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[pt] Nesse artigo, estudamos os leilões de petróleo e gás no Brasil para medir o impacto de requerimentos de conteúdo local nos lances realizados, o que nos permite estimar o seu impacto na receita obtida pelo governo. O caso brasileiro é particularmente atrativo, uma vez que houve variação significativa das exigências de conteúdo local ao longo do anos. Nas rodadas em que os requerimentos foram mais altos, observamos uma mudança significativa no comportamento dos participantes dos leilões: o bônus de assinatura médio em blocos offshore caiu de uma média de 57 milhões de reais nas primeiras rodadas para apenas 10,6 milhões de reais, e o número médio de lances por bloco caiu de 0,92 para 0,12. Nosso objetivo é responder em que medida a elevação das exigências de conteúdo local afetaram participação e receita nos leilões. Nós desenvolvemos e estimamos um modelo estrutural de leilões dentro do arcabouço de valores comuns que inclui a decisão de entrada e lances em múltiplas dimensões, incluindo um bônus e um percentual de conteúdo local. Nossos resultados mostram que as exigências de conteúdo local elevam os custos em blocos em águas profundas em 14 por cento. A receita governamental em leilões nessas áreas poderia ser muito maior em um contrafactual sem exigências de conteúdo local, contabilizando 17 bilhões de reais em receita de bônus de assinatura apenas em águas profundas. Em blocos em terra, não encontramos diferenças significativas de custo local e estrangeiro. / [en] In this paper, we study the case of Brazilian oil and gas auctions to assess the impact of local content requirements in bidding behavior, allowing us to estimate its impact on government revenue. The Brazilian case is particularly appealing, as there were significant changes in these requirements throughout the years. In the sales with increased local content requirements there was a dramatic change in the bidders behavior: the average signing bonus for offshore tracts dropped from an average of 57 million reais in the first sales to only 10.6 million reais and the average number of bids per tract plunged from 0.92 to 0.12. We aim to answer how much the increased local content requirements affected participation and revenue in the auctions. We develop and estimate a structural auction model within the mineral rights framework that includes an entry decision and bids in multiple dimensions, including a bonus and a local content percentage. Our results show that local content requirements increase costs in deep water areas in 14 por cento. Government revenue in auctions in these areas could be much larger in a counterfactual with no local content requirements, amounting to an extra 17 billion reais in signing bonus only for deep-water tracts. For onshore areas, we did not find any significant difference between local and foreign costs.

The field of explainable artificial intelligence (XAI) has gained momentum in recent years following the increased use of AI systems across industries leading to bias, discrimination, and data security concerns. Several conceptual frameworks for how to reach AI systems that are fair, transparent, and understandable have been proposed, as well as a number of technical solutions improving some of these aspects in a research context. However, there is still a lack of studies examining the implementation of these concepts and techniques in practice. This research aims to bridge the gap between prominent theory within the area and practical implementation, exploring the implementation and evaluation of XAI models in the Swedish credit scoring industry, and proposes a three-step framework for the implementation of local explanations in practice. The research methods used consisted of a case study with the model development at UC AB as a subject and an experiment evaluating the consumers' levels of trust and system understanding as well as the usefulness, persuasive power, and usability of the explanation for three different explanation prototypes developed. The framework proposed was validated by the case study and highlighted a number of key challenges and trade-offs present when implementing XAI in practice. Moreover, the evaluation of the XAI prototypes showed that the majority of consumers prefers rulebased explanations, but that preferences for explanations is still dependent on the individual consumer. Recommended future research endeavors include studying a longterm XAI project in which the models can be evaluated by the open market and the combination of different XAI methods in reaching a more personalized explanation for the consumer. / Under senare år har antalet AI implementationer stadigt ökat i flera industrier. Dessa implementationer har visat flera utmaningar kring nuvarande AI system, specifikt gällande diskriminering, otydlighet och datasäkerhet vilket lett till ett intresse för förklarbar artificiell intelligens (XAI). XAI syftar till att utveckla AI system som är rättvisa, transparenta och begripliga. Flera konceptuella ramverk har introducerats för XAI som presenterar etiska såväl som politiska perspektiv och målbilder. Dessutom har tekniska metoder utvecklats som gjort framsteg mot förklarbarhet i forskningskontext. Däremot saknas det fortfarande studier som undersöker implementationer av dessa koncept och tekniker i praktiken. Denna studie syftar till att överbrygga klyftan mellan den senaste teorin inom området och praktiken genom en fallstudie av ett företag i den svenska