The SARS-CoV-2 coronavirus is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database and the PPI network. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the PPI network in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs, and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The majority types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to known PPI network nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation. / Graduate

Hydrogen economy is capitalizing the decarbonization of transport and industrial sectors. Ammonia is an attractive intermediate to store and transport hydrogen, due to its low production cost, well developed storage and transportation infrastruc- ture, high hydrogen density in its liquified form (for transportation) and the potential production from renewable energy sources. Although there have been significant ad- vancements in catalyst development for ammonia decomposition, the potential of this technology cannot be fully exploited until significant process development is made. In this sense, catalytic membrane reactors show promising features and performances. In this work, ammonia decomposition has been studied using the following ap- proach: (1) Catalytic Packed Bed Reactor (CPBR) and kinetic modeling, (2) Cat- alytic Packed Bed Membrane Reactor (CPBMR) modeling and (3) CPBMR scale-up. Stage (1) was performed using Ru-K/CaO and Co-Ce catalysts over a wide range of experimental conditions (including pressures up to 16 bar). Stage (2) includes 1-D and 2-D models that were further validated experimentally, also using different software to tackle the stage (3), which aims to give the optimized geometry and properties of a CPBMR for a production of 5 N m3 h−1 of high purity H2 . The results presented in this Thesis enabled to: (1) obtain a reliable kinetic model capable of describing the ammonia decomposition under a wide range of operating conditions, using Ru-K/CaO and Co-Ce catalysts. (2) identify a range of operat- ing conditions where the CPBMR performs better than the CPBR in terms of NH3 conversion, H2 recovery and H2 purity. This range includes: reaction temperature between 250◦C and 500◦C; reaction pressures between 1 and 16 bar; space times be- tween 1 and 15 gcat h mol−1 and H2 permeate pressure higher than the atmospheric pressure (up to 5 bar). (3) scale-up the CPBMR for ammonia decomposition at a pilot scale, encountering that a pilot plant for a production of 5 N m3 h−1 of pure H2 ( >99.99%) could be obtained with a relatively small multitubular arraignment, that might be even smaller than the needed for the same product using other technology.

Nous considérons des problèmes d'optimisation stochastique et de théorie des jeux avec des mesures de risque. Dans une première partie, nous mettons l'accent sur la cohérence temporelle. Nous commençons par prouver une équivalence entre cohérence temporelle et l'existence d'une formule imbriquée pour des fonctions. Motivés par des exemples bien connus dans les mesures de risque, nous étudions trois classes de fonctions: les fonctions invariantes par translation, les transformées de Fenchel-Moreau et les fonctions supremum. Ensuite, nous étendons le concept de cohérence temporelle à la cohérence entre joueurs, en remplaçant le temps séquentiel par un ensemble non ordonné et les fonctions par des relations binaires. Enfin, nous montrons comment la cohérence entre joueurs est liée à des formes de décomposition séquentielles et parallèles en optimisation. Dans une seconde partie, nous étudions l'impact des mesures de risque sur la multiplicité des équilibres dans les problèmes de jeux dynamiques dans les marchés complets et incomplets. Nous concevons un exemple où l'introduction de mesures de risque conduit à l'existence de trois équilibres au lieu d'un dans le cas risque neutre. Nous analysons la capacité de deux algorithmes différents à trouver les différents équilibres. Nous discutons des liens entre la cohérence des joueurs et les problèmes d'équilibre dans les jeux. Dans une troisième partie, nous étudions l'optimisation robuste pour l'apprentissage automatique. En utilisant des mesures de risque convexes, nous fournissons un cadre unifié et proposons un algorithme adapté couvrant trois ensembles d'ensembles d'ambiguïté étudiés dans la littérature / We consider stochastic optimization and game theory problems with risk measures. In a first part, we focus on time consistency. We begin by proving an equivalence between time consistent mappings and the existence of a nested formula. Motivated by well-known examples in risk measures, we investigate three classes of mappings: translation invariant, Fenchel-Moreau transform and supremum mappings. Then, we extend the concept of time consistency to player consistency, by replacing the sequential time by any unordered set and mappings by any relations. Finally, we show how player consistency relates to sequential and parallel forms of decomposition in optimization. In a second part, we study how risk measures impact the multiplicity of equilibria in dynamic game problems in complete and incomplete markets. We design an example where the introduction of risk measures leads to the existence of three equilibria instead of one in the risk neutral case. We analyze the ability of two different algorithms to recover the different equilibria. We discuss links between player consistency and equilibrium problems in games. In a third part, we study distribution ally robust optimization in machine learning. Using convex risk measures, we provide a unified framework and propose an adapted algorithm covering three ambiguity sets discussed in the literature

