Magister Legum - LLM / The so called business judgment rule (hereinafter referred to as ―the BJR or the rule‖) that serves to protect directors from liability for negative consequences of honest, reasonable business decisions that went wrong, was developed by the American judiciary in the early 19th Century.2 Percy v. Millaudon, a Louisiana Supreme Court decision quoted above, articulated what is now referred to as the BJR.3 This case provides the earliest expression of the American BJR.4 Delaware courts subsequently issued a series of cases formulating the BJR as a presumption.5 Although the earliest expression of the rule was provided by a Louisiana court, the dissertation will focus on the Delaware case law formulation of the rule.6 The essence of the BJR is that judges should not second guess directors‘ decisions if certain elements of the BJR are fulfilled.7 Courts are required to exercise caution when dealing with claims brought by either stakeholders or shareholders against directors who have made bona fide, also referred to as good faith, business decisions.8 In order to be protected by the BJR and for it to act as a safe harbour, the court will determine whether certain requirements have been met before applying the rule.9 The Delaware courts formulated the BJR as a presumption and in order for directors to be protected by the rule they must have made an informed business decision, in good faith and in the honest belief that the decision will be in the best interest of the company.10 As will be discussed later, this formulation of the rule is referred to as the traditional BJR. In addition to the aforementioned formulation, another formulation was provided by the American Law Institute (hereafter referred to as the ―ALI formulation‖).11 Initially there had been difficulties codifying the ALI version of the rule but later it was successfully codified in paragraph 4.01(c) of the ALI Corporate Governance Project.12 This formulation requires a director to ensure that he has no personal interest in the matter, he is reasonably informed of the matter prior to making the decision and he rationally believes the decision will be in the best interest of the company.13 If the director complies with the aforementioned requirements, the director will be considered to have acted in good faith.14 Directors owe fiduciary duties to the company and in instances where they breach one or more of these duties they can incur personal liability.15 The rule thus emerged because of the need to protect directors and it serves as a safe harbour for those individuals who made a decision in conformity with the aforementioned requirements.16 In commercial terms the rule bestows economic freedoms and freedom of entrepreneurship to directors guided, in any case, by ―the best interest of the company‖.17 The most commonly cited reasons for the existence of the rule are that it promotes risk taking, encourages competent persons to serve as directors, prevents judicial second-guessing and promotes judicial efficiency. It further provides directors with sufficient freedom to manage the company and it ensures that the interest of shareholders and those of directors are balanced.18

Combustion instability due to feedback coupling between unsteady heat release and natural acoustic modes can cause catastrophic failure in liquid rocket engines and to predict and prevent these instabilities the mechanisms that drive them must be further elucidated. With this goal in mind, the objective of this thesis was to develop techniques that improve the understanding of the specific underlying physical processes involved in these driving mechanisms. In particular, this work sought to develop a small-scale, optically accessible liquid rocket engine simulator and to apply modern, high-speed diagnostic techniques to characterize the reacting flow and acoustic field within the simulator. Specifically, high-speed (10 kHz), simultaneous data were acquired while the simulator was experiencing a 170 Hz combustion instability using particle image velocimetry, OH planar laser induced fluorescence, CH* chemiluminescence, and dynamic pressure measurements. In addition, this work sought to develop approaches to reduce the large quantities of data acquired, extracting key physical phenomena involved in the driving mechanisms. The initial data reduction approach was chosen based on the fact that the combustion instability problem is often simplified to the point that it can be characterized by an approximately linear constant coefficient system of equations. Consistent with this simplification, the experimental data were analyzed by the dynamic mode decomposition method. The developed approach to apply the dynamic mode decomposition to simultaneously acquired data located a coupled hydrodynamic/combustion/acoustic mode at 1017 Hz. On the other hand, the dynamic mode decomposition's assumed constant operator approach failed to locate any modes of interest near 170 Hz. This led to the development of two new data analysis techniques based on the dynamic mode decomposition and Floquet theory that assume that the experiment is governed by a linear, periodic system of equations. The new periodic-operator data analysis techniques, the Floquet decomposition and the ensemble Floquet decomposition, approximate, from experimental data, the largest moduli Floquet multipliers, which determine the stability of the periodic solution trajectory of the system. The unstable experiment dataset was analyzed with these techniques and the ensemble Floquet decomposition analysis found a large modulus Floquet multiplier and associated mode with a frequency of 169.6 Hz. Furthermore, the approximate Rayleigh criterion indicated that this mode was unstable with respect to combustion instability. Overall, based on the positive finding that the ensemble Floquet decomposition was able to locate an unstable combustion mode at 170 Hz when the operator's time period was set to 1 ms, suggests that the dynamic mode decomposition based 1017 Hz mode parametrically forces the 170 Hz mode, resulting in what could be characterized as a parametric combustion instability.

