Transport infrastructure and economic development in least developed countries: the case of Afghanistan

This PhD thesis tries to show the impact of transport infrastructure in economic development in least developed countries and in particular in the case of Afghanistan. Some least developed countries during 1990 to 1999 experienced lack of investment in transportation. Lack of investment further increased the economic development gap between developed and least developed countries. Moreover, lack of literature and research in poor countries such as Afghanistan encouraged me to do my research in this country in order to unveil the problems, facing poor people who are living in inaccessible places and suffer from lack of economic opportunities and long term unemployment. This thesis shows the effect of inaccessibility and immobility in economic opportunities and basic social services in Afghanistan. This thesis is important because it covers the role of transport infrastructures at the moment that international community promised to rebuild the infrastructures of post conflict Afghanistan.

Regularization methods for the solution of a nonlinear least-squares problem in tomography

In this work we study a polyenergetic and multimaterial model for the breast image reconstruction in Digital Tomosynthesis, taking into consideration the variety of the materials forming the object and the polyenergetic nature of the X-rays beam. The modelling of the problem leads to the resolution of a high-dimensional nonlinear least-squares problem that, due to its nature of inverse ill-posed problem, needs some kind of regularization. We test two main classes of methods: the Levenberg-Marquardt method (together with the Conjugate Gradient method for the computation of the descent direction) and two limited-memory BFGS-like methods (L-BFGS). We perform some experiments for different values of the regularization parameter (constant or varying at each iteration), tolerances and stop conditions. Finally, we analyse the performance of the several methods comparing relative errors, iterations number, times and the qualities of the reconstructed images.

A systematic review of the survival and complication rates of implant-supported fixed dental prostheses (FDPs) after a mean observation period of at least 5 years

The objective of this systematic review was to assess the 5- and 10-year survival of implant-supported fixed dental prostheses (FDPs) and to describe the incidence of biological and technical complications.

Rifaximin treatment for the irritable bowel syndrome with a positive lactulose hydrogen breath test improves symptoms for at least 3 months

While rifaximin was able to improve symptoms in patients with irritable bowel syndrome (IBS) in phase III trials, these results are yet to be repeated in phase IV studies.

Classification Using Generalized Partial Least Squares

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression based on a previous approach, Iteratively ReWeighted Partial Least Squares, i.e. IRWPLS (Marx, 1996). We compare our results with two-stage PLS (Nguyen and Rocke, 2002A; Nguyen and Rocke, 2002B) and other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying bias correction to the likelihood to avoid (quasi)separation, we often get lower classification error rates.