Analysis of recent evolution of healthy life expectancy in the MENA region, Algeria

Introduction Healthy life expectancy is a significant indicator for assessing a population’s health status. It guides policymakers in designing efficient prevention strategies and global health programs. Furthermore, it enables comparisons of health status over time and space. Methods This paper examines the recent evolution of healthy life expectancy in the MENA region over the last two decades using the data from the Global Burden of Disease and some independent studies. Algeria has been given special consideration. Results The findings reveal two facts. First, while women live longer lives than men, men live healthier lives. Second, the MENA region is globally experiencing an expansion of morbidity. Nevertheless, Algeria enjoys better health conditions than the majority of MENA countries. Conclusion In the MENA region, there is a evident lack of data and research on healthy life expectancy. Thus, MENA countries are encouraged to strengthen their health information systems and provide independent national estimates of healthy life expectancy

Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors

Over the last few years, Multi-label classification has received significant attention from researchers to solve many issues in many fields. The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels at once. To overcome the manual annotation drawback, and to take advantages from the large amounts of unlabeled data, many semi-supervised approaches were proposed in the literature to give more sophisticated and fast solutions to support the automatic labeling of the unlabeled data. In this paper, a Collaborative Bagged Multi-label K-Nearest-Neighbors (CobMLKNN) algorithm is proposed, that extend the co-Training paradigm by a Multi-label K-Nearest-Neighbors algorithm. Experiments on ten real-world Multi-label datasets show the effectiveness of CobMLKNN algorithm to improve the performance of MLKNN to learn from a small number of labeled samples by exploiting unlabeled samples

Zirconium-Based Metal Organic Frameworks for the Capture of Carbon Dioxide and Ethanol Vapour. A Comparative Study

International audienceThis paper reports on the comparison of three zirconium-based metal organic frameworks (MOFs) for the capture of carbon dioxide and ethanol vapour at ambient conditions. In terms of efficiency, two parameters were evaluated by experimental and modeling means, namely the nature of the ligands and the size of the cavities. We demonstrated that amongst three Zr-based MOFs, MIP-202 has the highest affinity for CO2 (−50 kJ·mol−1 at low coverage against around −20 kJ·mol−1 for MOF-801 and Muc Zr MOF), which could be related to the presence of amino functions borne by its aspartic acid ligands as well as the presence of extra-framework anions. On the other side, regardless of the ligand size, these three materials were able to adsorb similar amounts of carbon dioxide at 1 atm (between 2 and 2.5 μmol·m−2 at 298 K). These experimental findings were consistent with modeling studies, despite chemisorption effects, which could not be taken into consideration by classical Monte Carlo simulations. Ethanol adsorption confirmed these results, higher enthalpies being found at low coverage for the three materials because of stronger van der Waals interactions. Two distinct sorption processes were proposed in the case of MIP-202 to explain the shape of the enthalpic profiles