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The effective moment of inertia of a CO impurity molecule in 4HeN and p-(H2)N solvent clusters initially increases with N but then commences a nonclassical decrease at N=4 (4He) or N=6 (p-H2). This suggests molecule-solvent decoupling and a transition to microscopic superfluidity. However, the quantum decoupling mechanism has not been elucidated. To understand the decoupling mechanism, a one-dimensional model is introduced in which the 4He atoms are confined to a ring. This model captures the physics and shows that decoupling happens primarily because of bosonic solvent-solvent repulsion. Quantum Monte Carlo and basis set calculations suggest that the system can be modeled as a stirred Tonks-Girardeau gas. This allows the N-particle time-dependent Schrödinger equation to be solved directly. Computations of the integrated particle current reveal a threshold for stirring and current generation, indicative of superfluidity.

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Development and adoption of artificial intelligence (AI) in Africa has occurred slowly relative to developed countries. A vibrant AI ecosystem is growing on the continent. Due to the unique geographical, cultural and political nature of the continent, the 4th industrial revolution on the continent is evolving differently from its global counterparts. The motivations for development of AI systems, the parties involved, and the impact of the AI ecosystem on the continent are therefore best analyzed and framed through a unique African lens. This paper seeks to begin this process by developing a conceptual framework to characterize the parties involved in the African AI ecosystem. i.e., the African AI stakeholder. Identification of these stakeholders will aid in determining their interests, responsibilities and accountability and will provide a basis for the development and implementation of an equitable AI ecosystem. It is our goal that this framework, ultimately, be used to guide the contributions from the African AI perspective in global dialogues on ethics, bias, inclusion and similar topics in the AI sphere.

Climate change is predicted to exacerbate Africa’s, already, precarious food security. Climate models, by accurately forecasting future weather events, can be a critical tool in developing countermeasures to reduce crop loss, decrease adverse effects on animal husbandry and fishing, and even help insurance companies determine risk for agricultural insurance policies – a measure of risk reduction in the agricultural sector that is gaining prominence. In this paper, we investigate the efficacy of various open-source climate change models and weather datasets in predicting drought and flood weather patterns in northern and western Kenya and discuss practical applications of these tools in the country’s agricultural insurance sector. We identified two models that may be used to predict flood and drought events in these regions. The combination of Artificial Neural Networks (ANNs) and weather station data was the most effective in predicting future drought occurrences in Turkana and Wajir with accuracies ranging from 78% to 90%. In the case of flood forecasting, Isolation Forests models using weather station data had the best overall performance. The above models and datasets may form the basis of a more objective and accurate underwriting process for agricultural index-based insurance, as we expound in the paper.