to explore the influence of multifactorial dynamics from nature, humanity, and policy on this evolution. Initially, the study utilizes land use transition matrices and land use dynamic degree methods to understand the spatiotemporal characteristics of land conversion within the study area. Subsequently, it analyzes the spatiotemporal changes in habitat quality from 2000–2020 based on the InVEST model and tools like spatial autocorrelation (Moran’s I) in ArcGIS. Finally, 14 potential influencing factors are selected from natural environment, socio-human, and policy regulation aspects and analyzed in the Geodetector software to understand the factors affecting the spatiotemporal evolution of habitat quality. The results show that, during the study period, the land area of 18 nature reserves in Hubei Province increased from 2000 to 2020, while the water area decreased. There were slight increases in farmland, construction land, and forest land, with significant decreases in grassland and water areas. This reveals the erosion of water bodies due to artificial lake filling during rapid urbanization, leading to a decline in overall habitat quality within the reserves and a gradual increase in spatial heterogeneity. Among the influencing factors, single-factor influences such as land use intensity and distance to county roads and slopes have a strong negative linear relationship with habitat quality, with land use intensity being the most significant human activity factor. The interaction strength among different types of influencing factors in the bivariate interaction detection results is ranked as follows: the interaction between natural geographical and socio-human factors &gt; the interaction within socio-human factors &gt; the interaction within natural geographical factors. This study has diverged from the past focus on the selection of a single continuous natural reserve as the empirical subject. Consequently, it allows for an integrated analysis of physical geographical dimensions such as locational topography with socio-cultural and policy elements including land use and transportation facilities, thereby facilitating a multifactorial assessment of the interactive impacts on habitat quality.

Study on the Spatial Distribution Characteristics and Influencing Factors in the Reuse of National Industrial Heritage Sites in China

Industrial heritage is a valuable spatial resource for urban stock updates, and its preservation and reuse play an important role in the transmission of urban history and culture. Typological analysis, descriptive statistical analysis, relevant spatial analysis using ArcGIS 10.8, and geographic probes were employed to explore the spatial distribution characteristics and influencing factors regarding the current status of the reuse of 196 heritage sites selected from five batches of China’s National Industrial Heritage (NIH) lists. The results reveal the following: (1) The spatial distribution of China’s NIH sites is uneven and cohesive, forming a dense circle with the Yangtze River Delta region and the Beijing–Tianjin–Hebei region at its core. (2) Three-fourths of the NIH sites have entered the reuse stage, and high-density and relatively high-density clusters have formed in the eastern and central regions. (3) The conservation and reuse directions of China’s NIH sites are mainly divided into publicization and marketization. (4) The spatial distribution differentiation of the reuse of NIH sites is not due to a single cause but, rather, a combination of various contributing factors. Natural geographic and socio-economic factors exert varying degrees of influence on the spatial distribution of reused heritage sites, with tourism resources and government support playing primary roles in shaping this distribution. These findings establish a fundamental database of China’s NIH sites and provide guidance for the current conservation and reuse of industrial heritage.

Spatial Distribution and Influencing Factors of Traditional Villages in Inner Mongolia Autonomous Region

This paper takes 207 traditional villages in Inner Mongolia as the research object and uses the ArcGIS10.7 software platform, using the nearest neighbor index, coefficient of variation analysis, spatial autocorrelation analysis, imbalance index, kernel density estimation method, geographical detector, and other methods to explore the spatial distribution characteristics and influencing factors of traditional villages in Inner Mongolia. The research shows that: (1) the spatial distribution of traditional villages in Inner Mongolia is condensed; the distribution of cities is uneven; and the overall distribution pattern of ‘two main and two vice’ is presented. (2) The traditional villages are mainly distributed in the altitude area of 500–1500 m, and their spatial distribution characteristics are positively correlated with the annual average temperature, annual precipitation, total population, the proportion of the primary industry, and the number of intangible cultural heritage, and negatively correlated with the slope, river distance, highway density, per capita GDP, urbanization, and the proportion of the secondary industry. (3) The results of GeoDetector2018 software show that socio-economic factors are the primary factors affecting the spatial distribution of traditional villages in Inner Mongolia, followed by natural geographical factors. The interaction and synergy between the influencing factors have increased significantly, which jointly affects the spatial pattern of the distribution of traditional villages in Inner Mongolia. The purpose of the study is to provide reference for the protection and development of traditional villages in Inner Mongolia and the implementation of the national rural revitalization strategy.

Spatiotemporal change and drivers of ecosystem quality in the Loess Plateau based on RSEI: A case study of Shanxi, China

The fragile ecological environment and intensive human activities in the Loess Plateau region of China have led to serious soil erosion and adverse weather disasters. To address these issues, the Chinese government has implemented a series of large-scale ecological restoration projects with significant investments and broad public participation. Throughout this process, the ecosystems have undergone substantial artificial intervention. This study utilizes MODIS data to establish a remote sensing ecological index (RSEI) for evaluating the ecosystem quality of Shanxi Province, located in the eastern Loess Plateau, over the past 20 years (2000–2020). Furthermore, the study analyzed the natural and socio-economic factors that influence the local ecosystem quality using Geodetector analysis. The research findings revealed the following: 1) Over the 20-year period of large-scale ecological restoration projects, the ecosystem quality in Shanxi has significantly improved, as indicated by the average RSEI index increasing from 0.48 to 0.57. 2) The southern part of the province has experienced relatively better ecosystem recovery, while the ecosystem quality in the northern and western regions, particularly along the Yellow River, remains poor. 3) Overall, the major factors influencing ecosystem quality are natural geographical factors such as temperature and slope. However, with rapid urbanization, the negative impacts from socio-economic factors represented by GDP have intensified, particularly in the northern regions. Additionally, this study demonstrated that the geographically weighted regression model can better explain the local variations affecting the RSEI and exhibits a high degree of reliability. The research findings provide scientific and effective references for evaluating the ecosystem quality in the Loess Plateau and implementing ecological restoration projects.

