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In bioinformatics one important task is to distinguish between native and mirror protein models based on the structural information. This information can be obtained from the atomic coordinates of the protein backbone. This thesis tackles the problem of distinction of these conformations, looking at the statistics of the dihedral angles’ distribution regarding the protein backbone. This distribution is visualized in Ramachandran plots. By means of an interpretable machine learning classification method – Generalized Matrix Learning Vector Quantization – we are able to distinguish between native and mirror protein models with high accuracy. Further, the classifier model supplies supplementary information on the important distributional regions for distinction, like α-helices and β-strands.
The games industry has significantly grown over the last 30 years. Projects are getting bigger and more expensive, making it essential to plan, structure and track them more efficiently.
The growth of projects has increased the administrative workload for producers, project managers and leads, as they have to monitor and control the progress of the project in order to keep a permanent overview of the project. This is often accompanied by a lack of insight into the project and basic communication within the team. Therefore, the goal of this thesis is to enhance conventional project management methods with process structures that occur frequently in game development.
This thesis initially elaborates on what project management in the game industry actually is: Here, methods are considered, especially agile methods and progress tracking prac-tices, which were created for software development and have become a standard in game development. Subsequently, an example is used to demonstrate how process management can function within the development of video games. Based on this, the ideal is depicted, which is implemented and used in a tool at the German games studio KING Art GmbH. This ideal is compared with expert interviews in order to verify its gen-eral validity in the industry.
By integrating process structures, the administrative effort can be reduced, communica-tion within game development can be simplified, while the current project status can be permanently presented. This benefits both project management and leads, as well as the entire team. Further application tests of this theory would have to be organized to check scalability and to draw comparisons to other applications.
Brassica oleracea like all crucifers plants have a defense mechanism against natural enemies, which are chemical compounds formed form the enzymatic degradation of glucosinolates. In the presence of epithiospecifier proteins (ESP), the hydrolysis of glucosinolates will form epithionitriles or nitriles depending on the glucosinolate structure, This research proved that three predicted sequences (ESP) taken from NCBI database has a role in the enzymatic hydrolysis of glucosinolates in Brassica oleracea.
Laser welding of hidden T-joints, connecting the web-sheet through the face-sheet of the joint can provide advantages like increased lightweight potential in manufacturing sandwich structures with thin-walled cores. However, maintaining the correct positioning of the beam relative to the joint is challenging. A method to reduce the effort of positioning is using optical coherence tomography (OCT), that interferometrically measures the reflection distance inside of the keyhole during laser deep penetration welding. In this study new approaches for targeted data processing of the OCT-signal to automatically detect misalignments are presented. It is shown that considering multiple components from the inference pattern and the respective signal intensities improve the detection accuracy of misalignments.
The emerging Internet of Things (IoT) technology interconnects billions of embedded devices with each other. These embedded devices are internet-enabled, which collect, share, and analyze data without any human interventions. The integration of IoT technology into the human environment, such as industries, agriculture, and health sectors, is expected to improve the way of life and businesses. The emerging technology possesses challenges and numerous
security threats. On these grounds, it is a must to strengthen the security of IoT technology to avoid any compromise, which affects human life. In contrast to implementing traditional cryptosystems on IoT devices, an elliptic curve cryptosystem (ECC) is used to meet the limited resources of the devices. ECC is an elliptic curve-based public-key cryptography which provides equivalent security with shorter key size compared to other cryptosystems such as Rivest–Shamir–Adleman (RSA). The security of an ECC hinges on the hardness to solve the elliptic curve discrete logarithm problem (ECDLP). ECC is faster and easier to implement and also consumes less power and bandwidth. ECC is incorporated in internationally recognized standards for lightweight applications due to the
benefits ECC provides.
In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGeneralized Learning Vector Quantization classifier. Specifically, we proved that the bottlenecks of an autoencoder serve as an "information filter" which tries to best represent the desired output in that particular layer in the statistical sense of mutual information.
Autoencoder model was trained for purely unsupervised task and leveraged the advantages by learning feature representations. As a result, the model got the significant value of the accuracy. Implementation and tuning of the model was carried out using Tensor Flow [1].
An extra study has been dedicated to improve traditional GLVQ algorithm taken from sklearn-lvg [2] using the bottleneck from an autoencoder.
The study has revealed potential of bottlenecks of an autoencoder as pre-processing tool in improving the accuracy of GLVQ. Specifically, the model was capable to identify 75% improvements of accuracy in GLVQ comparing to original one, which has about 62%. Consequently, the research exposed the need for further improvement of the model in the present problem case.
