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Routing and designing are essential for transportation networks. With effective routing and designing policies, transportation networks can work safely and efficiently. There are two transportation problems: hazardous materials (hazmat) transportation and warehouse logistics. This dissertation addresses the routing of networks for both problems. For hazmat transportation, the routing can be regulated via network design. Due to catastrophic consequences of potential accidents in hazmat transportation, a risk-averse approach for routing is necessary. In this dissertation, we consider spectral risk measures, for risk-averse hazmat routing. In addition, we introduce a network design problem to select a set of closed road segments for hazmat traffic with conditional value-at-risk (CVaR) to regulate hazmat routing. In warehouses, the routing of electric forklifts with sufficient battery levels is for material handling. The optimization model of dynamic wireless charging lane location is proposed under the workflow congestion in parallel-aisle warehouses. Considering the uncertainty of demands, the wireless charging lane location problem is formulated as a two-stage stochastic programming model. We confirm the efficiency of the proposed algorithms in solving these problems and the key advantages of use the proposed routing and designing policies via case studies.

Title: Conditional abilities of Czech ice hockey players in NHL according to the anaerobic Wingate test Aims: The aim of this thesis was to find out and evaluate the level of anaerobic readiness of selected Czech forwards and defenders playing in NHL between 2001/2002 and 2015/2016 seasons. The evaluation was based on comparison of the results in Wingate test of the research groups and specified standards of the first league (ELH) players. The other purpose was to find out whether there are differences between the results of the forwards and defenders. Methods: There were used the research methods of quantitative analysis including effect size. Research sample consists of the group of 26 forwards (mean age = 20.5 ± 4.4) and the group of 16 defenders (mean age = 23.2 ± 4.5) who have played in NHL. Results of the main parameters, i.e. peak power output, peak power relative to 1 kg of body mass, anaerobic capacity, anaerobic capacity relative to 1 kg of body mass, of the 30 s anaerobic Wingate were used. The data were collected from database of Faculty of PE and sport, Charles University in Prague and confronted with the first league ELH standards from the longitudinal research (Heller, Vodička 2014). The comparison was based on the mean values and dispersion of performances, effect sizes were...

Physical systems are usually not closed and insight about their internal structure is experimentally derived by scattering. This is efficiently described by resonance eigenfunctions of non-Hermitian quantum systems with a corresponding classical dynamics that allows for the escape of particles. For the phase-space distribution of resonance eigenfunctions in chaotic systems with partial and full escape we obtain a universal description of their semiclassical limit in terms of classical conditional invariant measures with the same decay rate. For partial escape, we introduce a family of conditionally invariant measures with arbitrary decay rates based on the hyperbolic dynamics and the natural measures of forward and backward dynamics. These measures explain the multifractal phase-space structure of resonance eigenfunctions and their dependence on the decay rate. Additionally, for the nontrivial limit of full escape we motivate the hypothesis that resonance eigenfunctions are described by conditionally invariant measures that are uniformly distributed on sets with the same temporal distance to the quantum resolved chaotic saddle. Overall we confirm quantum-to-classical correspondence for the phase-space densities, for their fractal dimensions, and by evaluating their Jensen–Shannon distance in a generic chaotic map with partial and full escape, respectively. / Typische physikalische Systeme sind nicht geschlossen, sodass ihre innere Struktur mit Hilfe von Streuexperimenten untersucht werden kann. Diese werden mit Hilfe einer nicht-Hermiteschen Quantendynamik und deren Resonanzeigenzuständen beschrieben. Die dabei zugrunde liegende klassische Dynamik berücksichtigt den Verlust von Teilchen. Für die semiklassische Phasenraumverteilung solcher Resonanzeigenzustände in chaotischen Systemen mit partieller und voller Öffnung entwickeln wir eine universelle Beschreibung mittels bedingt invarianter Maße gleicher Zerfallsrate. Für partiellen Zerfall stellen wir eine Familie bedingt invarianter Maße mit beliebiger Zerfallsrate vor, welche auf der hyperbolischen Dynamik und den natürlichen Maßen der vorwärts gerichteten und der invertierten Dynamik aufbauen. Diese Maße erklären die multifraktale Phasenraumstruktur der Resonanzzustände und deren Abhängigkeit von der Zerfallsrate. Darüber hinaus motivieren wir für den nicht trivialen Grenzfall voll geöffneter Systeme die Hypothese, dass Resonanzeigenzustände durch ein bedingt invariantes Maß beschrieben werden, welches gleichverteilt auf solchen Mengen ist, die den gleichen zeitlichen Abstand zum quantenunscharfen chaotischen Sattel haben. Insgesamt bestätigen wir die quantenklassische Korrespondenz für die Phasenraumdichten, deren fraktale Dimensionen und durch Auswertung ihres Jensen–Shannon Abstandes in einer generischen chaotischen Abbildung sowohl für partielle als auch für volle Öffnung.

