The revolutionary technology blockchain began with cryptocurrencies like bitcoin but has since expanded beyond the worlds of finance and banking. One relatively unexplored application domain is medical and water waste. The production of medical/water waste is an integral part of healthcare operations. However, Health care and water waste management methods can also pose a health and environment risk if the various steps in the management process are not carried out correctly. The objective of this paper is to propose conception of a system, based on the Blockchain and IoT, that ensures the control of these wastes in order to effectively manage, coordinate and monitor their disposal. An initial design of this system and an evaluation of the system's performance are also presented.
Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.
The increase of mobile devices, the availability of several features, and the decrease in terms of cost of smartphones made them useful not only for communication, but also for learning. Similarly, the importance of English in higher education makes it one of the most fundamental foreign languages that students want to learn. Many students look for different ways to learn English. Among these ways is Mobile Learning which becomes nowadays a useful tool for language learning due to the evolution of mobile technology. However, little is known about the attitudes of Moroccan students towards English Mlearning. Thus, the purpose of this study is to examine the attitudes of using Mlearning by university students to learn English. The study involved 286 students representing different universities, levels, and departments. A questionnaire was distributed and interviews were carried out to answer the research questions. Results revealed that university students have positive attitudes towards English Mlearning. The total average mean score obtained for the used scale was M=3.80. Additionally, the qualitative findings confirmed some results of the quantitative data and displayed other important findings related to feelings and obstacles towards English Mlearning. The findings are of great importance for teachers, practitioners, administrators, and websites/apps programmers.
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