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. Ad

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI’s predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

Open-source software (OSS) has emerged as a transformative tool whose implementation has the potential to modernise many libraries around the world in the digital age. OSS is a type of software which permits its users to inspect, share, modify, and enhance through its freely accessed source code. The accessibility and openness of the source code permits users to manipulate, change, and improve the way in which a piece of software, program, or application works. OSS solutions therefore provide cost-effective alternatives that enable libraries to enhance their technological infrastructure without being constrained by proprietary systems. Hence, many countries have initiated and formulated policies and legislative frameworks to support the implementation and use of OSS library solutions such as DSpace, Alfresco, and Greenstone. The purpose of the study reported on was to investigate the leveraging of OSS to modernise public libraries in South Africa. Content analysis was adopted as the research methodology for this qualitative study, which was based on a literature review integrating insights from the researchers’ experiences with the use of OSS in libraries The findings of the study reveal that the use of OSS has the potential to modernise public libraries, especially those located outside cities or urban areas. These libraries are often less well equipped with the necessary technology infrastructure to meet the demands of the digital age, such as online books and open access materials. The study culminated in an OSS framework that may be implemented to modernise public libraries. This framework may help public libraries to integrate OSS solutions and further allow users access to digital services.

Artificial intelligence chatbots can be used to conduct research effectively and efficiently in the fifth industrial revolution. Artificial intelligence chatbots are software applications that utilize artificial intelligence technologies to assist researchers in various aspects of the research process. These chatbots are specifically designed to understand researchers’ inquiries, provide relevant information, and perform tasks related to data collection, analysis, literature review, collaboration, and more. The purpose of this study is to investigate the use of artificial intelligence chatbots for conducting research in the fifth industrial revolution. This qualitative study adopts content analysis as its research methodology, which is grounded in literature review incorporating insights from the researchers’ experiences with utilizing artificial intelligence. The findings reveal that researchers can use artificial intelligence chatbots to produce quality research. Researchers are exposed to various types of artificial intelligence chatbots that can be used to conduct research. Examples are information chatbots, question and answer chatbots, survey chatbots, conversational agents, peer review chatbots, personalised learning chatbots and language translation chatbots. Artificial intelligence chatbots can be used to perform functions such as literature review, data collection, writing assistance and peer review assistance. However, artificial intelligence chatbots can be biased, lack data privacy and security, limited in creativity and critical thinking. Researchers must be transparent and take in consideration issues of informed content and data privacy and security when using artificial intelligence chatbots. The study recommends a framework on artificial intelligence chatbots researchers can use to conduct research in the fifth industrial revolution.

The rise of internet-based pharmacies has transformed the healthcare sector, giving patients access to medications, information, and direct interaction with pharmacists. While online pharmacies have become popular around the world, there are challenges hindering their widespread use in developing countries due to a limited understanding of the factors affecting their acceptance and usage. To bridge this knowledge gap, a study utilized a model combining the unified theory of acceptance and use of technology (UTAUT 2) with the technology acceptance model (TAM) to explore the drivers behind online pharmacy usage in Oman. Through this framework, twelve hypotheses were. A survey involving 378 individuals familiar with online pharmacies was conducted. Structural equation modeling (SEM) was applied to analyze the data and test these hypotheses. The results indicate that factors such as perceived expectancy effort expectancy and facilitating conditions hedonic motivation, habit perceived risk, technology trust, and technology awareness play roles in influencing the adoption of online pharmacies in Oman. The findings suggest that personal innovation plays a moderating role in the connection between perceived risk and behavioral intention, while it has a negative moderating influence on the relationship between technology trust and behavioral intention. Word of mouth was identified as a moderator in enhancing the correlation between behavioral intention and online pharmacy adoption. This research emphasizes the moderating relationship of personal innovation and word of mouth on shaping consumer attitudes towards online pharmacies and their acceptance. In summary, these results add to the existing knowledge on pharmacy adoption and in developed areas such as provide practical insights for online pharmacy providers to improve their offerings and attract a larger customer base.

This study explores approaches to optimizing inclusive education through international and local perspectives. It examines the role of educators in inclusive settings, highlights strategies for early detection of children’s developmental needs, and evaluates inclusive school management practices. Using qualitative case study methods, the research includes comprehensive observations and interviews at Fatma Kenanga Islamic Character School. Findings emphasize the importance of individualized learning plans, shadow teacher involvement, and collaborative stakeholder engagement. Integrating global insights, this study contributes to advancing inclusive education practices in Indonesia and beyond.