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DNA encodes the genetic instructions for the development and function of organisms and hence maintaining genomic integrity is essential for the propagation of life. However, DNA molecules are under constant threat of metabolic and environmental insults resulting in DNA damages including DNA double strand breaks (DSB), which are considered as a serious threat to cell survival. The majority of these DSB are repaired by Non-homologous end joining (NHEJ). Unrepaired DSB can lead to genomic instability resulting in cell cycle arrest, apoptosis, and mutations. Thus, delineating this DNA repair process is important in understanding the molecular mechanisms of aging and malignant progression. B lymphocytes undergo physiological DNA breaks and NHEJ-mediated DNA repair during their bone marrow differentiation and peripheral class switch recombination (CSR), thus lending them as a good model system in which to delineate the DNA repair mechanisms. To determine the effect of aging on NHEJ, B lymphocytes from old mice were analyzed. The results showed compromised DNA repair in cells from old mice compared to cells from adult mice. These results suggest that NHEJ is compromised during aging and might play critical roles in the aging process and age-associated conditions. To delineate the role of a CT in regulating the immune system, transgenic mice expressing NUP98-HOXD13 (NHD13) were analyzed for B lymphocyte differentiation, peripheral development, CSR, and antibody production. The results showed impaired B cell development and antibody production, which worsened with antigenic stimulation, suggesting the role of NHD13 in immune regulation. These studies explored the possibility of altered NHEJ-mediated DNA repair as a contributing reason for aging process and age-associated conditions. Also, the results from NHD13 study suggested that a primary CT can result in impaired NHEJ and regulate immune cell development and function. Furthermore, the results pointed to the possibility that a primary CT may lead to secondary mutations through altered NHEJ. Thus, these studies shed insight into the molecular mechanisms of altered NHEJ and may help in developing preventive or therapeutic strategies against accumulation of DNA damage, aging process and secondary mutations. / Ph. D.

Yes / With the advent of Wireless Sensor Networks (WSN) and its immense popularity in a wide range of applications, security has been a major concern for these resource-constraint systems. Alongside security, WSNs are currently being integrated with existing technologies such as the Internet, satellite, Wi-Max, Wi-Fi, etc. in order to transmit data over long distances and hand-over network load to more powerful devices. With the focus currently being on the integration of WSNs with existing technologies, security becomes a major concern. The main security requirement for WSN-integrated networks is providing end-to-end security along with the implementation of in-processing techniques of data aggregation. This can be achieved with the implementation of Homomorphic encryption schemes which prove to be computationally inexpensive since they have considerable overheads. This paper addresses the ID-issue of the commonly used Castelluccia Mykletun Tsudik (CMT) [12] homomorphic scheme by proposing an ID slotting mechanism which carries information pertaining to the security keys responsible for the encryption of individual sensor data. The proposed scheme proves to be 93.5% lighter in terms of induced overheads and 11.86% more energy efficient along with providing efficient WSN scalability compared to the existing scheme. The paper provides analytical results comparing the proposed scheme with the existing scheme thus justifying that the modification to the existing scheme can prove highly efficient for resource-constrained WSNs.

LTE heterogeneous wireless networks promise significant increase in data rates and improved coverage through (i) the deployment of relays and cell densification, (ii) carrier aggregation to enhance bandwidth usage and (iii) by enabling nodes to have dual connectivity. These emerging cellular networks are complex and large systems which are difficult to optimize with centralized control and where mobiles need to balance spectral efficiency, power consumption and fairness constraints. In this dissertation we focus on how decentralized and autonomous mobiles in multihop cellular systems can optimize their own local objectives by taking into account end-to-end or network-wide conditions. We propose several link-adaptive schemes where nodes can adjust their transmit power, aggregate carriers and select points of access to the network (relays and/or macrocell base stations) autonomously, based on both local and global conditions. Under our approach, this is achieved by disseminating the dynamic congestion level in the backhaul links of the points of access. As nodes adapt locally, the congestion levels in the backhaul links can change, which can in turn induce them to also change their adaptation objectives. We show that under our schemes, even with this dynamic congestion feedback, nodes can distributedly converge to a stable selection of transmit power levels and points of access. We also analytically derive the transmit power levels at the equilibrium points for certain cases. Moreover, through numerical results we show that the corresponding system throughput is significantly higher than when nodes adapt greedily following traditional link layer optimization objectives. Given the growing data rate demand, increasing system complexity and the difficulty of implementing centralized cross-layer optimization frameworks, our work simplifies resource allocation in heterogeneous cellular systems. Our work can be extended to any multihop wireless system where the backhaul link capacity is limited and feedback on the dynamic congestion levels at the access points is available. / Ph. D.

