abstract: Designing studies that use latent growth modeling to investigate change over time calls for optimal approaches for conducting power analysis for a priori determination of required sample size. This investigation (1) studied the impacts of variations in specified parameters, design features, and model misspecification in simulation-based power analyses and (2) compared power estimates across three common power analysis techniques: the Monte Carlo method; the Satorra-Saris method; and the method developed by MacCallum, Browne, and Cai (MBC). Choice of sample size, effect size, and slope variance parameters markedly influenced power estimates; however, level-1 error variance and number of repeated measures (3 vs. 6) when study length was held constant had little impact on resulting power. Under some conditions, having a moderate versus small effect size or using a sample size of 800 versus 200 increased power by approximately .40, and a slope variance of 10 versus 20 increased power by up to .24. Decreasing error variance from 100 to 50, however, increased power by no more than .09 and increasing measurement occasions from 3 to 6 increased power by no more than .04. Misspecification in level-1 error structure had little influence on power, whereas misspecifying the form of the growth model as linear rather than quadratic dramatically reduced power for detecting differences in slopes. Additionally, power estimates based on the Monte Carlo and Satorra-Saris techniques never differed by more than .03, even with small sample sizes, whereas power estimates for the MBC technique appeared quite discrepant from the other two techniques. Results suggest the choice between using the Satorra-Saris or Monte Carlo technique in a priori power analyses for slope differences in latent growth models is a matter of preference, although features such as missing data can only be considered within the Monte Carlo approach. Further, researchers conducting power analyses for slope differences in latent growth models should pay greatest attention to estimating slope difference, slope variance, and sample size. Arguments are also made for examining model-implied covariance matrices based on estimated parameters and graphic depictions of slope variance to help ensure parameter estimates are reasonable in a priori power analysis. / Dissertation/Thesis / Ph.D. Educational Psychology 2011

This paper investigates different stakeholder groups’ perceptions on how Chinese investments contribute to the development of Ethiopia in the capital city Addis Ababa. To delimit the thesis, one recently completed infrastructural project was analyzed, namely the Light Railway Transit (LRT) in Addis. This project was a collaboration between the Ethiopian and the Chinese governments and to 85% funded by Chinese loans. By conducting twenty-six semi-structured interviews with interviewees from six strategically selected stakeholder groups, varying perceptions on Ethio-China relations and the LRT project were encountered. The main findings of the study include that the Ethio-China relations have been beneficial for the two governments and that Chinese investments have affected Ethiopia’s economic growth positively. However, in the case of the LRT project, the main observable was that it contributed to developmental image-building and failed to meet some of the other objectives pursued by it, such as providing reliable transportation service to the citizens. Moreover, experts and representatives from international organizations, the Ethiopian private sector and trade unions as well as citizens brought up problems with respect to skill transfer and working conditions related to Chinese investments, among other things. Further, they claimed that Ethio-China relations might negatively affect Ethiopia’s potential for democratization.

In research where experiments on humans and animals is performed, it is in advance important to determine how many observations that is needed in a study to detect any effects in groups and to save time and costs. This could be examined by power analysis, in order to determine a sample size which is enough to detect any effects in a study, a so called “power”. Power is the probability to reject the null hypothesis when the null hypothesis is false. Mälardalen University and the Caroline Institute have in cooperation, formed a study (The Climate Friendly and Ecological Food on Microbiota) based on individual’s dietary intake. Every single individual have been assigned to a specific diet during 8 weeks, with the purpose to examine whether emissions of carbon dioxide, CO2, differs reliant to the specific diet each individuals follows. There are two groups, one treatment and one control group. Individuals assigned to the treatment group are supposed to follow a climatarian diet while the individuals in the control group follows a conventional diet. Each individual have been followed up during 8 weeks in total, with three different measurements occasions, 4 weeks apart. The different measurements are Baseline assessment, Midline assessment and End assessment. In the CLEAR-study there are a total of 18 individuals, with 9 individuals in each group. The amount of individuals are not enough to reach any statistical significance in a test and therefore the sample size shall be examined through power analysis. In terms of, data, every individual have three different measurements occasions that needs to be modeled through mixed-design ANOVA and linear mixed models. These two methods takes into account, each individual’s different measurements. The models which describes data are applied in the computations of sample sizes and power. All the analysis are done in the programming language R with means and standard deviations from the study and the models as a base. Sample sizes and power have been computed for two different linear mixed models and one ANOVA model. The linear mixed models required less individuals than ANOVA in terms of a desired power of 80 percent. 24 individuals in total were required by the linear mixed model that had the factors group, time, id and the covariate sex. 42 individuals were required by ANOVA that includes the variables id, group and time. / Inom forskning där försök, dels utförs på människor och djur, vill man försäkra sig om en lämplig urvalsstorlek för att spara tid och kostnad samtidigt som en önskad statistisk styrka uppnås. Mälardalens högskola och Karolinska institutet har gjort en pilotstudie (CLEAR) som undersöker människors koldioxidutsläpp i förhållande till kosthållning. Varje individ i studien har fått riktlinjer om att antingen följa en klimatvänlig- eller en konventionell kosthållning i totalt 8 veckor. Individerna följs upp med 4 veckors mellanrum, vilket har resulterat i tre mättillfällen, inklusive en baslinjemätning. I CLEAR-studien finns variabler om individernas kön, ålder, kosthållning samt intag av makro- och mikronäringsämnen. Nio individer i respektive grupp finns, där grupperna är klimat- och kontrollgruppen. Totala antalet individer i pilotstudien är för få för att erhålla statistisk signifikans vid statistiska tester och därför bör urvalsstorleken undersökas genom att göra styrkeberäkningar. Styrkan som beräknas är sannolikheten att förkasta nollhypotesen när den är falsk. För att kunna beräkna urvalsstorlekar måste modeller skapas utifrån strukturen på data, vilket kommer att göras med metoderna mixed-design ANOVA och linjära mixade modeller. Metoderna tar hänsyn till att varje individ har fler än en mätning. Modellerna som beskriver data tillämpas i beräkningarna av styrka. Urvalsstorlekarna och styrkan som beräknats är simuleringsbaserad och har analyserats i programspråket R med modellerna och värden från pilotstudien som grund. Styrka och urvalsstorlekar har beräknats för två linjära mixade modeller och en ANOVA. De linjära mixade modellerna kräver färre individer än ANOVA för en önskad styrka på 80 procent. Av de linjära mixade modellerna som krävde minst individer behövdes totalt 24 individer medan mixed design-ANOVA krävde 42 individer totalt.

