Cette thèse a pour but de développer des approches déterministes et heuristiques pour résoudre certaines classes des problèmes d'optimisation en télécommunication et la mobilité d'un réseau de transport : problèmes de routage, problèmes de covoiturage, problèmes de contrôle de l'alimentation dans un réseau sans fil, problèmes d'équilibrage du spectre dans les réseaux DSL. Il s'agit des problèmes d'optimisation non convexe de très grande taille. Nos approches sont basées sur la programmation DC&DCA, méthode de décomposition proximale et la méthode d'étiquetage des graphes. Grâce aux techniques de formulation/reformulation et de pénalité exacte, nous avons établi des programmes DC équivalents en vue de leur résolution par DCA. Selon la structure de ces problèmes, on peut fournir des décompositions DC appropriées ou de bons points initiaux de DCA. Nos méthodes ont été programmées sous MATLAB, C/C++. Ils montrent la performance de nos algorithmes par rapport à des méthodes existantes. / In this thesis, we focus on developing deterministic and heuristic approaches for solving some classes of optimization problems in Telecommunication and Mobility & Transport domain: Routing problems, Car pooloing problems, Power control problems in wireless network, Optimal spectrum balancing problems in DSL networks. They are large-scale nonconvex optimization problems. Our methodologies are focus on DC programming and DCA, Proximal decomposition method and Labeling method in graph theory. They are well-known as powerful tools in optimization. The considered problems were reformulated using the DC formulation/reformulation and exact penalty techniques and the DCA was used to obtain the solution. Also, depending on the structure of considered problems, we can provide appropriate DE decompositions or good initial points for DCA. All these proposed methods have been implemented with MATLAB, C/C++ to confirm the practical aspects and enhance our research works.

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Listening comprehension and listening strategies plays a crucial role in the process of acquiring a language. This study aims to investigate to what extent the listening skill is practised in upper secondary schools in the south of Sweden. Research studies within the field of listening are few which indicates that the listening skill is not considered as essential in second language teaching as the other three skills: reading, writing and speaking. Previous studies indicate that teachers should educate students metacognitive awareness when teaching listening. The results summarize the teachers’ answers, reflections and attitudes conducted from semi-structured interviews. The analysis of the results focuses on the four categories distinguished from the teachers’ answers: Teaching Approaches, National Exams, The Individual Student and Metacognitive Awareness. Some of the teachers do not possess the knowledge of how to teach listening that develops students' listening proficiency. As a conclusion, the study shows that a hierarchy exists among the four skills to which teachers adjust to, and this may be detrimental in achieving educational aims.

7 Abstract (AJ) Introduction Radiology examination using specialized modern imaging methods, including CT and MRI, is essential in the diagnosis of congenital and acquired diseases of the musculoskeletal system. The first part of the dissertation deals with certain congenital defects of the short femur, known in the literature as proximal femoral focal deficiency (PFFD). This part summarizes our experience with the radiological findings in the preoperative and postoperative period, with the main attention to the vascular supply to the affected area. The second part of the presentation deals with some aspects of autologous chondrocyte transplantation fixed at two different carriers implanted into post-traumatic articular cartilage defects of the distal femur. Radiological findings are evaluated in the relation to the histopathological findings. Objectives The first part of the study after the distribution of patients with PFFD by current commonly used radiographic classification sets the objective in the extent of scans of the hip joints to specify diagnosis PFFD in each patient and to evaluate in detail changes in the area of disability, especially a course of blood vessels. The evaluation of the radiation burden of repeated X-ray measurements was done with respect to the age of the patients. Tissue samples...

