This thesis describes two novel Bayesian methods: the Iterative Ensemble Square Filter (IEnSRF) and the Warp Ensemble Square Root Filter (WEnSRF) for solving the barcode detection problem, the deconvolution problem in well testing and the history matching problem of facies patterns. For the barcode detection problem, at the expanse of overestimating the posterior uncertainty, the IEnSRF efficiently achieves successful detections with very challenging real barcode images which the other considered methods and commercial software fail to detect. It also performs reliable detection on low-resolution images under poor ambient light conditions. For the deconvolution problem in well testing, the IEnSRF is capable of quantifying estimation uncertainty, incorporating the cumulative production data and estimating the initial pressure, which were thought to be unachievable in the existing well testing literature. The estimation results for the considered real benchmark data using the IEnSRF significantly outperform the existing methods in the commercial software. The WEnSRF is utilised for solving the history matching problem of facies patterns. Through the warping transformation, the WEnSRF performs adjustment on the reservoir features directly and is thus superior in estimating the large-scale complicated facies patterns. It is able to provide accurate estimates of the reservoir properties robustly and efficiently with reasonably reliable prior reservoir structural information.

Studien bidrar med fördjupad kunskap om en partsintermediärs, ett bransch- och arbetsgivarförbund, möjligheter till parts- och arbetsgivarsamverkan kring kompetensförsörjningsåtgärder för medlemsföretag och regional utveckling på en lokal arbetsmarknad. Fokus ligger på intermediärens villkor som samordnare av utbildning för maskinförares etablering på arbetsmarknaden. Studiens teoretiska referensram tar sin grund i tidigare forskning om intermediärer och de teoretiska utgångspunkterna från ett boundary-crossing perspektiv och omsluts av lärandemekanismer och dialektiska lärandeprocesser. Det empiriska materialet baseras på 12 intervjuer och två fokusgrupper. Slutsatserna som dras är att partsintermediären verkar i en dualistisk samverkansstruktur i tre nivåer i och mellan två praktikgemenskaper: organisatorisk, interpersonell och intrapersonell. Det som möjliggör samverkan gällande kompetensförsörjning för samordning av utbildning är de formella kunskapsråd mellan parterna på nationell och regional nivå samt juridiska stödstrukturer genom yrkesutbildningsavtal. Begränsningen för överskridande samordning är omfattande matchningsproblem mellan utbildning och medlemsföretag, genom frånvaron av ekonomiska och pedagogiska stödfunktioner för den lokala arbetsmarknaden. / The study provides in-depth knowledge of an intermediary, an industry and employers' federation, opportunities for party and employer cooperation on competence-providing measures for member companies and regional development in a local labor market. The focus is on the intermediary's conditions as coordinator of education for machine operators' establishment in the labor market. The theoretical reference framework of the study is based on previous research on intermediates and the theoretical starting points from a boundary-crossing perspective, and is encompassed by learning mechanisms and dialectical learning processes. The empirical material is based on 12 interviews and two focus groups. The conclusions drawn are that the intermediary acts in a dual-level collaboration structure in three levels in and between two communities of practice: organizational, interpersonal and intrapersonal. What allows co-operation on the provision of skills for coordination of education is the formal knowledge council between the parties at national and regional level and legal support structures through vocational training agreements. The overriding coordination constraints are extensive matching problems between education and member companies, due to the absence of financial and educational support for the local labor market.

De nos jours, avec l'utilisation croissante des technologies numériques, l'éducation à la préservation de la vie privée joue un rôle important en particulier pour les adolescents. Bien que plusieurs plateformes d'apprentissage en ligne à la sensibilisation à la vie privée aient été mises en œuvre, elles sont généralement basées sur des techniques traditionnelles d'apprentissage. Plus particulièrement, ces plateformes ne permettent pas aux étudiants de coopérer et de partager leurs connaissances afin d’améliorer leur apprentissage ensemble. En d'autres termes, elles manquent d'interactions élève-élève. Des recherches récentes sur les méthodes d'apprentissage montrent que la collaboration entre élèves peut entraîner de meilleurs résultats d'apprentissage par rapport à d'autres approches. De plus, le domaine de la vie privée étant fortement lié à la vie sociale des adolescents, il est préférable de fournir un environnement d'apprentissage collaboratif où l’on peut enseigner la préservation de la vie privée, et en même temps, permettre aux étudiants de partager leurs connaissances. Il serait souhaitable que ces derniers puissent interagir les uns avec les autres, résoudre des questionnaires en collaboration et discuter de problèmes et de situations de confidentialité. À cet effet, ce travail propose « Teens-online », une plateforme d'apprentissage en ligne collaborative pour la sensibilisation à la vie privée. Le programme d'études fourni dans cette plateforme est basé sur le Référentiel de formation des élèves à la protection des données personnelles. De plus, la plateforme proposée est équipée d'un mécanisme d'appariement de partenaires basé sur la théorie des jeux. Ce mécanisme garantit un appariement élève-élève stable en fonction des besoins de l'élève (comportement et / ou connaissances). Ainsi, des avantages mutuels seront obtenus en minimisant les chances de coopérer avec des pairs incompatibles. Les résultats expérimentaux montrent que l'utilité moyenne obtenue en appliquant l'algorithme proposé est beaucoup plus élevée que celle obtenue en utilisant d'autres mécanismes d'appariement. Les résultats suggèrent qu'en adoptant l'approche proposée, chaque élève peut être jumelé avec des partenaires optimaux, qui obtiennent également en retour des résultats d'apprentissage plus élevés. / Nowadays, with the increasing use of digital technologies, especially for teenagers, privacy education plays an important role in their lives. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. In particular, these platforms do not allow students to cooperate and share knowledge with each other in order to achieve mutual benefits and improve learning outcomes. In other words, they lack student-student interaction. Recent research on learning methods shows that the collaboration among students can result in better learning outcomes compared to other learning approaches. Motivated by the above-mentioned facts, and since privacy domain is strongly linked to the social lives of teens, there is a pressing need for providing a collaborative learning platform for teaching privacy, and at the same time, allows students to share knowledge, interact with each other, solve quizzes collaboratively, and discuss privacy issues and situations. For this purpose, this work proposes “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the Personal Data Protection Competency Framework for School Students. Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to a student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by minimizing the chances of cooperating with incompatible students. Experimental results show that the average learning-related utility obtained by applying the proposed partner-matching algorithm is much higher than the average utility obtained using other matching mechanisms. The results also suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn helps them reach their highest learning outcomes.