Detailed history of golf, golf carts and vehicles was described in analytic part of DT. Components and technical aspects of golf carts were listed in the technical part of the analysis. It also gives an overview of modern technologies available in these types of vehicles. Design part of DT maps current products on the market and their components influencing appearance of the vehicle. The final solution was chosen from three variants and brought the greatest added value. The original idea includes the way how to make handling of the vehicle easier, simplifies maintenance and reduces unnecessary preparation time of golf equipment for the game. New features have been presented in a single comprehensive and unique design.

The main contribution of this work is a new perspective on design of a golf cart for 2 players. The aim is to create a cart which will be innovative in the design but retaining necessary technical, ergonomic, aesthetic and ecological requirements. The final draft should be able to compete to existing products in this market.

>Magister Scientiae - MSc / Background and Aims Human Immunodeficiency Virus (HIV) type 1 group M subtype C (HIV-1C) accounts for nearly half of global HIV-1 infections, with South Africa (SA) being one of the countries with the highest infection burden. In recent years, SA has made great strides in tackling its HIV epidemic, resulting in the country being recognized globally as the one sub-Saharan country with the largest combination antiretroviral therapy (cART) programme. Regardless of the potency of cART, the efficacy of the treatment is limited and hampered by the emergence of drug resistance. The majority of research on HIV-1 infections, effect of antiretroviral (ARV) drugs and understanding resistance to ARV drugs has been extensively conducted, but mainly on HIV-1 subtype B (HIV-1B), with less information known about HIV-1C. HIV-1’s viral Integrase (IN) enzyme has become a viable target for highly specific cART, due to its importance in the infection and replication cycle of the virus. The lack of a complete HIV-1C IN protein structure has negatively impacted the progress on structural studies of nucleoprotein reaction intermediates. The mechanism of HIV-1 viral DNA’s integration has been studied extensively at biochemical and cellular levels, but not at a molecular level. This study aims to use in silico methods that involve molecular modeling and molecular dynamic (MD) simulations to prioritize mutations that could affect HIV-1C IN binding to DNA and the IN strand-transfer inhibitor (INSTI) dolutegravir (DTG). The purpose is to help tailor more effective personalized treatment options for patients living with HIV in SA. This study will in part use patient derived sequence data to identify mutations and model them into the protein structure to understand their impact on the HIV-1C IN protein structure folding and dynamics. Methods Our sample cohort consisted of 11 sample sequences derived from SA HIV-1 treatmentexperienced patients who were being treated with the INSTI raltegravir (RAL). The sequences were submitted to the Stanford HIV resistance database (HIVdb) to screen for any new/novel variants resulting from possible RAL failure. Some of these new variants were analyzed to analyse their effect, if any, on the binding of DTG to the HIV-1C IN protein. Additionally, an HIV-1C IN consensus sequence constructed from SA’s HIV-1 infected population was used to model a complete three-dimensional wild type (WT) HIV-1C IN homology model. All samples were sequenced by our collaborators at the Division of Medical Virology, Stellenbosch University together with the National Health Laboratory Services (NHLS), SA. The HIV-1CZA WT-IN protein enzyme was predicted using SWISS-MODEL, and the quality of the resulting model validated. Various analyses were conducted in order to study and assess the effect of the selected new variants on the protein structure and binding of DTG to the IN protein. The mutation Cutoff Scanning Matrix (mCSM) program was used to predict protein stability after mutation, while PyMol helped to study any changes in polar contact activity before and after mutation. PyMol was also used to generate four mutant HIV-1C IN complex structures and these structures together with the WT IN were subjected to production MD simulations for 150 nanoseconds (ns). Trajectory analyses of the MD simulations were also conducted and reported. Results A total of 21 new variants were detected in our sample cohort, from which only six were chosen for further analyses within the study. A homology model of HIV-1C IN was successfully constructed and validated. The structural quality assessment indicated high reliability of the HIV-1C IN tetrameric structure, with more than 90.0% confidence in modelled regions. Of the six selected variants, only one (S119P) was calculated to be slightly stabilizing to the protein structure, with the other five found to be destabilizing to the IN protein structure. Variant S119P showed a loss in polar contacts that could destabilize the protein structure, while variant Y143R, resulted in the gain of polar contacts which could reduce flexibility of the 140’s region affecting drug binding. Similarly, mutant systems P3 (S119P, Y143R) and P4 (V150A, M154I) showed reduced hydrogen bond formation and the weakest non-bonded pairwise interaction energy. These two systems, P3 and P4, also showed significantly reduced to none polar contacts between DTG, magnesium (MG) ions and the IN protein, compared to the WT IN and P2 mutant IN systems. Interestingly, the WT structure and systems P1 (I113V) and P2 (L63I, V75M, Y143R) showed the highest non-bonded interaction energy, compared to systems P3 and P4. This was further supported by the polar interaction analyses of simulation clusters from the WT IN and mutant IN system P2 (L63I, V75M, Y143R), which were the only protein structures that formed polar contacts with DTG, MG ions and DDE motif residues, while P1 only made contacts with DNA and IN residues. Conclusion Findings from this study leads to a conclusion that double mutants (S119P, Y143R) and (V150A, M154I) may result in a reduction in the efficacy of DTG, especially when in combination. Furthermore, variants identified in systems P1 and P2 may still allow for effective DTG binding to IN and outcompete viral DNA for host DNA to prevent strand transfer. To the best of our knowledge, this is the first study that uses the consensus WT HIV1C IN sequence to build an accurate 3D homology model to understand the effect of less frequently detected/reported variants on DTG binding in a South African context. https://etd.

