Paper Title
Hard water forms compact and adherent deposit to the walls of pipes and industrial or domestic installations. Several studies have been done in order to reduce the adverse scaling consequences and to achieve effective softening, but most of inhibitors used are chemical compounds that have damaging effects on health and the environment. There for we have used in this work an under natural alimentary product as scaling inhibitor. Our study focuses on softening hard water of Bounouara having a hard

Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that “look like” good communities for the application of interest.In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior. Keywords: Numerical, algorithms, Conductance, Investigation, Community Detection, Flow-Based Methods.