A Protein is a large molecule that consists of a vast number of atoms; one can only imagine the complexity of such a molecule. Protein is a series of amino acids that bind to each other to form specific sequences known as peptide chains. Proteins fold into three-dimensional conformations (or so-called protein’s native structure) to perform their functions. However, not every protein folds into a correct structure as a result of mutations occurring in their amino acid sequences. Consequently, this mutation causes many protein misfolding diseases. Protein folding is a severe problem in the biological field. Predicting changes in protein stability free energy in relation to the amino acid mutation (ΔΔG) aids to better comprehend the driving forces underlying how proteins fold to their native structures. Therefore, measuring the difference in Gibbs free energy provides more insight as to how protein folding occurs. Consequently, this knowledge might prove beneficial in designing new drugs to treat protein misfolding related diseases. The protein-energy profile aids in understanding the sequential, structural, and functional relationship, by assigning an energy profile to a protein structure. Additionally, measuring the changes in the protein-energy profile consequent to the mutation (ΔΔE) by using an approach derived from statistical physics will lead us to comprehend the protein structure thoroughly. In this work, we attempt to prove that ΔΔE values will be approximate to ΔΔG values, which can lead the future studies to consider that the energy profile is a good predictor of protein binding affinity as Gibbs free energy to solve the protein folding problem.
In the following bachelor thesis the current trends and potential applications of digitalization in the service industry will be discussed. With the nowadays surging demand on digitalization in all industries, there are branches of the service industry where digitalization is yet to be exploited to its full potential. However, it is difficult to pick and choose which branches of the industry should be fully digitized and which should be partially digitized. The result of this work should therefore facilitate the process of applying digitization in the consulting services where face to face human interaction has been the key to the industry for years. For this purpose, essential factors to be taken into account were identified, which are to be sought after through the analysis, in the specification of the system requirements as well as in the performance of a utility value analysis.
Probabilistic Micropayments
(2022)
Probabilistic micropayments are important cryptography research topics in electronic commerce. The Probabilistic micropayments have the potential to be researched in order to obtain efficient algorithms with low transaction costs and high speeding computer power. To delve into the topic, it is vital to scrutinize the cryptographic preliminaries such as hash functions and digital signatures. This thesis investigates the important probabilistic methods based on a centralized or decentralized network. Firstly, centralized networks such as lottery-based tickets, Payword, coin-flipping, and MR2 are described, and an approach based on blind signatures is also discussed. Then, decentralized network methods such as MICROPAY3, a transferable scheme on the blockchain network, along with an efficient model for cryptocurrencies, are explained. Then we compare the different probabilistic micropayment methods by improving their drawback with a new technique. To set the results from the theoretical analysis of different methods into some context, we analyze the attacks that reduce the security and, therefore, the system’s efficiency. Particularly, we discuss various methods for detecting double-spending and eclipse attacks occurrence
Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.
Bitcoin's energy consumption and social costs in relation to its capacity as a settlement layer
(2021)
Bitcoin runs on energy. The decentralized network’s amount of energy consumption has resulted in multifaceted discussions about its efficiency and environmental impact. To put Bitcoin’s energy consumption into perspective, we propose to relate (a) the energy consumption in TWh and (b) resulting social costs in the form of carbon emissions to the Dollar value settled on the Bitcoin network. Both metrics allow to relate and quantify the capacity of Bitcoin as a settlement layer to the network’s energy consumption and resulting carbon missions, or social costs. We find that in early 2021 Bitcoin (a) settles between $2,333 and $7,555 for each Dollar spent on energy and (b) that, on average, a Dollar settled on the Bitcoin blockchain causes in social costs between 0.007% and 0.01%, depending on the estimated energy consumption converted into the costs of carbon emissions. These results help to assess the efficiency, cost and sustainability of Bitcoin and may allow a comparison of Bitcoin with existing settlement base layers such as Fedwire or gold
The application described in this thesis has been created, built and designed to help nurses or any medical personnel all around the world in being able to access a real-time database to store patient records like Patient Name, Patient ID, Patient Age and Date of Birth, and the Symptoms that the patient is experiencing. A real-time database is a live database where all changes made to it are reflected across all devices accessing it. This application will be beneficial especially in countries where access to a computer or medical equipment is not always possible. A phone is always ready use and at the reach of the hand, users of this application will always be able to access the data at any given time and place. We will be able to add a new patient or search for existing patients. In addition, this application allows us to take RAW medical images that can be used to identify anomalies in the blood sample. RAW images are important for this application because they’re uncompressed, which means, they do not lose any quality or details. The users of this application are the medical personnel that will be taking care of the patients. These users will have to create a profile on the database in order to use the application, since their data, like user ID, will be used in order to control the behaviour of the data retrieved and stored. We will also discuss the current and future features of this application, as well as, the benefits of this application when it comes to the medical personnel, as well as patients. Finally, we will also go
over the implementation of such application from a hardware perspective, as well as a software one.
