The master's thesis is focused on optimal selection of transportation solution provider as a key activity of inbound logistics department of manufacturing company ABC s.r.o. The summary of general activities and objectives of inbound logistics are followed by the analysis of last selection procedure. Then there is mentioned suggestion of new selection procedure using the methods of Multiple Criteria Decision Making which is based on the knowledge from specialized logistical literature. This work assume that using the methods of Multiple Criteria Decision Making will result in more effective selection procedure in company ABC s.r.o.

The objective of this master thesis is a working out of project documentation of building „polyfunctional bulding“ containing all the requirements according to currently valid orders and standards. New bulding is located on plot No. 2636/5 flat terrain in the urban area of the village of Jablunkov. The building has 3 floors and a basement is partially. The roof is designed as a flat. The first floor is planned with space for library and cafe. In the second and third floor residental units are located on each floor of the two. In the basement of the building is designed background residental units. Connecting of utilities can be made of existing infrastructure.

Master thesis of "Multifunctional Building Kounicova" is composed in the form of project documentation. The building is designed as a shopping mall and office space and it is situated in the centre of Brno city. The building has four floors and it is partly basement. The load-bearing structure consists of a monolithic reinforced concrete skeleton with flat roof. The drawings are processed in CAD program.

The purpose of the master thesis is the Residential house, Nachod - Tepenska. The building is situated near by the city center of Nachod. It contains 6 floors. In the first floor there are two comercial areas, technical room and parking lot. The roof of parking lot serves as a terace for the second floor. In other floors are situated flats. Whole building is covered by flat roof. The ETICS is applied. The insulation from mineral wool is used. The master thesis includes the blue prints of the building.

The work is focused on the issue of cold production using absorbent circulation driven by thermal energy solar collectors recovered. The work can be divided into several main parts. In the first part of this work is given an overview of the possible principles of solar cooling system. In the next section followed by a description of the principle of absorption cycles. In the practical part is solved design proposal absorption refrigeration units with a cooling power of 6 kW. This is followed by a proposal to link solar cooling system with the absorption unit. Final section is made of basic technical-economic comparison of solar refrigeration unit with the compressor refrigeration units.

This master thesis is dealing with the German-Austrian relations between 1998 and 2005 with an emphasis on a period of sanctions against Austria in 2000 in which Germany participated. The analysis was done by using the concept of civilian power to analyze the German foreign policy and by using the theory of asymmetrical dyads to analyze the German-Austrian relations. Outside the above mentioned topic and period of time this thesis concerns itself also briefly with the history of the German-Austrian relations before 1998 and the Austrian political party FPÖ. Due to the thematic limitations this thesis doesn't cover the domestic policy of both countries during that era or the German-Austrian Relations outside the designed period of time.

With the wide application of machine learning technologies in advanced driver assistance systems of vehicles, object classification on obstacles has attracted much attention. Ultrasonic sensors are mainly used to measure the distance to obstacles under the condition of low-speed movement. The existing ultrasonic sensor is low-cost, and its good performance and robustness are sufficient for obstacle avoidance. Recent progress on ultrasonic has attempted to classify obstacles with the combination of ultrasonic sensors and machine learning. It shows that deep neural networks are able to classify objects using only ultrasonic sensors. In the thesis, we focus on the object classification on sizes of obstacles and expect our proposed neural networks model can solve the classification task under the simulation environment, thus contributing to the application of ultrasonic sensors in vehicles. The ultrasonic sensor arrays are built in COMSOL Multiphysics and can provide ultrasonic data with different objects. After many simulation experiments, the ultra-sonic data from objects are labeled and stored in datasets. Then we process the ultrasonic data from datasets and feed them to the proposed neural networks. The ultrasonic data obtained by experiments are examined by the distribution of reflected ultrasonic rays in simulation. The analysis results are in line with our expectations. The trained neural networks are divided into two groups. The first networks group is trained with data from cubes and shows over 80% accuracy on five object categories. The second group of networks is trained with data from cubes and S1 objects. There is an approximate 5% drop in classification performance as the difficulty of the classification task increases.

The aim of this thesis is to design improvements to increase the quality of the living in the territory of Jaroslavice and its surroundings. This South Moravian rural area located near the Austria–Czech Republic border may currently be considered as a No man's land. Given the subject of the thesis, the author suggests the conceptual solution of this problem. Focusing on the life in a border region analyses offer of the local development, amenities and opportunities, not forgetting about the education. The work suggests an entirely new local education system from the very beginning - from kindergartens to secondary education. The missing high school forces students to travel or to move to nearby towns such as Znojmo. what usually leads to their breakaway from the local environment. This could be avoided by building a new secondary school in Jaroslavice. It would link to the rich history of this village from the pre-war period, when it was considered an important transition point between the cities of Brno and Vienna. Besides, Czech-German school can re-establish mutual cooperation between both countries. Designated school is based on a current education needs, e.g. practical training experiences in local businesses, which is costs saving and immediately applicable. The subject of this thesis is placed into the area of the ruined mansion under the local castle. Besides school, there is also Food Center and Research Institute. All three institutions cooperate together, similarly to the concept of manor court (meierhof) in the past. Educational facilities are arranged in U-shape form with the inner courtyard and the large greenhouse on one side. Each wing of the building is self-sufficient and has a different character. The west wing is two-storey, on the ground floor there is a library with classrooms for social sciences and the heart of the house - room for theatre and performances. On the second floor there is a school headquarters facing the courtyard with the ability to check the daily routine. The central wing with entrance consists of classrooms of natural sciences and another social science classrooms located towards the courtyard for eventual outdoor lessons. Last part consists of computer classrooms made for the technical sciences. This wing leads to the lower floor with side entrance to a parking lot. Greenhouse, designed for both the public and the students, is an inseparable part of the school.

