This thesis is based on critical analysis of office development to determine the development potential of this real estate segment and therefore also predict the future evolution of market characteristics (supply, demand, vacancy rates, investments, rents, yields and prices) and a model scenarios of development towards an optimistic, realistic or pessimistic. The work includes a theoretical part, which defines the basic concepts needed to understand the problem. Furthermore, the development and current situation related to that topic and the actual forecast of the market for office space.

The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

This dissertation thesis deals with the application of expert systems and soft computing methods in field of power oil transformers. The main work is divided into theoretical and practical part. First, the theoretical part presents the basic elements of the transformer, and approaches to its diagnosis. The work focused mainly on the diagnostics of the insulation system, and diagnostic methods and approaches in this specific area. Next part describes the basics of expert systems and other soft computing methods such as: fuzzy logic, neural networks, genetic algorithms and their combinations and extensions. At the end of the theoretical part, the possibility of optimization approaches by means of artificial intelligence and its application in fuzzy model optimization are described. The practical part begins with description of the used data file that runs through the entire work. The work is then divided into four parts, namely in parts which deal with the expert system for transformer diagnostics, DGA module, prediction module, and optimization using artificial intelligence. The section describing the expert system gives specific information about the particular expert system. The means and techniques used for constructing given system are described, and then the complete system design and description of all subsystems and modules are presented. The next section describes the developed DGA module and all selected approaches to its implementation and expansion. At the end of the chapter, the results of comparison between all implemented methods are evaluated. The third part deals with the prediction module and describes its design and construction, including description of the main parts which are based on the selected predictive approaches. Also, the predictions of selected quantities from the data file are included. There are two predictive approaches being used: the one step prediction, and the multiple step prediction. The comparison of prediction accuracy and computational cost of given methods is presented at the end of this chapter. The last part deals with the possibilities of optimization using artificial intelligence methods, namely differential evolution, PSO, and genetic algorithms. Both the single-objective and the multi-objective optimization are considered. The methods are compared in a series of synthetic tests and then applied to optimize the fuzzy models of DGA tests from an earlier part of this work. The dissertation also includes chapters: "The Aims", "The Contribution of the Work", and a list of publications, products, and projects of the author.

The main focus of this dissertation thesis is on methods and tools which can increase the speed of software development process for embedded processors used in mechatronics applications. The first part of this work introduces software and hardware tools suitable for a rapid development and prototyping of new applications used today. This work focuses on two main topics from the mentioned application field. The first topic is a development of tools for an automatic code generation from the Simulink environment for an embedded processor. The second topic is a development of tools enabling execution time prediction based on a Simulink model. Next chapter of this work describes various aspects and properties of the Cerebot blockset, which is a toolset for a fully automatic code generation from a Simulink environment for an embedded processor. Following chapter describes various methods that are suitable for predicting the execution time on an embedded processor based on a Simulink model. Main contribution of this work presents the created support for a fully automatic code generation from a Simulink software for the MX7 cK hardware, which enables a code generation supporting also a complex peripheral (a graphic display unit). The next important contribution of this work presents the developed method for an automatic prediction of the software execution time based on a Simulink model.

This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.

An important part of maintaining network security is collecting and processing information about cyber threats, both from network operator's own detection tools and from third parties. A commonly used type of such information are lists of network entities (IP addresses, domains, URLs, etc.) which were identified as malicious. However, in many cases, the simple binary distinction between malicious and non-malicious entities is not sufficient. It is beneficial to keep other supplementary information for each entity, which describes its malicious activities, and also a summarizing score, which evaluates its reputation numerically. Such a score allows for quick comprehension of the level of threat the entity poses and allows to compare and sort entities. The goal of this work is to design a method for such summarization. The resulting score, called Future Maliciousness Probability (FMP score), is a value between 0 and 1, assigned to each suspicious network entity, expressing the probability that the entity will do some kind of malicious activity in a near future. Therefore, the scoring is based of prediction of future attacks. Advanced machine learning methods are used to perform the prediction. Their input is formed by previously received alerts about security events and other relevant data related to the entity. The method of computing the score is first described in a general way, usable for any kind of entity and input data. Then a more concrete version is presented for scoring IPv4 address by utilizing alerts from an alert sharing system and supplementary data from a reputation database. This variant is then evaluated on a real world dataset. In order to get enough amount and quality of data for this dataset, a part of the work is also dedicated to the area of security analysis of network data. A framework for analysis of flow data, NEMEA, and several new detection methods are designed and implemented. An open reputation database, NERD, is also implemented and described in this work. Data from these systems are then used to evaluate precision of the predictor as well as to evaluate selected use cases of the scoring method.