La maladie d'Alzheimer (MA) est une maladie cérébrale dégénérative qui entraîne une perte progressive de la mémoire, un déclin cognitif et une détérioration graduelle de la capacité d'une personne à faire face à la complexité et à l'exigence des tâches quotidiennes nécessaires pour vivre en autonomie dans notre société actuelle. Les traitements pharmacologiques actuels peuvent ralentir le processus de dégradation attribué à la maladie, mais ces traitements peuvent également provoquer certains effets secondaires indésirables. L'un des traitements non pharmacologiques qui peut soulager efficacement les symptômes est la thérapie assistée par l'animal (T.A.A.). Mais en raison de certaines limitations telles que le prix des animaux et des problèmes d'hygiène, des animaux virtuels sont utilisés dans ce domaine. Cependant, les animaux virtuels animés, la qualité d'image approximative et le mode d'interaction unidirectionnel des animaux qui attendent passivement les instructions de l’utilisateur, peuvent difficilement stimuler le retour émotionnel entre l'utilisateur et les animaux virtuels, ce qui affaiblit considérablement l'effet thérapeutique. Cette étude vise à explorer l'efficacité de l'utilisation d'animaux virtuels à la place d’animaux vivants et leur impact sur la réduction des émotions négatives chez le patient. Cet objectif a été gardé à l'esprit lors de la conception du projet Zoo Therapy, qui présente un environnement immersif d'animaux virtuels en 3D, où l'impact sur l'émotion du patient est mesuré en temps réel par électroencéphalographie (EEG). Les objets statiques et les animaux virtuels de Zoo Therapy sont tous présentés à l'aide de modèles 3D réels. Les mouvements des animaux, les sons et les systèmes de repérage spécialement développés prennent en charge le comportement interactif simulé des animaux virtuels. De plus, pour que l'expérience d'interaction de l'utilisateur soit plus réelle, Zoo Therapy propose un mécanisme de communication novateur qui met en œuvre une interaction bidirectionnelle homme-machine soutenue par 3 méthodes d'interaction : le menu sur les panneaux, les instructions vocales et le Neurofeedback. La manière la plus directe d'interagir avec l'environnement de réalité virtuelle (RV) est le menu sur les panneaux, c'est-à-dire une interaction en cliquant sur les boutons des panneaux par le contrôleur de RV. Cependant, il était difficile pour certains utilisateurs ayant la MA d'utiliser le contrôleur de RV. Pour accommoder ceux qui ne sont pas bien adaptés ou compatibles avec le contrôleur de RV, un système d'instructions vocales peut être utilisé comme interface. Ce système a été reçu positivement par les 5 participants qui l'ont essayé. Même si l'utilisateur choisit de ne pas interagir activement avec l'animal virtuel dans les deux méthodes ci-dessus, le système de Neurofeedback guidera l'animal pour qu'il interagisse activement avec l'utilisateur en fonction des émotions de ce dernier. Le système de Neurofeedback classique utilise un système de règles pour donner des instructions. Les limites de cette méthode sont la rigidité et l'impossibilité de prendre en compte la relation entre les différentes émotions du participant. Pour résoudre ces problèmes, ce mémoire présente une méthode basée sur l'apprentissage par renforcement (AR) qui donne des instructions à différentes personnes en fonction des différentes émotions. Dans l'expérience de simulation des données émotionnelles synthétiques de la MD, la méthode basée sur l’AR est plus sensible aux changements émotionnels que la méthode basée sur les règles et peut apprendre automatiquement des règles potentielles pour maximiser les émotions positives de l'utilisateur. En raison de l'épidémie de Covid-19, nous n'avons pas été en mesure de mener des expériences à grande échelle. Cependant, un projet de suivi a combiné la thérapie de RV Zoo avec la reconnaissance des gestes et a prouvé son efficacité en évaluant les valeurs d'émotion EEG des participants. / Alzheimer’s disease (AD) is a degenerative brain disease that causes progressive memory loss, cognitive decline, and gradually impairs one’s ability to cope with the complexity and requirement of the daily routine tasks necessary to live in autonomy in our current society. Actual pharmacological treatments can slow down the degradation process attributed to the disease, but such treatments may also cause some undesirable side effects. One of the non-pharmacological treatments that can effectively relieve symptoms is animal-assisted treatment (AAT). But due to some limitations such as animal cost and hygiene issues, virtual animals are used in this field. However, the animated virtual animals, the rough picture quality presentation, and the one-direction interaction mode of animals passively waiting for the user's instructions can hardly stimulate the emotional feedback background between the user and the virtual animals, which greatly weakens the therapeutic effect. This study aims to explore the effectiveness of using virtual animals in place of their living counterpart and their impact on the reduction of negative emotions in the