L'omniprésence de dispositifs interconnectés amène à un intérêt massif pour la sécurité informatique fournie entre autres par le domaine de la cryptographie. Pendant des décennies, les spécialistes en cryptographie estimaient le niveau de sécurité d'un algorithme cryptographique indépendamment de son implantation dans un dispositif. Cependant, depuis la publication des attaques d'implantation en 1996, les attaques physiques sont devenues un domaine de recherche actif en considérant les propriétés physiques de dispositifs cryptographiques. Dans notre dissertation, nous nous concentrons sur les attaques profilées. Traditionnellement, les attaques profilées appliquent des méthodes paramétriques dans lesquelles une information a priori sur les propriétés physiques est supposée. Le domaine de l'apprentissage automatique produit des modèles automatiques et génériques ne nécessitant pas une information a priori sur le phénomène étudié.<p><p>Cette dissertation apporte un éclairage nouveau sur les capacités des méthodes d'apprentissage automatique. Nous démontrons d'abord que les attaques profilées paramétriques surpassent les méthodes d'apprentissage automatique lorsqu'il n'y a pas d'erreur d'estimation ni d'hypothèse. En revanche, les attaques fondées sur l'apprentissage automatique sont avantageuses dans des scénarios réalistes où le nombre de données lors de l'étape d'apprentissage est faible. Par la suite, nous proposons une nouvelle métrique formelle d'évaluation qui permet (1) de comparer des attaques paramétriques et non-paramétriques et (2) d'interpréter les résultats de chaque méthode. La nouvelle mesure fournit les causes d'un taux de réussite élevé ou faible d'une attaque et, par conséquent, donne des pistes pour améliorer l'évaluation d'une implantation. Enfin, nous présentons des résultats expérimentaux sur des appareils non protégés et protégés. La première étude montre que l'apprentissage automatique a un taux de réussite plus élevé qu'une méthode paramétrique lorsque seules quelques données sont disponibles. La deuxième expérience démontre qu'un dispositif protégé est attaquable avec une approche appartenant à l'apprentissage automatique. La stratégie basée sur l'apprentissage automatique nécessite le même nombre de données lors de la phase d'apprentissage que lorsque celle-ci attaque un produit non protégé. Nous montrons également que des méthodes paramétriques surestiment ou sous-estiment le niveau de sécurité fourni par l'appareil alors que l'approche basée sur l'apprentissage automatique améliore cette estimation. <p><p>En résumé, notre thèse est que les attaques basées sur l'apprentissage automatique sont avantageuses par rapport aux techniques classiques lorsque la quantité d'information a priori sur l'appareil cible et le nombre de données lors de la phase d'apprentissage sont faibles. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Power Analysis Attacks pose serious threats to hardware implementations of cryptographic systems. To retrieve the secret key, the attackers can exploit the mutual information between power consumption and processed data / operations through monitoring the power consumption of the cryptosystems. Field Programmable Gate Arrays (FPGA) have emerged as attractive implementation platforms for providing hardware-like performance and software-like flexibility for cryptosystem developers. These features come at the expense of larger power consumption, which makes FPGAs more vulnerable to power attacks. Different countermeasures have been introduced in the literature, but as they have originally been developed for Application-Specific Integrated Circuits (ASIC), mapping them onto FPGAs degrades their effectiveness. In this work, we propose a logic family based on pass transistors, which essentially consists of hardware replication, that can be used to build FPGAs with constant power consumption. Since the power consumption is no longer related to processed data and operations, a quadruple robustness to attacks based on dynamic power consumption, static power consumption, glitches, and early evaluation effect is achieved. Such a secured-by-design FPGA will relieve the cryptosystems developers from doing advanced analog design to secure the cryptosystem implementation. Our pass-transistor logic family can also be used in implementing ASICs. The silicon area overhead costs are shown to be less than prior art, which makes our FPGA attractive to cryptosystems developers. / Graduate / 2018-07-26

