Abstract In the year 2019 the vehicle workshops services returned to the Swedish Armed Forces. For six years they had been an integrated part of the Swedish Defence Materiel Administration and the business was set up in a corporate fashion. When returning to the Swedish Armed Forces the workshops will continue to use that corporate business model, and at the same time adjust to military setting and logic. Among other things, the workshops will be organized into military units, under the same conditions as all other parts of the Armed Forces. The purpose of this thesis is to examine and understand the meaning and consequences of the corporate and the military logic coexisting within the Vehicle Workshop Services units. A typology made specifically for this reason is used to depict the different configurations that occur when those two logic structures coexist. The study draws the conclusions that the dualistic relationship between the corporate and the military logic ends up in compromises that makes the goal and purpose of the organisation vague and the personnel involved experience a difference between how it is supposed to be functioning and how it actually does. Furthermore, the study states that the conditions for developing the Vehicle Workshop Services units into a strong and resilient organisation from a HR and symbolic perspective is less favourable. The Swedish Armed Forces need to address this actively to create a common understanding of the factors that are in play by all personnel involved in the matter. The study gives five hands on suggestions to mitigate those problems. Finally, some suggestions are given how to re-use the typology that was created for the study in other contexts.

Under de senaste åren har stora datamängder blivit allt vanligare för beslutsfattande och analys. Maskininlärning och matematiska beräkningar är två avgörande metoder som används för detta. Dessa beräkningar kan dock vara tidskrävande, och de kräver högpresterande datorer som är utmanande att skala upp. Raspberry Pi är en liten, kraftfull och billig dator som lämpar sig för parallella beräkningar. Kubernetes är en öppen källkodsplattform för att hantera containerbaserade applikationer som tillåter automatisk skalning av mjukvaruapplikationer. Genom att kombinera Raspberry Pi med Kubernetes kan ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning skapas. I denna studie undersöks möjligheten att bygga ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning med hjälp av Raspberry Pi och Kubernetes. Det kommer att göras teoretisk forskning kring Kubernetes och Raspberry Pi, designa ett system för matematiska beräkningar och maskininlärning, implementera systemet genom att installera och konfigurera Kubernetes på flera Raspberry Pi:s, mäta och utvärdera systemets prestanda och skalbarhet samt presentera studiens resultat. Resultatet visade att användningen av Raspberry Pi i kombination med Kubernetes för att utföra matematiska beräkningar är både kostnadseffektiv och skalbar. När det gäller prestanda kunde systemet hantera intensiva beräkningsuppgifter på ett tillfredsställande sätt, vilket visar sin potential som en lösning för storskalig dataanalys. Förbättringar i systemdesign och mjukvaruoptimering kan ytterligare öka effektiviteten och prestanda / In the recent years, large data sets have become more often used for decision-making and analysis. Machine learning and mathematical calculations are two crucial methods employed for this. However, these computations may be time-consuming, and they require highperformance computers that are challenging to scale up. Raspberry Pi is a small, powerful, and cheap computer suitable for parallel calculations. Kubernetes is an open-source platform for managing container-based applications that allows automatic scaling of software applications. By combining Raspberry Pi with Kubernetes, a cost-effective and scalable system for mathematical calculations and machine learning can be created. In this study, the possibility of building a cost-effective and scalable system for mathematical calculations and machine learning using Raspberry Pi and Kubernetes is investigated. There will be theoretical research on Kubernetes and Raspberry Pi, design a system for mathematical calculations and machine learning, implement the system by installing and configuring Kubernetes on multiple Raspberry Pi's, measure and evaluate the system's performance and scalability, and present the study's results. The result showed that the use of Raspberry Pi in combination with Kubernetes to perform mathematical calculations is both cost-effective and scalable. In terms of performance, the system was able to handle intensive computational tasks satisfactorily, demonstrating its potential as a solution for large-scale data analysis. Improvements in system design and software optimization can further increase efficiency and performance.

