Recent developments in hardware and information technology have enabled the emergence of billions of connected, intelligent devices around the world exchanging information with minimal human involvement. This paradigm, known as the Internet of Things (IoT), is progressing quickly, with an estimated 27 billion devices by 2025 (almost four devices per person) [1, 2]. These smart devices help improve our quality of life, with wearables to monitor health, vehicles that interact with traffic centers and other vehicles to ensure safety, and various home appliances offering comfort. This increase in the number of IoT devices and successful IoT services has generated tremendous data. The International Data Corporation report estimates that by 2025 this data will grow from 4 to 140 zettabytes [3].

Various macroeconomic and institutional factors hinder FDI inflows, including corruption, trade openness, access to finance, and political instability. Existing research mostly focuses on country-level data, with limited exploration of firm-level data, especially in developing countries. Recognizing this gap, recent calls for research emphasize the need for qualitative data analysis to delve into FDI determinants, particularly at the regional level. This paper proposes a novel methodology, based on text mining and social network analysis, to get information from more than 167,000 online news articles to quantify regional-level (sub-national) attributes affecting FDI ownership in African companies. Our analysis extends information on obstacles to industrial development as mapped by the World Bank Enterprise Surveys. Findings suggest that regional (sub-national) structural and institutional characteristics can play an important role in determining foreign ownership.

The work show in this paper progresses through a sequence of physics-based increasing fidelity models that are used to design the robot controllers that respect the limits of the robot capabilities, develop a reference simple controller applicable to a large subset of tracking conditions, which include mostly non-invasive or highly dynamic movements and define path geometry following the control problem and develop both a simple geometric control and a dynamic model predictive control approach. In this paper, we propose for a nonlinear model with disturbance effect, the mathematical modeling of the longitudinal and lateral movements using PID with a feed-forward controller. This study proposes a feedforward controller to eliminate the disturbance effect.

The Bonassama District Hospital in Douala, Cameroon and six other African hospitals are adopting SOPHiA to – no matter their experience in genomic testing – get up to speed and analyze genomic data to identify disease-causing mutations in patients' genomic profiles, and decide on the most effective care. A release from the global leader in Data-Driven Medicine, Sophia Genetics, says in addition to the Bonassama district hospital, the modern technology is being adopted by Pharma Process in Casablanca, Morocco; ImmCell in Rabat, Morocco; The Al Azhar Oncology Center in Rabat, Morocco; The Riad Biology Center in Rabat, Morocco; The Oudayas, Medical Analysis Laboratory, Morocco;and The Center for Proteomic & Genomic Research (CPGR) in Cape Town, South Africa. As new users of SOPHiA, they become part of a larger network of 260 hospitals in 46 countries that share clinical insights across patient cases and patient populations, which feeds a knowledgebase of biomedical findings to accelerate diagnostics and care. Speaking about the adoption of SOPHiA in Africa, Jurgi Camblong, Sophia Genetics' CEO and co-founder, declared: "Since inception, our vision has been to develop innovative technological solutions that analyze patients' genomic profiles to offer better diagnosis and care to the greatest number of patients, wherever they live. Today, I am very proud that SOPHiA is triggering a technological leapfrog movement in healthcare across Africa."