Introduction: Number of strata (NoS) is an informative descriptor of forest structure and is therefore useful in forest management. Collection of NoS as well as other forest properties is performed by fieldworkers and could benefit from automation. Objectives: This study investigates automated prediction of NoS from airborne laser scanned point clouds over Swedish forest plots.Methods: A previously suggested approach of using vertical gap probability is compared through experimentation against the geometric neural network PointNet++ configured for ordinal prediction. For both approaches, the mean accuracy is measured for three datasets: coniferous forest, deciduous forest, and a combination of all forests. Results: PointNet++ displayed a better point performance for two out of three datasets, attaining a top mean accuracy of 46.2%. However only the coniferous subset displayed a statistically significant superiority for PointNet++. Conclusion: This study demonstrates the potential of geometric neural networks for data mining of forest properties. The results show that impediments in the data may need to be addressed for further improvements.

>Magister Scientiae - MSc / This study sought to identify and fit the appropriate extreme value distribution to flood data, using the method of maximum likelihood. To examine the uncertainty of the estimated parameters and evaluate the goodness of fit of the model identified. The study revealed that the three parameter Weibull and the generalised extreme value (GEV) distributions fit the data very well. Standard errors for the estimated parameters were calculated from the empirical information matrix. An upper limit to the flood levels followed from the fitted distribution.

Ensemble simulation is a commonly used technique in operational forecasting of weather and floods. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists and hydrologists are interested in understanding the uncertainties associated with the simulation; specifically variability between the ensemble members. The visualization of ensemble members is currently accomplished through spaghetti plots or hydrographs. To improve visualization techniques and tools for forecasters, we conducted a userstudy to evaluate the effectiveness of existing uncertainty visualization techniques on 1D and 2D synthetic datasets. We designed an uncertainty evaluation framework to enable easier design of such studies for scientific visualization. The techniques evaluated are errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. Although we did not find a consistent order among the four techniques for all tasks, we found that the efficiency of techniques used highly depended on the tasks being performed. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. With results from the user-study, we iteratively developed a tool named ‘Noodles’ to interactively explore the ensemble uncertainty in weather simulations. Uncertainty was quantified using standard deviation, inter-quartile range, width of the 95% confidence interval, and by bootstrapping the data. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers and avoiding the parametrizations leading to these outliers. Additionally, they could identify spatial regions with high uncertainty thereby determining poorly simulated storm environments and deriving physical interpretation of these model issues. We also describe uncertainty visualization capabilities developed for a tool named ‘FloodViz’ for visualization and analysis of flood simulation ensembles. Simple member and trend plots and composited inundation maps with uncertainty are described along with different types of glyph based uncertainty representations. We also provide feedback from a hydrologist using various features of the tool from an operational perspective.

This diploma project design studio work focus on the introduction to the problematics of vacant lots within the contemporary city centres. The opening represents basic typology of the lots. The work also presents a strategy how to develop the potential of vacant lots with its recreation as public spaces before the housing developments. The whole project book is divided into three parts. Analythical one shows the vacants lots within the Brno city centre and their typology. The strategy is illustrated by a sort of a „health kit“ tool box.And the main aim of the design part is to explore various possibilities of development for a vacant lot on Vesela street.

