The master thesis deals with a block subdivision into parcels using procedural modeling. The main goal focuses on improving of the existing method called OBB-based Subdivision that uses auxiliary envelopes of rectangle shape for splitting. It is required to adapt the method to Czech conditions as well. The first part focuses on the analysis of scientific publications, which describes the development and current situation of procedural modeling issues. Then the methodology of suggested algorithm is described that tests a change of the envelope to the trapezium shape, respectively to the quadrangle shape. In conclusion, the proposed algorithms are tested on the real data in CityEngine, which offered the possibility of using procedural approach to automatically generate 3D parcels. The whole process is controlled by the limitations of development plans of the selected urban areas. Powered by TCPDF (www.tcpdf.org)

With the acceleration of global urbanization trends, more and more intentions are put on multi-storey buildings. As the world leading area of wood construction, European countries started the construction of multi-storey timber building for a decade ago. However, unlike the traditional buildings made of reinforced concrete, the design of wooden high-rise timber buildings would face a substantial amount of new challenges because such high-rise timber buildings are touching the limitations of the timber engineering field. In this thesis, a parameterized three-dimensional FE-model (in ABAQUS) of a multi-storey timber frame building is created. Variable geometrical parameters, connection stiffness as well as boundary connections and applied wind and gravity loads are defined in a Python script to make it possible to analyze the influence of these parameters on the global structural behavior of the studied multi-storey timber frame building. The results and analysis implied that the script successfully worked and was capable to create different complex building geometries in an wasy way for the finite element analysis.

More and more companies use a REST architecture to implement applications for an easy to use API. One important quality attribute of an application is the performance. To be able to understand how the application will perform it is important to know how the selected framework perform. By testing the performance of different frameworks it will become easier for software developers to choose the right framework to achieve their requirements and goals. At the time when this paper was written the research in this area was limited. This paper answered the question of which framework between Express, .NET Core, Spring and Flask that had the best performance. To be able to know how frameworks performed the author needed to measure them. One way of measuring performance is with response time from the server. The author used a controlled experiment to collect raw data from which the results was drawn. The author found out that Spring had the best overall performance between the different categories. By analysing the results the author also found out that performance differed a lot between the frameworks in some categories.

Ce travail de thèse consiste au développement méthodologique et logiciel d'une méthode de docking moléculaire dite inverse. Cette méthode propose à travers le programme AMIDE — Automatic Inverse Docking Engine — de distribuer un grand nombres de simulations d'amarrage moléculaire sur des architectures HPC (clusters de calcul) avec les applications AutoDock 4.2 et AutoDock Vina. Le principe de cette méthode consiste à tester de petites molécules sur un ensemble de protéines cibles potentielles. Les paramètres optimaux ont été définis à partir d'une étude pilote et le protocole a été validé sur des ligands et peptides liants les protéines MMPs et EBP de la matrice extracellulaire. Cette méthode montre qu'elle permet d‘améliorer la recherche conformationnelle lors du calcul de docking sur des structures expérimentales par rapport à des protocoles existants (blind docking). Il est montré que le programme AMIDE permet de discriminer des sites de fixation privilégiés lors d'expériences de criblage inverse de protéines de manière plus performante que par blind docking. Ces résultats sont obtenus par la mise en place de méthodes de partitionnement de l'espace de recherche qui permettent également à travers un système de distribution hybride de déployer un ensemble de tâches indépendantes pour un traitement autorisant le passage d'échelle. / This work is a methodological and software development of so-called inverse molecular docking method. This method offers through an in house program AMIDE — Automatic Reverse Docking Engine — to distribute large numbers of molecular docking simulations on HPC architectures (com- puting clusters) with AutoDock 4.2 and AutoDock Vina applications. The principle of this method is to test small molecules on a set of potential target proteins. The program optimum parameters were defined from a pilot study and the protocol was validated on ligands and peptides binding MMPs and EBP extracellular matrix proteins. This method improves the conformational search in docking computation on experimental structures compared to existing protocols (blind docking). It is shown that the AMIDE program is more efficient to discriminate preferred binding sites in inverse proteins screening experiments than blind docking. These results are obtained by the implemen- tation of methods for partitioning the search space that also allow through a hybrid distribution system to deploy a set of independent embarassingly parallel tasks perfectly scalable.

