If sufficiently persistent, semi-volatile organic contaminants (SVOCs) can travel long distances through the atmosphere from their points of release and become concentrated in cold, remote regions. As air is sampled for SVOCs to establish both their presence and the success of emission reduction efforts, it becomes helpful to determine sampling site proximity to sources and the origin of the sampled air masses. Comparing three increasingly sophisticated methods for quantifying source proximity of sampling locations, it was judged necessary to account for the actual history of the sampled air through construction of an airshed, especially if wind is highly directional and population distribution is very non-uniform. The airshed concept was improved upon by introducing a ‘geodesic’ grid of equally spaced cells, rather than a simple latitude/longitude grid, to avoid distortion near Earth’s poles and to allow for the comparison of airshed shapes. Assuming that a perfectly round airshed reveals no information about sources allows the significance of each cell of an airshed to be judged based on its departure from roundness. Combining air-mass histories with a 2 year-long series of SVOC air concentrations at Little Fox Lake in Canada’s Yukon Territory did not identify distinct source regions for most analytes, although γ-hexachlorocyclohexane appears to originate broadly in north-eastern Russia and/or Alaska. Based on this remoteness from sources, the site is judged to be well suited to monitor changes in the hemispheric background concentrations of SVOCs. A model-based exploration revealed wet-gaseous deposition as the dominant process responsible for cold-trapping SVOCs in mountain soils. Such cold trapping is particularly effective if precipitation rate increases with altitude and if temperature differences along the mountain are large. Considerable sensitivity of the modeled extent of cold-trapping to parameters as diverse as scale, mean temperature, atmospheric particle concentration and time relative to emission maxima is consistent with the wide variety of observed enrichment behaviour. Concentration gradients of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in air and soil measured on four Western Canadian mountains with variable distance from sources revealed source proximity as the main driver of concentrations at both the whole-mountain scale and along individual mountain transects.

The work is focused on 3-Box model and its usage in a selected international IT company. The first part describes the pillars of 3-Box model: HR Business Partners, HR Shared Service Centers and HR Centers of Excellence. The second part introduces the HR organization in the company and describes HR Direct, which represents HR Shared Service Centre in the 3-Box model. Based on findings, the improvement is designed.

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Le déploiement d'une infrastructure de supervision permet une gestion plus intelligente des réseaux de distribution d'électricité comparé à un renforcement traditionnel pour répondre aux nouveaux enjeux de la maitrise de l'énergie (Consommations, EnR, VE, ...). Pour acheminer les données, les Courants Porteurs en Ligne (CPL) possèdent un atout majeur. En effet, cette technologie permet de superposer un signal de plus haute fréquence au signal électrique 50/60 Hz. Toutefois, le support de transmission est difficile et non maîtrisable. Ces travaux de recherche ont pour objectif d'apporter une contribution à cette problématique par l'élaboration d'une plateforme de simulation des réseaux pour des fréquences allant jusqu'à 1 MHz dans un but de transmission de données. Des éléments clés des réseaux sont traités de façon séparés puis assemblés pour estimer les performances des CPL « Outdoor » actuels. La variation du comportement des réseaux en fonction du temps et de la fréquence, en particulier des perturbations en tête d'installation clients sur 24h est étudiée. Les transformateurs entre les réseaux HTA et BT sont modélisés sous la forme d'un « modèle à constantes localisées » et d'un « modèle boite noire ». Les deux modèles sont appliqués sur un transformateur H61 100 kVA. Par la suite, une modélisation des câbles de distribution est proposée sous forme d'un « modèle cascadé ». Celle-ci est appliquée sur un câble souterrain BT. Chaque modèle est obtenu à l'aide de mesures d'impédances, et validé par des mesures de transmissions. Pour compléter, une étude préliminaire sur les communications radio mobile est réalisée pour la supervision des réseaux de distribution. / Establishing a supervisory infrastructure allows a better smart management than an expensive strengthening of distribution network to respond to new constraints at the energies control (Consumption, REN, EV ...). To transmit data, Power Line Communication (PLC) technologies present an advantage in this context. In fact, it enables a superposition of High Frequency (HF) signals on electrical signal 50/60 Hz. However, electric networks have not been developed to this application because of difficult propagation conditions. This research work makes a contribution to develop a simulation platform in objective to transmit data to 1 MHz. In first time, each network element is studied singly and in second time, together, to estimate "Outdoor PLC" transmission performance. The first element studied is the networks variation in function of frequency and time. Several 24h disturbance measurements on LV customers are presented. The second element is the transformers which established connection between Medium Voltage (MV) and Low Voltage (LV). The proposed modeling method is based on a "lumped model" and a "black box model". These models are applied to a 100 kVA H61 transformer most commonly used by French distribution system operator in rural and suburban networks. The third element is the power line used in MV and LV networks. The proposed modeling method is based on a "cascaded model" from the theory of transmission line. This model is applied to one power line used in LV underground network. Each model is obtained from various impedance measurements. To complete, an introductory study on mobile radio communication is performed to remote network distribution.