In this work we study two problems motivated by Risk Management: the optimal design of financial products from an investor's point of view and the calculation of bounds and approximations for sums involving non-independent random variables. The element that interconnects these two topics is the notion of conditioning, a fundamental concept in probability and statistics which appears to be a useful device in finance. In the first part of the dissertation, we analyse structured products that are now widespread in the banking and insurance industry. These products typically protect the investor against bearish stock markets while offering upside participation when the markets are bullish. Examples of these products include capital guaranteed funds commercialised by banks, and equity linked contracts sold by insurers. The design of these products is complex in general and it is vital to examine to which extent they are actually interesting from the investor's point of view and whether they cannot be dominated by other strategies. In the academic literature on structured products the focus has been almost exclusively on the pricing and hedging of these instruments and less on their performance from an investor's point of view. In this work we analyse the attractiveness of these products. We assess the theoretical cost of inefficiency when buying a structured product and describe the optimal strategy explicitly if possible. Moreover we examine the cost of the inefficiency in practice. We extend the results of Dybvig (1988a, 1988b) and Cox & Leland (1982, 2000) who in the context of a complete, one-dimensional market investigated the inefficiency of path-dependent pay-offs. In the dissertation we consider this problem in one-dimensional Levy and multidimensional Black-Scholes financial markets and we provide evidence that path-dependent pay-offs should not be preferred by decision makers with a fixed investment horizon, and they should buy path-independent structures instead. In these market settings we also demonstrate the optimal contract that provides the given distribution to the consumer, and in the case of risk- averse investors we are able to propose two ways of improving the design of financial products. Finally we illustrate the theory with a few well-known securities and strategies e.g. dollar cost averaging, buy-and-hold investments and widely used portfolio insurance strategies. The second part of the dissertation considers the problem of finding the distribution of a sum of non- independent random variables. Such dependent sums appear quite often in insurance and finance, for instance in case of the aggregate claim distribution or loss distribution of an investment portfolio. An interesting avenue to cope with this problem consists in using so-called convex bounds, studied by Dhaene et al. (2002a, 2002b), who applied these to sums of log-normal random variables. In their papers they have shown how these convex bounds can be used to derive closed-form approximations for several of the risk measures of such a sum. In the dissertation we prove that unlike the log-normal case the construction of a convex lower bound in explicit form appears to be out of reach for general sums of log-elliptical risks and we show how we can construct stop-loss bounds and we use these to construct mean preserving approximations for general sums of log-elliptical distributions in explicit form. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Den bostadspolitik som förts de senaste två decennierna har gett oss en alltmer segregerad stadskärna i Stockholm. Byggsubventionernas avveckling på 90-talet har lett till en historiskt låg byggtakt och ett byggande som ensidigt riktat sig till resursstarka grupper i samhället. Parallellt med detta har stora delar av hyresbeståndet ombildats till bostadsrätter vilket medfört att många relativt billiga hyreslägenheter i äldre fastigheter har försvunnit ur beståndet. Dessa är svåra att ersätta med nyproduktion, då dessa är dyrare. Kärnproblemet är förstås att det är dyrt att bygga hus och om man antar att den nuvarande bostadspolitiken består är det ytterst angeläget att utveckla kostnadseffektiva byggmetoder för att möjliggöra lägenheter med överkomliga hyror som kan bidra till att motverka boendesegregationen på sikt. Vårt förslag består av ett bostadshus i Stadshagen. Fastigheten är indelad i två byggnadskroppar som länkas samman via ett system av loftgångar kopplade till ett trapphus. Husen är uppbyggda av volymelement som staplats på en platsgjuten sockel. Elementens konstruktion baseras på transportcontainrar vilket ger en rationell byggprocess och hög grad av prefabrikation. I socklarna ryms en tunnelbanenedgång och en uthyrbar lokal samt gemensamma lokaler för de boende. Lägenheterna finns i tre storlekar: 1:or, 1,5:or och 4:or.  De större lägenheterna är i två plan. / The housing policy conducted over the past two decades has resulted in an increasingly segregated city center in Stockholm. The abolishment of housing subsidies in the 90s led to a historic low rate of construction and an urban development unilaterally directed towards the wealthy. Parallel to this, much of the housing stock has been converted to condominiums which caused many relatively cheap rental apartments in older buildings to disappear from the stock. These are difficult to replace with new housing, as they are more expensive. The core issue is of course that it is expensive to build houses and assuming that the current housing policy will last, ​​it is highly relevant to develop cost-effective construction methods in order to make possible apartments with affordable rents that can help to combat segregation over time. Our proposal consists of a residential building in Stadshagen. The property is divided into two buildings that are linked together by a system of access balconies connected to a stairwell. The houses are constructed with volume elements stacked on a concrete base. The design of the elements is based on shipping containers which provides a rational construction process and a high degree of prefabrication. The base contains an underground entrance, a business premise and common areas for the residents. The apartments are available in three sizes whereof the larger apartments have two floors.