Conflict researchers have validated many different theories on the causes of organized violence, but there are significant gaps in knowledge concerning how these theories interact with one another. In this thesis, I identify a body of the most prominent theories of organized violence and model them in an environment suitable for capturing these complex interactions. I formulate six causal categories to which these theories belong: Geography; Economy; Conflict History & Insecurity; Liberty & Inclusion; Natural Resources; and Structures of Governance. I then construct a cross-national, time-series sample of country-year observations and create a general model of organized violence using a machine learning technique called Classification and Regression Trees (CART). The results from this first model indicates a substantial negative effect owing to Peace Years, a count of the number of years since the country last experienced an internal conflict. Subsequently, I construct three more models, each investigating different subsets of country-year observations based on their Peace Years value. My models indicate that the country-years most likely to experience a high number of deaths from organized violence are those where conflict occurred in the previous year, the population size is high, and the net rate of male secondary school enrollment is low. The models also reveal several novel results under the presence of certain conditions, including: nonlinear relationships between deaths from organized violence and both oil exports and mass education; and a negative relationship between economic inequality and deaths from organized violence, wherein higher inequality results in fewer deaths. These findings highlight the importance of complexity-based modeling for both future conflict research and policymaking oriented towards violence reduction.

The aim of this thesis is the relocation of roads II/431 and III/4317 near Kojátky village. This is due to the unsuitable layout of current routes which were poorly arranged because of too many horizontal and vertical curves with small radius. Another reason was a bad technical state of bridge structures, one of them was in serious disrepair. The project deals with the relocation of current roads to more suitable routes including the option allowing access to the nearby estates and the connection to the existing cart tracks. The project also addresses two bridge structures across the streams.

Understanding the movement phenology of the four-toed salamander (Hemidactylium scutatum) is essential to guide management practices. I examined the relationship between environmental variables and the directionality, timing, and magnitude of four-toed salamander movements at two locations in eastern Massachusetts. Four-toed salamanders move from upland habitats to wetland areas in early spring and move away from wetlands in late spring. Adult movements increased with more precipitation and less moon light. Juvenile movements were similarly affected, and in addition they were more likely to move when temperatures were warm and days long. My results can be used to implement management strategies aimed at reducing human-related impacts on migrating four-toed salamanders (e.g., road closures to reduce road mortality). In addition, I developed and evaluated the accuracy of classification and regression tree (CART) models at multiple spatial scales to predict suitable habitat and potential species occurrences of the four-toed salamander (Hemidactylium scutatum) in Massachusetts. I analyzed four-toed salamander Element Occurrence (EO) observations reported during 1990-2009 in response to fifteen environmental predictor variables at six different local and landscape-scales. The landscape-scale model measured at 2000 m was most successful at predicting four-toed salamander habitat. It correctly classified 92.4% of the training data and 87.7% of the verification data. When the 2000 m model was applied statewide, 30,195 wetlands were determined to be potentially suitable habitat for the four-toed salamander. The results of this study confirm the potential and value of classification and regression tree models for identifying potential habitat for rare or cryptic species.

Elever som kombinerar sin gymnasietid med elitidrottande ställs inför ett flertal utmaningar som bland annat leder till ökad stress. För att de på bästa möjliga vis ska kunna ta sig an dessa utmaningar finns de anställda coacherna på riksidrottsgymnasierna till deras förfogande. Coacherna spenderar mycket tid med eleverna och planerar träningen, ger råd och bedömer dessa i ämnet specialidrott. Men hur ser egentligen elevernas relation med tränarna ut, och skiljer den sig åt beroende på kön, årskurs eller typ av idrott? Data samlades in på 61 elever med hjälp av CART-Q-enkäten samt med frågor som berörde just kön, årskurs och typ av idrott. Resultatet visar på att eleverna enligt CART-Q uppfattar relationen som god (m = 6,00). Inga signifikanta skillnader sågs mellan de olika bakgrundsvariablerna och coach-atletrelationen. Denna goda syn på relationen kan möjligtvis leda till goda eller förbättrade prestationer samt bidra till ett bättre mående utanför idrotten. Mer forskning behövs för att säkerställa hur coach-atletrelationen påverkar elever på riksidrottsgymnasier.