My work has focused on a variety of different types of diamond-based, core-shell particles. These particles are formed with inert cores and poly(allylamine)/nanodiamond shells. Their intended purpose is to form an LC stationary phase that is stable from pH 1 – 14 and at elevated temperatures. At the beginning of my studies, the particles that had been made in the Linford laboratory were pH stable, but irregular and had poor mechanical stability. Since that time, I have worked to improve the particles by using more spherical zirconia and carbon cores, and I have improved their mechanical stability via chemical crosslinking with epoxides. I have performed van Deemter and van’t Hoff analyses to understand the properties of these columns. Efficiencies greater than 100,000 N/m are routinely achieved with these carbon/nanodiamond-based phases. In addition I contributed to two patents that show innovations in diamond functionalization. My contributions involved reduction of an oxidized diamond surface with LiAlH4 prior to functionalization with isocyanates. I also wrote some application notes for the Flare mixed-mode column, which was recently introduced to the market and contains particles comprised of a carbon core and a polymer/nanodiamond shell. These application notes show the gradient separations of four essential oils (lavender, melaleuca, peppermint and eucalyptus), and the isocratic separations of various triazine herbicides and a mixture of β2-agonists and amphetamines.This dissertation contains the following sections. Chapter 1 is a review of liquid chromatographic history and theory. It also includes a history of the use of diamonds in liquid chromatography. Chapter 2 is a study on a glassy carbon core - polymer/nanodiamond shell particle made in our laboratory. Stability studies at pH 11.3 and 13 were performed and different analytes were retained and/or separated on the column. Chapter 3 is a study performed on the Flare mixed-mode column. Separations of tricyclic antidepressants, β2-andrenergic receptor agonists, and linear chain alkylbenzenes were demonstrated with this phase. Van Deemter and van’t Hoff studies were also performed to probe the efficiency and selectivity of this column with different classes of analytes. Chapter 4 chronicles, via SEM and van Deemter analysis, the improvements that have taken place in our column after many iterations of improved synthetic methods and new materials. These include better particle uniformity, particle stability, and column efficiency. Three different carbon cores were analyzed, each better than the previous one. Appendices 1 – 6 are application notes published by Diamond Analytics of β2-andrenergic receptor agonists and amphetamines, triazine herbicides, and lavender, melaleuca, eucalyptus and peppermint essential oils. Appendices 7 and 8 are patents that contain ideas and research contributed by the author.

In recent years, knowledge graphs(KGs) have become essential tools for visualizing concepts and retrieving contextual information. However, constructing KGs for new and specialized domains like Positive Energy Districts (PEDs) presents unique challenges, particularly when dealing with unstructured texts and ambiguous concepts from academic articles. This study focuses on various strategies for constructing and inferring KGs, specifically incorporating entities related to PEDs, such as projects, technologies, organizations, and locations. We utilize visualization techniques and node embedding methods to explore the graph's structure and content and apply filtering techniques and t-SNE plots to extract subgraphs based on specific categories or keywords. One of the key contributions is using the longest path method, which allows us to uncover intricate relationships, interconnectedness between entities, critical paths, and hidden patterns within the graph, providing valuable insights into the most significant connections. Additionally, community detection techniques were employed to identify distinct communities within the graph, providing further understanding of the structural organization and clusters of interconnected nodes with shared themes. The paper also presents a detailed evaluation of a question-answering system based on the KG, where the Universal Sentence Encoder was used to convert text into dense vector representations and calculate cosine similarity to find similar sentences. We assess the system's performance through precision and recall analysis and conduct statistical comparisons of graph embeddings, with Node2Vec outperforming DeepWalk in capturing similarities and connections. For edge prediction, logistic regression, focusing on pairs of neighbours that lack a direct connection, was employed to effectively identify potential connections among nodes within the graph. Additionally, probabilistic edge predictions, threshold analysis, and the significance of individual nodes were discussed. Lastly, the advantages and limitations of using existing KGs(Wikidata and DBpedia) versus constructing new ones specifically for PEDs were investigated. It is evident that further research and data enrichment is necessary to address the scarcity of domain-specific information from existing sources.

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The plot twist is a complex narrative surprise in which a revelation retroactively transforms readers’ understanding of the preceding events. Readers discover belatedly that the situation depicted in the narrative had all along been quite different from what they thought. Although the term “plot twist” was first used in the early twentieth century, many of the best-known works of fiction of the nineteenth century were revealed, in retrospect, to be twist narratives. This dissertation studies twist narratives and their readers in the period before the plot twist became a known device. Through case studies of Jane Austen’s Emma, Charles Dickens’s Great Expectations, Guy de Maupassant’s “The Necklace” and Agatha Christie’s The Murder of Roger Ackroyd, the chapters investigate what kinds of knowledge-making practices readers engage in during first-time readings and rereadings of twist narratives, as well as before and after reading. Across these chapters I make the case that twist narratives demonstrate the crucial and interconnected roles of knowledge and temporality in any narrative experience. What we know, and when, and especially what we don’t (yet) know, is crucial to how narratives work and why we enjoy them.