La pollution atmosphérique liée aux aérosols organiques secondaire (SOA) représente un des enjeux majeurs du XXIème siècle. La photochimie multiphasique des SOA constitue le coeur et l'originalité de cette thèse.Le réacteur photochimique permet de simuler en laboratoire, l'oxydation en phase aqueuse atmosphérique des composés organiques volatils biogéniques (BVOC), et notamment, la méthyl vinyl cétone (MVK), afin d'étudier la formation ces SOA.Nous étudions la réactivité de la MVK en présence de ●OH et sa capacité à oligomériser en fonction des concentrations initiales de MVK, d'oxygène, et de ●OH. Une large stratégie analytique basée sur la chromatographie liquide couplée à la spectrométrie de masse (MS) permet d'identifier des produits de réaction, et d'établir un mécanisme réactionnel, expliquant la formation des oligomères, leurs rendements et leur vieillissement.Les données colligées servent d'entrées à un modèle de boîte multiphasique, afin d'explorer la sensibilité de l'oligomérisation aux conditions atmosphériques.Ensuite, nous comparons la réactivité de la MVK en présence de ●OH à celle induite par la photolyse de l'acide pyruvique; puis nous mesurons la tension de surface engendrée par ces deux systèmes d'oligomères. Enfin, la mobilité ionique couplée à la MS permet d'observer la co-oligomérisation d'une gamme étendue de BVOC en présence de ●OH.L'oligomérisation atmosphérique implique (i) une concentration minimale de précurseurs pouvant être atteinte dans les aérosols humides via la co-oligomérisation; (ii) une réactivité en compétition avec l'oxygène dissous dans la phase aqueuse, et dont la pertinence atmosphérique reste à explorer. / Air pollution caused by secondary organic aerosol (SOA) is one of the major challenges of this century. We focus this thesis on SOA , through an innovative approach, i.e. multiphase photochemistry.The photochemical reactor allows to simulate in laboratory, the atmospheric aqueous phase oxidation of biogenic volatile organic compounds (BVOC) and in particular, methyl vinyl ketone (MVK), and thus, to study SOA.We study the reactivity of MVK in the presence of ●OH and its ability to oligomerize under various initial concentrations of oxygen, MVK and ●OH. A wide analytical strategy based on liquid chromatography-mass spectrometry is used to identify the reaction products, and establish a chemical mechanism. We focus on these oligomers systems, formation, yield and aging. Collected data are used as inputs to a multiphase box model to explore the sensitivity of oligomerization to the variations of physical and chemical atmospheric parameters. The photochemistry of pyruvic acid generates radical chemistry and initiates MVK oligomerization. We closely compare this reaction to MVK ●OH oxidation. Then, we measure the surface activity of both systems. The ability of oligomers to partition to the interface could affect the climate. Finally, we used ion mobility - mass spectrometry to observe ●OH co-oligomerization of a mixture of organic compounds most representative of the atmosphere.Atmospheric oligomerization implies (i) a minimal concentration of precursors that could be reached in wet aerosol via the co-oligomerization; (ii) a reactivity in competition with the addition of the dissolved oxygen, whose the atmospheric relevance remains to be explored.

