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https://www.selleckchem.com/products/uk5099.html With technological progress in particular telemedicine and health care, the information should meet and serve as well the needs of people and in particular whom with reduced mobility, the elderly as well as people with difficulties to access to medical resources and services. These services should be achieved in a fast and reliable manner based on case priorities. One of the major challenges in health care is the routing and scheduling problem to meet people's needs. Of course, the objective is to considerably minimize costs while respecting priorities according to cases that will face. Through this article, we propose a new technique for home healthcare routing and scheduling problem purely based on an artificial intelligence technique to optimize the offered services within a distributed environment. The automatic learning and search method seem to be interesting to optimize the allocation of visits to beneficiaries. The proposed approach has several advantages in terms of especially cost, efforts, and gaining time. A comparative study was carried out to evaluate the effectiveness of the planned technique compared to previous work.2019-nCoV is a virulent virus belonging to the coronavirus family that caused the new pneumonia (COVID-19) which has spread internationally very rapidly and has become pandemic. In this research paper, we set forward a statistical model called SIR-Poisson that predicts the evolution and the global spread of infectious diseases. The proposed SIR-Poisson model is able to predict the range of the infected cases in a future period. More precisely, it is used to infer the transmission of the COVID-19 in the three Maghreb Central countries (Tunisia, Algeria, and Morocco). Using the SIR-Poisson model and based on daily reported disease data, since its emergence until end April 2020, we attempted to predict the future disease period over 60 days. The estimated average number of contacts by an infe

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