TITLE: Work with offenders and victims of crime in the agenda of a probation worker AUTHOR: Mgr. Martin Jelínek DEPARTMENT: Faculty of Education SUPERVISOR: doc. PaedDr. Eva Šotolová, Ph.D. ABSTRACT: The thesis presents the agenda of the Probation and Mediation Service and it's approach to offenders and victims of crime in the agenda of probation workers. It focuses on the area of re-offending and the possibility of reducing reoffending through appropriate follow-up services for conditionally released persons with probation supervision. The thesis analyses the problems resulting in the unsuccessful reconciliation of persons released from prison, in accordance with the aim of the thesis based on combined research (respondents were all probation workers in the Czech republic), as well as proposing possibilities for further development of work with conditionally released persons with probation supervision, and suggests systematic measures that could reduce the risk and rate of re-offending. These measures relate to the areas of dependency, employment, and education. KEYWORDS: Probation, mediation, imprisonment, re-offending, resocialisation, conditional release

The Swedish procurement process has a yearly value of 706 Billion SEK over approximately 18 000 procurements. With each process comes many documents written in different formats that need to be understood to be able to be a possible tender. With the development of new technology and the age of Machine Learning it is of huge interest to investigate how we can use this knowledge to enhance the way we procure. The goal of this project was to investigate if public procurements written in Swedish in PDF format can be parsed and segmented into a structured format. This process was divided into three parts; pre-processing, annotation, and training/evaluation. The pre-processing was accomplished using an open-source pdf-parser called pdfalto that produces structured XML-files with layout and lexical information. The annotation process consisted of generalizing a procurement into high-level segments that are applicable to different document structures as well as finding relevant features. This was accomplished by identifying frequent document formats so that many documents could be annotated using deterministic rules. Finally, a linear chain Conditional Random Field was trained and tested to segment the documents. The models showed a high performance when they were tested on documents of the same format as it was trained on. However, the data from five different documents were not sufficient or general enough to make the model able to make reliable predictions on a sixth format that it had not seen before. The best result was a total accuracy of 90,6% where two of the labels had a f1-score above 95% and the two other labels had a f1-score of 51,8% and 63,3%.

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The law does not regulate the bidding process; it’s the seller who, in consultation with the estate agent, decides the rules. If the estate agent does not inform all the bidders about conditions  attached to the bids,it means that a bidder bid against something he s not fully informed about. This means that the lack of transparency in the bidding process leads to asymmetric information between the bidders and this could be detrimental to a bidder and for the benefit of another. This paper has been written in order to investigate how consumers and the housing market is Affected by the law not regulating the estate agent to inform all the bidders of conditions attached To the bids. Previously nothing has been written on this subject, which means that this paper is of an explorative--‐  and investigative character. Since there was no access to any secondary data in this study, the major part of the work was to collect primary data. The collection of data consisted of a survey to estate agents, a survey/interview to  uyers/speculators And interviews with a number of individuals with special expertise in the subject. The investigations carried out, resulted in a discussion and analysis of how consumers are affected by The estate agent not having to provide information about conditional bids to all bidders. The answers in interviews is not conclusive, there are both pros and cons of not having the bidding Process regulated by law. The conclusion also includes a discussion on how housing prices can be affected by the bidding not being regulated by law regarding conditions. This conclusion is that the prices could both increase and decrease by a regulation of the bidding process, depending on the scenario.

A bank borrowing some money has to give some securities to the lender, which is called collateral. Different kinds of collateral can be posted, like cash in different currencies or a stock portfolio depending on the terms of the contract, which is called a Credit Support Annex (CSA). Those contracts specify eligible collateral, interest rate, frequency of collateral posting, minimum transfer amounts, etc. This guarantee reduces the counterparty risk associated with this type of transaction. If a CSA allows for posting cash in different currencies as collateral, then the party posting collateral can, now and at each future point in time, choose which currency to post. This choice leads to optionality that needs to be accounted for when valuing even the most basic of derivatives such as forwards or swaps. In this thesis, we deal with the valuation of embedded optionality in collateral contracts. We consider the case when collateral can be posted in two different currencies, which seems sufficient since collateral contracts are soon going to be simplified. This study is based on the conditional independence approach proposed by Piterbarg [8]. This method is compared to both Monte-Carlo simulation and finite- difference method. A practical application is finally presented with the example of a contract between Natixis and Barclays.

Deep learning models have been used widely in various areas and applications of our everyday lives. They could also change the way non-profit organizations work and help optimize fundraising results. In this thesis, sequential models are applied in fundraising to compare their performance against the traditional machine learning model. Sequential model is a type of neural network that is specialized for processing sequential data. Although some research utilizing machine learning algorithms in fundraising context exists, it is based on the data extracted from the specific time window, which does not take time-dependency of features into account; therefore, time-series features are independent at each data point relative to others. This approach results in loss of time notion. In this thesis, we experiment with the application of time-dependent sequential models including Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and their variants in the fundraising domain to predict the alumni monetary contribution to the university. We also expand our study by including the architecture that treats time-invariant demographic data as a condition to the sequential layers. In this model, the time-dependent data is concatenated after running the sequential model. Sequential deep learning is empirically evaluated and compared against the traditional machine learning models. The results demonstrate the potential use of both traditional machine learning and sequential deep learning in the prediction of fundraising outcomes and offer non-profit organizations solutions to achieve their mission. / Graduate