This Thesis deals with the power of a statistical test and the associated problem of determining the appropriate sample size. It should be large enough to meet the requirements of the probabilities of errors of both the first and second kind. The aim of this Thesis is to demonstrate theoretical methods that result in derivation of formulas for minimum sample size determination. For this Thesis, three important probability distributions have been chosen: Normal, Bernoulli, and Exponential.

Avec l'essor des nouvelles technologies d'intégration sur silicium submicroniques, la consommation de puissance dans les systèmes sur puce multiprocesseur (MPSoC) est devenue un facteur primordial au niveau du flot de conception. La prise en considération de ce facteur clé dès les premières phases de conception, joue un rôle primordial puisqu'elle permet d'augmenter la fiabilité des composants et de réduire le temps d'arrivée sur le marché du produit final. / Shifting the design entry point up to the system-level is the most important countermeasure adopted to manage the increasing complexity of Multiprocessor System on Chip (MPSoC). The reason is that decisions taken at this level, early in the design cycle, have the greatest impact on the final design in terms of power and energy efficiency. However, taking decisions at this level is very difficult, since the design space is extremely wide and it has so far been mostly a manual activity. Efficient system-level power estimation tools are therefore necessary to enable proper Design Space Exploration (DSE) based on power/energy and timing.

This thesis contains three chapters, each of which covers a different topic in experimental economics.<br><br>The first chapter investigates power and power analysis in economics experiments. Power is the probability of detecting an effect when a true effect exists, which is an important but under-considered concept in empirical research. Power analysis is the process of selecting the number of observations in order to avoid issues with low power. However, it is often not clear ex-ante what the required parameters for a power analysis, like the effect size and standard deviation, should be. <br>This chapter considers the use of Quantal Choice/Response (QR) simulations for ex-ante power analysis, as it maps related data-sets into predictions for novel environments. <br>The QR can also guide optimal design decisions, both ex-ante as well as ex-post for conceptual replication studies. This chapter demonstrates QR simulations on a wide variety of applications related to power analysis and experimental design.<br><br>The second chapter considers a question of interest to computer scientists, information technology and security professionals. How do people distribute defenses over a directed network attack graph, where they must defend a critical node? Decision-makers are often subject to behavioral biases that cause them to make sub-optimal defense decisions. Non-linear probability weighting<br>is one bias that may lead to sub-optimal decision-making in this environment. An experimental test provides support for this conjecture, and also other empirically important biases such as naive diversification and preferences over the spatial timing of the revelation of an overall successful defense. <br><br>The third chapter analyzes how individuals resolve an exploration versus exploitation trade-off in a laboratory experiment. The experiment implements the single-agent exponential bandit model. The experiment finds that subjects respond in the predicted direction to changes in the prior belief, safe action, and discount factor. However, subjects also typically explore less than predicted. A structural model that incorporates risk preferences, base rate neglect/conservatism, and non-linear probability weighting explains the empirical findings well. <br>

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With increasing focus on climate change more research for net-zero emission are being made in the aviation industry.This project focuses on electric propulsion on a unmanned aerial vehicle (UAV) with a blended wing body (BWB) design. More specifically finding a solution for a propulsion system using electric ducted fan (EDF) engines for a scaled version of the KTH Aerospace project Green Raven. The system consists of a powerplant and power supply i.e engine(s) and a sufficient battery package. The goal is to find a solution to power this 7 kg aerial vehicle for 60 minutes with a consistent cruising speed of 30 m/s. To accomplish this an understanding of thrust and drag profile is essential in order to determine the requirements for the EDFs. Understanding the limitations of the scaled Green Raven is also necessary in order to provide a feasible solution for power supply. The result is to use 2x 50 mm EDF engines providing a total thrust of 16.7 Newtons that is integrated in the main body. To supply these engines two battery sets (one per EDF) composed of three different battery types have been chosen, giving a total capacity of 24 000 mAh for one hour flight time. This propulsion setup fulfils the requirements, though not without flaws because of the choice of integrating the EDFs. An alternative solution would be having the engines externally mounted in order to free up the space in the body for more efficient batteries.