Graph search is used in many areas of computer science. It is well-known that the scalability of graph-search algorithms such as A* is limited by their memory requirements. In this dissertation, I describe three complementary strategies for reducing the memory requirements of graph-search algorithms, especially for multiple sequence alignment (a central problem in computational molecular biology). These search strategies dramatically increase the range and difficulty of multiple sequence alignment problems that can be solved. The first strategy uses a divide-and-conquer method of solution reconstruction, and one of my contributions is to show that when divide-and-conquer solution reconstruction is used, a layer-by-layer strategy for multiple sequence alignment is more memory-efficient than a bestirst strategy. The second strategy is a new approach to duplicate detection in external-memory graph search that involves partitioning the search graph based on an abstraction of the state space. For graphs with sufficient local structure, it allows graph-search algorithms to use external memory, such as disk storage, almost as efficiently as internal memory. The third strategy is a technique for reducing the memory requirements of sub-alignment search heuristics that are stored in lookup tables. It uses the start and goal states of a problem instance to restrict the region of the state space for which a table-based heuristic is needed, making it possible to store more accurate heuristic estimates in the same amount of memory. These three strategies dramatically improve the scalability of graph search not only for multiple sequence alignment, but for many other graph-search problems, and generalizations of these search strategies for other graph-search problems are discussed throughout the dissertation.

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Rising levels of uncertainty and distrust of governments and mass printing of fiat currencies in conjunction with pandemic-related events have led to a rotation into different assets such as cryptos. Without solid fundamentals, cryptocurrencies have spiked in price levels in the last few years; while popularity rises it remains heavily misunderstood. This study looks into the factors that specifically influence the cryptocurrency Ethereum's price volatility. According to previous literature and existing theories, it gathers seven explanatory variables that should impact the volatility and applies a GARCH (1,1) model. The study finds that the variables Google trends, hash rate, S&P 500, address count, and trade volume impacted the volatility. With only the hash rate and S&P 500 lowering the volatility. Both the ARCH and GARCH terms were significant, with the latter having the bigger coefficient, implying that the past volatility should be accounted for when forecasting future volatility. The findings within this study align with previous literature and other studies on different cryptos. It concludes that while Ethereum is still volatile and is in its growing phase it is headed in a positive direction in terms of stabilization. Further research or a repeated identical/similar study should be conducted again once the market has matured further.

This degree project investigates social and technological aspects of human-vehicle interactions with regards to driverless autonomous utilitarian vehicles in urban context and proposes the use of LED lighting as an external Human Machine Interface for vehicle pedestrian signaling.

<p> Using established spur gear design practice, a user-oriented computer-aided design package is created by which a user requires minimal knowledge of, or experience in either FORTRAN, optimization or gear design. although this practice is risky since the designer a judgement should be employed to the same extent as in a manual design. The package is essentially material independent with options to specify the design variables as constant, standard or variable. The great flexibility incorporated in the routine enables the designer a full range of design features from control to power gearing. The structure of the package enables implementation of new theory or new optimization criteria with relative ease. </p> / Thesis / Master of Engineering (MEngr)