After the attacks of 9/11 Americans asked, `Why do they hate us so much?' The answer has been framed in terms of a range of `clashes', none of which has addressed emotion, which is at the centre of the question. Emotion, and particularly humiliation, has begun to be addressed within the literature of IR. Numerous scholars have highlighted the pervasiveness of a discourse of humiliation in the Middle East and its relationship to the swelling ranks of recruits who are willing to act as human bombs. The purpose of this article is to examine the emotional dynamics of this relationship. The first section undertakes a conceptual analysis of humiliation and betrayal. The second section explores how these emotions have been given coherent meaning in the narrative of Islamists from the region. This is followed by an historical analysis of how this narrative has provided a framework for giving meaning to a range of national, regional and international interactions, particularly since 1967, and has contributed to the emergence of Islam as the basis for transnational identity in what had become a highly secular region. Section three examines flaws in the logic of both militant Islamists and the US-led `War on Terrorism', arguing that both have exacerbated feelings of humiliation in the region rather than contributing to a restoration of dignity. The conclusion builds on the principle of human dignity to rethink the international approach to political violence.

TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract Artificial neural networks theory creates, with other theories and algorithms, a new science. This science deals with the human body as an excellent source, through which it can simulate some biological basics and systems, to be used in solving many scientific, and engineering problems. Neural networks are tested successfully in so many fields as pattern recognition or intelligent classifier, prediction, and correlation development. Recently, Neural network has gained popularity in petroleum applications. In this paper we applied this technique in PVT parameters determinations. The application interests in the estimation of the bubble point pressure through a designed neural network. As this value well estimated, it then used with other variables in a second network to determine oil FVF at this value of bubble point pressure. A comparison study between the performance of neural network and other published correlations has shown an excellent response with smallest absolute relative average error, and highest correlation coefficient for the designed networks among all correlations.

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