Spatial variation of ruminant distribution in Chamchamal district

Ruminants are an important component of agricultural income and play a role in providing food for the population in the study area. This study was conducted in Chamchamal district with the aim of presenting the variation in the distribution of livestock among the administrative units in the Chamchamal district and the relationship of this variance with natural and human geographical factors, The surface is one of two groups of factors, which are divided into three parts, also climate, and the average annual rainfall, another factor analyzed in the study is soil, which is consist of(5)types, water resources and natural plants factors each has an influential role, and other factors such as labor, Agricultural and Fodder and Pasture policy factors are described, As explained at the end of the first topic, veterinary services. Another purpose of the study is to analysis and present the number and type variation of ruminant, which shows the most common type is sheep and the most number are in(Sangw)sub-district, it reaches(115717)heads, while the least widespread is cow, and the most number of cows is in(Aghjalar)sub-district, it reache(6633)heads, Therefore, the study showed a clear variance of distribution. These analyzes have been carried out and based on data, maps, interviews and Survey form.

Retracted: Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui-Style Prints.

[This retracts the article DOI: 10.1155/2022/4627215.].

The Natural Geographic Factors of Golf Tourism and Their Impacts

The Natural Geographic Factors of Golf Tourism and Their Impacts

Hydro-geochemical assessment of ground water for drinking and agricultural purposes and potential human health risk in Aligarh city, India

Health effects are related globally to water quality. The residents of Aligarh city are solely rely on groundwater for drinking and domestic purposes. The aim of the study was to find out association between ground water quality and water borne diseases. For this purpose, the present study evaluated physicochemical quality of drinking water. The water quality was evaluated for both pre-monsoon (PRM) and post-monsoon (POM) seasons based on samples collected from 19 stations. To find the potential health risk and waterborne diseases vulnerability, questionnaires-based survey was carried out. Although most of the samples range slightly above the Bureau of Indian Standard's (BIS) permissible limit, the results revealed the sign of contamination from the surface of the earth. According to result of water quality index, the city is classified as having well to moderate water quality throughout the year. The correlation, HCA, and Trilinary plot demonstrate that natural pollution sources, human activities, and geographical factors affecting water quality in the study area. During PRM, 84.21% area falls under slight to moderate sodium hazard category where as the result gets better during POM where 68.42% area falls under the same category. Overall, the water was fit to be used for irrigation purposes. The accuracy of WBD's susceptibility maps was assessed by RMSE. During PRM, chemical parameters in groundwater, WQI and WBD's RMSE were found to be 0.1483, 0.3157 and 0.212 whereas during POM the results were 0.1822, 0.3172 and 0.1966, respectively. The results obtained from susceptibility assessment shows that drinking water will not have any major waterborne diseases. However, the result revealed that POM season is more vulnerable to some waterborne diseases than the PRM season.

Identification and characterization of codon usage pattern and influencing factors in HFRS-causing hantaviruses.

Hemorrhagic fever with renal syndrome (HFRS) is an acute viral zoonosis carried and transmitted by infected rodents through urine, droppings, or saliva. The etiology of HFRS is complex due to the involvement of viral factors and host immune and genetic factors which hinder the development of potential therapeutic solutions for HFRS. Hantaan virus (HTNV), Dobrava-Belgrade virus (DOBV), Seoul virus (SEOV), and Puumala virus (PUUV) are predominantly found in hantaviral species that cause HFRS in patients. Despite ongoing prevention and control efforts, HFRS remains a serious economic burden worldwide. Furthermore, recent studies reported that the hantavirus nucleocapsid protein is a multi-functional protein and plays a major role in the replication cycle of the hantavirus. However, the precise mechanism of the nucleoproteins in viral pathogenesis is not completely understood. In the framework of the current study, various in silico approaches were employed to identify the factors influencing the codon usage pattern of hantaviral nucleoproteins. Based on the relative synonymous codon usage (RSCU) values, a comparative analysis was performed between HFRS-causing hantavirus and their hosts, suggesting that HTNV, DOBV, SEOV, and PUUV, were inclined to evolve their codon usage patterns that were comparable to those of their hosts. The results indicated that most of the overrepresented codons had AU-endings, which revealed that mutational pressure is the major force shaping codon usage patterns. However, the influence of natural selection and geographical factors cannot be ignored on viral codon usage bias. Further analysis also demonstrated that HFRS causing hantaviruses adapted host-specific codon usage patterns to sustain successful replication and transmission chains within hosts. To our knowledge, no study to date reported the factors influencing the codon usage pattern within hantaviral nucleoproteins. Thus, the proposed computational scheme can help in understanding the underlying mechanism of codon usage patterns in HFRS-causing hantaviruses which lend a helping hand in designing effective anti-HFRS treatments in future. This study, although comprehensive, relies on in silico methods and thus necessitates experimental validation for more solid outcomes. Beyond the identified factors influencing viral behavior, there could be other yet undiscovered influences. These potential factors should be targets for further research to improve HFRS therapeutic strategies.