Ο σκοπός της παρούσας εργασίας είναι η ανάπτυξη ενός daemon που θα ελέγχει ένα σύνολο από virtual machines, και θα τα χρησιμοποιεί προκειμένου να εξομοιώνονται λειτουργίες δικτύων υπολογιστών. Κάθε ενεργό στιγμιότυπο ενός virtual machine θα έχει ασφαλώς το ρόλο ενός (εικονικού) υπολογιστή. Οι λειτουργίες που υλοποιεί ο κώδικας είναι η δημιουργία, ο έλεγχος και η μεταβολή ενός δικτύου εικονικών υπολογιστών, μέσω ενός μενού επιλογών, το οποίο ταυτόχρονα παρέχει πληροφορίες για κάθε εικονικό υπολογιστή. Η υλοποίηση του δικτύου γίνεται με τη χρήση του User Mode Linux (ενός Linux virtual machine)ενώ κάθε ενεργό UML στιγμιότυπο (UML instance) του virtual machine έχει το ρόλο ενός εικονικού υπολογιστή. Η ευελιξία που παρέχεται από τα Virtual Machines είναι τεράστια, και τα καθιστά ιδανικά για το δίκτυο που θα κάνουμε. Η συμπεριφορά του εξάλλου θα είναι όμοια με αυτή ενός πραγματικού δικτύου. / Creation of a daemon process, used to control a large number of Linux Virtual Machines and create a Virtual Machine network out of them.

M. Tech. Industrial Engineering / In industrial manufacturing systems, one often encounters situations in which motion in two, three or more hydraulic cylinders actuators need to be synchronized. The need for the design of a reconfigurable bending press machine (RBPM) control system prompted the research in the development of an automatic and synchronized system, suitable for the press tool operations, versatile in raising and thrusting of multiple- cylinders with odd numbers. The aim of this research is to design and develop a controller that will control all the modules of a reconfigurable bending press machine for bending box-type sheet metal components.

Understanding the relationship between genomic markers and complex disease could have a profound impact on medicine, but the large number of potential markers can make it hard to differentiate true biological signal from noise and false positive associations. A standard approach for relating genetic markers to complex disease is to test each marker for its association with disease outcome by comparing disease cases to healthy controls. It would be cost-effective to use control groups across studies of many different diseases; however, this can be problematic when the controls are genotyped on a platform different from the one used for cases. Since different platforms genotype different SNPs, imputation is needed to provide full genomic coverage, but introduces differential measurement error. In Chapter 1, we consider the effects of this differential error on association tests. We quantify the inflation in Type I Error by comparing two healthy control groups drawn from the same cohort study but genotyped on different platforms, and assess several methods for mitigating this error. Analyzing genomic data one marker at a time can effectively identify associations, but the resulting lists of significant SNPs or differentially expressed genes can be hard to interpret. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways reduces the dimensionality of the problem and could improve models by making them more biologically grounded and interpretable. The kernel machine framework has been proposed to model pathway effects because it allows nonlinear associations between the genes in a pathway and disease risk. In Chapter 2, we propose kernel machine regression under the accelerated failure time model. We derive a pseudo-score statistic for testing and a risk score for prediction using genes in a single pathway. We propose omnibus procedures that alleviate the need to prespecify the kernel and allow the data to drive the complexity of the resulting model. In Chapter 3, we extend methods for risk prediction using a single pathway to methods for risk prediction model using multiple pathways using a multiple kernel learning approach to select important pathways and efficiently combine information across pathways.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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Transposons are DNA segments that can move or transpose themselves to new positions within the genome of an organism. Biologists need to predict preferred insertion sites of transposons to devise strategies in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called Vstep, is of significant importance in the target-site selection process. We considered the Vstep profiles of insertion sites and developed predictors based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM). We trained our ANN and SVM predictors with the Sleeping Beauty transposonal data, and used them for identifying preferred individual insertion sites (each 12bp in length) and regions (each 100bp in length). Running a five-fold cross-validation showed that (1) Both ANN and SVM predictors are more successful in recognizing preferred regions than preferred individual sites; (2) Both ANN and SVM predictors have excellent performance in finding the most preferred regions (more than 90% sensitivity and specificity); and (3) The SVM predictor outperforms the ANN predictor in recognizing preferred individual sites and regions. The SVM has 83% sensitivity and 72% specificity in identifying preferred individual insertion sites, and 85% sensitivity and 90% specificity in recognizing preferred insertion regions.

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