Knowledge regarding a nuclear power plants potential and limitations is of utmost importance when working in the nuclear field. One way to extend the knowledge is using fuel performance codes that to its best ability mimics the real-world phenomena. Fuel performance codes involve a system of interlinked and complex models to predict the thermo-mechanical behaviour of the fuel rods. These models use several different model parameters that can be imprecise and therefore the parameters need to be fitted/calibrated against measurement data. This thesis presents two methods to calibrate model parameters in the presence of unknown sources of uncertainty. The case where these methods have been tested are the oxidation and hydrogen pickup of the zirconium cladding around the fuel rods. Initially, training and testing data were sampled by using the Dakota software in combination with the nuclear simulation program TRANSURANUS so that a Gaussian process surrogate model could be built. The model parameters were then calibrated in a Bayesian way by a MCMC algorithm. Additionally, two models are presented to handle unknown sources of uncertainty that may arise from model inadequacies, nuisance parameters or hidden measurement errors, these are the Marginal likelihood optimization method and the Margin method. To calibrate the model parameters, data from two sources were used. One source that only had data regarding the oxide thickness but the data was extensive, and another that had both oxide data and hydrogen concentration data, but less data was available.  The model parameters were calibrated by the use of the presented methods. But an unforeseen non-linearity for the joint oxidation and hydrogen pick-up case when predicting the correlation of the model parameters made this result unreliable.

The government of Ghana borrows from both domestic and foreign sources to finance the budget deficit. By the year 2013, the domestic debt was 55% of the public debt. Government domestic borrowing is competitive and can potentially crowd out the private corporate sector. Therefore, the specific research problem addressed in this study was whether the Ghanaian government's domestic debt (DEBT) caused financial crowding out (FCO) in Ghana. FCO theory is not conclusive and not proven specifically for Ghana, so the purpose of this research was to investigate its presence in Ghana. The neoclassical theory of FCO underpinned the research. The 2 research questions investigated FCO along the quantity and cost channels. The research examined the relationship between DEBT as the independent variable, the quantity of private sector credit (PSCREDIT), and the net interest margin (NIM) of banks as dependent variables. Covariates were macroeconomic and banking industry variables. The research population was the banking sector of the financial services industry. The research was correlational, and it used time series data from the Bank of Ghana and the World Bank. Data analysis used the autoregressive distributed lag method. The analysis returned a negative relationship between DEBT and PSCREDIT, and a positve relationship between NIM and DEBT. These results indicated the presence of FCO along both the quantity and cost channels. The research provides policymakers a means of quantifying the extent and effects of fiscal policies. The study may contribute to positive social change by promoting the revision of fiscal policies to favor the private corporate sector to invest, create jobs, and grow the Ghanaian economy.

Indiana University-Purdue University Indianapolis (IUPUI) / In a multifidelity structural design process, depending on the required analysis, different levels of structural models are needed. Within the aerospace design, analysis and optimization community, there is an increasing demand for automatic generation of parametric feature tree (build recipe) attributed multidisciplinary models. Currently, this is mainly done by creating separate models for different disciplines such as mid-surface model for aeroelasticity, outer-mold line for aerodynamics and CFD, and built-up element model for structural analysis. Since all of these models are built independently, any changes in design parameters require updates on all the models which is inefficient, time-consuming and prone to deficiencies. In this research, Engineering Sketch Pad (ESP) is used to create attribution and maintain consistency between structural models with different fidelity levels. It provides the user with the ability to interact with a configuration by building and/or modifying the design parameters and feature tree that define the configuration. ESP is based an open-source constructive solid modeler, named OpenCSM, which is built upon the OpenCASCADE geometry kernel and the EGADS geometry generation system. The use of OpenCSM as part of the AFRL’s CAPS project on Computational Aircraft Prototype Syntheses for automatic commercial and fighter jet models is demonstrated. The rapid generation of parametric aircraft structural models proposed and developed in this work will benefit the aerospace industry with coming up with efficient, fast and robust multidisciplinary design standardization of aircraft structures. Metallic aircraft wings are usually not optimized to their fullest potential due to shortage of development time. With roughly \$1000 worth of potential fuel savings per pound of weight reduction over the operational life of an aircraft, airlines are trying to minimize the weight of aircraft structures. A stiffness based strategy is used to map the nodal data of the lower-order fidelity structural models onto the higher-order ones. A simple multi-fidelity analysis process for a parametric wing is used to demonstrate the advantage of the approach. The loads on the wing are applied from a stick model as is done in the industry. C program is created to connect the parametric design software ESP, analysis software Nastran, load file and design configuration file in CSV format. This problem gets compounded when it comes to optimization of composite wings. In this study, a multi-level optimization strategy to optimize the weight of a composite transport aircraft wing is proposed. The part is assumed to initially have some arbitrary number of composite super plies. Super plies are a concept consisting of a set of plies all arranged in the same direction. The thickness and orientation angles of the super plies are optimized. Then, each ply undergoes topometry optimization to obtain the areas of each super ply taking the least load so that it could be cut and removed. Each of the super plies are then optimized for the thickness and orientation angles of the sub plies. The work presented on this paper is part of a project done for Air Force Research Laboratory (AFRL) connecting the parametric geometry modeler (ESP) with the finite element solver (Nastran).

