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Thіs bachеlоr thеsіs was еxеcutеd fоr Іntеrpіpе cоmpany and іt cоncеntratеs оn іts busіnеss stratеgy оn іntеrnatіоnal markеts, еspеcіally оn Mіddlе Еastеrn pіpеs markеt. Chооsіng an іnapprоprіatе еntry busіnеss stratеgy can lеad tо sіgnіfіcant nеgatіvе cоnsеquеncеs, busіnеss stratеgy sеlеctіоn оn іntеrnatіоnal markеts іs оnе оf thе mоst crіtіcal dеcіsіоns іn іntеrnatіоnal tradе systеm. Thе thеоrеtіcal framеwоrk оf thе bachеlоr thеsіs іs prоvіdеd іn thе sеcоnd chaptеr, whіch was maіnly cоllеctеd by dеsktоp studyіng. Thе thеоry rеvіеw cоntaіns dеscrіptіоn оf varіоus fоrеіgn markеt stratеgіеs, mеthоds and mеchanіsms оf dеcіsіоn-makіng, lеvеls and typеs оf busіnеss еnvіrоnmеnt. A cоmbіnatіоn оf thеоrіеs іs adоptеd tо facіlіtatе thе prоcеss оf gathеrіng thе rеquеstеd іnfоrmatіоn. Thе thіrd chaptеr cоntaіns іnfоrmatіоn abоut Іntеrpіpе Cоmpany und іt´s еcоnоmіc actіvіty іn thе hоst cоuntry and abrоad. Gеnеral іnfоrmatіоn abоut Іntеrpіpе Cоmpany, іts currеnt pоsіtіоn, busіnеss dеvеlоpmеnt stratеgy fоr 2015-2016 yеars arе prеsеntеd. Thе sіtuatіоn оn thе pіpеs and whееls markеt іn Ukraіnе durіng thе pеrіоd 2014-2015 was analyzеd and оn accоunt оf thіs thе rеasоns оf dеclіnе іn prоfіts and salеs wеrе еducеd. Cоіncіdеntly thе pеnеtratіоn fоrms оf Іntеrpіpе Cоmpany tо fоrеіgn cоuntrіеs wеrе cоnsіdеrеd. Іn thіs rеgard thе mоst succеssful еntry fоrms arе suggеstеd tо bе accеptеd as thе maіn kеy stratеgy оf pеnеtratіоn tо thе іntеrnatіоnal markеt. Thе fоrth chaptеr prоvіdеs thе іnfоrmatіоn abоut thе cеrtaіn apprоach оf pеnеtratіоn оf Іntеrpіpе Cоmpany tо thе Mіddlе Еastеrn pіpе markеt. Thе purpоsе іs tо іncrеasе thе numbеr оf dеlіvеrіеs tо оіl and gas cоmpanіеs іn thіs rеgіоn and cоntіnuе еstablіshіng оf іts rеlatіоns wіth kеy agеnts and dіstrіbutоrs. Thе prоjеct aіms tо еlеvatе currеnt pоsіtіоn оf thе еntеrprіsе оn Mіddlе Еastеrn pіpеs markеt and adjust advantagеоus іntеrnatіоnal rеlatіоns fоr bоth cоuntеrparts. Data іs cоllеctеd frоm varіоus sоurcеs, іncludіng: bооks and jоurnals іn thе thеоrеtіcal framеwоrk, nеwspapеrs, cоmpany’s publіshеd rеpоrts, prеss rеlеasеs, catalоguеs, bullеtіns, brоchurеs, prеsеntatіоn, Іntеrnеt rеsоurcеs еtc. іn thе еmpіrіcal study.
This thesis aims to research the platform YouTube and whether “being a YouTuber” qualifies as a profession or not and what leads to this. The author combines existing scientific data and information provided by YouTubers doing this as a job and uses the compilation method. The author merges that material and uses it to create a bachelor thesis that covers both the theoretical and practical approach. The aim was to find out if there is a success recipe that can be followed that leads to views and clicks which are essential for the profession as a YouTuber. To do this, the author created two channels to see how the factors mentioned in this thesis are applied and if the approach leads to success. The findings of this thesis showed, that although the profession of a YouTuber can be classified as a job, it needs to be viewed differently from commonly known and in society accepted careers. Becoming a YouTuber and making money from this business, therefore, cannot be guaranteed.
