One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

To ensure greater success in its regular projects, Abu-Dhabi Police (ADP) is working on: training their project managers, applying project management software, utilising the services of engineering consultants, etc. However, the performance and outcomes of its projects are still not fully meeting the desired expectations. Therefore, a study to understand some of the undermining factors was carried out. A critical literature review was carried out initially where it was established that project delivery and outcomes are affected in part by the three overarching factors of: project managers’ characteristics, organizational culture and project management culture. On this basis a conceptual framework was developed highlighting how these 3 compound factors affect project performance and outcome; and in particular how ADP’s values relate with the competencies of their project managers. The empirical aspects involved the use of mixed methods where the first part was a quantitative survey of the understanding and achievement of both ADP’s 5 values (part of organizational culture) and 15 competencies (part of project manager’s characteristics), as well as the impact of the former on the later. A questionnaire was administered to 157 people for data collection and 71 fully completed responses were obtained, representing a response rate of 45%. Descriptive statistics were used to evaluate the levels of achievement of ADP’s values and competencies, which were found to be high. The analysis went on to use ordered logistic regression to examine the association between the attainment of ADP’s values and competencies. The findings showed that the 5 values impact on the competencies of ADP’s project managers differently; for example, the value of ‘integrity and honesty’ impacts heavily on the 5 competencies of Integration management, Scope management, Time management, Achieving and action, and Leadership; while the value of ‘effective communication’ impacts heavily on the 3 competencies of Scope management, Cost management, and Achieving and action. An advanced training programme was subsequently developed for ADP to further increase the attainment of values and competencies by their project managers. This programme was developed in focus group discussions that involved some selected project managers who had long working experience and high understanding of ADP project schemes. A further round of focus group discussions was also used to validate this advanced training programme.

Botswana is a middle-income economy. It has become dependent on non-renewable resources. Agriculture and manufacturing have failed to develop in a significant way. The small population has not provided adequate demand. The Government has tried a number of strategies in order to diversify the economy. One of these is the promotion of Small and Medium Enterprises (SMEs). Unfortunately, SMEs have failed to thrive, so far. A number of researchers have concluded that SMEs will not thrive due to the fact that Botswana have low entrepreneurial skills. High failure rates are experienced where enterprises are established. The thesis identifies an alternative strategy in the development of enterprises in Botswana. It is generally believed that a franchisee does not need high levels of entrepreneurial skills to succeed. If this is so, Botswana can solve her problem of lack of sufficient enterprises by promoting franchising. The thesis uses the interview technique to assess whether existing franchisees in Botswana have low levels of entrepreneurial skills. Indeed it proves that franchisees have low skill levels compared to non-franchised entrepreneurs. Secondly, the thesis proves that franchisees in Botswana operate as employee-managers. Thirdly, the thesis establishes that franchisees perform better than non-franchised entrepreneurs, even though they have low entrepreneurial skill levels. Lastly, the thesis, using evidence from findings above, and from responses of experts interviewed, establishes that the promotion of franchising is a viable alternative strategy to one that depends solely on non-franchised enterprises.While there are some methodological limitations, like those stemming from a low and unknown franchisee population in Botswana, the use of ordinal data, use of techniques to rate their own skills, and a relatively small sample for franchised and non-franchised entrepreneurs, the statistical techniques used are powerful enough to generate reliable findings. / Graduate School of Business Leadership / D.B.L

Trees are one of the most fundamental structures in computer science. Standard pointer-based representations consume a significant amount of space while only supporting a small set of navigational operations. Succinct data structures have been developed to overcome these difficulties. A succinct data structure for an object from a given class of objects occupies space close to the information-theoretic lower-bound for representing an object from the class, while supporting the required operations on the object efficiently. In this thesis we consider representing trees succinctly. Various succinct representations have been designed for representing different classes of trees, namely, ordinal trees, cardinal trees and labelled trees. Barring a few, most of these representations are static in that they do not support inserting and deleting nodes. We consider succinct representations for cardinal trees that also support updates (insertions and deletions), i.e., dynamic cardinal trees. A cardinal tree of degree k, also referred to as a k-ary cardinal tree or simply a k-ary tree is a tree where each node has place for up to k children with labels from 1 to k. The information-theoretic lower bound for representing a k-ary cardinal tree on n nodes is roughly (2n+n log k) bits. Representations that take (2n+n log k+ o(n log k ) ) bits have been designed that support basic navigations operations like finding the parent, i-th child, child-labeled j, size of a subtree etc. in constant time. But these could not support updates efficiently. The only known succinct dynamic representation was given by Diego, who gave a structure that still uses (2n+n log k+o(n log k ) ) bits and supports basic navigational operations in O((log k+log log n) ) time, and updates in O((log k + log log n)(1+log k /log (log k + log log n))) amortized time. We improve the times for the operations without increasing the space complexity, for the case when k is reasonably small compared to n. In particular, when k=(O(√(log n ))) our representation supports all the navigational operations in constant time while supporting updates in O(√(log log n )) amortized time.