Insurance is built on the principle that a group of people contributes to a common pool of money which will be used to cover the costs for individuals who suffer from the insured event. In a competitive market, an insurance company will only be profitable if their pricing reflects the covered risks as good as possible. This thesis investigates the recently proposed Combined Actuarial Neural Network (CANN), a model nesting the traditional Generalised Linear Model (GLM) used in insurance pricing into a Neural Network (NN). The main idea of utilising NNs for insurance pricing is to model interactions between features that the GLM is unable to capture. The CANN model is analysed in a commercial insurance setting with respect to two research questions. The first research question, RQ 1, seeks to answer if the CANN model can outperform the underlying GLM with respect to error metrics and actuarial model evaluation tools. The second research question, RQ 2, seeks to identify existing interpretability methods that can be applied to the CANN model and also showcase how they can be applied. The results for RQ 1 show that CANN models are able to consistently outperform the GLM with respect to chosen model evaluation tools. A literature search is conducted to answer RQ 2, identifying interpretability methods that either are applicable or are possibly applicable to the CANN model. One interpretability method is also proposed in this thesis specifically for the CANN model, using model-fitted averages on two-dimensional segments of the data. Three interpretability methods from the literature search and the one proposed in this thesis are demonstrated, illustrating how these may be applied. / Försäkringar bygger på principen att en grupp människor bidrar till en gemensam summa pengar som används för att täcka kostnader för individer som råkar ut för den försäkrade händelsen. I en konkurrensutsatt marknad kommer försäkringsbolag endast vara lönsamma om deras prissättning är så bra som möjligt. Denna uppsats undersöker den nyligen föreslagna Combined Actuarial Neural Network (CANN) modellen som bygger in en Generalised Linear Model (GLM) i ett neuralt nätverk, i en praktiskt och kommersiell försäkringskontext med avseende på två forskningsfrågor. Huvudidén för en CANN modell är att fånga interaktioner mellan variabler, vilket en GLM inte automatiskt kan göra. Forskningsfråga 1 ämnar undersöka huruvida en CANN modell kan prestera bättre än en GLM med avseende på utvalda statistiska prestationsmått och modellutvärderingsverktyg som används av aktuarier. Forskningsfråga 2 ämnar identifiera några tolkningsverktyg som kan appliceras på CANN modellen samt demonstrera hur de kan användas. Resultaten för Forskningsfråga 1 visar att CANN modellen kan prestera bättre än en GLM. En literatursökning genomförs för att svara på Forskningsfråga 2, och ett antal tolkningsverktyg identifieras. Ett tolkningsverktyg föreslås också i denna uppsats specifikt för att tolka CANN modellen. Tre av tolkningsverktygen samt det utvecklade verktyget demonstreras för att visa hur de kan användas för att tolka CANN modellen.

Insurance is built on the principle that a group of people contributes to a common pool of money which will be used to cover the costs for individuals who suffer from the insured event. In a competitive market, an insurance company will only be profitable if their pricing reflects the covered risks as good as possible. This thesis investigates the recently proposed Combined Actuarial Neural Network (CANN), a model nesting the traditional Generalised Linear Model (GLM) used in insurance pricing into a Neural Network (NN). The main idea of utilising NNs for insurance pricing is to model interactions between features that the GLM is unable to capture. The CANN model is analysed in a commercial insurance setting with respect to two research questions. The first research question, RQ 1, seeks to answer if the CANN model can outperform the underlying GLM with respect to error metrics and actuarial model evaluation tools. The second research question, RQ 2, seeks to identify existing interpretability methods that can be applied to the CANN model and also showcase how they can be applied. The results for RQ 1 show that CANN models are able to consistently outperform the GLM with respect to chosen model evaluation tools. A literature search is conducted to answer RQ 2, identifying interpretability methods that either are applicable or are possibly applicable to the CANN model. One interpretability method is also proposed in this thesis specifically for the CANN model, using model-fitted averages on two-dimensional segments of the data. Three interpretability methods from the literature search and the one proposed in this thesis are demonstrated, illustrating how these may be applied. / Försäkringar bygger på principen att en grupp människor bidrar till en gemensam summa pengar som används för att täcka kostnader för individer som råkar ut för den försäkrade händelsen. I en konkurrensutsatt marknad kommer försäkringsbolag endast vara lönsamma om deras prissättning är så bra som möjligt. Denna uppsats undersöker den nyligen föreslagna Combined Actuarial Neural Network (CANN) modellen som bygger in en Generalised Linear Model (GLM) i ett neuralt nätverk, i en praktiskt och kommersiell försäkringskontext med avseende på två forskningsfrågor. Huvudidén för en CANN modell är att fånga interaktioner mellan variabler, vilket en GLM inte automatiskt kan göra. Forskningsfråga 1 ämnar undersöka huruvida en CANN modell kan prestera bättre än en GLM med avseende på utvalda statistiska prestationsmått och modellutvärderingsverktyg som används av aktuarier. Forskningsfråga 2 ämnar identifiera några tolkningsverktyg som kan appliceras på CANN modellen samt demonstrera hur de kan användas. Resultaten för Forskningsfråga 1 visar att CANN modellen kan prestera bättre än en GLM. En literatursökning genomförs för att svara på Forskningsfråga 2, och ett antal tolkningsverktyg identifieras. Ett tolkningsverktyg föreslås också i denna uppsats specifikt för att tolka CANN modellen. Tre av tolkningsverktygen samt det utvecklade verktyget demonstreras för att visa hur de kan användas för att tolka CANN modellen.