The food supply chain in South Africa faces significant challenges related to transparency, traceability, and consumer trust. As concerns about food safety, quality, and sustainability grow, there is an increasing need for innovative solutions to address these issues. Blockchain technology has emerged as a promising tool to enhance transparency and accountability across various industries, including the food sector. This study sought to explore the potential of blockchain technology in revolutionizing through promoting transparency that enable the achievement of sustainable food supply chain infrastructure in South Africa. The study found that blockchain technology used in food supply chain creates an immutable and decentralized ledger of transactions that has the capacity to provide real-time, end-to-end visibility of food products from farm to table. This increased transparency can help mitigate risks associated with food fraud, contamination, and inefficiencies in the supply chain. The study found that blockchain technology can be leveraged to enhance supply chain efficiency and trust among stakeholders. This technology used and/or applied in South Africa can reshape the agricultural sector by improving production and distribution processes. Its integration in the food supply chain infrastructure can equally improve data management and increase transparency between farmers and food suppliers.There is need for policy-makers and scholars in the fields of service delivery and food security to conduct more research in blockchain technology and its roles in creating a more transparent, efficient, and trustworthy food supply chain infractructure that address food supply problems in South Africa. The paper adopted a qualitative methodology to collect data, and document and content analysis techniques were used to interpret collected data. 

The sense of belonging in any organization is vital to generate a work motivation with the objective of a good organizational performance, because of this, companies usually take this point into account, ensuring that this leads to greater performance. For this reason, the objective of this article is to determine the relationship between the sense of belonging and the work motivation in the workers of a small Peruvian research company. For this purpose, a quantitative methodology was used, with a cross-sectional descriptive design. The instrument used was a survey consisting of 10 items, which were interpreted using the Likert scale. The survey was conducted and delivered to 24 workers, who were selected by non-probabilistic convenience sampling. After verifying the validity of the instrument and the study variables by means of Cronbach's Alpha statistic, we proceeded to determine the existence of correlation between the variables, which, using Spearman's Rho coefficient, obtained a 70.2% which demonstrates a moderate positive correlation, therefore it indicates that employees feel highly motivated as they feel an indispensable part of the company, therefore they feel job satisfaction by being part of the organization.

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.

This study delves into the complex flow dynamics of magnetized bioconvective Ellis nanofluids, highlighting the critical roles of viscous dissipation and activation energy. By employing a MATLAB solver to tackle the boundary value problem, the research offers a thorough exploration of how these factors, along with oxytactic microorganism’s mobility, shape fluid behavior in magnetized systems. Our findings demonstrate that an increase in the magnetization factor  leads to a decrease in both velocity and temperature due to enhanced interparticle resistance from the Lorentz force. Additionally, streamline analysis reveals that higher mixed convection parameters  intensify flow concentration near surfaces, while increased slip parameters reduce shear stress and boundary layer thickness. Although isotherm analysis shows that higher Ellis fluid parameters enhance heat conduction, with greater porosity values promoting efficient thermal dissipation. These insights significantly advance our understanding of nanofluid dynamics, with promising implications for bioengineering and materials science, setting the stage for future research in this field.