The agriculture sectors in Canada are highly vulnerable to a wide range of inter-related weather risks linked to seasonal climate variability (e.g., El Ni ̃no Southern Oscillation(ENSO)), short-term extreme weather events (e.g., heatwaves), and emergent disease(e.g., grape powdery mildew). All of these weather-related risks can cause severe crop losses to agricultural crop yield and crop quality as Canada grows a wide range of farm products, and the changing weather conditions mainly drive farming practices. This dissertation presents three machine learning-based statistical models to assess the weather risks on the Canadian agriculture regions and to provide reliable risk forecasting to improve the decision-making of Canadian agricultural producers in farming practices. The first study presents a multi-scale, cluster-based Principal Component Analysis(PCA) approach to assess the potential seasonal impacts of ENSO to spring wheat and barley on agricultural census regions across the Canada prairies areas. Model prediction skills for annual wheat and barley yield have examined in multi-scale from spatial cluster approaches. The ’best’ spatial models were used to define spatial patterns of ENSO forcing on wheat and barley yields. The model comparison of our spatial model to non-spatial models shows spatial clustering and ENSO forcing have increase model performance of prediction skills in forecasting future cereal crop production. The second study presents a copula-Bayesian network approach to assess the impact of extreme high-temperature events (heatwave events) on the developments of regional crops across the Canada agricultural regions at the eco-district-scale. Relevantweather variables and heatwave variables during heatwave periods have identified and used as input variables for model learning. Both a copula-Bayesian network and Gaussian-based network modeling approach is evaluated and inter-compared. The copula approach based on ’vine copulas’ generated the most accurate predictions of heatwave occurrence as a driver of crop heat stress. The last study presents a stochastic, hybrid-Bayesian machine-learning approach to explore the complex causal relationships between weather, pathogen, and host for grape powdery mildew in an experimental farm in Quebec, Canada. This study explores a high-performance network model for daily disease risk forecast by using estimated development factors of pathogen and host from recorded daily weather variables. A fungicide strategy for disease control has presented by using the model outputs and forecasted future weather variability. The dissertation findings are beneficial to Canada’s agricultural sector. The inter-related weather risks explored by the three separate studies in multi-scales provide a better understanding of the interactions between changing weather conditions, extreme weather, and crop production. The research showcases new insights, methods, and tools for minimizing risk in agricultural decision-making / Graduate / 2021-08-19

Information and communication technology (ICT) greatly influence today’s business processes be it in public or private sectors. Everything that is done in business requires ICT in one way or the other. Research in ICTs is therefore critical. So much research was and is still carried out in projects that develop or enhance ICT but it is still apparent that the success rate of these projects is still very low. The extensive coverage of ICTs implies that if the success rate is still that low, many resources are being wasted in the failed projects; therefore, more research is needed to improve the success rate. Previous research has focussed on factors which are critical for the success of ICT projects, assuming that all ICT projects are the same. As a result, literature is full of different suggestions and guidelines of the factors critical to ICT projects’ success. This scenario brings challenges to project managers who end up using their own personal judgement to select which factors to consider for any project at hand. The end result is the high failure rate of ICT projects since there is a very high chance of applying the same critical success factors to different types of ICT projects. This research answered the question: which factors are critical to the success of computer networking projects in Zimbabwe and how these factors could be used for building a model that determines in advance the success of such projects? Literature reviewed indicated that most CSFs were not focused on specific types of ICT projects, hence were generalised. No literature was found on ICT projects’ CSFs in Zimbabwe. More so, no CSFs were found for computer networking projects as a specific instance of ICT projects. No model existed that predicts computer networking projects’ success. This study addressed the gaps by developing a CSF framework for ICT projects in Zimbabwe, determining CSFs for computer networking projects in Zimbabwe and the development of a logistic regression analysis model to predict computer networking projects’ success in Zimbabwe. Data was collected in Zimbabwe using a unique three-staged process which comprise metasynthesis analysis, questionnaire and interviews. The study was motivated by the fact that most available research focused on CSFs for general ICT projects and that no research was found on CSFs influencing projects in computer networking. Meta-synthesis analysis was therefore conducted on literature in order to identify CSFs as given in literature. The approach was appropriate since the researcher had noticed that there were extensive ICT projects’ CSFs and that no such research has been carried out in Zimbabwe. These CSFs formed the basis for the determination (using a questionnaire) of ICT projects CSFs for Zimbabwe in particular. Project practitioners’ viewpoints were sought through questionnaires. Once CSFs for ICT projects in Zimbabwe were determined, they formed the basis for the determination of unique critical success factors for computer networking projects in Zimbabwe. Interviews were used to get further information that would have been left out by questionnaires. The interview questions were set to clarify some unclear or conflicting responses from the questionnaire and providing in-depth insights into the factors critical to computer networking projects in Zimbabwe. The data i.e. critical success factors for computer networking projects guided the development of the logistic regression analysis model for the prediction of computer networking projects’ success in Zimbabwe. Data analysis from the questionnaire was analysed using SPSS Version 23.0. Factor analysis and principal component analysis were some of the techniques used in the analysis. Interview data was analysed through NVivo Version 10.0. From the results it was deduced that factors critical to ICT project management in Zimbabwe were closely related to those found in the literature. The only apparent difference was that CSFs for ICT projects in Zimbabwe were more specific thereby enhancing their applicability. Computer networking projects had fewer CSFs than general ICT projects. In addition, CSFs for general ICT projects were different from those critical to computer networking projects in Zimbabwe. The development of a comprehensive set of general ICT projects’ CSFs was the first contribution of this study. This was achieved through meta-synthesis analysis. The other contribution was the development of a CSF framework for ICT projects specific to Zimbabwe and those specific to computer networking projects in Zimbabwe. The major contribution was the development of the logistic regression analysis model that predicts computer networking projects’ success in Zimbabwe. These contributions will provide literature on ICT project management in Zimbabwe which will subsequently assist ICT project managers to concentrate on specific factors. The developed prediction model can be used by project managers to determine possible success or failure of ICT projects; thereby possible reducing wastage of resource. / School of Computing

Market makers such as large banks are exposed to market risk in fixed income by acting as a counterparty for customers that enter swap contracts. This master thesis addresses the problem of creating a cost-effective hedge for a realistic swap book of a market maker in a multiple yield curve setting. The proposed hedge model is the two-stage stochastic optimisation problem created by Blomvall and Hagenbjörk (2020). Systematic term structure innovations (components) are estimated using six different component models including principal component analysis (PCA), independent component analysis (ICA) and rotations of principal components. The component models are evaluated with a statistical test that uses daily swap rate observations from the European swap market. The statistical test shows that for both FRA and IRS contracts, a rotation of regular principal components is capable of a more accurate description of swap rate innovations than regular PCA. The hedging model is applied to an FRA and an IRS swap book separately, with daily rebalancing, over the period 2013-06-21 to 2021-05-11. The model produces a highly effective hedge for the tested component methods. However, replacing the PCA components with improved components does not improve the hedge. The study is conducted in collaboration with two other master theses, each done at separate banks. This thesis is done in collaboration with Swedbank and the simulated swap book is based on the exposure of a typical swap book at Swedbank, which is why the European swap market is studied.

Indiana University-Purdue University Indianapolis (IUPUI) / The link between volatile organic compounds (VOCs) from breath and various diseases and specific conditions has been identified since long by the researchers. Canine studies and breath sample analysis on Gas chromatography/ Mass Spectroscopy has proven that there are VOCs in the breath that can detect and potentially predict hypoglycemia. This project aims at developing a smart nanosensor system to detect hypoglycemia from human breath. The sensor system comprises of 1-Mercapto-(triethylene glycol) methyl ether functionalized goldnanoparticle (EGNPs) sensors coated with polyetherimide (PEI) and poly(vinylidene fluoride -hexafluoropropylene) (PVDF-HFP) and polymer composite sensor made from PVDF-HFP-Carbon Black (PVDF-HFP/CB), an interface circuit that performs signal conditioning and amplification, and a microcontroller with Bluetooth Low Energy (BLE) to control the interface circuit and communicate with an external personal digital assistant. The sensors were fabricated and tested with 5 VOCs in dry air and simulated breath (a mixture of air, small portion of acetone, ethanol at high humidity) to investigate sensitivity and selectivity. The name of the VOCs is not disclosed herein but these VOCs have been identified in-breath and are identified as potential biomarkers for other diseases as well. The sensor hydrophobicity has been studied using contact angle measurement. The GNPs size was verified using Ultra-Violent-Visible (UV-VIS) Spectroscopy. Field Emission Scanning Electron Microscope (FESEM) image is used to show GNPs embedded in the polymer film. The sensors sensitivity increases by more than 400\% in an environment with relative humidity (RH) of 93\% and the sensors show selectivity towards VOCs of interest. The interface circuit was designed on Eagle PCB and was fabricated using a two-layer PCB. The fabricated interface circuit was simulated with variable resistance and was verified with experiments. The system is also tested at different power source voltages and it was found that the system performance is optimum at more than 5 volts. The sensor fabrication, testing methods, and results are presented and discussed along with interface circuit design, fabrication, and characterization. / 2022-05-8

The essence of face recognition within the image is generally computer vision, which provides methods and algorithms for the implementation. Some of them are described just in this work. Whole process is split in to three main phases. These are detection, aligning of detected faces and finally its recognition. Algorithms which are used to applied in given issue and which are still in progress from todays view are mentioned in every phase. Implementation is build up on three main algorithms, AdaBoost to obtain the classifier for detection, method of aligning face by principal features and method of Eigenfaces for recognizing. There are theoretically described except already mentioned algorithms neural networks for detection, ASM - Active Shape Models algorithm for aligning and AAM - Active Appearance Model for recognition. In the end there are tables of data retrieved by implemented system, which evaluated the main implementation.

In this thesis, we explore the proprietary Environmental, Social and Corporate Governance (ESG) scores and analyze their impacts on firm valuation using the sample of 166 companies operating in 35 emerging and frontier markets. Three methods of ESG scores, Principal Component Analysis and regression analysis are used. The results indicate an economically significant relationship between the overall ESG measure and firm value mainly driven by the “Environmental” and “Capital allocation” sub-scores. An exploratory principal component analysis and an extensive list of firm characteristics is also employed in our regression analysis to address problems identified in previous studies - construct validity and endogeneity. The PCA revealed dominance of Environmental and Social components in the variance of the total ESG score. Finally, the strengths and weaknesses of proprietary ESG score and PCAderived index are recognized based on sector- and region level comparison and the opportunities to improve the ESG scorecard framework are identified. / In den uppsatsen, forskning går på Environmental, Social and Corporate Governance (ESG) poäng och analyserar deras påverkan på företagsvärdering genom att använda ett urval av 166 företag som verkar i 35 frontier och tillväxtmarknader. Tre metoder av ESG mätning, Principal Component Analysis och regressionsanalyser tillämpades. Resultat tyder på ett ekonomiskt signifikant förhållande mellan totala ESG mätning och företagsvärdering vilket drivs av miljö och kapitalallokering delpoäng. Principalkomponentanalys och en utförlig lista av företagsegenskaper tillämpades också i våra regressionsanalyser för att adressera problem identifierade i tidigare studier - begreppsvaliditet och endogenitetsproblem. PCA tydde på dominans av miljöoch sociala aspekter i varians av den totala ESG poängen. Avslutningsvis, styrkor och svagheter av ESG-poäng och PCA-härlett index baserat på bransch- samt regionaljämförelser och möjligheterna för förbättring av ESG-mätning ramverk identifierades.

Social factors moderate the impacts of natural hazards, which means that households are affected differently when exposed to the same hazard. This differential impact of hazards can be explained by the concept of social vulnerability, which is commonly assessed to inform disaster preparedness and response action. Most of these assessments, however, focus their analyses on large administrative units and, consequently, neglect the heterogeneity of households within these units. This thesis leverages data from Malawi’s social registry (the UBR) to construct a Household Social Vulnerability Index for Nsanje – one of the most disaster-prone districts in Malawi. In Nsanje, geocoded socio-economic data was collected using a census-sweep approach with the goal of registering 100% of the district’s residents. From this dataset, indicators are deductively selected and analyzed using Principal Component Analysis to produce a social vulnerability score for each household. These index scores are mapped at a spatial resolution of 0,01°. By repurposing a social registry to inform a new set of actors, including humanitarian and disaster risk management practitioners, the thesis highlights the considerable scope for collaboration within the realm of data and information by actors and policy fields that traditionally largely have operated in isolation from one another.

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Die Risikoprämie einer Unternehmensanleihe dient prinzipiell der wirtschaftlichen Kompensation für die Übernahme zusätzlicher Risiken gegenüber den Risiken der Benchmark. Allerdings findet sich in der bisher veröffentlichen Literatur eine Vielzahl von den praktischen Messkonzepten, die in vielen Fällen nicht fehlerfrei und problemlos zustande gekommen sind. Daher ist die präzise und quantitative Messung der Risikoprämien von Unternehmensanleihen eine betriebswirtschaftliche Notwendigkeit. In der vorliegenden Arbeit werden im Hinblick auf die Erreichbarkeit drei alternative Messkonzepte bezüglich der Risikoprämien von Unternehmensanleihen vorgestellt und miteinander verglichen. Einige bisherige Studien sind der Auffassung, dass die Risikoprämien von Unternehmensanleihen zumeist von den Nicht-Kreditkomponenten beeinflusst werden. Um diese Marktanomalien zu erklären, verwenden die vorliegenden Untersuchungen das statistische lineare Faktor-Modell. In diesem Zusammenhang wird die Untersuchung von LITTERMAN/SCHEINKMAN (1991) auf die risikobehafteten Unternehmensanleihen übertragen. Im Kern steht die Frage, welche Risikoarten bzw. wie viele Einflussfaktoren wirken sich auf die Risikoprämien von Unternehmensanleihen in wieweit aus. Das Ziel ist ein sparsames lineares Faktor-Modell mit wirtschaftlicher Bedeutung aufzubauen. Somit leistet diese Dissertationsschrift einen wesentlichen Beitrag zur Gestaltung der Anleiheanalyse bzw. zur Portfolioverwaltung.

Multinational companies have consistently not been able to explain the impact currency and interest rates fluctuations have on their profits. To be able to account for these effects, thorough visibility is required. Epiroc Örebro is a global supplier of products and services within mining and infrastructure, with sales in more than 150 countries. The largest markets are Europe, North and South America and Asia. Naturally, with exposure to many different currencies and interest rates, it lies in the company’s interest to fully grasp and visualize the effects of these risk factors. The aim of this study is to provide and apply a performance attribution model to Epiroc Örebro, in order to fully grasp and visualize, how foreign exchange rates and interest rates affect the profits of the company’s operations on a daily basis. Main focus is on incorporating inventory into the performance attribution model. To fulfill the purpose of this thesis, literature studies on performance attribution models, foreign exchange risk, and interest rate risks were conducted. Epiroc Group and Epiroc Örebro were studied to get the full picture of their risk exposures. Consequently, a generic framework for performance attribution was extended, established and provided to their daily operations. The rigorous framework describes profit decomposition (ΔNPVt) with respect to risk factors. In summary, this mathematical model comprises of: a Taylor approximation for changes in price with several error terms, terms accounting for holding foreign currencies and assets, purchasing and sales of currencies and assets and lastly, a term accounting for currency fluctuations. See eq. (4.25) to eq. (4.35). The focus of this report is the addition of inventory into the existing performance attribution model. Inventory is valued to last purchase price and the value of inventory is only affected by price changes and exchange rate fluctuations. The main result of this study is that inventory can be incorporated into the performance attribution model. The model is comprehensive and fully explains the company’s NPV changes on a daily basis in detail. Furthermore, the conclusion is that the model can be extended to handle inventory, but several additions and adjustments are still to be added. Work regarding data extraction and cash flow prognosis will be required to scale the model and to enable real time use. / <p>Examensarbete i Finans från Civilingenjörsprogrammet i Industriell Ekonomi.</p>