Workload Optimization Techniques for Password
Guessing Algorithms on Distributed Computing Platforms
(2019)
The following thesis covers several ways to optimize distributed computing platforms for cryptanalytic purposes. After an introduction on password storage, password guessing attacks and distributed computing in general, a set of inital benchmark results for a variety of different devices will be analyzed. The shown results are mainly based on utilization of the open source password recovery tool Hashcat. The second part of this work shows an algorithmic implementation for information retrieval and workload generation. This thesis can be used for the conception of a distributed computing system, inventory analysis of available hardware devices, runtime and cost estimations for specific jobs and finally strategic workload distribution.
In this thesis, the changes in economy and society and the resulting effects on the labor market are being outlined. Current studies show that the shrinking labor market and the increasing digitalization result in a lack of skilled tech talent and a transition from an employer market to a clear employee market. Derived from the findings of the scientific research on this topic and conducted expert interviews, practical recommendations for recruitment actions within the scope of employer branding will be defined in order to help corporations to gain the needed tech skill set and drive innovation.
This work concentrates on the frequently used marketing instrument brand personality. Its effect on the consumer and how it drives consumer behaviour through TV advertis-ing are the focus. Scientific material, utilising research results of the last 20 years, has been analysed to investigate this subject. Furthermore, the example of Southern Comfort provides an insight of brand personality being applied to the real world of marketing business.
This paper explores the origins of Maori images in New Zealand film history. Discussing the history of Maori and their society brings us closer to a, once almost extinct, race and its struggle for self-representation and self-governance. By taking an in-depth look at New Zealands film history we get to understand how Maori were the subject of the earliest films and at what time they started making their own films. Combining those elements gives us the opportunity to understand how early images of Maori were created by Pakeha directors. By looking at different films throughout film history shows how Maori images evolved in time, especially when Maori started depicting themselves. This paper not only answers questions about Maori images in film but also tries to make people realise what odds Maori had to overcome in their daily struggle for selfdetermination.
The following is a description and outline of the work done at the Cornell Lab of Ornithology developing Nation Feathers VR, a virtual reality game for learning about bird calls and songs. The goal was to develop a game which is intuitive, educational and entertaining. Furthermore, the software needed to be structured in a way that allows for feasible future expansion. This required careful data saving and retrieval. The game gives the player an opportunity to learn and apply that knowledge, all while maintaining a shorter runtime in order to reduce the total time spent in the virtual world. This is meant to prevent any discomfort to the player that may result from extended use of the VR headset.
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.
Protein structures are essential elements in every biological system evolved on earth, where they function as stabilizing elements, signaltransducers or replication machin eries. They are consisting of linear-bonded amino acids, which determine the three-dimensional structure of the protein, whereas the structure in turn determines the function. The native and biological active structure ofa protein can be understood as the folding state of a polypeptide chain at the global minimum of free energy.
By means of protein energy profiling, which is an approach derived from statistical physics it is possible to assign a so called energy profile to a protein structure. Such an energy profile describes the local energetic interaction features of every amino acid within the structure and introduces an energetic point of view, instead of a structural or sequential onto proteins.
This work aims to give a perspective to the question of how we may gain pattern information out of energy profiles. The concrete subjects are energy-mapped Pfam family alignments and investigations on finding motifs or patterns indiscretizised energy profile segments.
To enable smart devices of the internet of things to be connected to a blockchain, a blockchain client needs to run on this hardware. With the Trustless Incentivized Remote Node Network, in short Incubed, it will be possible to establish a decentralized and secure network of remote nodes, which enables trustworthy and fast access to a blockchain for a large number of low-performance IoT devices. Currently, Incubed supports the verification of Ethereum data. To serve a wider audience and more applications this paper proposes the verification of Bitcoin data as well, which can be achieved due to the modularity of Incubed. This paper describes the proof data that is necessary for a client to prove the correctness of a node’s response and the process to verify the response by using this proof data as well. A proof-object which contains the proof data will be part of every response in addition to the actual result. We design, implement and evaluate Bitcoin verification for Incubed. Creation of the proof data for supported methods (on the server-side) and the verification process using this proof data (on the client-side) has been demonstrated. This enables the verification of Bitcoin in Incubed.