In recent years the term Cloud has become popular in the world of technology. It is used to describe many different Information Technology offerings, but people are adapting this word without truly understanding it. “Demystifying the Cloud – Drawing the Lines between Technologies and Concepts” by Kevin Arnot takes a look at many levels of the Cloud and gives a comprehensive overview of the technologies and ideas that make it a paradigm shift. The author analyzes the term methodically by leveraging appropriate information from the Internet as well as from experts. An important milestone in understanding the Cloud accurately is differentiating between its components. These include: underlying technologies, the three Cloud Service Models (SaaS, IaaS and PaaS) and how it is deployed, publically or privately. The result is to understand that a Cloud can be composed in different ways and therefore serves exactly the needs of its users. Furthermore, the author describes challenges that individuals and busi-nesses have to deal with equally and reviews possible solutions. Cloud technology will continue to evolve; however, the future business value of the term “Cloud” will depend on how companies continue using or misusing it.
Several algorithms have been proposed for the testing of series-parallel graphs in linear time. We give our alternate algorithms for testing series-parallel graphs, their tree decompositions, and the independence number when the input is undirected biconnected series-parallel graphs, which run (approximately) linearly in polynomial time.
Purpose: The study is aimed to determine the Incentives for German SMEs to offshore their business activities in India and China.
Design: This study is based on quantitative approach. Primary and secondary data is being used in the study. The data was collected from individuals working in different SMEs in Germany, having relative offshoring experience. Theories from the articles, peer reviewed journal along with relevant books were consulted throughout the study.
Findings: The findingssuggest that the benefits and advantages of offshoring strategy in India and China are cost efficiency and technology. Moreover, the challenges that are being faced by the firms while executive offshoring strategy is cultural mix especially language/cultural barriers, security issues and loss of market performance.
Originality and Value: The study on incentives of German SMEs to offshore business activities in India and China enables me to understand why companies are interested in offshoring strategy in low cost countries for expanding their business while evaluating the challenges, merits and demerits of offshoring
VQ-VAE is a successful generative model which can perform lossy compression. It combines deep learning with vector quantization to achieve a discrete compressed representation of the data. We explore using different vector quantization techniques with VQ-VAE, mainly neural gas and fuzzy c-means. Moreover, VQ-VAE consists of a non-differentiable discrete mapping which we will explore and propose changes to the original VQ-VAE loss to fit the alternative vector quantization techniques.
With the growing market of cryptocurrencies, blockchain is becoming central to various research areas relevant from a mathematical and cryptographic point of view. Moreover, it is capable of transforming the traditional methods involving centralized network operations into decentralized peer-to-peer functionalities. At the same time, it provides an alternative to digital payments in a robust and tamperproof manner by adding the element of cryptography, consequently making it traversable for an individual who is a part of the blockchain network. Furthermore, for a blockchain to be optimal and efficient, it must handle the blockchain trilemma of security, decentralization, and scalability constraints in an effective manner. Algorand, a blockchain cryptocurrency protocol intended to solve blockchain’s trilemma, has been studied and discussed. It is a permissionless (public) blockchain protocol and uses pure proof of stake as its consensus mechanism.
We propose a method for edge detection in images with multiplicative noise based on Ant Colony System (ACS). To adapt the Ant Colony System algorithm to multiplicative noise, global pheromone matrix is computed by the Coefficient of Variation. We carried out a performance comparison of the edge detection Ant Colony System algorithm among several techniques, the best results were found in the gradient and the coefficient of variation.
Due to the intractability of the Discrete Logarithm Problem (DLP), it has been widely used in the field of cryptography and the security of several cryptosystems is based on the hardness of computation of DLP. In this paper, we start with the topics on Number Theory and Abstract Algebra as it will enable one to study the nature of discrete logarithms in a comprehensive way, and then, we concentrate on the application and computation of discrete logarithms. Application of discrete logarithms such as Diffie Hellman key exchange, ElGamal signature scheme, and several attacks over the DLP such as Baby-step Giant-step method, Silver Pohlig Hellman algorithm, etc have been analyzed. We also focus on the elliptic curve along with the discrete logarithm over the elliptic curve. Attacks for the elliptic curve discrete logarithm problem, ECDLP have been discussed. Moreover, the extension of several discrete logarithms-based protocols over the elliptic curve such as the elliptic curve digital signature algorithm, ECDSA have been discussed also.
In dieser Arbeit wurden neuartige Proteasen aus psychrotoleranten Bakterienstämmen isoliert und auf ihre biochemischen Eigenschaften charakterisiert. Des Weiteren konnten S8 Familie Proteasen Gene amplifiziert werden und Unterschiede in der Aminosäuresequenz konnten in Zusammenhang mit den biochemischen Eigenschaften der Proteasen in Verbindung gebracht werden.