In this paper, we shall study the relationship between renewable energy, economic growth (GDP), carbon dioxide emissions and with control variable that are estimated into realized volatility and to verify if the EKC hypothesis is accepted or not. This study is focussed on the Algerian situation during the periods of 1995-2016 and we employed the VECM procedure and Granger causality to estimate the short and long-run coefficients. We found with VECM that an increase in carbon dioxide emissions, fossil energy consumption and production will raise the level of economic growth, while an increase in GDP, fossil energy consumption and production will upsurge the level of carbon dioxide emissions, but an increase in renewable energy consumption will reduce both GDP and carbon dioxide emissions. We concluded in the short-term that there's bidirectional causality between carbon dioxide emissions and GDP and there is unidirectional causality running from renewable energy consumption to carbon dioxide emissions.
The aim of this paper is to study, both theoretically and empirically, tourism as a channel of Migration and Development. Relaying on migration networks and trade literature, the study suppose that migration networks affect positively tourism flows to the origin countries. Theoretically, global migration networks effect on tourism is composed of migrant generations, transactions, preferences and emigrants’ life style effects. Such effects could adapt, promote and advertise tourism flows to origin countries. Empirically, the gravity model has been used to estimate the global effect of networks on Moroccan tourism inflows from the eight principal immigration countries during the periods (2000, 2010, 2011, 2012, 2013 and 2014). Our study reveals that a ten-percent rise in the emigration rate from Morocco increases the real value of Moroccan tourism inflows by 1.3 %.
Key words: Migration, Networks, Tourism, Development.
Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.
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