The number of Internet of Things (IoT) devices is increasing rapidly. The Trustless Incentivized Remote Node Network, in short IN3 (Incubed), enables trustworthy and fast access to a blockchain for a large number of low-performance IoT devices. Although currently IN3 only supports the verification of Ethereum data, it is not limited to one blockchain due to modularity. This thesis describes the fundamentals, the concept and the implementation of the Bitcoin verification in IN3.
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This study explores the opportunities and risks associated with user-generated content (UGC) in the communication strategies of marketing departments from a business perspective. With the rise of social media and online platforms, UGC has become a powerful tool for brands to engage with their audience, build trust, and enhance brand awareness. However, implementing UGC also comes with inherent risks, including the loss of control over brand messaging, potential negative user-generated content, and legal implications.
To investigate these dynamics, an empirical mixed-methods approach was employed, including expert interviews and a comprehensive literature review. The findings indicate that UGC offers significant opportunities for marketing departments, such as increased customer loyalty, enhanced authenticity, brand awareness, as well as a diverse set of possible content. However, the study also reveals the potential risks associated with UGC, highlighting the importance of managing these risks effectively.
In the following study the properties of the superabsorbent polymer Broadleaf P4 were investigated according to the aim to apply that polymer within constructed wetlands. The application of the polymer in constructed wetlands shall result in an improvement of the removal of pesticides. For that the polymer was given into lab-scale wetlands together with pumice and were compared to a control wetland, which was filled with gravel. The wetlands were running for several weeks in which the nutrient removal was recorded. The polymer was also tested according to its property to adsorb the pesticides before adding the pesticides to the wetland beds.
In this thesis, we implement, correct, and modify the compartmental model described in “Transmission Dynamics of Large Coronavirus Disease Outbreak in Homeless Shelter, Chicago, Illinois, USA, 2020”. Our objective is to engage in reading and understanding scientific literature, reproduce the results, and modify or generalize an existing mathematical model. We provide an overview of epidemiological models, focusing on simple compartmental SEIR models. We correct inaccuracies and misprints in the original implementation and use the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm to fit the model’s parameters. Furthermore, we modify the model by introducing an additional compartment. The resulting model has a more intuitive interpretation and relies on fewer assumptions. We also perform the fitting process for this alternative model. Finally, we demonstrate the advantages of our modified implementations and discuss other possible approaches.
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [12]. In many tasks of classification, different variants of LVQ are considered while training a model. In this thesis, the two variants of LVQ, Generalized Matrix Learning Vector Quantization(GMLVQ) and Generalized Tangent Learning Vector Quantization(GTLVQ) have been discussed. And later, transfer learning technique for different variants of LVQ has been implemented, visualized and we have compared the results using different datasets.
Community acquired pneumonia (CAP) is a very common, yet infectious and sometimes lethal disease. Therefor, this disease is connected to high costs of diagnosis and treatment. To actually reduce the costs for health care in this matter, diagnosis and treatment must get cheaper to conduct with no loss in predictive accuracy. One effective way in doing so would be the identification of easy detectable and highly specific transcriptomic markers, which would reduce the amount of work required for laboratory tests by possibly enhanced diagnosis capability.
Transcriptomic whole blood data, derived from the PROGRESS study was combined with several documented features like age, smoking status or the SOFA score. The analysis pipeline included processing by self organizing maps for dimensionality and noise reduction, as well as diffusion pseudotime (DPT). Pseudotime enabled modelling a disease run of CAP, where each sample represented a state/time in the modelled run. Both methods combined resulted in a proposed disease run of CAP, described by 1476 marker genes. The additional conduction of a geneset analysis also provided information about the immune related functions of these marker genes.
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.
Influenza A viruses are responsible for the outbreak of epidemics as well as pandemics worldwide. The surface protein neuraminidase of this virus is responsible, among other things, for the release of virions from the cell and is thus of interest in pharmacological research. The aim of this work is to gain knowledge about evolutionary changes in sequences of influenza A neuraminidase through different methods. First, EVcouplings is used with the goal of identifying evolutionary couplings within the protein sequences, but this analysis was unsuccessful. This is probably due to the great sequence length of neuraminidase. Second, the natural vector method will be used for sequence embedding purposes, in hopes to visualize sequential progression of the virus protein over time. Last, interpretable machine learning methods will be applied to examine if the data is classifiable by the different years and to gain information if the extracted information conform to the results from the EVcouplings analysis. Additionally to using the class label year, other labels such as groups or subtypes are used in classification with varying results. For balanced classes the machine learning models performed adequately, but this was not the case for imbalanced data. Groups and subtypes can be classified with a high accuracy, which was not the case for the years, continents or hosts. To identify the minimal number of features necessary for linear separation of neuraminidase group 1 subtypes, a logistic regression was performed at last, resulting in the identification of 15 combinations of nine amino acid frequencies. Since the sequence embedding as well as the machine learning methods did not show neuraminidase evolution over time, further research is necessary, for example with focus on one subtype with balanced data.
