Located at the North-Eastern region of Algeria, the coastal aquifer of Taleza has a potential storage of groundwater. It contains hundreds of private wells installed by the population for several purposes including drinking water. Recently, the groundwater has become quite salinized. Furthermore, some people have given rise to the presence of a terrible odor, which smells as rotten eggs, in the groundwater pumped from their wells. Our first provided answer mentions that groundwater quality is mostly controlled by two factors: 1) the natural processes such as aquifer’s lithology, and soil/rock interactions with groundwater, and 2) anthropogenic activities such as agricultural production, industrial growth, urbanization with increasing exploitation of water resources, and private sanitation systems. However, the anthropogenic activities can be considered as the most serious sources of groundwater contamination. Unfortunately, most of Taleza’s people don’t have any idea about the anthropogenic groundwater contamination, and their raised question is « Is there still a chance to save the groundwater in Taleza aquifer? ». An urgent, efficient and durable groundwater protection planning is required in order to limit, and potentially improve the effects of the anthropogenic sources on groundwater quality within aquifer. In fact, determining the origin of saline components in aquifer of Taleza may provide more precision about the development of salinization process, and constitutes a useful tool to propose alternative solutions for ensuring the long-term protection of water resources. Accordingly, it is planned to perform a Multi-tracer study, combining isotopic and hydro-geochemical analysis, to identify the origin and fate of groundwater salinization. But firstly, the studying process will be started by reviewing the potential anthropogenic sources of groundwater contamination related to the Taleza territory. Our proposed communication describes a review of these potential anthropogenic sources, which potentially have led to the degradation of groundwater quality.

Recently, the identification of an individual race has become an important research topic in face recognition systems, especially in large-scale face images. In this paper, we propose a new large-scale race classification method which combines Local Binary Pattern (LBP) and Logistic Regression (LR) on Spark framework. LBP is used to extract features from facial images, while spark’s logistic regression is used as a classifier to improve the accuracy and speedup the classification system. The race recognition method is performed on Spark framework to process, in a parallel way, a large scale of data. The evaluation of our proposed method has been performed on two large face image datasets CAS-PEAL and Color FERET. Two major races were considered for this work, including Asian and Non-Asian races. As a result, we achieve the highest race classification accuracy (99.99%) compared to Linear SVM, Naive Bayesian (NB), Random Forest(RF), and Decision Tree (DT) Spark’s classifiers. Our method is compared against different state-of-the-art methods on race classification, the obtained results show that our approach is more efficient in terms of accuracy and processing time.

In this work, we propose a secure version of ElGamal public key cryptosystem, and prove that it is semantically secure assuming the hardness of what we call the two-dimensional decisional Diffie-Hellman (2DDDH) problem, this cryptosystem is distinguished by the speed of encryption and decryption processes and by its resistance to active adversaries. Since the 2DDDH problem is harder than the decisional Diffie-Hellman (DDH) problem (as it will be seen), one may conclude that our model reinforces the exchange security compared to the existed cryptosystems falling within the same context, also we discuss the difficult problems that guarantee its security.