This study explored the challenges faced by enterprises in adapting to the rapid digital transformation driven by Industry 4.0 technologies. Evaluating there turn on investment and measuring the business value of digital solutions can be difficult. Furthermore, it investigated the feasibility of enhancing the business value of the cloud-based application, Digital Dealer, through change management. It has a particular focus on the manufacturing and automation industry. The research was shaped by the literature review, which provided a theoretical basis and guided the case study design, leading to the development of a conceptual framework. The thesis employed an exploratory approach, utilizing an inductive methodology and gathering data through interviews and document analysis. The results shows that the definition of a digital tool’s contribution to business value varies among stakeholders. Effective change management is crucial, while barriers to adoption include perceived value, resistance to change, and technical issues. To increase and maintain the business value of digital tools companies should prioritize clear communication, stakeholder engagement, and continuous evaluation. This is concluded to enable effective implementation and enhancement of new technologies in their operations. / Denna studie undersökte de utmaningar som företagen står inför när de ska anpassa sig till den snabba digitala omvandlingen som drivs av Industri 4.0-teknik. Det kan vara svårt att utvärdera avkastningen på investeringen och mäta affärsvärdet av digitala lösningar. Fortsättningsvis, undersöktes möjligheten att öka affärsvärdet för den molnbaserade applikationen Digital Dealer genom förändringsledning. Den har ett särskilt fokus på tillverknings- och automationsindustrin. Forskningen formades av litteratur studien, som gav en teoretisk grund och vägledde utformningen av fallstudien, vilket ledde till utvecklingen av ett konceptuellt ramverk. Avhandlingen har en explorativ ansats med en induktiv metodik och datainsamling genom intervjuer och dokumentanalys. Resultaten visar att definitionen av ett digitalt verktygs bidrag till affärsvärdet varierar mellan olika aktörer. Effektiv förändringsledning är avgörande, medan hinder för adoption inkluderar upplevt värde, motstånd mot förändring och tekniska problem. För att öka och bibehålla affärsvärdet av digitala verktyg bör företag prioritera tydlig kommunikation, engagemang från aktörer och kontinuerlig utvärdering. Detta för att möjliggöra en effektiv implementering och förbättring av ny teknik i verksamheten.

Cloud computing and containerization has been on the rise in recent years and have become important areas of research and development in the field of computer science. One of the challenges in distributed and cloud computing is to predict the resource utilization of the nodes that run the applications and services. This is especially relevant for container-based platforms such as Kubernetes. Predicting the resource utilization of a Kubernetes cluster can help optimize the performance, reliability, and cost-effectiveness of the platform. This thesis focuses on how well different resources in a cluster can be predicted using machine learning techniques. The approach consists of 3 main steps: data collection, data extraction and pre-processing, and data analysis. The data collection step involves stressing the system with a load-generator called Locust and collecting data from Locust and collecting data from Kubernetes with the use of Prometheus. The data pre-processing and extraction step involves extracting relevant data and transforming it into a suitable format for the machine learning models. The final step involves applying different machine learning models to the data and evaluating their accuracy. The results of this thesis illustrate that machine learning can work well for predicting resources in a cluster based on how stressed the system is and that the best performing machine learning model tested was Support Vector Machine with a polynomial kernel. / Cloud computing och containerisering har ökat de senaste åren och har blivit viktiga områden för forskning och utveckling inom datavetenskap. En av utmaningarna inom distribuerad och cloud computing är att förutsäga resursutnyttjandet av de noder som kör applikationerna och tjänsterna. Detta är särskilt relevant för containerbaserade plattformar som Kubernetes. Att förutsäga resursutnyttjandet av ett Kubernetes-kluster kan hjälpa med att optimera plattformens prestanda, tillförlitlighet och kostnadseffektivitet. Denna avhandling fokuserar på hur väl olika resurser i ett kluster kan förutsägas med hjälp av maskininlärningstekniker. Tillvägagångssättet består av 3 huvudsteg: datainsamling, dataextraktion och för-processering, samt dataanalys. Datainsamlingssteget innebär att stressa systemet med en load-generator som heter Locust och samla in data från Locust och även samla in data från Kubernetes med hjälp av Prometheus. Steget för för-processering och extrahering av data innefattar att extrahera relevant data och omvandla den till ett lämpligt format för maskininlärningsmodellerna. Det sista steget innefattar att tillämpa olika maskininlärningsmodeller på data och utvärdera deras noggrannhet. Resultaten av denna avhandling demonstrerar att maskininlärning kan fungera bra för att förutsäga resurser i ett kluster baserat på hur stressat systemet är och att den bäst presterande maskininlärningsmodellen som testades var Support Vector Machine med en polynom-kernel.