patient. This approach has been implemented in the Zoo Therapy project, which presents an immersive 3D virtual reality animal environment, where the impact on the patient’s emotion is measured in real-time by using electroencephalography (EEG). The static objects and virtual animals in Zoo Therapy are all presented using real 3D models. The specially developed animal movements, sounds, and pathfinding systems support the simulated interactive behavior of virtual animals. In addition, for the user's interaction experience to be more real, the innovation of this approach is also in its communication mechanism as it implements a bidirectional human-computer interaction supported by 3 interaction methods: Menu panel, Speech instruction, and Neurofeedback. The most straightforward way to interact with the VR environment is through Menu panel, i.e., interaction by clicking buttons on panels by the VR controller. However, it was difficult for some AD users to use the VR controller. To accommodate those who are not well suited or compatible with VR controllers, a speech instruction system can be used as an interface, which was received positively by the 5 participants who tried it. Even if the user chooses not to actively interact with the virtual animal in the above two methods, the Neurofeedback system will guide the animal to actively interact with the user according to the user's emotions. The mainstream Neurofeedback system has been using artificial rules to give instructions. The limitation of this method is inflexibility and cannot take into account the relationship between the various emotions of the participant. To solve these problems, this thesis presents a reinforcement learning (RL)-based method that gives instructions to different people based on multiple emotions accordingly. In the synthetic AD emotional data simulation experiment, the RL-based method is more sensitive to emotional changes than the rule-based method and can automatically learn potential rules to maximize the user's positive emotions. Due to the Covid-19 epidemic, we were unable to conduct large-scale experiments. However, a follow-up project combined VR Zoo Therapy with gesture recognition and proved the effectiveness by evaluating participant's EEG emotion values.

This thesis is concerned with the development of novel numerical methods for solving nondifferentiable convex optimization problems in real Hilbert spaces and with the investigation of their asymptotic behavior. To this end, we are also making use of monotone operator theory as some of the provided algorithms are originally designed to solve monotone inclusion problems. After introducing basic notations and preliminary results in convex analysis, we derive two numerical methods based on different smoothing strategies for solving nondifferentiable convex optimization problems. The first approach, known as the double smoothing technique, solves the optimization problem with some given a priori accuracy by applying two regularizations to its conjugate dual problem. A special fast gradient method then solves the regularized dual problem such that an approximate primal solution can be reconstructed from it. The second approach affects the primal optimization problem directly by applying a single regularization to it and is capable of using variable smoothing parameters which lead to a more accurate approximation of the original problem as the iteration counter increases. We then derive and investigate different primal-dual methods in real Hilbert spaces. In general, one considerable advantage of primal-dual algorithms is that they are providing a complete splitting philosophy in that the resolvents, which arise in the iterative process, are only taken separately from each maximally monotone operator occurring in the problem description. We firstly analyze the forward-backward-forward algorithm of Combettes and Pesquet in terms of its convergence rate for the objective of a nondifferentiable convex optimization problem. Additionally, we propose accelerations of this method under the additional assumption that certain monotone operators occurring in the problem formulation are strongly monotone. Subsequently, we derive two Douglas–Rachford type primal-dual methods for solving monotone inclusion problems involving finite sums of linearly composed parallel sum type monotone operators. To prove their asymptotic convergence, we use a common product Hilbert space strategy by reformulating the corresponding inclusion problem reasonably such that the Douglas–Rachford algorithm can be applied to it. Finally, we propose two primal-dual algorithms relying on forward-backward and forward-backward-forward approaches for solving monotone inclusion problems involving parallel sums of linearly composed monotone operators. The last part of this thesis deals with different numerical experiments where we intend to compare our methods against algorithms from the literature. The problems which arise in this part are manifold and they reflect the importance of this field of research as convex optimization problems appear in lots of applications of interest.

