An autonomous system consisting of an unmanned aerial vehicle (UAV) in cooperation with an unmanned ground vehicle (UGV) is of interest in applications for military reconnaissance, surveillance and target acquisition (RSTA). The basic idea of such a system is to take advantage of the vehicles strengths and counteract their weaknesses. The cooperation aspect suggests that the UAV is capable of autonomously landing on the UGV. A fundamental part of the landing is to localise the UAV with respect to the UGV. Traditional navigation systems utilise global navigation satellite system (GNSS) receivers for localisation. GNSS receivers have many advantages, but they are sensitive to interference and spoofing. Therefore, this thesis investigates the feasibility of autonomous landing in a GNSS-denied scenario. The proposed landing system is divided into a control and an estimation system. The control system uses a proportional navigation (PN) control law to approach the UGV. When sufficiently close, a proportional-integral-derivative (PID) controller is used to match the movements of the UGV and perform a controlled descent and landing. The estimation system comprises an extended Kalman filter that utilises measurements from a camera, an imu and ultra-wide band (UWB) impulse radios. The landing system is composed of various results from previous research. First, the sensors used by the landing system are evaluated experimentally to get an understanding of their characteristics. The results are then used to determine the optimal sensor placements, in the design of the EKF, as well as, to shape the simulation environment and make it realistic. The simulation environment is used to evaluate the proposed landing system. The combined system is able to land the UAV safely on the moving UGV, confirming a fully-functional landing system. Additionally, the estimation system is evaluated experimentally, with results comparable to those obtained in simulation. The overall results are promising for the possibility of using the landing system with the presented hardware platform to perform a successful landing.

The purpose of this thesis is to investigate different control strategies on a differential drive vehicle. The vehicle should be able to drive in turns at high speed and slowly when it should park next to a charger. In both these cases, good precision in both orientation and distance to the path is important. A PID and an LQ controller have been implemented for this purpose. The two controllers were first implemented in a simulation environment. After implementing the controllers on the system itself, tests to evaluate the controllers were made to imitate real-life situations. This includes tests regarding driving with different speeds in different turns, tests with load distributions, and tests with stopping accuracy. The existing controller on the system was also tested and compared to the new controllers. After evaluating the controllers, it was stated that the existing controller was the most robust. It was not affected much by the load distribution compared to the new controllers. However, the LQ controller was slightly better in most cases, even though it was highly affected by the load distribution. The PID controller performed best regarding stopping accuracy but was the least robust controller by the three. Since the existing controller has a similar performance as the LQ controller but is more robust, the existing controller was chosen as the best one.

A study of pre-service primary school teachers in Singapore and the United States revealed superior performance by the Singaporeans on proportional reasoning problems. Analysis of solutions showed the Singapore future teachers were more likely to use unitary and benchmark approaches than were their American counterparts. Conclusions include suggestions for programs intended to improve the performance of prospective elementary school teachers on proportional reasoning problems.

This doctoral thesis was written under supervision of my supervisor Prof. Ing. Přemysl Janicek, DrSc. and under supervision of my specialist supervisor Ing. Petr Skalka, Ph.D. The thesis deals with the influence of various operating conditions on crack behaviour in in a wheel rim and/or tire. The dissertation topic was motivated by cooperation with Pardubice University, Faculty of Jan Perner. The first part of the thesis presents a literature search, which provides a current state-of-art in the field of contact loading, crack propagation behaviour under mixed-mode and thermal field generated during braking. The next part of thesis deals with the solution of the given problem and the obtained results are presented. Specifically, crack behaviour under various operating conditions including the crack growth rate is simulated. Also, an influence of thermal field on crack is investigated. The linear elastic fracture mechanics approach is considered to treat the above mentioned problems. Last part of thesis summarizes the obtained results which are discussed in a broader context.