The advances in deep neural networks in recent years have opened up the possibility of using image classification as a valuable tool in various areas, such as medical diagnosis from x-ray images. However, training deep neural networks requires large amounts of annotated data which has to be labelled manually, by a person. This process always involves a risk of data getting the wrong label, either by mistake or ill will, and training a machine learning model on mislabelled images has a negative impact on accuracy. Studies have shown that deep neural networks are so powerful at memorization that if they train on mislabelled data, they will eventually overfit this data, meaning learning a data representation that does not fully mirror real data. It is therefore vital to filter out these images. Area under the margin is a method that filters out mislabelled images by observing the changes in a network’s predictions during training. This method does however not take into consideration the overconfidence in deep neural networks and the uncertainty of a model can give indications of mislabelled images during training. Calibrating the confidence can be done through label smoothing and this thesis aims to investigate if the performance of Area under the margin can be improved when combined with different smoothing techniques. The goal is to develop a better insight into how different types of label noise affects models in terms of confidence, accuracy and the impact it has depending on the dataset itself. Three different label smoothing techniques will be applied to evaluate how well they can mitigate overconfidence, prevent the model from memorizing the mislabelled samples and if this can improve the filtering process for the Area under the margin method. Results show when training on data with noise present, adding label smoothing improves accuracy, an indication of noise robustness. Label noise is seen to decrease confidence in the model and at the same time reduce the calibration. Adding label smoothing prevents this and allows the model to be more robust as the noise rate increases. In the filtering process, label smoothing was seen to prevent correctly labelled samples to be filtered and received a better accuracy at identifying the noise. This did not improve the classification results on the filtered data, indicating that it is more important to filter out as many mislabelled samples as possible even if this means filtering out correctly labelled images as well. The label smoothing methods used in this work was set up to preserve calibration, a future topic of research could be to adjust the hyperparameters to increase confidence instead, focusing on removing as much noise as possible. / De senaste årens framsteg inom djupa neurala nätverk har öppnat för möjligheten att använda bildklassificering som ett värdefullt verktyg inom olika områden, såsom medicinsk diagnos från röntgenbilder. Men att träna djupa neurala nätverk kräver stora mängder annoterad data som måste märkas antingen av människor eller datorer. Denna process involverar alltid med en risk för att data får fel etikett, antingen av misstag eller av uppsåt och att träna en maskininlärningsmodell på felmärkta bilder har negativ inverkan på resultatet. Studier har visat att djupa neurala nätverk är så kraftfulla att memorera att om de tränar på felmärkta data, kommer de så småningom att överanpassa dessa data, vilket betyder att de kommer att lära sig en representation som inte helt speglar verklig data. Det är därför viktigt att filtrera bort dessa bilder. Area under marginalen är en metod som filtrerar bort felmärkta bilder genom att observera förändringarna i ett nätverks beteende under träning. Denna metod tar dock inte hänsyn till översäkerhet i djupa neurala nätverk och osäkerheten i en modell kan ge indikationer på felmärkta bilder under träning. Kalibrering av förtroendet kan göras genom etikettutjämning och denna uppsats syftar till att undersöka om prestandan för Area under marginalen kan förbättras i kombination med olika tekniker för etikettutjämning. Målet är att utveckla en bättre insikt i hur olika typer av brusiga etiketter påverkar modeller när det gäller tillförlitlighet, noggrannhet och den påverkan det har beroende på själva datasetet. Tre olika tekniker för etikettutjämning kommer att tillämpas för att utvärdera hur väl de kan mildra översäkerheten, förhindra modellen från att memorera de felmärkta bilderna och om detta kan förbättra filtreringsprocessen för Area under marginalen-metoden. Resultaten visar att när man tränar på data innehållande felmärkt data, förbättrar etikettutjämning noggrannheten vilket indikerar på robusthet mot felmärkning. Felmärkning tycks minska säkerheten hos modellen och samtidigt minska kalibreringen. Att lägga till etikettutjämning förhindrar detta och gör att modellen blir mer robust när mängden brusiga etiketter ökar. I filtreringsprocessen sågs att etikettutjämning förhindrar att korrekt märkt data filtreras bort och fick en bättre noggrannhet vid identifiering av bruset. Detta förbättrade dock inte klassificeringsresultaten på den filtrerade datan, vilket indikerar att det är viktigare att filtrera bort så mycket felmärkta prover som möjligt även om detta innebär att filtrera bort korrekt märkta bilder. Metoderna för etikettutjämning som används i detta arbete sattes upp för att bevara kalibreringen, ett framtida forskningsämne kan vara att justera hyperparametrarna för att istället öka förtroendet, med fokus på att ta bort så mycket felmärkta etiketter som möjligt.