The theoretical part of the thesis analyzes several selected methodologies and best-practices related to information technology risks management, with focus on documents and guidance developed by ISACA. It builds a set of ideas and basic requirements for effective model of an IT risk register. Strong emphasis is placed on mapping CobiT 4.1 based Risk IT to COBIT 5. The practical part describes implementation of an exploratory web-based IT risk register in Python programming language utilizing the Django framework and employs concepts from the analysis.

This report aims to solve a problem for the   waxers in the Swedish National Cross-country Ski Team, which hereafter will   be referred to as the national team. The problem in hand is that currently,   the national team lacks a system for book-keeping of ski pairs and ski tests.   Also, the project intends to provide a tool for predicting the best ski pairs   in given conditions. The report describes cross-country skis and factors that   affect the performance of these skis. Moreover, this report presents the   testing procedure of the national team. The project provides a solution to   the problem in hand by developing a web service based on Django and Django   REST Framework and an iOS application to handle the user interaction. The app   was tested and approved by the waxers of the national team. To predict the   best performing skis in given conditions, the three Machine Learning   algorithms Support Vector Machine (SVM), Decision Tree, and Artificial Neural   Network (ANN) is implemented and evaluated. Experimental results indicate   that the ANN algorithm has better accuracy than the Decision Tree, and that   the SVM algorithms and that the SVM was performing slightly worse than the   other two, when applied on test data which is artificially generated based on   the experience of the national team. All three Machine Learning algorithms   perform better in terms of mean accuracy which is significantly higher   compared to the accuracy of a baseline algorithm. The report suggests that   the accuracy of the ANN algorithm is high enough to be useful for the   national team.

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Regression testing is an essential part of developing and maintaining software. It helps verify that changes to the software have not introduced any new bugs, and that the functionality still works as intended. However, for this verification to be valid, the executed tests must be assumed to be deterministic, i.e. produce the same output under the same circumstances. Unfortunately, this is not always the case. A test that exhibits non-deterministic behavior is said to be flaky. Flaky tests can severely inhibit the benefits of regression testing, as developers must figure out whether a failing test is due to a bug in the system under test (SUT) or test flakiness. Moreover, the non-deterministic nature of flaky tests poses several problems. Not only are the failures difficult to reproduce and debug, but developers are more likely to ignore the outcome of flaky tests, potentially leading to overlooked bugs in the SUT. The aim of this thesis was to investigate the applicability of execution tracing techniques as a means of providing root cause analysis for flaky tests in the randomness and network categories. This involved reproducing and studying flakiness, as well as implementing and evaluating a prototype with the ability to analyze runtime behavior in flaky tests. To gain a better understanding of reproducibility and common traits among flaky tests in the selected categories, a pre-study was conducted. Based on the outcome of the pre-study and findings in related literature, the network category was dropped entirely, and two techniques were chosen to be implemented. The implementation process resulted in the FlakyPy tool, a plugin for pytest that provides root cause analysis aimed at randomness flakiness. When run against a dataset of 22 flaky tests, the tool was able to identify potential root causes in 15 of these. This serves as an indication that execution tracing has the potential of detecting possible root causes in flaky randomness tests in Python. However, more research is needed to evaluate how developers perceive the usefulness of such tools.

Nowadays, the traffic tomography represents an integral component in converged networks and systems for detecting their behavioral characteristics. The dissertation deals with research of its implementation with the use of evolutionary algorithms. The research was mainly focused on innovation and solving behavioral detection data flows in networks and network anomalies using tomography and evolutionary algorithms. Within the dissertation has been proposed a new algorithm, emerging from the basics of the statistical method survival analysis, combined with a genetics’ algorithm. The proposed algorithm was tested in a model of a self-created network probe using the Python programming language and Cisco laboratory network devices. Performed tests have shown the basic functionality of the proposed solution.

This thesis deals with controlling the irrigation system. In the introduction there is a theoretical analysis of the irrigation units, the designed system and the programs used. The core of the paper is a description of the software development and visualization software developed. Subsequently, the process of designing printed circuit boards is described. The conclusion of the work is to verify the functionality of the irrigation system and the summary of the results achieved.