Iron and copper increased the decomposition of nitrite significantly during the first two hours of reaction when 150 parts per million (ppm) of nitrite and nitrogen was added to an acidic soil. Manganese was found to have a slight effect. There was no additional effect of the added metals after two hours reaction time. The higher concentration of metals was not effective in catalyzing the decomposition of the added nitrite in nitrogen. All the added nitrite nitrogen was not recovered and the deficit could be due to the formation of nitrogen gas.

Dans un contexte de grande concurrence internationale, la simulation numérique du comportement joue un rôle primordial dans le domaine aéronautique, permettant de réduire les délais et les coûts de conception, d'évaluer la pertinence de nouvelles solutions technologiques avant de se lancer dans les investissements qu'elles imposent. Visant la simulation de structures assemblées, ce travail de thèse a consisté a développer une méthode de décomposition de domaine mixte, multiéchelle, s’appuyant sur le solveur LaTIn. Afin de simplifier le traitement discret des quantités d'interface, la méthode proposée utilise un représentant des interefforts qui évolue dans le même espace que les déplacements d’interface (H^1/2). Elle s'appuie sur le produit scalaire associé à ces quantités pour le calcul des travaux d'interface. Délicat à calculer, ce produit scalaire est traité par une approximation validée numériquement. Le calcul de la matrice de masse pleine en découlant est récompensé par un taux de convergence montré indépendant du pas du maillage et de la taille des sous-domaines sur plusieurs cas-tests faisant intervenir notamment du contact. / Mechanical industries' need of liability in numerical simulations leads to evermore fine and complex models taking into account complicated physical behaviours. With the aim of modelling large complex structures, a non-overlapping mixed domain decomposition method based on a LaTIn-type iterative solver is proposed.The method relies on splitting the studied domain into substructures and interfaces which can both bear mechanical behaviors so that perfect cohesion, contact, delamination can be modelled by the interfaces. The associated solver enables to treat at small scales nonlinear phenomena and, as commonly done, scalabilty is ensured by a coarse problem. The method presented uses the Riesz representation theorem to represent interface tractions in H^1/2 in order to discretize them accordingly to the displacements. Independence of convergence and search direction's optimal value from mesh size is evidenced and high precision can be reached in few iterations.Different test-cases assess the method for perfect and contact interfaces.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第20477号 / エネ博第346号 / 新制||エネ||69(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 坂 志朗, 教授 髙部 圭司, 准教授 河本 晴雄 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DGAM

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Despite the importance of power systems in today’s societies, they suffer from aging infrastructure and need to improve the efficiency, reliability, and security. Two issues that significantly limit the current grid’s efficient energy delivery and consumption are: loadollowing generation dispatch, and energy theft. A loadollowing generation dispatch is usually employed in power systems, which makes continuous small changes so as to account for differences between the actual energy demand and the predicted values. This approach has led to an average utilization of energy generation capacity below 55% [49]. Moreover, energy theft causes several billion dollar losses to U.S. utility companies [31] [16], while in developing countries it can amount to 50% of the total energy delivered [48]. Recently, the Smart Grid has been proposed as a new electric grid to modernize current power grids and enhance its efficiency, reliability, and sustainability. Particularly, in the Smart Grid, a digital communication network is deployed to enable two-way communications between users and system operators. It thus makes it possible to shape the users’ load demand curves by means of demand response strategies. Additionally, in the Smart Grid, traditional meters will be replaced with cyber-physical devices, called smart meters, capable of recording and transmitting users’ real-time power consumption. Due to their monitoring capabilities, smart meters offer a great opportunity to detect energy theft in smart grids, but also raise serious concerns about users’ privacy. In this dissertation, we design optimal load scheduling schemes to enhance system efficiency, and develop energy theft detection algorithms that can preserve users’ privacy.