A content of this thesis is an analysis of a steel castings production mismatch in Tatra Metallurgy foundry. Most common defects found on castings are gas holes. Thesis shows a theoretical research on casting defects caused by gases in steel castings. It also describes melting and degassing problematics of molten steel and procedures to reduce gas contents. The last part of theoretical research describes oxidation of steel in contact with silica mold. Practical part of thesis analysis production of steel castings to determine the cause of origin of gas holes. Main attention is paid to mold mixture, chemical composition, casting temperatures and casting time. The last chapter metallographically analysis two defected castings to determine the origin of defects. Main attention is paid to shape and chemical composition of gas holes and to morphology of steel inclusions.

In all organisms are rising a reactive oxygen and nitrogen species by the effects of various stress factors and these species have a negative impact on the organism. Due to this species plants have built up an efficient antioxidant system, that helps them to resist negative effects of reactive oxygen and nitrogen species. In this work was researched the effect of hydrogen peroxide and sodium benzoate on the production of hydrogen peroxide, superoxide, reactive nitrogen species and malondialdehyde, contained in the root and above-ground part of maize (Zea mays L.). By use of the fluorescence microscopy there were obtained images of cross-cut of root from which was determined the intensity of fluorescence of individual parts of the root and was examined the effect of the intensity of fluorescence markers of oxidative stress in dependence on the type of the fluorescence filter used.

The main of the master’s thesis is to determine current opportunities of investment life insurance in the Czech Republic. In the theoretical part of the thesis, the basic concepts of life insurance and investment life insurance are specified. In the practical part, the statistical analysis of insurance market development in the Czech Republic is presented. The work further provides the comparison of investment life insurance products of selected insurance institutions. The comparison is made on the basis of selected parameters and model examples. Conclusions and recommendations of this study could help to everybody participated in pension system and with a focus on modern form of life insurance.

The objective of this diploma thesis is to state the influence of porosity, DAS and shape factor on mechanical characteristics for the cast transmission cover made in Kovolit Modrice a.s.Which was molten from AlSi9Cu3. It was used an imaging software (Olympus Five) for classification of metallographical structures.

The objective of this diploma thesis is an evaluation of die-castings porosity, eventually the evaluation of seats with local squeeze in connection with their mechanical and structural properties. The swatches of alloy AISi9Cu3 were taken from the engine block made by  koda Auto Company, Mlad Boleslav. To the evaluation and the comparison of the results there were the other VUT FSI Brno students´ theses used.

Aim of this work is to study the impact of inaccuracy in execution of required task (inaccuracy in subject’s behavioral response to experimental stimulation) by person who undergoes fMRI examination. The work is solved in several stages. First, theoretical analysis of inaccuracy in fMRI experiment was performed, and simulations with synthetic data were created. Several variables in general linear model and t-statistics were followed. We found that estimated effect size depends linearly on covariance between the corresponding columns of X and D matrices or their linear combination. The component of residual variance caused by inaccuracy is negligible at real-life noise levels. In such case, moreover, the dependence of t-statistics on inaccuracy becomes linear. Next, our theoretical results (dependencies/characteristics of variables) were verified using real data. All results were confirmed. Last, I focused on possible practical use of the uncovered characteristics and dependencies. Optimization of experimental design with respect to inaccuracy, correction of inaccurate results and reliability of inaccurate results are introduced and discussed. Especially, the calculation of maps of maximal tolerable inaccuracy can be useful to find robust or weak (tending to be not detected or to be significantly different from accurate value) activation in real fMRI experiments.

As in many other fields, biology is faced with enormous amounts ofdata that contains valuable information that is yet to be extracted. The field of proteomics, the study of proteins, has the luxury of having large repositories containing data from tandem mass-spectrometry experiments, readily accessible for everyone who is interested. At the same time, there is still a lot to discover about proteins as the main actors in cell processes and cell signaling. In this thesis, we explore several methods to extract more information from the available data using methods from statistics and machine learning. In particular, we introduce MaRaCluster, a new method for clustering mass spectra on large-scale datasets. This method uses statistical methods to assess similarity between mass spectra, followed by the conservative complete-linkage clustering algorithm.The combination of these two resulted in up to 40% more peptide identifications on its consensus spectra compared to the state of the art method. Second, we attempt to clarify and promote protein-level false discovery rates (FDRs). Frequently, studies fail to report protein-level FDRs even though the proteins are actually the entities of interest. We provided a framework in which to discuss protein-level FDRs in a systematic manner to open up the discussion and take away potential hesitance. We also benchmarked some scalable protein inference methods and included the best one in the Percolator package. Furthermore, we added functionality to the Percolator package to accommodate the analysis of studies in which many runs are aggregated. This reduced the run time for a recent study regarding a draft human proteome from almost a full day to just 10 minutes on a commodity computer, resulting in a list of proteins together with their corresponding protein-level FDRs. / <p>QC 20160412</p>

I investigate how to use machine learning to predict fuel consumption in heavy vehicles. I examine data from several different sources describing road, vehicle, driver and weather characteristics and I find a regression to a fuel consumption measured in liters per distance. The thesis is done for Scania and uses data sources available to Scania. I evaluate which machine learning methods are most successful, how data collection frequency affects the prediction and which features are most influential for fuel consumption. I find that a lower collection frequency of 10 minutes is preferable to a higher collection frequency of 1 minute. I also find that the evaluated models are comparable in their performance and that the most important features for fuel consumption are related to the road slope, vehicle speed and vehicle weight. / Jag undersöker hur maskininlärning kan användas för att förutsäga bränsleförbrukning i tunga fordon. Jag undersöker data från flera olika källor som beskriver väg-, fordons-, förar- och väderkaraktäristiker. Det insamlade datat används för att hitta en regression till en bränsleförbrukning mätt i liter per sträcka. Studien utförs på uppdrag av Scania och jag använder mig av datakällor som är tillgängliga för Scania. Jag utvärderar vilka maskininlärningsmetoder som är bäst lämpade för problemet, hur insamlingsfrekvensen påverkar resultatet av förutsägelsen samt vilka attribut i datat som är mest inflytelserika för bränsleförbrukning. Jag finner att en lägre insamlingsfrekvens av 10 minuter är att föredra framför en högre frekvens av 1 minut. Jag finner även att de utvärderade modellerna ger likvärdiga resultat samt att de viktigaste attributen har att göra med vägens lutning, fordonets hastighet och fordonets vikt.