Parkinson’s disease is an age-related neurodegenerative disorder. The major symptoms of Parkinson’s disease are closely linked to the pathology of the disease. The main pathology of Parkinson’s disease consists of the degeneration of neurons of the substantia nigra pars compacta (SNpc), which leads to reduced amounts of dopamine in the brain. One of the treatment strategies in Parkinson’s disease is to conserve dopamine by inhibiting the enzymes responsible for its catabolism. The monoamine oxidase (MAO) B isoform catalyses the oxidation of dopamine in the central nervous system and is therefore an important target for Parkinson’s disease treatment. Inhibition of MAO-B provides symptomatic relief for Parkinson’s disease patients by increasing endogenous dopamine levels as well as enhancing the levels of dopamine after administration of levodopa (L-dopa), the metabolic precursor of dopamine. Recent studies have shown that phthalide can be used as a scaffold for the design of reversible MAO inhibitors. Although phthalide is a weak MAO-B inhibitor, substitution on the C5 position of phthalide yields highly potent reversible MAO-B inhibitors. In the present study, sesamol and benzodioxane were used as scaffolds for the design of MAO inhibitors. The structures of sesamol and benzodioxane closely resemble that of phthalide, which suggests that these moieties may be useful for the design of MAO inhibitors. This study may be viewed as an exploratory study to discover new scaffolds for MAO inhibition. Since substitution at C5 of phthalide with a benzyloxy side chain yielded particularly potent MAO inhibitors, the sesamol and benzodioxane derivatives possessed the benzyloxy substituent in the analogous positions to C5 of phthalide. These were the C5 and C6 positions of sesamol and benzodioxane, respectively. The sesamol and benzodioxane derivatives were synthesised by reacting sesamol and 6- hydroxy-1,4-benzodioxane, respectively, with an appropriate alkyl bromide in the presence of potassium carbonate (K2CO3) in N,N-dimethylformamide (DMF). 6-Hydroxy-1,4- benzodioxane, in turn, was synthesised from 1,4-benzodioxan-6-carboxaldehyde. The structures of the compounds were verified with nuclear magnetic resonance (NMR) and mass spectrometry (MS) analyses, while the purities were estimated by high-pressure liquid chromatography (HPLC). Sixteen sesamol and benzodioxane derivatives were synthesised. To determine the inhibition potencies of the synthesised compounds the recombinant human MAO-A and MAO-B enzymes were used. The inhibition potencies were expressed as the corresponding IC50 values. The results showed that the sesamol and benzodioxane derivatives are highly potent and selective inhibitors of MAO-B and to a lesser extent MAOA. The most potent MAO-B inhibitor was 6-(3-bromobenzyloxy)-1,4-benzodioxane with an IC50 value of 0.045 μM. All compounds examined displayed selectivity for the MAO-B isoform over MAO-A. Generally the benzodioxane derivatives were found to be more potent inhibitors of human MAO-A and MAO-B than the sesamol derivatives. The reversibility and mode of MAO-B inhibition of a representative derivative, 6-(3- bromobenzyloxy)-1,4-benzodioxane, was examined by measuring the degree to which the enzyme activity recovers after dialysis of enzyme-inhibitor complexes, while Lineweaver- Burk plots were constructed to determine whether the mode of inhibition is competitive. Since MAO-B activity is completely recovered after dialysis of enzyme-inhibitor mixtures, it was concluded that 6-(3-bromobenzyloxy)-1,4-benzodioxane binds reversibly to the MAO-B enzyme. The Lineweaver-Burk plots constructed were linear and intersected on the y-axis. Therefore it may be concluded that 6-(3-bromobenzyloxy)-1,4-benzodioxane is a competitive MAO-B inhibitor. To conclude, the C6-substituted benzodioxane derivatives are potent, selective, reversible and competitive inhibitors of human MAO-B. These compounds are therefore promising leads for the future development of therapy for Parkinson’s disease. / MSc (Pharmaceutical Chemistry), North-West University, Potchefstroom Campus, 2015