Microplastics take hundreds to thousands of years to degrade in nature, and pose a threat to the environment. A photocatalytical degradation method have been developed to take advantage of solar light to degrade microplastics, however it takes several months to degrade microplastics with the process. The purpose of this study is to enhance the degradation rate of microplastics by synthesizing a material where photocatalysis is combined with Fenton reaction. A material with zinc oxide nanorods coated with tin oxide and decorated with iron particles (𝑍𝑛𝑂/𝑆𝑛𝑂2/𝐹𝑒0) was synthesized and used to degrade methylene blue, polystyrene and polypropylene. The result show that the degradation rate with a 𝑍𝑛𝑂/𝑆𝑛𝑂2/𝐹𝑒0 – sample is faster than with a 𝑍𝑛𝑂 – sample, and that it can be used to degrade polystyrene and polypropylene.Students’ view on researchers can affect the development of their interest and attitude towards science. Study visits to laboratories have been used to increase students’ interest and give them new experiences. The purpose of this study is to investigate what and how high school students learn during a study visit to a nanotechnology laboratory, and how the study visit affects high school students’ interest and motivation for research and learning. A study visit with 5 stations was organized, and students were given a questionnaire about what they learned during the study visit. Thematic analysis was used to analyze the students’ answers. The result shows that the study visit increased students’ interest in research, and the importance of designing stations so that they are connected to students’ previous knowledge and are within their proximal development zone. / Mikroplaster tar hundratals till tusentals år att bryta ner i naturen och utgör ett hot mot miljön. En fotokatalytisk nedbrytningsprocess har utvecklats där solljus utnyttjas för att bryta ner mikroplaster, dock tar det flera månader att bryta ner mikroplaster med den processen. Syftet med denna studie är att förbättra nedbrytningshastigheten av mikroplaster genom att syntetisera ett material där fotokatalys kombineras med Fenton-reaktion. Ett material med zinkoxid nanorör belagda med tennoxid och dekorerade med järnpartiklar (𝑍𝑛𝑂/𝑆𝑛𝑂2/𝐹𝑒0) syntetiserades och användes för att bryta ner metylenblått, polystyren och polypropen. Resultatet visar att nedbrytningshastigheten med 𝑍𝑛𝑂/𝑆𝑛𝑂2/𝐹𝑒0 – materialet är snabbare än med ett 𝑍𝑛𝑂 – material, och att 𝑍𝑛𝑂/𝑆𝑛𝑂2/𝐹𝑒0 – materialet kan användas för att bryta ned polystyren och polypropen.Elevers syn på forskning och forskare kan påverka utvecklingen av deras intresse och inställning till vetenskap. Studiebesök på laboratorier har använts för att öka elevernas intresse och ge dem nya erfarenheter. Syftet med denna studie är att undersöka vad och hur gymnasieelever lär sig under ett studiebesök i ett nanotekniklaboratorium och hur studiebesöket påverkar gymnasieelevernas intresse och motivation för forskning och lärande. Ett studiebesök med 5 stationer organiserades och eleverna fick ett frågeformulär om vad de lärde sig under studiebesöket. Tematisk analys användes för att analysera elevernas svar. Resultatet visar att studiebesöket ökade elevernas intresse för forskning och vikten av att utforma stationer så att de är kopplade till elevernas tidigare kunskaper och ligger inom deras proximala utvecklingszon.

