The focus of this essay is to demonstrate and analyze how Egypt responds strategically to massification issues in the context of the global knowledge economy and increasing national and international competition in the organization of its academic system. The analysis of the postsecondary system considers its size and structure, the diversity and core missions of different institutional types, degrees of institutional autonomy and academic freedom, mechanisms to expand equitable access and quality assurance, and finally, strategic and policy decisions and initiatives to address these realities.

Segmentation is one of the most important tasks in image processing. It classifies the pixels into two or more groups depending on their intensity levels and a threshold value. The classical methods exhaustively search the best thresholds for a spec image. This process requires a high computational effort, to avoid this situation has been incremented the use of evolutionary algorithms. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. This chapter introduces a multilevel thresholding (MT) algorithm based on the EMO and the Otsu's method as objective function. The combination of those techniques generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image reducing the number of iterations.