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Blockchain and other distributed ledger technologies are evolving into enabling infrastructures for innovative ICT-solutions. Numerous features, such as decentralization, programmability, and immutability of data, have led to a multitude of use cases that range from cryptocurrencies, tracking and tracing to automated business protocols or decentralized autonomous systems. For organizations that seek blockchain adoption, the overwhelming spectrum of potential application areas requires guidance reducing complexity and support the development of blockchain-based concepts. This paper introduces a classification approach to provide design and implementation guidance that goes beyond current textbook classifications. As an outcome, a typology for management and business architects is developed, before the paper concludes with an instantiation of existing use cases and a discussion of their classes.
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Proteins are macromolecules that consist of linear-bonded amino acids. They are essential elements in various metabolic processes. The three-dimensional structure of a protein is determined by the order of amino acids, also referred to as the protein sequence. This conformation corresponds to the structural state in which the protein is functionally active. However, relationships between protein sequence, structure and function have not been fully understood yet. Additionally, information about structural properties or even the entire protein structure are crucial for understanding the dynamics that define protein functionality and mechanisms. From this, the role of a protein in its molecular context can be described closely. For instance, interactions can be investigated and comprehended as a biological dynamic network that is sensitive to alternations, i.e. changes which are caused by diseases. Such knowledge can aid in drug design, whereas compounds need to be specifically tailored and adjusted to their molecular targets. Protein energy profile-basedmethods can be applied to investigate protein structures concerning dynamics and alternations. The publications enclosed to this work discuss in general the scientific potentials of energy profilebased techniques and algorithms. On the one hand, changes in stability caused by protein mutations and proteinligand interactions are discussed in the context of energy profiles. On the other hand, energetic relations to protein sequence, structure and function are elucidated in detail. Finally, the presented discussions focus on recent enhancements of the eProS (energy profile suite) database and toolbox. eProS freely provides all elucidated methodologies to the scientific community. Thus, one can address biological questions with the presented methods at hand. Additionally, eProS provides annotations related to foreign databases. This ensures a broad view on biological data and information. In particular, energetic characteristics can be identified which contribute to a protein’s structure and function.
Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers.
Target of this Diploma Thesis is the development of a thermal simulation card to analyze the thermal behavior of a LTE PCIe Mini data card for GSM/UMTS based wireless networks in different environments. The power consumption of modern wireless communication systems has increased dramatically during the last years. Especially for the next generation of wireless modem cards the thermal dissipations will be slightly on or even beyond the official guidelines of the components and the whole card. To gain knowledge about the behavior of the data card, it shall be simulated with software as well as real hardware. As the ASIC components are not available yet, a hardware emulation shall be developed. The thesis covers the whole development process from the idea, the conception, the layout to the assembly and the measurements. It starts with finding a way of emulating the mounted components, measuring and powering. Afterwards a card, incorporating the principles found before, will be developed. An additional software simulation gives comparative values against the measurements. After assembling the emulation cards and running reference measurements, trials for temperature improvements will be ran and compared with the simulations.
This study shows the potential for the make-or-buy theory in several scenarios – production, assembling and development. The evaluation of these possibilities is conducted, based on Bosch’s core competencies. A decision model is developed to support the decision making process. Based on these results, the serial production at RBAC in China is planned and suggestions for setting up the assembly line are given
This feasibility study shows possibilities, how logistical concepts can be
improved or reorganized. Therefore, the assambly line for hydraulic blocks at
Bosch Rexroth Changzhou is checked and new ideas are shown. To ensure
comparability, three different cases are considered. Based on this evaluation,
recommendations for further development are displayed.
This work emphasises the synergy between anthropologi-cal research on human skeletal remains and suitable doc-umentation strategies. Highlighting the significance of data recording and the use of digital databases in various aspects of anthropological work on bones, including scien-tific standards, skeletal collections, analysis of research re-sults, ethical considerations, and curation, it provides a comprehensive examination of these topics to demonstrate the value of investing time and resources in this field, countering the existing lack of funding that has led to sig-nificant deficiencies. Additionally, the paper outlines the requirements and challenges associated with standard data protocoling and suggests that digital data manage-ment frameworks and technologies such as ontologies and semantic web technologies for anthropological information should be a central focus in developing solutions.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.