I takt med att datakvantiteter växer och kraven på skalbarhet och tillgänglighet inom molntjänster växer, framhävs behovet av undersökningar kring dess prestanda och kostnadseffektivitet.  Dessa analyser är avgörande för att optimera tjänster och bistå företag med värdefulla rekommendationer för att fatta välgrundade beslut om datalagring i molnet. Detta examensarbete undersöker kostnad samt prestanda hos relationella och icke-relationella datalagringslösningar implementerade på Microsoft Azure och Google Cloud Platform. Verktyget Hyperfine används för att mäta latens och tjänsternas kostnadseffektivitet beräknas baserat på detta resultat samt dess beräknade månadskostnader. Studiens resultat indikerar att för de utvärderade relationella databastjänsterna uppvisar Azure SQL Database initialt en låg latens som sedan ökar proportionellt med datamängden, medan Google Cloud SQL visar en något högre latens vid lägre datamängder men mer konstant latens vid högre datamängder. Azure SQL visar sig vara mer kostnadseffektiv i förhållande till Google Cloud SQL, vilket gör den till ett mer fördelaktigt alternativ för företag som eftersträvar hög prestanda till lägre kostnader. Vid jämförelse mellan de två icke-relationella databastjänsterna Azure Cosmos DB och Google Cloud Datastore uppvisar Azure Cosmos DB genomgående jämförelsevis lägre latens och överlägsen kostnadseffektivitet. Detta gör Azure Cosmos DB till en fördelaktig lösning för företag som prioriterar ekonomisk effektivitet i sin databashantering. / As data volumes grow and the demands for scalability and availability within cloud services increase, the need for studies on their performance and cost-effectiveness is emphasized. These analyses are crucial for optimizing services and providing businesses with valuable recommendations to make well-grounded decisions about cloud data storage. This thesis examines cost and performance for relational and non-relational data storage solutions implemented on Microsoft Azure and Google Cloud Platform. The tool Hyperfine is used to evaluate latency and the cloud services cost efficiency is calculated using this result as well as their monthly cost. The study's results regarding relational data storage indicate that Azure SQL Database initially exhibits low latency, which then increases proportionally with the data volume, while Google Cloud SQL shows slightly higher latency at smaller data volumes but more consistent latency with more data. Azure SQL Database is more cost-effective, making it a more favorable option than Google Cloud SQL for companies seeking high performance at lower costs. Regarding evaluated services for non-relational data storage Azure Cosmos DB consistently demonstrates lower latency and superior cost efficiency compared to Google Cloud Datastore, making it the preferred solution for companies prioritizing economic efficiency in their database management.

人類慾望無窮而資源有限，所以如何將資源做最有效的使用一直是經濟學所追求的課題。有關效率的規範分析中，巴瑞圖最適（Pareto optimality）為接受度最高的準則。基於巴瑞圖效率的觀點，不論是營利組織或非營利組織其經營之基本理念皆是希望以最少的投入獲得最大的產出，因此衡量投入與產出間之相對表現即為效率的評估。非營利組織及公共部門因為有許多產出、投入不易量化，故其效率不易評定。此一情況一直到DEA（Data Envelopment Analysis；資料包絡分析法）發展才逐漸改善。另外，有關DEA的運用幾乎都集中在個別決策單位的效率衡量，將之應用在衡量總體經濟的表現上非常少見。所以論文第肆章是按傳統方式以DEA衡量我國地方政府的效率並分析造成效率差異的原因，第參章則將DEA的概念應用於經濟成長上，探討使經濟成長達到極大化之租稅負擔及租稅結構。又效率的追求為經濟學的主軸，但中央政府的效率目標與地方政府並不相同，由於目標不同，因此彼此所訂的租稅政策亦不相同。本文第伍章試圖提出一理論模型說明中央政府在面對異質地區的垂直外部性下如何有效率的訂定其租稅政策。 / The human desires are infinite but resources are scarce. Using resources effectively is the topic of the economics. In efficient analyses, the Pareto optimality is the highest criterion to accept. Based on Pareto efficiency, the basic idea is to obtain the most outputs by the least inputs. Therefore the efficiency measurement is to calculate the relative performance of inputs and outputs. The nonprofit organization and the public agencies have many outputs and inputs not easy to be quantified; hence, their efficiency is not easy to evaluate. This phenomenon doesn’t improve until DEA (Data Envelopment Analysis) was developed. However, the application of DEA concentrates nearly on the efficiency measurement of individual policy-making unit; the application is be rarely used in measuring the performance of the macro-economy. Chapter 4 of this dissertation measures and explains the variation in cost efficiency of the local governments in Taiwan area. Chapter 3 deals with the application the DEA to economic growth, estimating a combination of the tax burden(the ratio of tax revenue to GDP）and the tax structure(the ratio of indirect taxes to direct taxes)which would maximize the rate of growth of GDP of Taiwan. Efficiency is the core of economics, but the national government’s efficient goal is not as same as the local government’s. Because their goals are not consistent, thus the tax policies are different. Chapter 5 attempts to propose a theoretical model to explain how a national or federal government decides its tax policy in the vertical externalities happening in heterogeneous states.

The paper deals with the use of unmanned aircraft of the American national security policy. It examines the history of unmanned aviation, its military use, and cost- efficiency. It then examines the main obstacles and problems with their use in national security that this technology meets and will meet in the future after a higher level of autonomy is developed. These problems involve legal issues, international and domestic American law, the issue of civilian casualties, the role of the media, and public opinion. The final chapter focuses on the problems of technical, strategic and operational issues. In this section the research paper comes to the first conclusion which claims that the introduction of more autonomous systems to war will radically change its structure and, consequently, standard procedures and strategies. Case studies are included to illustrate how successful the drone strategy is applied in the five countries where the United States leads a war on terror. The research using the theory of the revolution in military affairs concludes that these UAVs pose the greatest challenge in history and it goes well beyond military matters. UAVs in national security affect the understanding of the basic principles of war in relation to the concepts of warrior ethos and just war.