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The power of converging and coupling Artificial Intelligence (AI), Fifth Generation Cellular(5G), Internet of Things (IoT), Big Data analytics, and Cyber-security has been touted to be the next revolutionary technologies. If applied correctly, the synergy of the aforementioned technologies is poised to unravel, transform and yield unparalleled economic efficiency and productivity globally across various sectors. It is important to note that each of these technologies has an enormous impact as a standalone in various industries/sectors but when properly combined, the technologies will create an array of opportunities that hugely impact widely all sectors of global economy. This paper will carry out a systematic review of applications of the above-mentioned technologies towards improving Africa's multi-sectoral economy, and their global impact. The findings of this paper will guide the development of techniques of converging various technologies towards enhancing efficiency and impacting sustainability in virtually all sectors of human endeavors. [ABSTRACT FROM AUTHOR]

In the era of digital agriculture, Precision agriculture (PA) data sources are diverse in terms of the range of technology options and the type of data they generate. Government institutions, scientists, and the private sectors generate much of the PA data at the innovation, validation, and dissemination phases. At scale-up phases, farmers also generate tremendous amounts of data that might have privacy and ownership concerns. There remains the possibility of a better way to integrate PA data continent-wide in Africa. Data privacy and ownership issues must be addressed while still maintaining the integration of PA data at scale. The objective of this paper is to review the major challenges of PA data harmonization in Africa and discuss the existing opportunities in relation to technological advancements in PA data applications to address data sharing without compromising data privacy, ownership, and stewardship. Finally, a new PA data sharing and reward model—'PrecisioNexion' is proposed to rationalize data network systems by establishing a robust and self-sustaining business model. The model uses AI and blockchain technology to track and stamp PA data using unique dataset_IDs or PrecisionPrint (like a fingerprint), determining credit amounts using 'pVouchers' (like eVouchers) and distributing credits between PA data owners or 'PrecisionProprietor', data clients or 'PrecisionClient' and funders or 'PrecisionPatron'. The proposed system provides a foundation for win–win–win PA data sharing and self-sustaining business models for data owners, technology solutions providers and funders, while ensuring a strong partnership between farmers' cooperatives, private sector, scientists, governments, and financial institutions, and countries. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, located 80 km north of Arba Minch in southern Ethiopia and covering just over 5250 km2, was selected as the study area. Bilate is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time series database of 75 boreholes, machine learning models, including multiple linear regression, multivariate adaptive regression splines, artificial neural networks, random forest regression, and gradient boosting regression (GBR), were constructed to predict the depth to the water table. The study considered 20 independent variables, including elevation, soil type, and seasonal data (spanning three seasons) for precipitation, specific humidity, wind speed, land surface temperature during day and night, and Normalized Difference Vegetation Index (NDVI). GBR performed the best of the approaches, with an average 0.77 R-squared value and a 19 m median absolute error on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model, with water levels ranging from 1.6 to 245.9 m. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water. [ABSTRACT FROM AUTHOR]