kreditupplysningsindustrin. Detta genom att föreslå ett ramverk för implementation av lokala förklaringar i praktiken och genom att utveckla tre förklaringsprototyper. Rapporten utvärderar även prototyperna med konsumenter på följande dimensioner: tillit, systemförståelse, användbarhet och övertalningsstyrka. Det föreslagna ramverket validerades genom fallstudien och belyste ett antal utmaningar och avvägningar som förekommer när XAI system utvecklas för användning i praktiken. Utöver detta visar utvärderingen av prototyperna att majoriteten av konsumenter föredrar regelbaserade förklaringar men indikerar även att preferenser mellan konsumenter varierar. Rekommendationer för framtida forskning är dels en längre studie, vari en XAI modell introduceras på och utvärderas av den fria marknaden, dels forskning som kombinerar olika XAI metoder för att generera mer personliga förklaringar för konsumenter.

The research presented in this thesis focuses on an innovative use of digital imaging, and the machine learning techniques to assess the body rumen fill scoring in dairy cows. This study aims to enhance the efficiency of monitoring and managing dairy cow health, which is crucial for the dairy industry's productivity and sustainability. The primary objective was to develop an automated annotation system fore valuating rumen fill status in dairy cows using digital images extracted from recorded videos. This system leverages advanced machine learning algorithms and neural networks, aiming to mimic manual assessments by veterinarians and specialists on farms. To achieve the above objectives, this thesis made use of already existing video records from a Swedish dairy farm hosting mainly the Swedish Redand the Swedish Holstein breeds. A subset of these images were then processed, manually classified using a modified rumen fill scoring system based on visual assessment, and supervised classification algorithms were trained on 277 manually annotated images. The thesis explored various machine learning techniques for classifying these images, including Logistic Regression, Support Vector Machine (SVM), and a Deep Neural Network using the VGG16 architecture. These models were trained, validated, and tested with a dataset that included variations in cow color patterns, aiming to determine the most effective approach for automated rumen fill scoring.The results indicated that while each model had its strengths and weaknesses, the simple logistic model was performing the best in terms of test accuracy and F1 score. This research contributes to the field of precision livestock farming, particularly in the context of dairy farming. By automating the process of rumen fill scoring, the study aims to provide dairy farmers with a reliable, efficient, and cost-effective tool for monitoring cow health. This tool has the potential to enhance dairy cow welfare, improve milk production, and support the sustainability of dairy farming operations. However, at the current state, the model accuracy of the best model was only moderate. There is a need for further improvement of the prediction performance possibly by adding more cow images, using improved image processing, and feature engineering.

Pinhole leaks in copper plumbing pipes are a big concern for the homeowners. The problem is spread across the nation and remains a threat to plumbing systems of all ages. Due to the absence of a single acceptable mechanistic theory no preventive measure is available to date. Most of the present mechanistic theories are based on analysis of failed pipe samples however an objective comparison with other pipes that did not fail is seldom made. The variability in hydraulic and water quality parameters has made the problem complex and unquantifiable in terms of plumbing susceptibility to pinhole leaks. The present work determines the spatial and temporal spread of pinhole leaks across United States. The hotspot communities are identified based on repair histories and surveys. An assessment of variability in water quality is presented based on nationwide water quality data. A synthesis of causal factors is presented and a scoring system for copper pitting is developed using goal programming. A probabilistic model is presented to evaluate optimal replacement time for plumbing systems. Methodologies for mechanistic modeling based on corrosion thermodynamics and kinetics are presented. / Master of Science

The development of medicine is generally a long and costly process, and one big factor is estimating the affinity of protein-drug binding. Leveraging machine learning in this field is a promising approach as it can streamline the prediction process and reduce the need for expensive experimental methods. Machine learning methods have already enabled significant advances in predicting protein-drug binding affinity, yet there remains room for improvement. The primary challenge is the quality of data used for these machine learning models. In this work, two ensemble machine learning models, Random Forest and Extreme Gradient Boosting Machine, have been tested and compared with a recent database of protein-ligand complex features calculated from molecular dynamics simulation. Additional features were also extracted from the PDB database through PLIP (Protein-Ligand interaction Profiler), aiming to improve the predictions further. The results indicate that while the features from the PDB database provided strong predictive power, including features from molecular dynamic simulations did not improve the models’ performance.