Proteins are involved in almost every aspect of life, mediating a wide range of cellular tasks. The protein sequence dictates the spatial arrangement of the residues and thus ultimately the function of a rotein. Huge effort is put into cumbersome structure eludication experiments which obtain models describing the observed spatial conformation of a protein, enabling users to predict their function, to understand their mode of action or to design tailored drugs to cure disease caused by misfolded or misregulated proteins.
However, the result of structure determination experiments are merely models of reality, made under simplifying assumptions - sometimes containing major undetected errors. On the other hand, such experiments are resource demanding and they cannot supply the actual demand.
Thus, scientists are predicting the structure of proteins in silico, resulting in models that are even
more prone to error.
In consequence, the structure biologists search after a practicable definition of structure quality and over the last two decades several model quality assessment programs emerged, measuring the local and global quality of peculiar structures. Seven representatives were studied, regarding the paradigms they follow and the features they use to describe the quality of residues. Their predications were compared, showing that there is almost no common ground among the tools.
Is there a way to combine their statements anyway?
Finally, the accumulated knowledge was used to design a novel evaluation tool, addressing problems previously spotted. Thereby, high quality of its predication as well as superior usability was
key. The strategy was compared to existing approaches and evaluated on suitable datasets.
Currently, the Internet of Things (IoT) is connected to the virtual world through the Web of Things (WoT), allowing efficient utilization of real-world objects with Internet technologies. The WoT facilitates abstract interaction between applications and connected IoT devices, allowing owners to switch between devices while using multiple ones. To achieve this, virtual assets in WoT devices can be tokenized through smart contracts and transferred using hashed proof as transactions within blockchain networks that support virtual currencies. The goal of Web of Things is to establish connectivity, interoperability, and integration among IoT devices using web standards and protocols, reducing reliance on device manufacturers. This enables easy integration of Web 3.0 cryptocurrency for device management. This study proposes a solution for WoT applications involving different cryptocurrency definitions. Finally, simulation results are presented to demonstrate the tokenization-based ownership transfer in the Web of Things.
Tokenization projects are currently very present when it comes to new blockchain technologies. After explaining the fundamentals of cross-chain interaction, the bachelor thesis will focus on tokenizing technology for Bitcoin on Ethereum. To get a more practical context, implementing the currently most successful decentralized tokenization project is described.
The financial world of blockchains is mostly covered by Bitcoin, taking up about 210 billion dollars in market cap. Despite the huge security and independence which the technology offers to the users, it's not quite easy to adapt with upcoming applications due to the regulated infrastructure behind. For small-scale transactions, everyday use applications or the access to a variety of crypto technologies and projects, Bitcoin is relatively limited in future development. The compatibility for most of those applications is covering currencies from more development-driven blockchains like Ethereum. Those want to reach out for the user base that's already in hold of Bitcoins and offer them a seamless transition to new applications without the risk of losing their funds. Within the article, atomic swaps and tokenization are covered up and current approaches compared. Both mechanisms are used to fulfill this symbiosis between Bitcoin and Ethereum.
To get a more practical view, an example on how to implement such a tokenization within an app is shown. This will give deeper insights and offers inspiration for digital identity-based app development.
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.
In this work a new method for the prediction of the Xaa-proline (where Xaa is any amino acid) cis/trans isomerization was investigated. By extraction of twelve structural features (real secondary structure, inside/outside classification, properties of the environment around proline and proline itself) a support vector machine (SVM) based prediction approach was evolved. The Java software Xaa-PIPT for structural feature extraction was developed. Based on 4397 (2199 cis and 2198 trans) prolines extracted from non-redundant, globular proteins a classifier was trained using the radial basis function (RBF) kernel. In ten-fold cross-validation it achieved an accuracy of 70.0478 % and a Matthews correlation coefficient (MCC) of 0.4223, a sensitivity of 0.5433 and a specificity of 0.8576. Based on this classifier a lightweight and easy-to-use Java software tool, called m Xaa-PIPT, for the prediction of the Xaa-proline cis/trans isomerization was devel-oped. It was shown that there are correlations between the proline surrounding environment and the isomerization state. m Xaa-PIPT can be used for the evaluation of low-resolution protein structures and theoretical models to improve their quality by the prediction of the Xaa-proline isomerization.