In the rapidly evolving field of autonomous driving, the integration of Deep Reinforcement Learning (DRL) promises significant advancements towards achieving reliable and efficient vehicular systems. This study presents a comprehensive examination of DRL’s application within a simulated autonomous driving context, with a focus on the nuanced impact of representation learning parameters on the performance of end-to-end models. An overview of the theoretical underpinnings of machine learning, deep learning, and reinforcement learning is provided, laying the groundwork for their application in autonomous driving scenarios. The methodology outlines a detailed framework for training autonomous vehicles in the Duckietown simulation environment, employing both non-end-to-end and end-to-end models to investigate the effectiveness of various reinforcement learning algorithms and representation learning techniques. At the heart of this research are extensive simulation experiments designed to evaluate the Proximal Policy Optimization (PPO) algorithm’s effectiveness within the established framework. The study delves into reward structures and the impact of representation learning parameters on the performance of end-to-end models. A critical comparison of the models in the validation chapter highlights the significant role of representation learning parameters in the outcomes of DRL-based autonomous driving systems. The findings reveal that meticulous adjustment of representation learning parameters markedly influences the end-to-end training process. Notably, image segmentation techniques significantly enhance feature recognizability and model performance.:Contents List of Figures List of Tables List of Abbreviations List of Symbols 1 Introduction 1.1 Autonomous Driving Overview 1.2 Problem Description 1.3 Research Structure 2 Research Background 2.1 Theoretical Basis 2.1.1 Machine Learning 2.1.2 Deep Learning 2.1.3 Reinforcement Learning 2.2 Related Work 3 Methodology 3.1 Problem Definition 3.2 Simulation Platform 3.3 Observation Space 3.3.1 Observation Space of Non-end-to-end model 3.3.2 Observation Space of end-to-end model 3.4 Action Space 3.5 Reward Shaping 3.5.1 speed penalty 3.5.2 position reward 3.6 Map and training dataset 3.6.1 Map Design 3.6.2 Training Dataset 3.7 Variational Autoencoder Structure 3.7.1 Mathematical fundation for VAE 3.8 Reinforcement Learning Framework 3.8.1 Actor-Critic Method 3.8.2 Policy Gradient 3.8.3 Trust Region Policy Optimization 3.8.4 Proximal Policy Optimization 4 Simulation Experiments 4.1 Experimental Setup 4.2 Representation Learning Model 4.3 End-to-end Model 5 Result 6 Validation and Evaluation 6.1 Validation of End-to-end Model 6.2 Evaluation of End-to-end Model 6.2.1 Comparison with Baselines 6.2.2 Comparison with Different Representation Learning Model 7 Conclusion and Future Work 7.1 Summary 7.2 Future Research

With the rapid development of autonomous driving and artificial intelligence technology, end-to-end autonomous driving technology has become a research hotspot. This thesis aims to explore the application of deep reinforcement learning in the realizing of end-to-end autonomous driving. We built a deep reinforcement learning virtual environment in the Carla simulator, and based on it, we trained a policy model to control a vehicle along a preplanned route. For the selection of the deep reinforcement learning algorithms, we have used the Proximal Policy Optimization algorithm due to its stable performance. Considering the complexity of end-to-end autonomous driving, we have also carefully designed a comprehensive reward function to train the policy model more efficiently. The model inputs for this study are of two types: firstly, real-time road information and vehicle state data obtained from the Carla simulator, and secondly, real-time images captured by the vehicle's front camera. In order to understand the influence of different input information on the training effect and model performance, we conducted a detailed comparative analysis. The test results showed that the accuracy and significance of the information has a significant impact on the learning effect of the agent, which in turn has a direct impact on the performance of the model. Through this study, we have not only confirmed the potential of deep reinforcement learning in the field of end-to-end autonomous driving, but also provided an important reference for future research and development of related technologies.

The diploma thesis on Development of Front-end Applications focuses on the design of computerization of enterprise processes covered by a unified front-end application. Exploratory research was used to suggest working methods to solve technological growth of the enterprise and how to evaluate business need of a front-end application. Later chapters present basic summary of development methodology used for automation of business processes. The diploma thesis innovatively proposes a role of a front-end specialist who does not need to create a program code and has closer relation to business departments. There are recommend analytical standards and development tools for automation of business processes as well as front-end implementation. Finally the work attaches a model example demonstrating relative simplicity and practical realization of new techniques for front-end application development. Powered by TCPDF (www.tcpdf.org)

During the past years, the usage of wood as construction material in multistory applications has increased. In Germany and Sweden, various activities have been, and are about to be performed, to accentuate and improve the position of wooden multistory houses. In line with that, this thesis tries to contribute to the understanding of how consumers perceive durable products; in the contextual frame of how German and Swedish consumers perceive wooden multistory houses. It was hypothesized that consumers’ perceptions on durable products differ, depending on their age, income, national and within-country habitation. Based on the Means-End Chain Theory, the Association Pattern Technique has been further developed to collect and analyze data for two samples. In Germany and Sweden, 31 respectively 34 laddering interviews have been carried out which formed the base for a survey-study in each country. Here, 229 surveys were received from German, and 503 from Swedish respondents. The results show that age, income, national and within-country habitation have significant impact on consumers’ perceptions of wooden multistory houses. Moreover, the extension of the Association Pattern Technique was validated. It allowed for additional data to be gathered, which can be regarded as rather important, since it appeared in the most dominant Means-End Chains of the respondents in both Germany and Sweden. This helps to understand consumers’ underlying reasons why one product is favoured over another.

NETI@home is an open-source software package that collects network performance statistics from end-systems. It has been written for and tested on the Windows, Solaris, and Linux operating systems, with testing for other operating systems to be completed soon. NETI@home is designed to run on end-user machines and collect various statistics about Internet performance. These statistics are then sent to a server at the Georgia Institute of Technology, where they are collected and made publicly available. This tool gives researchers much needed data on the end-to-end performance of the Internet, as measured by end-users. NETI@homes basic approach is to sniff packets sent from and received by the host and infer performance metrics based on these observed packets. NETI@home users are able to select a privacy level that determines what types of data are gathered, and what is not reported. NETI@home is designed to be an unobtrusive software system that runs quietly in the background with little or no intervention by the user, and using few resources.