Der Aufstieg des maschinellen Lernens (ML) und die rasante Digitalisierung der Wirtschaft haben die Entscheidungsprozesse in der Finanzbranche erheblich verändert. Finanzinstitute setzen zunehmend auf ML, um die Entscheidungsfindung zu unterstützen. Kreditscoring ist eine der wichtigsten ML-Anwendungen im Finanzbereich. Die Aufgabe von Kreditscoring ist die Unterscheidung ob ein Antragsteller einen Kredit zurückzahlen wird. Finanzinstitute verwenden ML, um Scorecards zu entwickeln, die die Ausfallwahrscheinlichkeit eines Kreditnehmers einschätzen und Genehmigungsentscheidungen automatisieren. Diese Dissertation konzentriert sich auf drei große Herausforderungen, die mit dem Aufbau von ML-basierten Scorekarten für die Bewertung von Verbraucherkrediten verbunden sind: (i) Optimierung von Datenerfassungs- und -speicherkosten bei hochdimensionalen Daten von Kreditantragstellern; (ii) Bewältigung der negativen Auswirkungen von Stichprobenverzerrungen auf das Training und die Bewertung von Scorekarten; (iii) Messung und Sicherstellung der Fairness von Instrumenten bei gleichzeitig hoher Rentabilität. Die Arbeit bietet und testet eine Reihe von Instrumenten, um jede dieser Herausforderungen zu lösen und die Entscheidungsfindung in Finanzinstituten zu verbessern. Erstens entwickeln wir Strategien zur Auswahl von Merkmalen, die mehrere unternehmensbezogene Zielfunktionen optimieren. Unsere Vorschläge reduzieren die Kosten der Datenerfassung und verbessern die Rentabilität der Modelle. Zweitens schlagen wir Methoden zur Abschwächung der negativen Auswirkungen von Stichprobenverzerrungen vor. Unsere Vorschläge gleichen die Verluste aufgrund von Verzerrungen teilweise aus und liefern zuverlässigere Schätzungen der künftigen Scorecard-Leistung. Drittens untersucht die Arbeit faire ML-Praktiken in Kreditscoring. Wir katalogisieren geeignete algorithmische Optionen für die Einbeziehung von Fairness-Zielen und verdeutlichen den Kompromiss zwischen Gewinn und Fairness. / The rise of machine learning (ML) and the rapid digitization of the economy has substantially changed decision processes in the financial industry. Financial institutions increasingly rely on ML to support decision-making. Credit scoring is one of the prominent ML applications in finance. The task of credit scoring is to distinguish between applicants who will pay back the loan or default. Financial institutions use ML to develop scoring models to estimate a borrower's probability of default and automate approval decisions. This dissertation focuses on three major challenges associated with building ML-based scorecards in consumer credit scoring: (i) optimizing data acquisition and storage costs when dealing with high-dimensional data of loan applicants; (ii) addressing the adverse effects of sampling bias on training and evaluation of scoring models; (iii) measuring and ensuring the scorecard fairness while maintaining high profitability. The thesis offers a set of tools to remedy each of these challenges and improve decision-making practices in financial institutions. First, we develop feature selection strategies that optimize multiple business-inspired objectives. Our propositions reduce data acquisition costs and improve model profitability and interpretability. Second, the thesis illustrates the adverse effects of sampling bias on model training and evaluation and suggests novel bias correction frameworks. The proposed methods partly recover the loss due to bias, provide more reliable estimates of the future scorecard performance and increase the resulting model profitability. Third, the thesis investigates fair ML practices in consumer credit scoring. We catalog algorithmic options for incorporating fairness goals in the model development pipeline and perform empirical experiments to clarify the profit-fairness trade-off in lending decisions and identify suitable options to implement fair credit scoring and measure the scorecard fairness.

Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises. / Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises.

In der Humanmedizin wird zur Beurteilung der Magenschleimhautproben das aktualisierte Sydney-System nach STOLTE (1997) angewendet. Das Ziel war es herauszufinden, ob das histologische Grading System auch in der Veterinärmedizin, für die Beurteilung von Pferdemagenschleimhautpräparaten, genutzt werden kann und ob daraus neue Erkenntnisse erwachsen. Von 60 Pferden wurden direkt post mortem Schleimhautproben aus der Pars glandularis (Drüsenschleimhaut), im Bereich der großen Kurvatur und dem Pylorus, entnommen. Die Patienten wurden in 4 Gruppen, 10 operierte (Kolik)pferde, 36 Pferde mit Kolik und infauster Prognose, 6 Pferde mit hochgradigen Magenulzera und 8 Pferde, die nicht auf Grund einer Kolik euthanasiert wurden, eingeteilt. Die makroskopische Beurteilung der 60 Pferdemägen erfolgte nach MURRAY et al. (1989) und MACALLISTER et al. (1995). Die histopathologische Beurteilung erfolgt in der Humanmedizin anhand der Helicobacter-like-Organismen Dichte, dem Grad der chronischen Entzündung, der Aktivität der Gastritis, der Atrophie und der intestinalen Metaplasie. Nach diesen Beurteilungsvariablen wurden die 120 Proben aus den 60 Pferdemägen beurteilt. Die ätiologischen Diagnosen sind in der Humanmedizin das Ergebnis jahrzehntelanger Forschung. Beim Pferd liegen dagegen zur Ätiologie der Gastritis noch keine gesicherten Erkenntnisse vor. Beim Pferd gibt es bestimmte Gastritisformen, die denen des Menschen ähnlich sind. Doch können die morphologischen Befunde in der Veterinärmedizin, nach den jetzigen Erkenntnissen, keinen Ätiologien zugeordnet werden. Die ätiologischen Diagnosen in dieser veterinärmedizinischen Studie beruhen auf der Diagnostik am Menschen und wurden noch nicht auf ihre Richtigkeit beim Pferd überprüft. Von den 60 untersuchten Pferdemägen wiesen 31 makroskopisch Läsionen in der Magenschleimhaut auf. 20 Pferde mit Veränderungen hatten diese in der Pars glandularis. Bei 44 der Pferde bestätigt der histologische Befund, nach dem aktualisierten Sydney- System, das makroskopische Grading. 13 der Pferde hatten nach dem aktualisierten Sydney-System histologisch einen pathologischen Befund, obwohl makroskopisch die Schleimhaut keine Auffälligkeiten aufwies. Bei nur 3 von den 60 Pferden konnte der histologische den makroskopischen Befund nicht bestätigen. Ätiologisch wurde, nach humanmedizinischen Beurteilungskriterien, bei 18 Pferden im Bereich der großen Kurvatur der Pars glandularis und, oder im Bereich des Pylorus eine C-Gastritis (chemische Gastritis), bei 11 Pferden eine like B-Gastritis (bakterielle Gastritis ohne den Nachweis von Helicobacter-like-Organismen), 3 Pferden eine B-Gastritis (bakterielle Gastritis mit dem Nachweis von Helicobacter-like-Organismen) und bei 9 Pferden eine Sonderform der Gastritis diagnostiziert. 6 Pferde bekamen die Diagnose: zur Zeit nicht klassifizierbar und 7 Pferde die deskriptive Diagnose erosive oder ulzerative Gastritis gestellt. 24 Pferde hatten keinen pathologischen Befund in einem der oben genannten Bereiche der Schleimhaut. Die histopathologischen Befunde der Pferde mit einer like-B-Gastritis oder einer B-Gastritis entsprachen nach humanmedizinischen Gesichtspunkten dem Bild einer Helicobacter-pylori-Gastritis beim Menschen. Bandartige Anordnung der Lymphozyten in der Lamina propria mucosae und neutrophile Granulozyten in Verbindung mit einer Atrophie des Drüsenkörpers, intestinaler Metaplasie und Erosionen. Bei drei Pferden konnte in der Warthin–Starry-Färbung und in der IHC-Reaktion Helicobacter-like-Organismen nachgewiesen werden. Die Pylorusschleimhaut war doppelt so häufig, im Vergleich zur Drüsenschleimhaut der großen Kurvatur, von einer like-B-Gastritis oder B-Gastritis betroffen. Die histologische Auswertung von Magenschleimhautbioptaten, in dieser Studie nach dem aktualisierten Sydney-System aus der Humanmedizin, komplettiert die makroskopische (endoskopische) Diagnostik. Nach den Ergebnissen der vorliegenden Studie gehört in der Pferdemedizin zu jeder Gastroskopie die Bioptatentnahme. Das aktualisierte Sydney-System kann in Zukunft in der Veterinärmedizin als Arbeitsgrundlage für die weitere wissenschaftliche Forschung genutzt werden. / Stolte’s updated Sydney system is used in the field of human medicine for grading the gastric mucosa (STOLTE 1997). The goal of this study was to determine whether this system could also be applied for histological parameter grading in veterinary medicine, in order to gain new insights into medications for treating equine gastric mucosa. Post mortem biopsies of mucosa were taken from 60 equines along the greater curve and pylorus of the pars glandularis. The test animals were divided into four groups: 10 post-colic surgery equines, 36 with colic and an infaust prognosis, six with chronic EGUS (equine gastric ulcer syndrome), and eight equines not euthanized for reasons other than colic. Macroscopic grading of the 60 equine stomachs was performed in accordance with MURRAY et al. (1989) and MACALLISTER et al. (1995). In human medicine, histological scoring is based on the following five parameters: density of Helicobacter-like organisms, grade of the chronic inflammation, level of gastric activity, atrophy, and intestinal metaplasia. A total of 120 biopsies taken from the 60 equines were graded according to these parameters. Etiologic diagnoses for humans are the outcome of decades of research, but the etiology of equine gastritis lacks an equivalent foundation. Equines exhibit forms of gastritis similar to those in humans, but their morphology cannot be classified into any specific etiology. In this study, the etiological diagnoses were based on human diagnostics, but their validity for equines has yet to be substantiated. Of the 60 equine stomachs examined, 31 showed lesions in the gastric mucosa, while 20 of those with changes had lesions in the pars glandularis. Histological findings of 44 equines confirmed the macroscopic grading according to the updated Sydney system. Thirteen equines exhibited pathological findings based on the updated Sydney system histology, although no abnormalities were discovered in the macroscopic examination. The histological diagnosis did not confirm the macroscopic grading for only three of the 60 subjects. The following etiological findings were reached in terms of human medicine: 18 equines with type C gastritis (chemical gastritis) along the greater curve of the pars glandularis and/or pylorus, 11 equines with type B-like gastritis (bacterial gastritis without evidence of H-like organisms), three equines with type B gastritis (bacterial gastritis with evidence of H-like organisms), and nine with a special form of gastritis. Six of the equines could not be classified, while seven showed erosive gastritis or ulceration. A total of 24 equines exhibited no pathological findings along any of the above-mentioned mucosae. The histopathological findings of the equines with either type B-like gastritis or type B gastritis corresponded with H pylori gastritis seen in humans, as ligamental lymphocytes in the lamina propria mucosae and neutrophilic granulocytes associated with atrophy of the glandular corpus, intestinal metaplasia, and erosion. Warthin-Starry staining and the IHC reaction confirmed H-like organisms in three of the equines. The frequency of type B-like gastritis or type B gastritis was observed to be twice as high in the pylorus mucosa as along the glandular mucosa of the greater curve. This study has demonstrated that histological analysis of gastric mucosa biopsies graded according to the updated Sydney system for human medicine significantly complements veterinary gastroscopy, which should therefore always include a biopsy. The updated Sydney system can thus serve as a platform for future scientific research in the field of veterinary medicine.

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