In modern times continuous casting is the preferred way to convert molten steel into solid forms to enable further processing. At Columbus Stainless the continuous casting machine cast slabs of constant thickness with varying width. One important aspect of the continuously cast strand that must be controlled, is the strand width. The strand width exiting from the casting machine, has a direct influence on the product yield which in turn influences the profitability of the company. In general, the strand width control on the austentic and ferritic type steels achieved is excellent with the exception of the 12% chrome non stabilised ferritic steel. This steel type exhibited different strand width changes when a sequence of different heats was cast. The strand width changes corresponded to the different heats in the sequence. Each heat has a unique chemistry and a relationship between the austenite and ferrite fraction at high temperature and the resulting strand width change was explained by Siyasiya[27]. The relationship between the heat composition and width change has in the past resulted in the development of a model that enabled the prediction of the expected width change of a specific heat before it is cast to enable preventative action to be taken. This model has been implemented as an on-line prediction model in the production environment with very encouraging results. This study was initiated because it was uncertain if the implemented model was the most accurate for this application. This study is concerned with the development of more models based on different techniques in an attempt to implement a more accurate model. The data mining techniques used include statistical regression, decision trees and fuzzy logic. The results indicated that the existing model was the most accurate and it could not be improved upon. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / MEng / unrestricted

Buildings are not only central to the day-to-day activities but also serve as critical indicators of urban development and transformation. The automatic extraction of building footprints from high-resolution Remote Sensing Imagery (RSI) has emerged as an important and popular tool in urban studies. It helps to enhance the understanding and management of urban sprawl, urban planning, population estimation, resource allocation, and post-disaster damage assessment. In this context, having an automated and robust building detection model is crucial. Deep Learning (DL) model and Object-Based Image Analysis (OBIA) techniques are the main and commonly used for automated building detection. This study investigates the efficacy of a pre-trained DL model and a rule-based model OBIA techniques in building detection across varied resolutions and geographic settings. Employing orthophotos from Luleå, Gävle, and Stockholm, the research assesses the adaptability and robustness of these methods under image properties and urban densities. The DL model was initially trained on 0.25m resolution data of Sweden by Lantmäteriet (Sweden mapping agency). The rule-based model was developed by applying OBIA techniques on behalf of this study. Models were analyzed through six feature agreement statistics including Critical Success Index (CSI), Precision, and Detection Probability (POD). The findings reveal that the DL model consistently outperformed the OBIA approach across all study areas, particularly at the original 25 cm resolution. Gävle showed superior precision with a CSI of 0.8139 for the DL model against a CSI of 0.7493 for OBIA at 25 cm.  The evaluation was improved by considering 50*50 sq. m subsets and building sizes. These evaluations highlight that building size and urban density significantly influence detection accuracy. Larger (> 2500 sq. m) buildings and less dense areas tend to yield higher accuracy across both detection methods. The DL model exhibited high CSI values for very large buildings (>5500 sq. m) in Gävle, surpassing 0.8, while the detection of very small (< 50 sq. m) buildings remained challenging for both methods. Overall, the pre-trained DL model is very sensitive to resolution changes compared to OBIA. Importantly, both give their best performance at the original resolution while DL is superior than OBIA. A rule-based OBIA model is affected by the geographical characteristics more heavily than a DL model. Both models have their best performance in the area with medium building density when medium to very large buildings exist. This study highlights how big the impact of building size, geographic characteristics, and image resolution on the performance of DL and OBIA techniques. However, further investigation is recommended to draw a strong conclusion regarding the impact of resolution on the model performance.

Mathematische Modellierung ist ein wichtiges Werkzeug biologischer Forschung geworden, was sich in der Entstehung von Systembiologie widerspiegelt. Eine erfolgreiche Anwendung mathematischer Methoden auf biologische Fragen erfordert die Zusammenarbeit zwischen experimentell und theoretisch arbeitenden Wissenschaftlern, auch um sicherzustellen, dass die Biologie im Modell adäquat dargestellt wird. Ich präsentiere hier zwei Untersuchungen zur Anpassung von Saccharomyces cere- visae an hyperosmotische Bedingungen: Eine biologisch detailgetreue Beschreibung der Signaltransduktion zur Aktivierung von Hog1 und ein Model, das Anpassung an osmotischen Stress in zellulärem Zusammenhang beschreibt. Die Studie zur Osmoadaptation in zellulären Kontext impliziert, dass Hog1 und Fps1, zwei wichtige Bausteine dieses Adaptationsvorgangs, miteinander in Wechselwirkung treten und dies zur Anpassung beiträgt. Dieses Ergebnis wird durch die Integration verschiedener Hefestämme mit zum Teil gegensätzlich wirkenden Mutationen ermöglicht. Diese Studie offenbart des weiteren, dass die Rolle von Glycerol in der langfristigen Anpassung bisher überschätzt wurde. Die hier präsentierten Ergebnisse zeigen, dass Glycerol als ’Not’-Osmolyt eingesetzt wird und andere Stoffe, z.B. Trehalose, erheblich zu dauerhafter Osmoadaptation beitragen. Durch die Betrachtung des Zustands mehrerer zellulärer Mechansimen wird deutlich, dass Osmoadaptation stark vom Kontext abhängig ist und nicht perfekt ist. Der Preis schlägt sich in langsamerem Wachstum nieder. Zeitabhängige Sensitivitätsanalyse des Modells untermauert diese Hypothese. Die gewählte Perspektive ermöglicht die Betrachtung von intrazellulären Signaltransduktionskomponenten, Metaboliten und des Wachstums. Der Vergleich mit einer Studie, die Anpassung an osmotischen Stress als perfekte Adaptation auf Grund eines vereinfachten Modells beschreibt, hebt die Rolle der gewählten Perspektive zum Verständnis biologischer Systeme hervor. / Mathematical modeling has become an important tool in biology, reflected in the emergence of systems biology. Successful application of mathematical methods to biological questions requires collaboration of experimental and theoretical scientists to identify and study the problem at hand and to ensure that biology and model match. In this thesis, I present two studies on adaptation to hyperosmotic conditions in the yeast Saccharomyces cerevisae: A biologically faithful description of the signaling pathways activating Hog1 and a model integrating the effects of Hog1-activity and cellular metabolism, describing osmoadaptation in cellular context. The study of osmoadaptation in cellular context suggests that Hog1 and Fps1, two crucial components of adaptation, interact upon hyperosmotic stress. This finding is facilitated by incorporating multiple strains with mutations leading to partly oppositional phenotypes. This study further reveals that the role of glycerol in long term adaptation has been overestimated so far. According to the results presented here, glycerol is utilized as an ’emergency’ osmoprotectant and other compounds, e.g. trehalose, contribute significantly to osmoadaptation. Accounting for the state of multiple cellular mechanisms (Hog1-activity, glycolysis, growth) shows that adaptation to hyperosmotic stress and the impact of the individual mechanisms of adaptation is context dependent and that adaptation to sustained osmostress is not perfect, the expense reflected in a reduced growth rate in hyperosmotic medium. Time-dependent sensitivity analysis supports the notion of context. The perspective chosen allows observations on intracellular signaling components, metabolites and growth speed. Comparison with a study that describes osmoadaptation as perfect adaptation highlights the role of this perspective for the conclusions drawn, thus emphasizing the importance of an integrative perspective for understanding biological systems.