Stressed Value-at-Risk (VaR) is a statistic used to measure an entity's exposure to market risk by evaluating possible extreme portfolio losses. Stressed VaR scenarios can be used as a metric to describe the state of the financial market and can be used to detect and counter procyclicality by allowing central clearing counterparities (CCP) to increase margin requirements. This thesis aims to implement and evaluate machine learning methods (e.g., neural networks) for selecting stressed VaR scenarios in price return stock datasets where one liquidity day is assumed. The models are implemented to counter the procyclical effects present in NASDAQ's dual lambda method such that the selection maximises the total margin metric. Three machine learning models are implemented together with a labelling algorithm, a supervised and unsupervised multilayer perceptron and a random forest model. The labelling algorithm employs a deviation metric to differentiate between stressed VaR and standard scenarios. The models are trained and tested using 5000 scenarios of price return values from historical stock datasets. The models are tested using visual results, confusion matrix, Cohen's kappa statistic, the adjusted rand index and the total margin metric. The total margin metric is computed using normalised profit and loss values from artificially generated portfolios. The implemented machine learning models and the labelling algorithm manage to counter the procyclical effects evident in the dual lambda method and selected stressed VaR scenarios such that the selection maximise the total margin metric. The random forest model shows the most promise in classifying stressed VaR scenarios, since it manages to maximise the total margin overall.