Multi-agent path planning is applied in a wide range of applications in robotics and autonomous vehicles, including aerial vehicles such as drones and other unmanned aerial vehicles (UAVs), to solve tasks in areas like surveillance, search and rescue, and transportation. In today's rapidly evolving technology in the fields of automation and artificial intelligence, multi-agent path planning is growing increasingly more relevant. The main problems encountered in multi-agent path planning are collision avoidance with other agents, obstacle evasion, and pathfinding from a starting point to an endpoint. In this project, the objectives were to create intelligent agents capable of navigating through two-dimensional eight-agent cost map environments to a static target, while avoiding collisions with other agents and simultaneously minimizing the path cost. The method of reinforcement learning was used by utilizing the development platform Unity and the open-source ML-Agents toolkit that enables the development of intelligent agents with reinforcement learning inside Unity. Perlin Noise was used to generate the cost maps. The reinforcement learning algorithm Proximal Policy Optimization was used to train the agents. The training was structured as a curriculum with two lessons, the first lesson was designed to teach the agents to reach the target, without colliding with other agents or moving out of bounds. The second lesson was designed to teach the agents to minimize the path cost. The project successfully achieved its objectives, which could be determined from visual inspection and by comparing the final model with a baseline model. The baseline model was trained only to reach the target while avoiding collisions, without minimizing the path cost. A comparison of the models showed that the final model outperformed the baseline model, reaching an average of $27.6\%$ lower path cost. / Multi-agent-vägsökning används inom en rad olika tillämpningar inom robotik och autonoma fordon, inklusive flygfarkoster såsom drönare och andra obemannade flygfarkoster (UAV), för att lösa uppgifter inom områden som övervakning, sök- och räddningsinsatser samt transport. I dagens snabbt utvecklande teknik inom automation och artificiell intelligens blir multi-agent-vägsökning allt mer relevant. De huvudsakliga problemen som stöts på inom multi-agent-vägsökning är kollisioner med andra agenter, undvikande av hinder och vägsökning från en startpunkt till en slutpunkt. I detta projekt var målen att skapa intelligenta agenter som kan navigera genom tvådimensionella åtta-agents kostnadskartmiljöer till ett statiskt mål, samtidigt som de undviker kollisioner med andra agenter och minimerar vägkostnaden. Metoden förstärkningsinlärning användes genom att utnyttja utvecklingsplattformen Unity och Unitys open-source ML-Agents toolkit, som möjliggör utveckling av intelligenta agenter med förstärkningsinlärning inuti Unity. Perlin Brus användes för att generera kostnadskartorna. Förstärkningsinlärningsalgoritmen Proximal Policy Optimization användes för att träna agenterna. Träningen strukturerades som en läroplan med två lektioner, den första lektionen var utformad för att lära agenterna att nå målet, utan att kollidera med andra agenter eller röra sig utanför gränserna. Den andra lektionen var utformad för att lära agenterna att minimera vägkostnaden. Projektet uppnådde framgångsrikt sina mål, vilket kunde fastställas genom visuell inspektion och genom att jämföra den slutliga modellen med en basmodell. Basmodellen tränades endast för att nå målet och undvika kollisioner, utan att minimera vägen kostnaden. En jämförelse av modellerna visade att den slutliga modellen överträffade baslinjemodellen, och uppnådde en genomsnittlig $27,6\%$ lägre vägkostnad.

This thesis offers an assessment design for collaborative learning, utilisation of blended learning support through current communication technologies and highlights the crucial role of the tutor. The thesis designed and tested a theoretical framework which encompassed an active learning environment and resulted in the development of the shamrock conceptual framework. To test the theoretical framework, clarify the role of the tutor and the impact on the learner experience two studies were undertaken using pedagogical models that combined the concepts of learner-centric, sociocultural and dialogic perspectives on collaborative learning and technology in meeting the needs of learners in the 21st Century. In the first study, the role of the tutor was found to be crucial in setting, implementing and guiding learners using the assessment design as part of a social constructivist pedagogical practice. The pedagogical approach adopted was to blend face-to-face and Wiki learning experiences and was found to promote learner ownership, engagement and the fostering of a learning community. The second study validated the first and provided additional asynchronous technology experiences in addition to the Wiki blend in the assessment design. Study 2 examined the role of the tutor and the learner whilst using current technologies comprising podcasts and video and a Wiki in the collaborative experience. Findings showed that the Wiki supported community and collaborative aspects of a sociocultural practice whilst learners were engaged in authentic learning activities and led to a well supported learning environment. The importance of technology design and use to accommodate collaborative and community aspects was found to be an essential component. It was found that technology is not simply an add-on but rather needs to be planned and considered purposefully by both tutors and learners when used in a blend to supplement learning on campus as part of an assessment design in higher education. This study has shown that, for this to happen, academics need to be provided with the appropriate support, knowledge and skills required in developing a blended learning experience using a Wiki supplemented by class contact on campus as part of an assessment design.

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