In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for addressing imbalanced datasets for binary classification; however, current methods do not address within-class imbalance and between-class imbalance at the same time. Similarly, there has been very little research work on addressing imbalanced datasets for ordinal regression. Although current standard oversampling methods can be used to improve the dataset class distribution, they do not consider the ordinal relationship between the classes. The class imbalance problem is a big challenge in classification problems. Most of the clinical datasets are highly imbalanced, which can weaken the performance of classifiers significantly. In this research, the imbalanced dataset classification problem is also examined in the context of a clinical application, particularly pelvic organ prolapse diagnosis. Pelvic organ prolapse (POP) is a major health problem that affects between 30-50% of women in the U.S. Although clinical examination is currently used to diagnose POP, there is still little evidence on specific risk factors that are directly related to particular types of POP and their severity or stages (Stage 0-IV). Data from dynamic MRI related to the movement of pelvic organs has the potential to improve POP prediction but it is currently analyzed manually limiting its exploration and use to small datasets. Moreover, POP is a disorder with multiple stages that are ordinal and whose distribution is highly imbalanced. The main goal of this research is two-fold. The first goal is to design new oversampling methods for imbalanced datasets for both binary classification and ordinal regression. The second goal is to automatically track, segment, and classify the trajectory of multiple organs on dynamic MRI to quantitatively describe pelvic organ movement. The extracted image-based data along with the designed oversampling methods will be used to improve the diagnosis of POP. The proposed research consists of three major objectives: 1) to design a new oversampling technique for binary imbalanced dataset classification; 2) to design a novel oversampling technique for ordinal regression with imbalanced datasets; and 3) to design a two-stage method to automatically track and segment multiple pelvic organs on dynamic MRI for improving the prediction of multi-stage POP with imbalanced datasets. The proposed research aims to provide robust oversampling techniques and image processing models that can (1) effectively handle highly imbalanced datasets for both binary classification and ordinal regression, and (2) automatically track and segment multiple deformable structures for feature extraction from low contrast and nonhomogeneous images and classify them using the resulted trajectories. This research will set the foundation towards a computer-aided decision support system that can automatically extract and analyze image and clinical data to improve the prediction of disorders where the dataset is highly imbalanced through personalized and evidence-based assessment.

This dissertation deals with three topics in descriptive set theory. First, the order topology is a natural topology on ordinals. In Chapter 2, a complete classification of order topologies on ordinals up to Borel isomorphism is given, answering a question of Benedikt Löwe. Second, a map between separable metrizable spaces X and Y preserves complete metrizability if Y is completely metrizable whenever X is; the map is resolvable if the image of every open (closed) set in X is resolvable in Y. In Chapter 3, it is proven that resolvable maps preserve complete metrizability, generalizing results of Sierpiński, Vainštein, and Ostrovsky. Third, an equivalence relation on a Polish space has the Laczkovich-Komjáth property if the following holds: for every sequence of analytic sets such that the limit superior along any infinite set of indices meets uncountably many equivalence classes, there is an infinite subsequence such that the intersection of these sets contains a perfect set of pairwise inequivalent elements. In Chapter 4, it is shown that every coanalytic equivalence relation has the Laczkovich-Komjáth property, extending a theorem of Balcerzak and Głąb.

<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>

It is believed that a large number of people experience remaining symptoms after COVID-19, so-called post-COVID. The formal definition and diagnostic criteria of post-COVID have been a scientific controversy. So far, there is no reliable system for distinguishing the severity of post-COVID. This type of measurement would be helpful in future targeted therapies. Therefore, this thesis aims to evaluate the relationship between an individual’s functional status today and the symptoms present as well as identify relevant groups of post-COVID based on these 17 long-term symptoms of post-COVID. Further, to produce a model for which of these groups an individual belongs to. By using cluster analysis and ordinal logistic regression, Post-COVID Syndrome scores are produced. That is based upon both subjects who were hospitalised and those who were not, collected through a project called COMBAT post-covid. The individuals are then divided into groups based on these scores, and a prediction model is made using ordinal logistic regression and backward deletion. Three well-separated groups of post-COVID are found based on the produced scores. The prediction model indicates that the nine variables Sex, BMI, Smoking, Snuff, Heart disease, Lung disease, Diabetes, Chronic pain and Symptom severity at the onset seem important for predicting someone’s group. This study showed that the remaining symptoms affected an individual’s functional status, including self-reported working ability and general health.

In today’s information era, many news outlets are competing for attention. One way to cut through the noise is to use images. Obtaining images can be both time-consuming and expen- sive for smaller news agencies. In collaboration with the Swedish news agency Newsworthy, we investigate the possibilities of using AI-generated images in a journalistic context. Using images generated with the text-to-image generation model Stable Diffusion, we aim to answer the research question How do the parameters in Stable Diffusion affect the applicability of the generated images for journalistic purposes? A total of 511 images are generated with different Stable Diffusion parameter settings and rated on a scale of 1-5 by three journalists at Newswor- thy. The data is analyzed using ordinal logistic regression. The results suggest that the optimal value for the Stable Diffusion parameter classifier-free guidance is around 10-12, the default 50 iterations are sufficient, and keywords do not significantly affect the image outcome. The parameter that has the single greatest effect on the outcome is the prompt. Thus, to generate photo-realistic images that can be used in a journalistic context, most thought and effort should be put towards formulating a suitable prompt.