The diploma thesis deals with the identification and analysis of misconceptions that are formed when the ninth grade pupils work with thematic maps of quantitative mapping methods, specifically with the choropleth map and proportional symbol map. The theoretical introduction defines the tested mapping methods, reviews previous foreign and Czech studies of map skills, and describes misconceptions that are formed both in cartography in general and specifically when working with thematic maps of quantitative mapping methods. The theoretical part is followed by the practical part - the research methodology and the design of an open conceptual test itself. The test is compiled according to the concept map, which was based on the literature review of previous empirical studies and theoretical publications. From the conceptual map eight propositions (statements) that are key to understanding the concept of choropleth map and proportional symbol map were determined based on an expert evaluation. The conceptual test was used to reveal misconceptions that are formed when working with these mapping methods. A total of 257 pupils from eight primary schools across Czechia took part in the testing and a total of 59 misconceptions of varying severity and frequency were revealed. These misconceptions were then...

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<p>This dissertation investigates whether dual credit students' academic performance in subsequent university courses is comparable to that of non-dual-credit students. The study uses data from a Midwest regional public university over a ten-year period and employs propensity score matching and proportional odds ordinal logistic regression to create balanced comparison groups and analyze the results. The findings indicate that students who completed the prerequisite course as dual credit have similar grades in subsequent university courses as those who completed the prerequisite course as a university student. The study also identifies significant predictors of academic performance in subsequent university courses, such as sex, historically underserved groups status, high school GPA, and course subject, regardless of dual credit status. However, first-generation status, SAT scores, and the time between courses are not statistically significant predictors. These results suggest that dual credit students are as prepared for subsequent university courses as non-dual-credit students. Nevertheless, academic outcomes differ based on certain factors, which should be considered when designing student success initiatives and allocating resources.</p>

Acute respiratory infections (ARI) caused by influenza and other respiratory viruses affect millions of people annually. Although usually self-limiting a more complicated or severe course may occur in previously healthy people but are more likely in individuals with underlying illnesses. The most common viral agent is rhinovirus whereas influenza is less frequent but is well known to cause winter epidemics. In primary care, rapid diagnosis of influenza virus infections is essential in order to provide treatment. Clinical presentations vary among the different pathogens but may overlap and may also depend on host factors. Predictive models have been developed for influenza but study results may be biased because only individuals presenting with fever were included. Most of these models have not been adequately validated and their predictive power, therefore, is likely overestimated. The main objective of this thesis was to compare different mathematical models for the derivation of clinical prediction rules in individuals presenting with symptoms of ARI to better distinguish between influenza, influenza A subtypes and entero-/rhinovirus-related illness in children and adults and to evaluate model performance by using data-splitting for internal validation. Data from a completed prospective cluster-randomized trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first episodes per season of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were randomly split by subjects into a derivation (2/3 of the dataset) and a validation population (1/3 of the dataset). All predictive models were developed in the derivation sets. Demographic factors and the classical symptoms of ARI were evaluated with logistic regression and Cox proportional hazard models using forward stepwise selection applying robust estimators to account for non-independent data and by means of recursive partitioning. The beta coefficients of the independent predictors were used to develop different point scores. These scores were then tested in the validation groups and performance between validation and derivation set was compared using receiver operating characteristics (ROC) curves. We determined sensitivities and specificities, positive and negative predictive values, and likelihood ratios at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate with areas under the receiver operating characteristic curves between 0.75 and 0.80 for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, performance of the models was slightly overestimated due to potential clustering of data and the results would first needed to be validated in a different population before application in clinical practice. / Thesis / Master of Science (MSc) / Every year, millions of people are attacked by "the flu" or the common cold. Certain signs and symptoms apparently are more discriminative between the common cold and the flu. However, the decision between starting a simple symptom orientated treatment, treating empirically for influenza or ordering a rapid diagnostic test that has only moderate sensitivity and specificity can be challenging. This thesis, therefore, aims to help physicians in their decision-making process by developing simple scores and decision trees for the diagnosis of influenza versus non-influenza respiratory infections. Data from a completed trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first seasonal episodes of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were split into a derivation and a validation set (=holdout group). Different mathematical models were applied to the derivation set and demographic factors as well as the classical symptoms of ARI were evaluated. The scores generated from the most important factors that remained in the model were then tested in the validation group and performance between validation and derivation set was compared. Accuracy was determined at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, the results would first needed to be validated in a different population before application in clinical practice.

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