La production du boson de Higgs associée à une paire de quarks top est l'un des modes de production de boson de Higgs les plus importants bien que toujours pas encore observé. Par conséquent, sa découverte est l'une des recherches les plus ambitieuse après la découverte Higgs: non seulement cela sera la première fois que nous pourrons observer ce mode de production du Higgs mais nous pourrons également en mesurer le couplage de Yukawa au quark top. Les résultats de ces mesures peuvent répondre aux questions fondamentales du Modèle Standard (MS) et peuvent également donner des indices de nouvelle physique au-delà du MS. Une analyse de la recherche de la production de boson de Higgs associée à une paire de quarks top dans des états finaux à trois leptons est présentée dans cette thèse. Cette analyse est réalisée avec des données collectées par le détecteur ATLAS en 2015 et 2016 pendant la campagne dite « Run 2 » et correspondant à une luminosité intégrée de 36.1 fb-1 à une énergie dans le centre de masse de 13 TeV. Elle utilise un algorithme d'arbre de décision renforcé pour discriminer le signal et le fond. Le bruit de fond dominant de faux leptons est estimé avec une méthode de matrice s’appuyant sur les données (Méthode de la Matrix). Pour un Higgs standard de 125 GeV, un excès d'événements par rapport au bruit de fond attendu d'autres processus MS est trouvé avec une signification observée de 2.2 écarts-types, comparé à une prédiction de 1.5 écart-type. Le meilleur ajustement pour la section efficace de production $t\bar tH$ est de $1.5^{+0.8}_{-0.7}$ fois l'espérance SM, consistant avec la valeur SM du couplage de Yukawa au quark top. / The production of the Higgs boson associated with a pair of top quarks is one of the most important Higgs boson production modes yet still not observed. Therefore, its discovery is one of the most challenging searches after the Higgs discovery: not only will it be the first time we can observe this Higgs production mode but also we will be able to measure its Yukawa coupling to the top quark. The measured results can answer the basic question of the Standard Model (SM) and can also search for any hints of new physics beyond the SM prediction. An analysis searching for the production of the Higgs boson associated with a pair of top quarks in three leptons final states is presented in this thesis. It is performed with the data collected by the ATLAS detector in 2015 and 2016 during the so-called « Run 2 » campaign corresponding to an integrated luminosity of 36.1 fb−1 at a center of mass energy of 13 TeV. It uses a boosted decision tree algorithm to discriminate between signal and background. The dominant background of fake leptons is estimated with the data-driven matrix method (Matrix Method). For a 125 GeV Standard Model Higgs boson, an excess of events over the expected background from other SM processes is found with an observed significance of 2.2 standard deviations, compared to an expectation of 1.5 standard deviations. The best fit for the $t\bar tH$ production cross section is $1.5^{+0.8}_{-0.7}$ times the SM expectation, consistent with the SM value of the Yukawa coupling to top quarks.

The so-called Industry 4.0 is by its agitators commonly denoted as the fourth industrial revolution and promises to turn the manufacturing sector on its head. However, everything that glimmers is not gold and in the backwash of hefty consultant fees questions arises: What are the drivers behind Industry 4.0? Which barriers exists? How does one prepare its manufacturing procedures in anticipation of the (if ever) coming era? What is the internet of things and what file sizes’ is characterised as big data? To answer these questions, this thesis aims to resolve the ambiguity surrounding the definitions of Industry 4.0, as well as clarify the fuzziness of a data-driven manufacturing approach. Ergo, the comprehensive usage of data, including collection and storage, quality control, and analysis. In order to do so, this thesis was carried out as a case study at GKN Aerospace Sweden (GAS). Through interviews and observations, as well as a literature review of the subject, the thesis examined different process’ data-driven needs from a quality management perspective. The findings of this thesis show that the collection of quality data at GAS is mainly concerned with explicitly stated customer requirements. As such, the data available for the examined processes is proven inadequate for multivariate analytics. The transition towards a data-driven state of manufacturing involves a five-stage process wherein data collection through sensors is seen as a key enabler for multivariate analytics and a deepened process knowledge. Together, these efforts form the prerequisites for Industry 4.0. In order to effectively start transition towards Industry 4.0, near-time recommendations for GAS includes: capture all data, with emphasize on process data; improve the accessibility of data; and ultimately taking advantage of advanced analytics. Collectively, these undertakings pave the way for the actual improvements of Industry 4.0, such as digital twins, machine cognition, and process self-optimization. Finally, due to the delimitations of the case study, the findings are but generalized for companies with similar characteristics, i.e. complex processes with low volumes.

Nano-particle aerosols are one of the major types of air pollutants in the urban indoor and outdoor environments. Therefore, determination of mechanisms of formation, dispersion, evolution, and transformation of combustion aerosols near the major source of this type of air pollution - busy roads and road networks - is one of the most essential and urgent goals. This Thesis addresses this particular direction of research by filling in gaps in the existing physical understanding of aerosol behaviour and evolution. The applicability of the Gaussian plume model to combustion aerosols near busy roads is discussed and used for the numerical analysis of aerosol dispersion. New methods of determination of emission factors from the average fleet on a road and from different types of vehicles are developed. Strong and fast evolution processes in combustion aerosols near busy roads are discovered experimentally, interpreted, modelled, and statistically analysed. A new major mechanism of aerosol evolution based on the intensive thermal fragmentation of nano-particles is proposed, discussed and modelled. A comprehensive interpretation of mutual transformations of particle modes, a strong maximum of the total number concentration at an optimal distance from the road, increase of the proportion of small nano-particles far from the road is suggested. Modelling of the new mechanism is developed on the basis of the theory of turbulent diffusion, kinetic equations, and theory of stochastic evaporation/degradation processes. Several new powerful statistical methods of analysis are developed for comprehensive data analysis in the presence of strong turbulent mixing and stochastic fluctuations of environmental factors and parameters. These methods are based upon the moving average approach, multi-variate and canonical correlation analyses. As a result, an important new physical insight into the relationships/interactions between particle modes, atmospheric parameters and traffic conditions is presented. In particular, a new definition of particle modes as groups of particles with similar diameters, characterised by strong mutual correlations, is introduced. Likely sources of different particle modes near a busy road are identified and investigated. Strong anti-correlations between some of the particle modes are discovered and interpreted using the derived fragmentation theorem. The results obtained in this thesis will be important for accurate prediction of aerosol pollution levels in the outdoor and indoor environments, for the reliable determination of human exposure and impact of transport emissions on the environment on local and possibly global scales. This work will also be important for the development of reliable and scientifically-based national and international standards for nano-particle emissions.