Thesis (Ph.D. (Agricultural Economics )) -- University of Limpopo, 2021 / Regardless of the strategies adopted globally and nationwide to fight food insecurity within communities, particularly in the rural context, poverty becomes a major constituent which translates to most rural households experiencing food insecurity shocks. Given the high unemployment rate in South Africa which triggers several household’s vulnerability to food insecurity, the country has diverse natural resources which include indigenous plants such as Indigenous Leafy Vegetables (ILVs), which can be used as food and for business purposes by its residents. Irrespective of the diversity of ILVs in South Africa, there is a significant decline in the production and consumption of ILVs particularly in rural areas where these vegetables are mostly available. In addition, production and consumption of ILVs may not only address food insecurity but these vegetables may benefit households through the income obtained from their sales. The income generated from sales of ILVs may therefore assist towards improving and sustaining rural livelihood needs. Given this background information, the study aimed at assessing ILV production for income generation and food security among rural households in the Eastern Cape Province (ECP) of South Africa. The study was conducted within the three district municipalities of the ECP which were selected because statistics report these districts to be the most affected areas by poverty within the province. These districts are OR Tambo District Municipality (ORTDM), Alfred Nzo District Municipality (ANDM) and Joe Gqabi District Municipality (JGDM). Multistage and proportional random sampling procedures were employed to select households which could participate in the study. Thus, 407 households within these three (3) districts municipalities were used for the purposes of the study. The study also included interviewing role players within the ILV production value chain, thus a snowball sampling procedure was used to select role players. Sixteen hawkers and three input suppliers were interviewed from the three district municipalities. In addition, from the 407 households that were interviewed, 260 households from the three district municipalities reported to be producers of ILVs. A structured questionnaire was therefore used to collect pertaining data allied in achieving the aim of the study. The collected data was captured using Excel 2016, after data cleaning, it was then exported to Statistical Package for Social Sciences (SPSS) version 25 for analysis. Numerous analytical models were used from SPSS 25. For instance, to identify and describe socio-economic characteristics of households, to assess the most produced ILVs from the study areas and to identify role players within the ILV production value chain, descriptive statistics in a form of means, percentages, frequencies, and standard deviation was used. To determine factors which influence production of ILVs, a Binary Logistic Regression Model was used. A Multinomial Logistic Regression model was used to determine factors which influence different uses of ILVs by households and to determine factors influencing food security status among households. A gross margin analysis was used to estimate viability from each ILV produced, harvested and sold, while Household Food Insecurity Access Scale (HFIAS) was used to measure food security status among households. Lastly, a correlation matrix was also used to determine the relationship between the role players and their functions among the ILV production value chain. Based on the results, the study therefore concluded that, from the three district municipalities used in the study, there are various ILVs growing naturally and produced. The production of ILVs from these municipalities is habituated by socio economic characteristics of households, wherein households use ILVs for various purposes which include these vegetables as source of food, medicine and livestock feed. The use of ILVs among households is influenced by socio-economic characteristics and seasonal availability of ILVs in ORTDM, while in ANDM and JGDM, the use of ILVs by households is conditioned by socio-economic characteristics of households, knowledge/ awareness related to nutrition and health benefits of ILVs and seasonal production of ILVs. Furthermore, this research concludes that, ILVs have a potential of diversifying diets and addressing food insecurity problems within rural parts of the three districts. Given the positive gross margins from the three districts, production and selling of ILVs has a potential to contribute to rural household income. Lastly, the study concludes that, the ILV production value chain system lacks governmental support in the form of institutional engagement since there is no evidence of extension officer support from these three district municipalities concerning ILVs production. To this end, the study recommends that, policy makers should further establish inclusion of ILVs in both farming and food systems. Also, government and related institutions which focus on sustainable rural development must intervene in promoting production of ILVs particularly within rural contexts since production of these vegetables may alleviate poverty through job creation, addressing food insecurity and income generation. Thus, a successful intervention of government and policy makers in ILV production would have a potential of translating to sustainable rural livelihoods / National Research Foundation (NRF)

As the transition from the use of fossil fuels torenewable sources takes place, consumption of electricity willgreatly increase. The shift in energy sources will have a deepimpact on how financial decisions in the grid will be made. Toensure that the necessary investments are made to meet the newneeds, many network administrators have used different typesof markets for capacity. This project reviews how the networkadministrator PJM in the US uses capacity markets to secure thesupply of electricity and stability in the grid. A literature studywas conducted together with a market simulation and the resultsfrom the simulation shows that the use of a separate capacitymarket is a successful concept for securing future electricitysupply and stability in the grid. / I takt med att övergången från användningen av fossila bränslen till förnybara källor sker, kommer elförbrukningen att öka kraftigt. Skiftet av energikällorkommer att ha en stor påverkan på hur finansiella beslut inom elnätet kommer att tas. För att se till att nödvändiga investeringar görs för att klara de nya behoven har många nätverksadministratörer använt sig av olika slags marknader för kapacitet. I detta projekt undersöks hur nätverksadministratören PJM i USA använder sig av kapacitetsmarknader för att säkra elproduktionen och stabiliteten i nätet. En litteraturstudie genomfördes tillsammans med en marknadssimulering och resultatet från undersökningen visar på att användningen av en separat kapacitetsmarknad är ett framgångsrikt koncept för att säkra framtida elförsörjning och stabilitet i nätet. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

No description available.

This thesis is, to my knowledge, the first study utilising data from the Swedish population and housing censuses between 1960 and 1990 merged with other data from the same period in order to estimate extensive margin labour supply responses to changes in municipal tax rate changes. Given that women historically have not faced the same structural labour market preconditions as men, the empirical strategy is designed to allow for an analysis of gender-based heterogeneity in labour supply responses. Using a weighted fixed effects framework, estimates of the average over time between municipal effects of tax rate increases are presented. Using the preferred main model specification, the estimate for the average tax rate elasticity is -0.165 for men and 0.3513 for women. Additionally, an attempt is made to estimate an effect using a difference-in-difference framework, treating the overall largest municipal tax rate changes as a form of quasi-experimental treatment. The results of the main analysis indicate the presence of gender-based heterogeneity in extensive margin labour supply responses during 1960 to 1990 within the administrative region in question.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24884号 / 医博第5018号 / 新制||医||1068(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 小濱 和貴, 教授 中島 貴子 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM