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

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.

Traumatic brain injury is highly prevalent in the United States yet there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data were gathered from literature sources and self-organizing maps were used to conduct a sensitivity analysis to rank considered parameters by importance. Fuzzy C-means clustering was applied to find any data patterns. The rankings and clustering for each data set varied, indicating that the strain rate and type of deformation influence the role of these parameters. Multivariate linear regression was applied to develop a model which can predict the mechanical response from different experimental conditions. Prediction of response depended primarily on strain rate, frequency, brain matter composition, and anatomical region.

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.

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>

The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.

With the help of new technology it has become much easier to apply for a job. Reaching out to a larger audience also results in a lot of more applications to consider when hiring for a new position. This has resulted in that many big companies uses statistical learning methods as a tool in the first step of the recruiting process. Smaller companies that do not have access to the same amount of historical and big data sets do not have the same opportunities to digitalise their recruitment process. Using topological data analysis, this thesis explore how clustering methods can be used on smaller data sets in the early stages of the recruitment process. It also studies how the level of abstraction in data representation affects the results. The methods seem to perform well on higher level job announcements but struggles on basic level positions. It also shows that the representation of candidates and jobs has a huge impact on the results. / Ny teknologi har förenklat processen för att söka arbete. Detta har resulterat i att företag får tusentals ansökningar som de måste ta hänsyn till. För att förenkla och påskynda rekryteringsprocessen har många stora företag börjat använda sig av maskininlärningsmetoder. Mindre företag, till exempel start-ups, har inte samma möjligheter för att digitalisera deras rekrytering. De har oftast inte tillgång till stora mängder historisk ansökningsdata. Den här uppsatsen undersöker därför med hjälp av topologisk dataanalys hur klustermetoder kan användas i rekrytering på mindre datauppsättningar. Den analyserar också hur abstraktionsnivån på datan påverkar resultaten. Metoderna visar sig fungera bra för jobbpositioner av högre nivå men har problem med jobb på en lägre nivå. Det visar sig också att valet av representation av kandidater och jobb har en stor inverkan på resultaten.