<p>Bacteriophages are viruses that infect and kill bacteria. They are the most abundant organism on the planet and the largest source of untapped genetic information. Every year, more bacteriophages are isolated from the environment, purified, and sequenced. Once sequenced, their genomes are annotated to determine the location and putative function of each gene expressed by the phage. Phages have been used in the past for genetic engineering and new research is being done into how they can be used for the treatment of disease, water safety, agriculture, and food safety. </p> <p>Despite the influx of sequenced bacteriophages, a majority of the genes annotated are hypothetical proteins, also known as No Known Function (NKF) proteins. They are expressed by the phages, but research has not identified a possible function. Wet lab research into the functions of the hundreds of NKF phages genes would be costly and could take years. Bioinformatics methods could be used to determine putative functions and functional categories for these hypothetical proteins. A new bioinformatics method using algorithms such as Domain Assignments, Hidden Markov Models, Structure Prediction, Sub-Cellular Localization, and iterative algorithms is proposed here. This new method was tested on the bacteriophage genome PotatoSplit and dropped the number of NKF genes from 57 to 40. A total of 17 new functions were found. The functional class was identified for an additional six proteins, though no specific functions were named. Structure Prediction and Simulations were tested with a focus on two NKF proteins within lytic phages and both returned possible functional categories with high confidence.</p> <p>Additionally, this research focuses on the possibility of phage therapy and FDA regulation. A database of phage proteins was built and tested using R Statistical Analysis to determine proteins significant to phage infecting <i>M. tuberculosis</i> and to the lytic cycle of phages. The statistical methods were also tested on both pharmaceutical products recalled by the FDA between 2012 and 2018 to determine ingredients/manufacturing steps that could affect product quality and on the FDA Adverse Event Reporting System (FAERS) data to determine if AERs could be used to judge the quality of a product. Many significant excipients/manufacturing steps were identified and used to score products on their quality. The AERs were evaluated on two case studies with mixed results. </p>

<div>Shock-wave/turbulent boundary-layer interactions (SWTBLIs) are characterized by low-frequency unsteadiness, amplified aerothermal loads, and a complex three-dimensional flowfield. Presence of a broad range of length and time-scales associated with compressible turbulence generates additional gasdynamic features that interact with different parts of the flowfield via feedback mechanisms. Determining the physics of such flows is of practical importance as they occur frequently in different components of a supersonic/hypersonic aircraft such as inlets operating in both on- and off-design conditions, exhaust nozzles, and control surfaces. SWTBLIs can cause massive flow separation which may trigger unstart by choking the flow in an inlet. On control surfaces, fatigue loading caused by low-frequency shock unsteadiness, coupled with high skin-friction and heat transfer at the surface, can result in failure of the structure.</div><div><br></div><div>The objective of this study is twofold. The first aspect involves examining the causes of unsteadiness in SWTBLIs associated with two geometries – a backward facing step flow reattaching on to a ramp, and a highly confined duct flow. Signal processing and statistical techniques are performed on the results obtained from Delayed Detached-Eddy Simulations (DDES) and Implicit Large-Eddy Simulations (ILES). Dynamic Mode Decomposition (DMD) is used as a complement to this analysis, by obtaining a low-dimensional approximation of the flowfield and associating a discrete frequency value to individual modes. </div><div><br></div><div>In case of the backward facing step, Fourier analysis of wall-pressure data brought out several energy dominant frequency bands such as separation bubble breathing, oscillations of the reattachment shock, shear-layer flapping, and shedding of vortices from the recirculation zone. The spectra of reattachment shock motion suggested a broadband nature of the oscillations, wherein separation bubble breathing affected the low-frequency motion and shear-layer flapping, and vortex shedding correlated well at higher frequencies. A similar exercise was carried out on the highly confined duct flow which featured separation on the floor and sidewalls. In addition to the low-frequency shock motions, the entire interaction exhibited a cohesive back-and-forth in the streamwise direction as well as a left-right motion along the span. Mode reconstruction using DMD was used in this case to recover complex secondary flows induced by the presence of sidewalls.</div><div><br></div><div>For the final aspect of this study, a flow-control actuator was computationally modeled as a sinusoidally varying body-force function. Effects of high-frequency forcing at F⁺ =1.6 on the flowfield corresponding to a backward facing step flow reattaching on to a ramp were examined. Conditionally averaged profile of streamwise velocity fluctuations, based on reattachment shock position, was used for the formulation of spatial distribution of the actuator. The forcing did not change the mean and RMS profiles significantly, but affected the unsteadiness of the interaction significantly. The effects of forcing were localized to the recirculation zone and did not affect the evolution of the shear-layer. The acoustic disturbances propagating through the freestream and recirculation zone drove the motion of the reattachment shock, and did not alter the low-frequency dynamics of the interaction.</div>