The objective of this Thesis is to devise a system for the "rheology-based design" of non-settling (homogeneous) slurry pipelines that is more conducive to application by practicing engineers without impairing its accuracy or utility for research purposes. The cornerstone is the development of a new rheological model and constitutive equation for homogeneous slurry based on the aggregation/deaggregation of the suspended mineral particles. This “yield plastic” model is shown to describe a family of models that includes the Newtonian, Bingham plastic and Casson models as special cases. It also closely approximates the results of many consistency models, including power law, yield power law, Cross and Carreau-Yasuda. The yield plastic model is then used to develop design equations to determine the pressure-gradient of laminar and turbulent pipe flow. A relative energy dissipation criterion is proposed for the laminar-turbulent transition and shown to be consistent with currently used transition models for Newtonian and Bingham fluids. Finally, a new dimensionless group (the “stress number”) is proposed that is directly proportional to the pressure-gradient and independent of the velocity. When the design equations are presented graphically in terms of the stress number and the plastic Reynolds number, the resulting “design curve diagram” is shown to be a dimensionless (pressure-gradient vs. velocity) pipe flow curve. The net result is that the hydraulic design of homogeneous slurry systems only requires the use of a single constitutive equation and three engineering design equations. The results are presented in a conceptually easy form that will foster an intuitive understanding of non-Newtonian pipe flow. This will assist engineers to understand the impact of slurry rheology when designing, operating and troubleshooting slurry pipelines and, in the future, other slurry related processes. / Applied Science, Faculty of / Mining Engineering, Keevil Institute of / Graduate

No description available.

No description available.

The elderly social care IT company Phoniro is developing solutions for deploying software using docker technologies. To secure quality in their deploy pipelines Phoniro would like to do automatic selenium testing within Docker containers. The project should set the framework and required technical solution to enable this and also run some basic test including suitable reporting on test success. This is a concept that is new to Phoniro that would allow them, if proven, to automate testing in a safe environment, and pushing their web applications to deployment faster. The purpose is to prove this concept by creating a framework that will easily let the user run web applications inside a Docker container. Then run this framework inside of a pipeline to see the testing capabilities. The user is supposed to be able to do this without any previous knowledge of how docker works. We have developed a framework in Python that enables the user to build and run their web application inside a docker container. We built a pipeline and connected it to the repository with the source code for the web application. The framework is run inside the pipeline to start the container, followed by simple selenium tests that we created to test the concept. We have proven the concept to run and test web applications in docker containers inside of pipelines to work. It is possible connect a web application in development to a repository, connect it to a pipeline and have it automatically test the application every time a change is made to the source code. By designing proper selenium tests the pipeline can save a lot of time and effort that is otherwise spent on manual testing

RNA-Seq (Whole Transcriptome Shotgun Sequencing) provides an ideal platform to study the complete set of transcripts for a specific developmental stage or physiological condition. It reveals not only expression-level changes, but also structural changes in the coding sequences, including gene rearrangements. In this dissertation, I present my contributions to the development of computational tools for the robust and efficient analysis of RNA-seq data to support cancer research. To automate the laborious and computationally intensive procedure of RNA-seq data management, I worked on the development of Hydra, an RNA-seq pipeline for the parallel processing and quality control of large numbers of samples. With user-friendly reports on quality control and running checkpoints, Hydra makes the data processing procedure fast, efficient and reliable. Here, I report my application of the pipeline to the analysis of patient-derived lymphoma xenograft samples, to show Hydra’s ability to detect abnormalities (e.g., mouse tissue contamination) in the sequencing data. Because fusions play an important role in carcinogenesis, fusion detection has become an important area of methodological research. Several computational methods have been developed to identify fusion transcripts from RNA-seq data. However, all these methods require realignment to the transcriptome, a computationally expensive task, unnecessary in many cases. Here, I present QueryFuse, a novel gene-specific fusion-detection algorithm for aligned RNA-seq data. It is designed to help biologists find and/or computationally validate fusions of interest quickly, and to annotate the detected events with visualization and detailed properties of the supporting reads. By focusing the fusion detection on read pairs aligned to query genes, we can not only reduce realignment time, but also afford to use a more accurate but computationally expensive local aligner. In the extensive evaluation I performed, I obtained comparable or better results compared with two widely adopted tools (deFuse and TophatFusion) on two simulated datasets, as well as on cell line datasets with known fusions. Finally, I contributed to the identification of a novel fusion event in lymphoma, with potential therapeutic implications in clinical samples. I validated this fusion in silico by my putative reference method before experimental validation.

No description available.

No description available.

No description available.