The theoretical foundations of enterprise management using information technology were reviewed; analysis of the effectiveness of the use of information systems in the enterprise; ways of improving the enterprise management mechanism using information systems (on example of Mars Wrigley Confectionery Belarus) have been developed.
The Media System of Malawi
(2010)
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The impact of organisational structure and organisational culture on the efficiency of a business
(2020)
The fear of losing flexibility and effectiveness due to an increased organisational structure induced by personal growth is causing SME's to defer structural changes. The purpose of this work is to examine whether the structural and cultural demands of employees match the structure and predominant culture within such a medium-sized company. As part of this, a survey was made to evaluate the current status and to suggest furthermore where and how changes would make sense to regain or even improve organisational efficiency.
The research of this thesis aims to analyze how a specific CSR approach from the Adidas Group on sustainability is perceived globally based on an analysis of the movements on the stock market combined with a sentiment analysis of tweet activities on Twitter. The thesis analyzed both positive feedback and critic from customers worldwide regarding the approach and other initiatives from the Adidas Group and their partner Parley for the Oceans, a non-governmental organization working towards a more sustainable world.
The topic of soulbound, non-transferable tokens is getting lots of interest within the blockchain space lately as decentralized societies become more tangible with Web3 social media applications and DAOs. In this article, I want to outline how such tokens function, their problems for adoption and standardization, and how they differ from verifiable credentials in the SSI field. As such soulbound assets will likely rely on extended recovery and asset management schemes to become viable identities that safely gain reputation and trust, features like social recovery and contract-based accounting are incorporated. By combining those new technologies and the theoretical crypto-native identity construct, the paper will give an impression of the future user-centric data economy.
This paper set out to determine what the effect of daily internet usage on a short attention span was and whether this had an effect on academic performance. As described briefly in the introduction this paper consisted of laying the groundwork through defining the relevant terminology, applying the methodology to the Hypotheses and making conclusive statements.
Two Hypotheses were presented to give the paper the aim. While Hypothesis 1 can be proven true through the two-step terminology applied, Hypothesis 2 does not stand up to the scrutiny. For lack of sufficient and specific evidence, the only conclusive statement that can be made regarding it is that it is untrue.
Approx. 80% of the population sample analysed were between the age of 19 – 30 which automatically reduces the analysis, extrapolations and scientific statements to a more specific age group. The other ages represented were almost all above, meaning that the findings could not accurately be applied to older age groups.
Nonetheless, the data collected was accurate and good be applied to prove Hypothesis 1, meaning that daily internet usage breeds and invites a short attention span. For lack of a fitting data collection method, physcial, social, mental factors along with motivation of an individual make up his academic performance. These were factors that could not be taken into consideration.
Conclusively, the author predicts that a present internet connection coupled with the growing popularity of digital technology attention spans will contin ue to stay as short as they are. Individuals will find ways to direct their short attention span where it is needed and apply it as necessary.
Both cryptocurrency researchers and early adopters of cryptocurrencies agree that they possess a special kind of materiality, based on the laborious productive process of digital ‘mining’ [1]. This idea first appears in the Bitcoin White Paper [2] that encourages Bitcoin adopters to construct and justify its value in metaphoric comparison to gold mining. In
this paper, I explore three material aspects of blockchain: physical infrastructure, human language and computer code. I apply the concept of 'continuous materiality' [3] to show how these three aspects interact in practical implementations of blockchain such as Bitcoin and Ethereum. I start from the concept of ‘digital metallism’ that stands for ‘fundamental value’ of cryptocurrencies, and end with the move of Ethereum to ‘proof-of-stake’, partially as a countermeasure against ‘evil miners’. I conclude that ignoring material aspects of blockchain technology can only further problematize complicated relations between their technical, semiotic and social materiality.