The goal of the thesis is to create a predictor of the effect of a mutation on protein solubility given its initial 3D structure. Protein solubility prediction is a bioinformatics problem which is still considered unsolved. Especially a prediction using a 3D structure has not gained much attention yet. A relevant knowledge about proteins, protein solubility and existing predictors is included in the text. The principle of the designed predictor is inspired by the Surface Patches article and therefore it also aims to validate the results achieved by its authors. The designed tool uses changes of positive regions of the electric potential above the protein's surface to make a prediction. The tool has been successfully implemented and series of computationally expensive experiments have been performed. It was shown that the electric potential, hence the predictor itself too, can be successfully used just for a limited set of proteins. On top of that, the method used in the article correlates with a much simpler variable - the protein's net charge.

This diploma thesis describes searching for long forgotten routes between ancient settlements in the mountainous environment of Central Bulgaria. It focuses on Stara Planina Mountains, where the highest peaks over two thousand metres are. Geographical features of this area were analysed using three-dimensional tools in GIS environment. I experimented with various route-designing tools and tested their capabilities against well-known ancient paths. The most accurate tools were then selected to predict routes on maps of ancient settlements from various historical periods. The data used to construct these maps had been collected from two different sources. The first source was a multidisciplinary project called TRAP, initiated by Mgr. Adéla Sobotková, the consultant of my thesis. This project's data was collected during spring and autumn surface survey campaigns in the Kazanluk region of Tundzha River valley. The second source was specialized monographs and journal articles about settlement and path connections in ancient Thracia in the period between 6th century BC and 4th century AD.

This master thesis aims to explore and predict counterproductive work behavior (CWB) targeted to the employer within a framework of the Theory of Planned Behavior (TPB). The question is examined by quantitative questionnaire survey, assessing attitudes, subjective norms, perceived behavioral control, intention and actual behavior. The aim of this study is to verify the psychometric properties of a non-standardized questionnaire designed in the theoretical framework of the Theory of Planned Behavior, analysis of functionality of the proposed model and the proposal of its possible improvements to be given. Unrepresentative sample (n = 71 in the first phase of research, n = 41 in the second phase) consists of working adults with a work history of at least one year. The proposed model of the Theory of Planned Behavior is analyzed using the method of multiple linear regression analysis, which shows a statistically significant relationship between the dependent variable and some of the independent variables in the first phase of the research. In the second phase, no significant relationships between variables are found. Since the psychometric properties of the measuring instrument are not satisfactory, the item analysis using principal component analysis is conducted, which identifies some problematic...

Title: Analysis and prognosis of elite male and female triathletes performance at the ITU World Triathlon Championships in 1989-2016 in Olympic triathlon Objectives: To analyse male and female performance in individual parts of the triathlon (swim, bike, run) as well as the whole race performance during the years 1989 - 2016. To determine the performance prediction of racers using the time series analysis for Olympic triathlon in ITU World Triathlon Championship in 2028. Methods: For statistical data processing we will apply the time series analysis using SPSS Statistics 22 software. We will then add the historical content and the actual conditions of the race to the final graphs. On the basis of the processed data, we will create a performance prediction for 2028 using Excel program. Results: In swimming, women are approaching men's performance and they are now on 92.2%. In the future, women will not come closer to men's times. Performance will improve slightly. In cycling, the gap between men and women is 10%. We do not expect any major change in the future. According to the trend of development, we find deterioration in both categories, especially in men. At the moment, the performance of women in running is 88.3% of men. We do not expect any change in the future. However, male and female times...