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This work is focused on the design of an algorithm for run-time verification over requirements given as formulas in metric temporal logic (MTL). Tree structure is used for verification of these requirements, which is similar to run of alternating timed automata from which the final algorithm is derivated. Designed algorithm is able to verify given MTL formulas over the runs of a program without a need to remember the whole program's trace. This allows to monitor a given program on potentially infinite runs.

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The importance of a satisfactory logistics system is becoming increasingly apparent as a consequence of growing competition and rising customer requirements regarding, amongst others, lead times, flexibility and delivery dependability. The increasing demands reduce profit margins and increase logistics costs, accelerating the  importance of a well-functioning logistics system. A way to meet the increased customer demands is to tailor the logistics solution by segmenting customers according to their unique needs. Segmenting the customers has the potential to reduce a company's logistics costs, partly because delivery service can be bespoke to the customers' different requirements, instead of offering all customers the best possible performance. When making changes to a logistics system, it is therefore also important to take customer satisfaction into account, as reduced customer satisfaction otherwise risks leading to lost sales. A company that faces challenges with increased customer demands regarding the logistics solution in parallel with a quest for reduced logistics costs is ASSA ABLOY, a company that manufactures and sells security solutions and security systems worldwide. Based on the above situation, the research purpose of the study is to \textit{through customer segmentation develop strategy proposals to reduce the total cost of ASSA ABLOY's logistics solution, taking customer satisfaction into account}. To create an understanding of the building blocks of the research purpose: logistical change, total cost analysis, customer segments and customer satisfaction, literature was examined in the mentioned areas. To link the study to ASSA ABLOY's unique situation, multiple unstructured and semi-structured interviews were conducted with people at, among others, ASSA ABLOY with good insight into the logistics solution. Based on the interview outcomes, a list of six strategy proposals for customer segmentation was produced. Based on additional interviews as well as the study's frame of reference, the list of strategic proposals was shortened to three strategies based on implementability. The total cost of the strategy proposals and the customer satisfaction impact were analyzed using empirical data in the form of data from interviews as well as order and cost data for 2021. During the analysis, constant discussions were held with supervisors at ASSA ABLOY to ensure the analysis' connection to reality. The results of the study were summarized into three strategy proposals that through data analysis could be demonstrated to lead to potential cost savings. The customer segmentation option \textit{accepted lead time} showed the by far largest potential cost saving, followed by \textit{geographical position} which also showed great savings potential. For both alternatives, customer satisfaction was estimated to slightly be improved. The third analyzed strategy proposal, customer segmentation based on \textit{average order value} indicated the lowest cost-saving and with a slight deterioration in customer satisfaction, which is why it was considered to be the worse of the three analysed strategic alternatives.

Roadside vegetation provides numerous environmental and psychological benefits to drivers. Previous studies have shown that natural landscapes can effectively lower crash rates and cause less stress and frustration to the driver. However, run-off-the-road crashes resulting in a collision with a tree are twice as likely to result in a fatality, thus reinforcing the need to examine the placement of vegetation within the clear zone. This study explores the relationship between the size of the clear zone and the presence of roadside vegetation on selected driver attributes, including both driver speed and lateral positioning. To evaluate the effect on the driver speed selection process, a static evaluation was employed. Completed by more than 100 drivers, the static evaluation was utilized to gather speed selections on both real and virtual roads containing four combinations of clear zone size and roadside vegetation density. Additionally, field data was collected to validate the findings of the static evaluation and to determine the extent to which roadside vegetation impacts driving attributes. When presented with a large clear zone, drivers positioned the vehicle further from the edge of the road as the vegetation density increased. Furthermore, the speeds observed in the field correlated with the speeds that participants selected when watching a video of the same road. Finally, the UMassSafe Traffic Safety Data Warehouse was utilized to link crash and roadway data, allowing for an in-depth analysis of run-off-the-road (ROR) crash severity. The results of this study further demonstrate the nature of the relationship between clear zone design and driver behavior.

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Climate change poses a significant challenge to economies throughout the world; understanding its potential economic impacts at the regional level is important for making informed policy decisions and mapping out effective adaptation strategies. This thesis examines the long-run effect of climate change in Sweden, an environmentally diverse, developed country with ambitious sustainability goals. By using a half-panel Jackknife Fixed Effect (HPJ-FE) method that estimates the effect of deviations in temperature and precipitation from long-run historical norms on the growth of different county-level economic performance indicators between 1968 and 2021. The study suggests that deviations in temperature have a positive effect on the growth rates of real GRP, real GRP per capita, and employment in Sweden, while productivity sees a negative effect. The results are compared to the United States and suggest that Sweden is seeing fewer negative effects and adapting faster to climate change than the United States.

This study examines the specific ways in which private equity firms influence their portfolio companies to enhance their value, with a focus on the relationship between the level of retained ownership and post-IPO performance. Private Equity firms influence their portfolio companies in specific ways to enhance their value. Private Equity firms are typically limited partnerships, and to realize the value created during the life of the investment, the exit strategy is crucial. An initial public offering is stated as the preferable exit. However, private equity firms usually stay invested in their portfolio companies for up to several years after an initial public offering. Their retained ownership is crucial for underpricing at the IPO and performance post-IPO. This study aims to discover this relationship and to determine its effects. It will contribute to understanding how the portfolio companies' price changes on the first day of trading and their performance, in the long run, is affected by the stake held by the private equity firms. This research will try to clarify the current uncertainty about the effect of underpricing that prevails. It will also fill the existing gap in the academic literature about performance. It can also be potentially helpful for investors. Given knowledge about how retained ownership by PE firms affects underpricing at the IPO and performance post-IPO, this study can help investors to make better investment decisions. However, it should not be seen as investment advice but rather as a contribution to increasing the investor's understanding and knowledge. Publicly listed portfolio companies in the Nordic region constitute the sample for the analysis, and pooled OLS is the econometric method used in this study. We utilized a panel dataset for performance and obtained 2411 unique observations. The long-run performance has been measured as 36 months following the IPO. Our findings indicate a positive relationship between the level of ownership held by the PE firm and both underpricing and performance. These relationships are both statistically significant on the 1% level. Control variables were also included to capture other possible factors that might impact our dependent variables. The positive relationship between the level of ownership held by the PE firm and performance was in line with previous similar research and our expectations. However, the relationship between the PE firm's level of ownership and underpricing was the opposite of what we expected. Previous research has also presented contradictory results, making it difficult to predict the relationship. We hope our results have contributed to clarity regarding underpricing and broadening existing literature about performance for private equity-backed companies.

This study introduces a dynamic approach to sorting, making use of predictions and data gathered during run-time to optimize the sorting of the current data set. This approach is used to develop a sorting algorithm called DynamicSort which partitions data and calculates a partial standard deviation for each partition to determine which of two sorting algorithms should be used to sort the partition. The algorithm is tested against Quicksort and radix sort on data sets of different sizes and standard deviation with the intent of finding advantages of the approach. In order to adapt to modern applications, the algorithm is tested in an environment utilizing parallel processing on multiple machines on data sets generated to mimic the characteristic size of big data. To accommodate this the data is divided at start and merged together after sorting using a k-way merge sort. While the tests conducted do not show any concrete gain in performance there are several factors that could be further optimized and evaluated. We find that it is not enough to simply consider the standard deviation in this approach. While no real instance of big data was used the algorithm was adapted for limited cache sizes and multiple hosts working in parallel.