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Where does the cocoa, which we consume on a regular basis, come from? Supply chains are not always transparent, much less easily comprehensible. The cocoa industry faces ongoing challenges. Whether it be the chocolate manufacturers’ promise to maintain a sustainable and ethical supply chain, the minimal impact on the environment or the maximum adherence to human rights in their production process. This paper revises important steps which lead to the compliance with UN standards and questions the role of consumers in the construct of ethical chocolate products.
The shape-memory Nitinol as a nickel-titanium alloy is widely used in actuator and medical applications. However, the connection of a flange to the rod is a critical point. Therefore, laser rod end melting enables material accumulations to generate a preform at the end of a rod, followed by die forming, so that the flange can be generated. This process has been successfully applied on 1.4301 steel. This study is aimed to investigate laser rod end melting of shape-memory Nitinol regarding the resultant surface quality of the preforms. The results showed that spherical preforms could be generated without visible surface discoloration due to oxidation. By using different scan rates, different solidification conditions occurred which led to significantly different surface structures. These findings show that laser rod end melting can principally be applied on Nitinol to generate preforms for flanges whereby the surface quality depends on the solidification conditions.
As economies are getting more and more interconnected, the importance of the global logistics sector grew accordingly. However, both structural challenges and current events lead to recent supply chain disruptions, exposing the vulnerabilities of the sector. Simultaneously, blockchain has emerged as a key innovative technology with use cases going far beyond the exchange of virtual currencies. This paper aims to analyze how the technology is transforming global logistics and its challenges. Therefore, six use cases, are presented to give an overview of the technological possibilities of blockchain and smart contracts. The analysis combines theoretical approaches from scientific journals and combines them with findings from real-world implementations. The paper finds that the technology can change supply chain design fundamentally, with processes and decisions being automated and power within supply chain structures changing. However, implementations also face technological, environmental, and organizational challenges that need to be solved for wide-spread adoption.
This thesis proposes a solution to the practical problem of supervising relatively basic mechanic processes in robotics by means of computervision. Supervision happens by comparing the tracked movement with a known, ideal recording of the movement that acts as a model.
First, this thesis analyzes possible approaches to the problem regarding data structures and representation, ways of extracting the data from the recording and ways to compare the data sets of two recordings. Then, a specific solution is implemented in C++ and explained.
Success story DAB in the UK
(2017)
The popularity of digital audio broadcasting in different countries can be explained mainly by means of historical development. In this work, the general technical conditions are explained and the mode of operation is explained. In addition, advantages, disadvantages and alternatives are presented. After that, the development of the digital radio in Germany and the UK is compared with the current situation in order to show how the differences have led to a different distribution and acceptance of the medium.
This master thesis covers the topics of Studying customers’ behavior on the example of skin care brand Nivea. There are presented theoretical basis for the following research about marketing, customers’ behavior and conducting marketing research properly. Then, there is the analysis of German market. Since Nivea is the brand of Beiersdorf company, there is a description of Beiersdorf’s activity and operation work. The main idea of the paper work is to analyze customers’ behavior of Nivea. Therefore, the work contains huge research about the brand along with its’ micro- and macroenvironment. There also were conducted an in-depth interview and a survey to understand customers’
current needs. With all the results the author of the work proposed some ideas for Nivea brand.
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
After the expression of the titin-Hsp27-construct with the following purification supplies no satisfied results which makes the realization of the atomic force microscopy not possible. The devel-opment of the structure model by using different bioinformatic methods can establish a model for the protein sequence. As bioinformatic methods the template search by different BLAST runs and free available software like SwissModel, Pcons, ModWeb and other tools are used. Nevertheless, the generated model is not the native conformation and has to be analyzed with other software until a stable conformation of the structure can be predicted. Depending on the time which is provided the generated model is a good approach for the aim this master thesis has.
More than 10 years after the invention of Bitcoin, the underlying blockchain technology is having an increasing effect on today’s society. Although one of the most popular application areas of blockchain is still the field of cryptocurrencies, the technological concepts are crossing into further application domains such as international supply chains. Fast-changing markets, high costs of time and risk management as well as biased relationships between the actors pose big challenges to an appropriate supply chain management. Based on a case study about sensor tracking, this paper explores the potential impact of blockchain on small and medium enterprises within an international supply chain. We will show that blockchain technologies offers a high potential to reduce inequalities of power relations between involved actors within supply chains. To achieve this, the requirements for the use of blockchain in supply chain management will be analyzed by means of a conducted case study and an expert survey of the companies concerned.
The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses.