Due to recent advances in technology that facilitate acquisition of multi-parameter defined phenotypes, new opportunities have arisen for predicting patient outcomes based on individual specific cell subset changes. The data resulting from these trials can be a challenge to analyze, as predictors may be highly correlated with each other or related to outcome within levels of other predictor variables. As a result, applying traditional methods like simple linear models and univariate approaches such as odds ratios may be insufficient. In this dissertation, we describe potential solutions including tree-based methods, ridge regression, mixed modeling, and a new estimator called a mixed ridge estimator with expectation-maximization (EM) algorithm. Data examples are provided. In particular, flow cytometry is a method of measuring a large number of particle counts at once by suspending them in a fluid and shining a beam of light onto the fluid. This is specifically relevant in the context of studying human immunodeficiency virus (HIV), where there exists a great potential to draw from the rich array of data on host cell-mediated response to infection and drug exposures, to inform and discover patient level determinants of disease progression and/or response to anti-retroviral therapy (ART). The data sets collected are often high dimensional with correlated columns, which can be challenging to analyze. We demonstrate the application and comparative interpretations of three tree-based algorithms for the analysis of data arising from flow cytometry in the first chapter of this manuscript. Specifically, we consider the question of what best predicts CD4 T-cell recovery in HIV-1 infected persons starting antiretroviral therapy with CD4 count between 200-350 cell/μl. The tree-based approaches, namely, classification and regression trees (CART), random forests (RF) and logic regression (LR), were designed specifically to uncover complex structure in high dimensional data settings. While contingency table analysis and RFs provide information on the importance of each potential predictor variable, CART and LR offer additional insight into the combinations of variables that together are predictive of the outcome. Specifically, application of tree-based methods to our data suggest that a combination of baseline immune activation states, with emphasis on CD8 T cell activation, may be a better predictor than any single T cell/innate cell subset analyzed. In the following chapter, tree-based methods are compared to each other via a simulation study. Each has its merits in particular circumstances; for example, RF is able to identify the order of importance of predictors regardless of whether there is a tree-like structure. It is able to adjust for correlation among predictors by using a machine learning algorithm, analyzing subsets of predictors and subjects over a number of iterations. CART is useful when variables are predictive of outcome within levels of other variables, and is able to find the most parsimonious model using pruning. LR also identifies structure within the set of predictor variables, and nicely illustrates relationship among variables. However, due to the vast number of combinations of predictor variables that would need to be analyzed in order to find the single best LR tree, an algorithm is used that only searches a subset of potential combinations of predictors. Therefore, results may be different each time the algorithm is used on the same data set. Next we use a regression approach to analyzing data with correlated predictors. Ridge regression is a method of accounting for correlated data by adding a shrinkage component to the estimators for a linear model. We perform a simulation study to compare ridge regression to linear regression over various correlation coefficients and find that ridge regression outperforms linear regression as correlation increases. To account for collinearity among the predictors along with longitudinal data, a new estimator that combines the applicability of ridge regression and mixed models using an EM algorithm is developed and compared to the mixed model. We find from a simulation study comparing our mixed ridge (MR) approach with a traditional mixed model that our new mixed ridge estimator is able to handle collinearity of predictor variables better than the mixed model, while accounting for random within-subject effects that regular ridge regression does not take into account. As correlation among predictors increases, power decreases more quickly for the mixed model than MR. Additionally, type I error rate is not significantly elevated when the MR approach is taken. The MR estimator gives us new insight into flow cytometry data and other data sets with correlated predictor variables that our tree-based methods could not give us. These methods all provide unique insight into our data that more traditional methods of analysis do not offer.

Accelerometers are objective monitors of physical activity (PA) that can be used to estimate energy expenditure (EE). Most accelerometer EE estimation equations are based on steady-state data and do not consider excess post-exercise oxygen consumption (EPOC) after exercise. PURPOSE: To quantify the error in accelerometer EE estimates due to EPOC after varying durations of high-intensity treadmill running. METHODS: Nine young, healthy, recreationally active males participated in three study visits. Visit 1 included a treadmill VO2 peak test to determine the treadmill speed correlating to 80% VO2 peak for visits 2 and 3. Visit 2 included a seated 20-min baseline and three short (30s, 60s, 120s) vigorous treadmill running bouts each followed by 20 minutes of seated rest. Visit 3 included a supine 60-min baseline and a 30-min treadmill running bout followed by 3 hours of supine rest. Twelve EE estimation equations each using either a non-dominant wrist or right hip ActiGraph GT3X+ accelerometer were compared to the true EE measured by the Parvomedics TrueOne 2400 indirect calorimeter. RESULTS: The Freedson 1998 EE estimation equation overestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: 19.3 [11.4, 27.2], 60s: 16.6 [8.5, 24.7], 120s: 13.4 [5.74, 21.1], 30min: 15.1 [6.69, 23.5]). The Crouter 2009 branching algorithm underestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: -8.59 [-10.6, -6.62], 60s: -11.6 [-13.7, -9.38], 120s: -15.0 [-18.1, -11.8], 30min: -11.0 [-14.3, -7.77]), but was partially corrected by adding in the measured EPOC. CONCLUSION: Estimated EE during lying or seated rest from linear accelerometer equations was heavily dependent on the y-intercept of the equation, which represents the estimated resting EE of the wearer, with the Crouter calibration study being the only one to directly measure resting EE. More sophisticated approaches, like the Crouter 2009 and newer machine learning algorithms, have better potential to more accurately estimate EE across various activity types. New accelerometer EE estimations should include resting in their calibration protocols in order to more accurately estimate EE during rest.

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