Modélisation de l’écoulement des eaux souterraines de l’aquifères quaternaire des palmeraies de Figuig et des plaines de Tisserfine, El Arja (Haut Atlas Oriental, Maroc)

Le présent travail a pour objectif la modélisation hydrodynamique de la nappe Quaternaire de Figuig ; généralement la nappe circule dans des alluvions limoneuses et des calcaires travertineux du Quaternaire avec un substratum imperméable constitué des marnes du Jurassique. La zone d’étude est caractérisée par un climat désertique. La modélisation hydrogéologique a été mise en œuvre par le logiciel MODFLOW. La nappe a été simulée en régime permanent et transitoire ; elle résout l’équation gouvernante de l’écoulement des eaux souterraines en milieux poreux continus et elle calcule la charge hydraulique par simulation des éléments comme : drain, puits, oued, etc. Le modèle numérique ainsi élaboré a permis d’estimer le bilan des aquifères des palmeraies de Figuig et de la plaine de Tisserfine-El Arja dans les deux cas : les conditions actuelles et le scénario de gestion simulant l’élimination des apports des barrages de Sfissif et de Rkiza, par l’oued Tisserfine jusqu’à l’oued Zouzfana.   The current study aims at providing a hydrodynamic model of the Quaternary water aquifer in Figuig; the water aquifer generally flows in a silty alluvial soil and travertine limestone of the Quaternary with an impermeable substratum of the Jurassic marl. The area of the study is characterised by a desert climate. The hydrogeological modelling was carried out using the MODFLOW software. The groundwater was simulated in permanent and transient regime; it solves the governing equation of groundwater flow in continuous porous media and calculates the hydraulic load by simulating elements such as: drain, well, river... Therefore, the digital model developed allowed us to estimate the aquifer balance of Figuig palm groves and Tisserfine-El Arja plain in the two cases: the current conditions and the management scenario simulating the elimination of the inflows from the Sfissif and Rkiza dams, through the Tisserfine wadi to the Zouzfana wadi

Prioritization of customers’ preferences in Islamic banking system: an artificial intelligence approach using Kano analysis

Purpose: There are variety of factors that influence a customer’s selection of a bank in general. However, there is a large gap in the literature that covers a customer decision to choose between an Islamic bank and a conventional one. To this end, we try to fill this gap by using a case study in Morocco to analyse factors contributing to a consumer’s bank selection. Methodology: The analysis presented in the paper is using a case in Morocco and applying an artificial intelligence method using KANO analysis. We apply it in three stages. First, customers preferences are identified and classified according to their impact on customer’s satisfaction. Second, a Satisfaction Increasing Index (SII) is formulated. Third a Dissatisfaction Decreasing Index (DII) is formulated. Findings: The analysis shows that Islamic banking attributes (Provision of profit-loss sharing financing , Operating on Islamic law and principles, Staff knowledge of Islamic banking, Provision of interest-free loans) are required by customers in selecting an Islamic bank as opposed to a conventional bank. However, these requirements do not necessarily contribute to increasing customers’ satisfaction. Significance: To the best of our knowledge, this is perhaps the first paper which uses a Kano analysis in the context of consumers selection of an Islamic or Conventional bank Research Limitations/Implications: This Paper has the main limitation of being conducted only in Morocco. It will be interesting to see how the results would change if the country context is changed. Practical and Social Implications: The findings using these techniques, should help financial institution, whether it be Islamic or conventional banks to tailor their offerings to match consumers requirements. KAUJIE Classification: L3, JEL Classification: D12, N3

Towards new framework for modelling project selection in crowdfunding platforms

Crowdfunding is a form of public funding via the internet to the entrepreneurs. The crowdfunding covers various models that range from simple donations to risky investments. The aim of this paper is to introduce a new framework for modeling project selection in crowdfunding platform using two artificial intelligence approaches: fuzzy logic and agent-based simulation. We propose a new model based on both donation and interest-free lending and we compare its performance with a pure donation

Entrepreneurial Bankruptcies and Moral Hazards at the times of prosperity and Crisis: An Artificial Intelligence Model Application to PLS and Debt Financing

In this paper we try to compare the performnace of profit and loss sharing contract and debt contracts in the face of economic crisis such as Covid 19. Our approch relies on an artificial intelligence model using Netlogo to predict the probability of defaults (banckrupcies) of entrepreneurial contracts. We have found simulation evidence that PLS contracts have lower number of defaults than debt contracts during a crisis. The fact that PLS contracts provide the advantage of sharing losses reduces the chances of banckrupcies compared to debt contracts where the entreprenurs bears wholly the risk of projects failure. On the other hand we found that Debt contracts provide less banckrupcies during normal conditions. This suggest that failure of PLS contracts is not only due to economic conditions but to high level of moral hasard

A performant deep learning model for sentiment analysis of climate change

International audienceClimate change is one of the most trend topics of the decade in the world. The recent years were the warmest in 139 years, however identifying deniers and believers of this subject still a very big issue. The challenge is to have an efficient tool to detect deniers in order to deploy the appropriate strategy facing this phenomenon. Moreover, Bidirectional Encoder Representations from Transformers (BERT) pre-trained model has taken Natural Language Processing tasks results so far. In this paper we presented an efficient technological tool based on deep learning model and BERT model for detecting people's opinions on climate change on social media platforms. We used convolutional neural network targeting the public opinions on climate change on Twitter. The results showed that our model outperforms the machine learning approaches: Naive Bays, Support Vector Machine and Logistic Regression. This model is able to analyze people's behavior and detect believers and deniers of this disaster with high accuracy results (98% for believers and 90% for deniers). Our model could be a powerful citizen sensing tool that can be used by governments for monitoring and governance, especially for smart cities

A gallbladder tumor revealing metastatic clear cell renal carcinoma: report of case and review of literature

Abstract Metastatic renal cell carcinoma in the gallbladder is extremely rare, with reported frequencies of less than 0.6% in large autopsy reviews. Only 40 cases were reported in the literature. We report a first case of gallbladder polypoid tumor revealing metastatic clear cell renal cell carcinoma, which demonstrates the importance of radiological tests, histology and immunohistochemistry when making a definitive diagnosis. These examinations also allow differentiating metastatic clear cell renal cell carcinoma from other polypoid lesions in the gallbladder with clear cell morphology. Cholecystectomy should be performed to obtain a definitive diagnosis and to improve survival in case of solitary metastatic renal cell carcinoma. Virtual slides The virtual slides’ for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/8956897238238989</p