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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.