While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using
static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious
exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.
Standard assembly time is an important piece of data in product development that is used to compare different product variants or manufacturing variants. In the presented approach, standard time is created with the use of a decision tree regarding standard manual and machine-manual operations, taking into consideration product characteristics and typical tools, equipment and layout. The analysed features include, among others: information determined during product development, such as product structure, parts characteristics (e.g. weight, size), connection type, as well as the information determined during assembly planning: tools (e.g. hand screw driver, power screw driver, pliers), equipment (e.g. press, heater), workstation layout (e.g. distance, way of feeding). The object-attribute-value (OAV) framework was applied for the assembly characteristic. An example of the decision tree application to predict standard assembly time was presented for a mechanical subassembly. The case study was dedicated to standard time prediction for a bearing assembly. The presented approach is particularly important for the enterprises which offer customized products.
The subject of the following paper is the analysis of global company motives for taking on sport sponsorships as a corporate social responsibility (CSR) initiative. This work is compilatory in nature because it is derived from literature released by experts as well as real-life case studies. The expert literature provides a basis of theories and models regarding the fundamental motives for CSR and sport sponsoring and visualizes them by means of statistics and real-life case studies. This paper aims to inform individuals, leaders and specifically global organizations about the benefits that taking on a sport sponsorship may have for fulfilling a company’s CSR objectives
As widely discussed in literature spatial patterns of amino acids, so-called structural motifs, play an important role in protein function. The functional responsible part of a protein often lies in an evolutionary highly conserved spatial arrangement of only few amino acids, which are held in place tightly by the rest of the structure. In general, these motifs can mediate various functional interactions, such as DNA/RNA targeting and binding, ligand interactions, substrate catalysis, and stabilization of the protein structure.
Hence, characterizing and identifying such conserved structural motifs can contribute to understanding of structurefunction relationships in diverse protein families. Therefore and because of the rapidly increasing number of solved protein structures, it is highly desirable to identify, understand and moreover to search for structural scattered amino acid motifs. The aim of this work was the development and the implementation of a matching algorithm to search for such small structural motifs in large sets of target structures. Furthermore, motif matches were extensively analyzed, statistically assessed and functionally classified. Following a novel approach, hierarchical clustering was combined with functional classification and used to deduce evolutionary structure-function relationships. The proposed methods were combined and implemented to a feature-rich and easy-to-use command line software tool, which is freely available and contributes to the field of structural bioinformatic research.
nicht vorhanden
We use machine learning for the selection and classification of single–molecule trajectories to replace commonly used user–dependent sorting algorithms. Measured fluorescence time series of labelled single molecules need to be sorted into ’good molecules’ and ’bad’ molecules before further kinetic and thermodynamic analysis.
Currently, processing, sorting and analysis of the data is mainly done with the help of laboratory specific programs.
Although there are freely available programs for processing smFRET data, they do not offer ’molecular sorting’ or it is purely empirical. Only recently, new approaches came up to solve this problem by means of machine learning. Here, we describe a sound terminology for molecular sorting of smFRET data and present an efficient workflow for manual annotation followed by the training of the ML algorithm. Descriptive statistics of our generated dataset are provided and will serve as the basis for supervised ML-based molecular sorting algorithms yet to be developed.
The intention of this thesis is to examine the beneficial impact of renewable energies in general and biogas technologies in particular on socioeconomic status of people, by considering all applicable sides affecting its development as per political, cultural, environmental, and institutional means. As energy and development are very much correlative with each other, biogas technologies figure prominently as part of a decentralized, sustainable, renewable, energy network especially in rural areas of Nepal.
This thesis was written in order to prove the expediency of startup ecosystem support and to develop practical recommendations for Belarusian government based on the analysis of successful practices in the U.S. and Lithuania.
It covers the essence of a “startup company” and a “startup ecosystem” as well as provides the analysis of socioeconomic impact of startup companies with particular focus on job creation. It sheds light on the best startup support policies in the U.S., where most prominent startup ecosystems are operating, and Lithuania as a country with similar to Belarusian preconditions and a rapidly
developing ecosystem. Furthermore, this paper deals with Belarus‘s peculiarities regarding fostering startup ecosystem growth. It assesses recent economic development of Belarusian IT sector and gives an insight into its competitive advantages and challenges.
The subsequent paper is based on internet research using articles, presentations, reports and studies, websites and official legal documents.