A large number of people of the fringe areas of Sundarban enter into the forests every year and encounter with the tigers simply for their livelihood. This study attempts to examine the extent and impact of human-animal conflicts in the Sundarban Reserve Forest (SRF) area in West Bengal, India. An intensive study of the data of the victims (both death and injury) between 1999 and 2014 reveals that, fishermen crab collector, honey collectors and woodcutters are generally victimized by the tiger attack. Pre monsoon period (April to June) and early winter period (Jan to March) are noted for the two-peak periods for casualties. Maximum casualty occurs between 8-10 am, and 2-4 pm. Jhilla (21.1%), Pirkhali (19.72 %), Chandkhali (11.72%), and Arbesi (9.35%) are the four most vulnerable forest blocks accounting more than 60 per cent occurrence of incidences. 67.24 per cent of the tiger attack victims were residents of Gosaba followed by Hingalganja (15%) and Basanti, (9.76%). The vulnerability rating puts the risk of tiger attack to 0.88 for every 10,000 residents of Gosaba block followed by 0.33 at Hingalganj Block and 0.11 at Bansanti Block. The majority of the victims (68%) were found to be males, aged between 30 and 50 years.

For this, the primary aim of this study was to analyze of the impact of cultural accessibility and ICT (information and communication technology) infrastructure on economic growth in Kazakhstan, employing regression models to asses a single country data from 2008 to 2022. The research focuses on two sets of variables: cultural development variables (e.g., number of theaters, museums, and others) and ICT infrastructure variables (e.g., number of fixed Internet subscribers, total costs of ICT, and others). Principal component analysis (PCA) as employed to reduce the dimensionality of the data and identify the most significant predictors for the regression models. The findings indicate that in the cultural development model (Model 1), the number of recreational parks and students are significant positive predictors of GDP per capita. In the ICT infrastructure model (Model 2), ICT costs are found to have a significant positive impact on GDP per capita. Conversely, traditional connectivity indicators, such as the number of fixed telephone lines, show a low dependence on economic growth, suggesting diminishing returns on investment in these outdated forms of ICT. These results suggest that investments in cultural and ICT infrastructure are crucial for economic development. The study provides valuable insights for policymakers, emphasizing the need for quality improvements in education and strategic modernization of communication technologies.

While some conflict can serve as a more sophisticated stimulus to student achievement, significant or unresolved conflict can delay or even frustrate even the best-planned curriculum. The aim of our study is to get a clear picture of the conflicts with whom and to what extent the international students studying on our campuses have conflicts that affect their performance, and how they can manage them. In our study, based on a questionnaire survey (n = 480), we revealed that the international students at our university have the most conflicts with other foreign students, and the least with Hungarians, including their teachers. On the other hand, we found that according to the Thomas-Kilmann Conflict Instrument, they solve their problems by the Compromising and Accommodating style. The results obtained by detailed socio-demographic aspects show significant differences, mainly between gender, age, and country groups. Knowledge of the revealed facts and connections can offer conscious and careful solutions to understand and reduce tensions, and this can improve the understanding and management of conflict in the classroom, in collaborative projects, and even in non-teaching environments on campuses.

This study aimed to explore the indirect effects of appearance-related anxiety (ARA) on Instagram addiction (IA) through sequential mediators, namely social media activity intensity (SMAI) and Instagram feed dependency (IFD). The study also aimed to provide theoretical explanations for the observed relationships and contribute to the understanding of the complex interplay between appearance-related concerns, social media usage, and addictive behaviors in the context of IA. A sample of 306 participants was used for the analysis. The results of the sequential mediation analysis (SMA) revealed several important findings. Firstly, the mediation model demonstrated that SMAI mediated the relationship between ARA and IA. However, there was no direct relationship observed between ARA and SMAI. Secondly, the analysis showed that IFD acted as a second mediator in the relationship between ARA and IA. Both ARA and SMAI had significant direct effects on IA, indicating their individual contributions to addictive behaviors. Furthermore, the total effect model confirmed a positive relationship between ARA and IA. This finding suggests that ARA has a direct influence on the development of IA. The examination of indirect effects revealed that ARA indirectly influenced IA through the sequential mediators of SMAI, IFD, and ultimately IA itself. The completely standardized indirect effect of ARA on IA through these mediators was found to be significant. Overall, this study provides evidence for the indirect effects of ARA on IA and highlights the mediating roles of SMAI and IFD. These findings contribute to our understanding of the psychological mechanisms underlying the complex relationship between appearance-related concerns, social media usage, and the development of IA.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

Soil erosion is characterized by the wearing away or loss of the uppermost layer of soil, driven by water, wind, and human activities. This process constitutes a significant environmental issue, with adverse effects on water quality, soil health, and the overall stability of ecosystems across the globe. This study focuses on the Anuppur district of Madhya Pradesh, India, employing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) tools to estimate and spatially analyze soil erosion and fertility risk. The various factors of the model, like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and cover management factor (C), have been computed to measure annual soil loss in the district. Each factor was derived using geospatial datasets, including rainfall records, soil characteristics, a Digital Elevation Model (DEM), land use/land cover (LULC) data, and information on conservation practices. GIS methods are used to map the geographical variation of soil erosion, providing important information on the area's most susceptible to erosion. The outcome of the study reveals that 3371.23 km2, which constitutes 91% of the district's total area, is identified as having mild soil erosion; in contrast, 154 km2, or 4%, is classified as moderate soil erosion, while 92 km2, representing 2.5%, falls under the high soil erosion category. Additionally, 50 km2, or 1.35%, is categorized as very high soil erosion and around 30 km2 of the study area is classified as experiencing severe soil erosion. The analysis further discovers that the annual soil loss in the district varies between 0 and 151 tons per hectare per year. This study indicates that most of the district is classified under low soil erosion; only a tiny fraction of the area is categorized as experiencing high and very high soil erosion. The study provides significant insights into soil erosion for policymakers and human society to bring their attention to the need for sustainable soil conservation practices in the undulating terrain/topography and agriculturally dominated district of Anuppur.