Tese de mestrado. Engenharia Electrotécnica e de Computadores (Área de especialização de Informática e Automação). Faculdade de Engenharia. Universidade do Porto. 2006

Estágio realizado no CERN, Suiça e orientado pelo Eng.º Ronny Billen e Chris Roderick / Tese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 2008

Tese de doutoramento. Engenharia Mecânica. Faculdade de Engenharia. Universidade do Porto. 2009

All cellular forms of life contain Deoxyribonucleic acid (DNA). DNA is a molecule that carries all the information necessary to perform both, basic and complex cellular functions. DNA is replicated to form new tissue/organs, and to pass genetic information to future generations. DNA replication ideally yield an exact copy of the original DNA. While replication generally occurs without error, it may leave DNA vulnerable to accidental changes via mistakes made during the replication process. Those changes are called mutations. Mutations range in magnitude. Yet, mutations of any magnitude range in consequences, from no effect on the organism, to disease initiation (e.g. cancer), or even death. In this thesis, we limit our focus to mutations in human DNA, and in particular, MMBIR mutations. Recent literature in human genomics has found Microhomology-mediated break-induced replication (MMBIR) to be a common mechanism producing complex mutations in DNA. MMBIRFinder is a tool to detect MMBIR regions in Yeast DNA. Although MMBIRFinder is successful on Yeast DNA, MMBIRFinder is not capable of detecting MMBIR mutations in human DNA. Among several reasons, one major reason for its deficiency with human DNA is the amount of computations required to process human large data. Our contribution in this regard is two fold: 1) We utilize parallel computations to significantly reduce the processing time consumed by the original MMBIFinder, and address several performance degrading issues inherent in the original design; 2) We introduce a new heuristic to detect MMBIR mutations that were not detected by the original MMBIRFinder, even in the case of small sized DNA, like Yeast DNA.

Large investments in sparsely populated areas have the reputation to increase the attractiveness of these areas. This is said to lead to more investments, followed by growths in population, unemployment rates and tax revenues. Usually, city officials in Sweden trust in this formula and have the tendency to provide financial and other incentives for companies to start large investment projects in their towns (Hrelja, Isaksson, and Richardson, 2012). This thesis investigates the actual impact of such projects by the example of the large retail chain IKEA in Sweden. The development of three medium-sized cities in sparsely populated areas in Sweden which recently opened IKEA stores are compared to close-by cities and benchmark cities of a comparable size with respect to their unemployment rates, income, population, retail sales and trade indexes. The results will be used to predict the impact of the new IKEA store planned in Umeå.

This dissertation introduces a novel three-dimensional pseudo-rigid-body model (3-D PRBM) for straight cantilever beams with rectangular cross sections. The model is capable of capturing the behavior of the neutral axis of a beam loaded with an arbitrary force end-load. Numerical integration of a system of differential equations yields approximate displacement and orientation of the beam's neutral axis at the free end, and curvatures of the neutral axis at the fixed end. This data was used to develop the 3-D PRBM which consists of two torsional springs connecting two rigid links for a total of 2 degrees of freedom (DOF). The 3-D PRBM parameters that are comparable with existing 2-D model parameters are characteristic radius factor (mean: γ = 0.8322), bending stiffness coefficient (mean: KΘ = 2.5167) and parametric angle coefficient (mean: cΘ = 1.2501). New parameters are introduced in the model in order to capture the spatial behavior of the deflected beam, including two parametric angle coefficients (means: cΨ = 1.0714; cΦ = 1.0087). The model is verified in a few locations using ANSYSTM and its use in the design of compliant mechanisms is illustrated through spatial compliant versions of crank slider and double slider mechanisms.

Recent scientific evidence shows the incredible potential of the brain to grow and change. Students with a growth mindset view errors and obstacles as opportunities for growth. These students welcome challenges and the opportunity to learn from their mistakes. Although some university instructors are incorporating growth mindset into their lectures, attitudes, and exams in small classes, the traditional exam method used in large lecture undergraduate mathematics classrooms follows a fixed mindset model. The growth mindset structured assessments developed for this study incorporate a testing center portion (matching, short answer, fill in the blank and free response) with structured rework opportunities, a written portion with peer reviews, and a group portion. The purpose of this study was to understand the relationship between (1) large lecture college algebra undergraduate growth mindset structured assessments and (2) students’ achievement, drop/fail/withdraw rates, mindsets, and anxiety. This relationship is determined using the final exam scores, the withdraw and fail rates, and the responses from a Likert scale survey and a Qualtrics free response survey. No statistically significant difference in mean final exam scores was found, however, withdrawal and fail rates were lower for the class participating in the growth mindset structured assessments than the control classes. Lower levels of math test anxiety and higher levels of growth mindset were demonstrated in the class participating in the growth mindset structured assessments.

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This thesis examines and identifies the problems of shape collapse in large deformation image registration. Shape collapse occurs in image registration when a region in the moving image is transformed into a set of near zero volume in the target image space. Shape collapse may occur when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image. We state that shape collapse is a problem in image registration because it may lead to the following consequences: (1) Incorrect pointwise correspondence between different coordinate systems; (2) Incorrect automatic image segmentation; (3) Loss of functional signal. The above three disadvantages of registration with shape collapse are illustrated in detail using several examples with both real and phantom data. Shape collapse problem is common in image registration algorithms with large degrees of freedom such as many diffeomorphic image registration algorithms. This thesis proposes a shape collapse measurement algorithm to detect the regions of shape collapse after image registration in pairwise and group-wise registrations. We further compute the shape collapse for a whole population of pairwise transformations such as occurs when registering many images to a common atlas coordinate system. Experiments are presented using the SyN diffeomorphic image registration algorithm and diffeomorphic demons algorithm. We show that shape collapse exists in both of the two large deformation registration methods. We demonstrate how changing the input parameters to the SyN registration algorithm can mitigate the collapse image registration artifacts.

Large datasets require efficient processing, storage and management to efficiently extract useful information for innovation and decision-making. This dissertation demonstrates novel approaches and algorithms using virtual memory approach, parallel computing and cyberinfrastructure. First, we introduce a tailored user-level virtual memory system for parallel algorithms that can process large raster data files in a desktop computer environment with limited memory. The application area for this portion of the study is to develop parallel terrain analysis algorithms that use multi-threading to take advantage of common multi-core processors for greater efficiency. Second, we present two novel parallel WaveCluster algorithms that perform cluster analysis by taking advantage of discrete wavelet transform to reduce large data to coarser representations so data is smaller and more easily managed than the original data in size and complexity. Finally, this dissertation demonstrates an HPC gateway service that abstracts away many details and complexities involved in the use of HPC systems including authentication, authorization, and data and job management.