[pt] Este trabalho apresenta uma metodologia para a determinação da localização ótima de suportes em linhas de tubulação de centrais nucleares, visando minimizar o número de suportes empregados. Incorporam-se a esta metodologia os critérios e restrições de projeto descritos em normas e relatórios técnicos específicos para sistemas de tubulações de centrais nucleares. Por exemplo, a localização dos suportes estruturais deve ser efetuada de tal forma que as tensões atuantes nos elementos da linha de tubulação, devido aos vários carregamentos impostos, estejam dentro dos limites especificados no código da ASME- American Society of Mechanical Engineers e da ANSI- American National Standards Institute e que os deslocamentos da linha de tubulação não excedam o valor do deslocamento máximo admissível. A etapa de análise para obtenção dos esforços e deformações é realizada utilizando-se um programa de elementos finitos, KWUROHR, desenvolvido pela Siemens especificamente para análise de tubulações. Empregam-se na modelagem dos tubos elementos de viga. A partir de uma análise preliminar formula-se o problema de otimização topológica com restrições de geometria, tensões e deslocamentos. A solução deste problema se dá empregando-se técnicas de programação matemática, mais especificamente, programação quadrática recursiva. Os esforços e deformações finais são obtidos por meio de nova análise para o sistema otimizado. / [en] This work presents a methodology for the determination of the optimal support locations for nuclear power plant pipelines. The objective here is to minimize the number of supports taking into account the specific design criteria present in the codes and regulatory guides for these special structures. The stress analysis is performed with a finite element program, KWUROHR, developed by Siemens specifically for the analysis of pipelines. On the tube modeling beam elements are employed. From a preliminary stress analysis performed on a trial structure, the topology optimization problem is formulated. Stresses and displacements, as well as the sensitivity analysis, obtained for this structure are the inputs for the optimization procedure. The solution of the optimization problem is obtained with mathematical programming techniques, more specifically with recursive quadratic programming. Final stresses and deformations are obtained through reanalysis of the optimal structure. / [es] Este trabajo presenta una metodología para la determinación de la localización óptima de los soportes, en líneas de tuberías de las centrales nucleares, minimizando el número de soportes empleados. Se Incorporan a esta metodología los criterios y restricciones del proyecto, que están descritos en las normas e informes técnicos específicos para sistemas de tuberías de centrales nucleares. Por ejemplo, la localización de los soportes extructurales debe ser efectuada de tal forma que las tensiones que actúan en los elementos de la línea de tubería, debido a las cargas impuestas, estén dentro de los límites especificados en el código de la ASME-American

<p> In this thesis we develop and evaluate automated "expert systems" for two applications: (i) gas/oil pipeline inspection using magnetic flux leakage information, (ii) treatment efficacy prediction and medical diagnosis using electroencephalograph (EEG) and clinical information. Both applications share the same methodology and procedure as they employ machine learning methods which learn their decision models using the training data (or past examples in real life/environment).</p> <p> The magnetic flux leakage (MFL) technique is commonly used for nondestructive testing (NDT) of oil and gas pipelines which are mostly buried underground. This testing involves the detection of metal defects and anomalies in the pipe wall, and the evaluation of the severity of these defects. The difficulty with the MFL method is the extent and complexity of the analysis of the MFL images. In this thesis we show how modern machine learning techniques can be used to considerable advantage in this respect.</p> <p> The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, an automated medical expert system is designed and then evaluated. The system is capable of predicting the treatment response for each individual patient at the outset of a therapy (i.e., using pre-treatment information) thus improving therapeutic efficiency and reducing personal and economic costs. Our experiments are focused on treatment planning and diagnosis of mood disorders and psychiatric illnesses. Through different experiments, we have shown that it is possible to predict treatment efficacy of a 'selective serotonin reuptake inhibitor' (SSRI) antidepressant and 'repetitive transcranial magnetic stimulation' (rTMS) therapies for patients with treatment-resistant major depressive disorder (MDD) or major depression. The predictions are based on pre-treatment quantitative EEG measurements. Also, prediction of post-treatment schizophrenia symptomatic scores, using pre-treatment EEG data, showed significant performance in patients treated with the drug clozapine. Clozapine is an antipsychotic medication of superior effectiveness in treating Schizophrenia but has several potentially severe side effects.</p> <p> Medical diagnosis is the second problem we consider in the neuroscience aspects of this thesis. In this research, an automated digital medical diagnosis methodology is developed to estimate/detect the type of a disease or illness that a patient is suffering. This intelligent diagnostic system can assist the physician/clinician by offering a second opinion on diagnosis. Several complex psychiatric illnesses may have many common symptoms and accurate diagnosis can, at times, be very difficult. Efficient diagnosis helps by avoiding prescription of wrong therapy /treatment to a patient. In our limited experiments, EEG data is used to make a diagnosis for distinguishing between various psychiatric illnesses including MDD, schizophrenia, and the depressed phase of bipolar affective disorder (BAD).</p> <p> In all problems considered in this thesis, specifically the neuroscience problem, a large number of candidate features are extracted from measurement data but most candidate features are found to be irrelevant and have little or no discriminative power. Finding a few most discriminating features that guarantee numerical efficiency and obtain a smooth and generalizable decision function, is a major challenge in this research. In this thesis, feature selection methods based on mutual information or Kullback-Leibler (KL) distance is employed to find the most statistically relevant features. For the multi-class diagnosis problem, to improve performance, a feature selection procedure denoted as feature combination feature selection is used which first finds discriminating features in all binary classification combinations, and then combines them into a larger feature subset to make a final multi-class decision. The two-dimensional (2D) representation of the feature data is also found to be useful for clustering analysis. The overall method was evaluated using a nested cross-validation procedure for which over 80% average prediction performance is obtained in all experiments. The results indicate that machine learning methods hold considerable promise in solving the challenging problems encountered in the two applications of concern.</p> / Thesis / Doctor of Philosophy (PhD)