Geografiska informationssystems användning blir allt viktigare i dagens samhälle där spatiala data kan lagras, hämtas, analyseras och visualiseras. Genom att sammanställa spatiala data kan en bild av verkligheten abstraheras. Detaljerad information om vägnat och företeelser (refuger, bullerplank, skyltar etcetera) för analys leder till ett effektivare drift- och underhållsarbete. Vilket i sin tur ger en ökad framkomlighet för trafikanter. Teknikföretaget Triona har en kartapplikation där utmaningar har uppstått gällande algoritmisk knytning av inmätta refuger (benämnd Norge-datasamlingen) till det norska vägnatet. En refug ar en upphöjning i gatan som avgränsar körfalt och påminner om en trottoar i utseendet. Denna fallstudie behandlade ett delproblem där klassificering av refuger skulle kunna underlätta knytningen och förutsättningarna for analys. Syftet med studien kan sammanfattas till att presentera förslag på metoder for att klassificera refugerna med övervakad maskininlärning. Med algoritmerna K-nearest neighbors (KNN) och Decision tree studerades möjligheten att automatiskt klassificera refugerna. En refug bestod av en vektorpolygon vilket är en lista med koordinater. Polygonens hörn bestod av koordinatparen latitud och longitud. Norge-datasamlingen var inte i forväg kategoriserad till sina elva typer och kunde därfor inte anvandas. En datasamling med 2157 refuger med sju typer från Portland, USA tillämpades i stället. De spatiala vektorpolygonerna transformerades med Elliptical Fourier Descriptors (EFD). Maskinlärningsmodellerna tränades på att klassificera refugerna baserat på matematiska approximationer av dess konturer från EFD. Slutsatser kunde dras genom att refugtypernas konturer analyserades och prestationer observerades. Prestationer utvärderades utifrån traffsäkerhet med kompletterande mätvarden som precision och återkallelse på Portland-datasamlingen. Traffsäkerhet är andelen rätta klassificeringar av refugerna. KNN uppnådde 64 % och Decisiontree 69 % traffsäkerhet. Då båda datasamlingarna var verkliga exempel på refuger i vägnat kunde ett antagande göras att det inte skulle bli en mycket högre traffsäkerhet om studiens metod appliceras på Norge-datasamlingen. Modellernas prestationer bedömdes därmed inte vara tillrackligt bra for en rekommendation. / Geographical information systems are becoming increasingly important in today´s society where spatial data can be stored, collected, analysed, and visualized. By compiling spatial data reality can be abstracted. Detailed information on road networks and objects (traffic islands, noise barriers, signs, etcetera) for analysis leads to more efficient operation and maintenance work. Which in turn provides increased accessibility for road users. The technology company Triona has a map application where algorithmic connection of traffic islands (Norway-dataset) to the Norwegian road network has been challenging. A traffic island is an elevation in the street that delimits lanes and is reminiscent of a sidewalk in appearance. This case study addressed a sub-problem where classification of traffic islands could facilitate the connection and prerequisites for analysis. The aim was to present methods that could classify the traffic islands with supervised machine learning. With the algorithms K-nearest neighbors (KNN) and Decision tree, the possibility of automatically classifying the traffic islands was studied. A traffic island consisted of a vector polygon which is a list storing its corners (latitude and longitude). The Norway-dataset was not previously labelled into its eleven types. A data collection of 2157 refuges with seven types from Portland, USA was therefore applied instead. The traffic islands were transformed with Elliptical Fourier Descriptors which extracted an approximation of its contours to train the machine learning models on. Conclusions could be drawn by analysing the contours and observing performance. Performance was evaluated based on accuracy with precision and recall on the Port-land-dataset. Accuracy is the proportion of correct classifications. KNN achieved 64% and Decision Tree 69% accuracy. As both datasets contained real traffic islands in road networks, an assumption could be made that the accuracy would not be much higher if applied on the Norway-dataset. The result was not considered sufficient for a recommendation.

Dans le cadre de cette thèse, des Décharges à Barrières Diélectrique (DBD) pulsées de volume et de surface ont été étudiées expérimentalement, dans le but de les appliquer respectivement à la précipitation électrostatique de particules submicroniques et à la régénération de surface.La caractérisation des DBD pulsées a consisté à effectuer des mesures électriques (courant, énergie) et optiques (imagerie ICCD), afin d'observer l'influence des paramètres électriques et géométriques, ainsi que des matériaux utilisés, sur les propriétés de la décharge. Il en ressort que pour les deux types de DBD (surface et volume), le courant présente deux pics durant les fronts montant et descendant du pulse de tension, et que les deux décharges ont des aspects différents (filamentaire ou diffus).Concernant la précipitation électrostatique, l'efficacité de collecte des électrofiltres a été déterminée par des mesures granulométriques. Les résultats montrent que celle-ci est influencée par les paramètres électriques et géométriques des électrofiltres. Une caractérisation des phénomènes EHD au sein des électrofiltres par métrologie optique (LDV et PIV) a été effectuée afin de comprendre les mécanismes de charge et de dérive des particules.Ensuite, la régénération de surface a été étudiée en analysant les images de la surface polluée, avant et après le traitement. Les résultats révèlent que les performances de la régénération sont influencées par les paramètres électriques, géométriques et les matériaux des réacteurs DBD. Les mécanismes de la régénération ont été analysés en relevant l'évolution de la taille et la concentration des particules générées lors du processus de régénération. / As part of this thesis, volume and surface pulsed Dielectric Barrier Discharges (DBD) were investigated experimentally for applications in the electrostatic precipitation of submicron particles and surface regeneration, respectively. First, the characterization of pulsed DBDs consisted of performing electrical measurements (current, power) and optical measurements (ICCD imaging) in order to observe the influence of electrical and geometrical parameters as well as materials on the properties of the discharge. It is demonstrated that for both types of DBD (surface and volume), the current has two peaks during the rising and falling times of the voltage pulse, corresponding to distinct streamer and glow discharge regimes, respectively.Next, the collection efficiency of electrostatic precipitation (ESP) was determined by granulometric measurements. The results show that the efficiency is influenced by the electrical and geometrical parameters of the precipitator. A characterization of EFD phenomena was performed inside the precipitator by optical metrology (LDV and PIV) to understand the mechanisms of charge and particle drift. Finally, surface regeneration was investigated by analyzing images of the contaminated surface, before and after treatment. The results reveal that the regeneration process is influenced by electrical and geometrical parameters and DBD reactor materials. The mechanisms of regeneration were analyzed in-situ by measuring changes in the size and concentration of the generated particles during the process of regeneration.