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The dynamics of self-adaptive multi-recombinant evolution strategies on the general ellipsoid model
(2014)
Nowadays, the area of customer management strives for omni-channel and state-of-the-art CRM concepts including Artificial Intelligence and the approach of Customer Experience. As a result, modern CRM solutions are essential tools for supporting customer processes in Marketing, Sales and Service. AI-driven CRM accelerates sales cycles, improves lead generation and qualification, and enables highly personalized marketing. The focus of this thesis is to present the basics of Customer Relationship Management, to show the latest Gartner insights about CRM and CX, and to demonstrate an AI Business Framework, which introduces AI use cases that are used as a basis for the expert interviews conducted in an international B2B company. AI will transform CX through a better understanding of customer behavior. The following research questions are answered in this thesis: In which AI use cases can Sales and CRM be improved? How can Customer Experience be improved with AI-driven CRM?
The presented master thesis of the study subject International Management and Leadership at the University of Applied Science Vorarlberg in Dornbirn handles the potential future influence of the EU Corporate Sustainability Due diligence on SMEs. First this thesis introduces the most important regulations that might come into place with this Due Diligence Act and gives a theoretical input when and how it will come into place, and also who it will affect directly and who will be affected indirectly. The empirical data resulted of several qualitative expert interviews and a following quantitative research. The expert interviews are split in two different groups, first the topic experts from institutions like chamber of commerce or chamber of labour and second experts from highly successful Austrian companies which are already handling the topic and the future challenges. Expected outcome of the qualitative interviews was a better view on the actual situation especially the impact on small and medium enterprises. On the basis of this results the quantitative survey was produced. In the quantitative survey the goal was to see, how much entrepreneurs and companies in the small and medium sector already are aware of the upcoming legal challenges throughout the supply chain. With all this collected data the practical outcome of this thesis is the Checklist, which helps entrepreneurs to find out if and how much they will be affected by the Act. And finally, the most important part is the Guideline, which introduces first risk assessment tools, that will help companies to prepare for future legislation and bring undoubtedly a certain advantage for the upcoming challenges.
Many test drives are carried out in the automotive environment. During these test drives many signals are recorded. The task of the test engineers is to find certain patterns (e.g. an emergency stop) in these long time series. Finding these interesting patterns is currently done with rule based processing. This procedure is very time consuming and requires a test engineer with expertise. In this thesis it is examined if the emerging field of machine learning can be used to support the engineers in this task. Active Learning, a subarea of machine learning, is used to train a classifier during the labeling process. Thereby it proposes similar windows to the already labeled ones. This saves the annotator time for searching or formulating rules for the problem. A data generator is worked out to replace the missing labeled data for tests. The custom performance measure “proportion of seen samples” is developed to make the success measurable. A modular software architecture is designed. With that, several combinations of Time Series Classification algorithms and query strategies are compared on artificial data. The results are verified on real datasets, which are open source available. The best performing, but computational intensive solution is an adapted RandOm Convolutional KErnel Transform (ROCKET). The custom query strategy “certainty sampling” shows the best results for highly imbalanced datasets.
Combining parallel pattern generation of electrohydrodynamic lithography with serial addressing
(2018)
For a given set of banks, how big can losses in bad economic or financial scenarios possibly get, and what are these bad scenarios? These are the two central questions of stress tests for banks and the banking system. Current stress tests select stress scenarios in a way which might leave aside many dangerous scenarios and thus create an illusion of safety; and which might consider highly implausible scenarios and thus trigger a false alarm. We show how to select scenarios systematically for a banking system in a context of multiple credit exposures. We demonstrate the application of our method in an example on the Spanish and Italian residential real estate exposures of European banks. Compared to the EBA 2016 stress test our method produces scenarios which are equally plausible as the EBA stress scenario but yield considerably worse system wide losses.
Towards a novel infrastructure for conducting high productive cloud-based scientific analytics
(2016)
Recently the use of microRNAs (miRNAs) as biomarkers for a multitude of diseases has gained substantial significance for clinical as well as point-of-care diagnostics. Amongst other challenges, however, it holds the central requirement that the concentration of a given miRNA must be evaluated within the context of other factors in order to unambiguously diagnose one specific disease. In terms of the development of diagnostic methods and devices, this implies an inevitable demand for multiplexing in order to be able to gauge the abundance of several components of interest in a patient’s sample in parallel. In this study, we design and implement different multiplexed versions of our electrochemical microfluidic biosensor by dividing its channel into subsections, creating four novel chip designs for the amplification-free and simultaneous quantification of up to eight miRNAs on the CRISPR-Biosensor X (‘X’ highlighting the multiplexing aspect of the device). We then use a one-step model assay followed by amperometric readout in combination with a 2-minute-stop-flow-protocol to explore the fluidic and mechanical characteristics and limitations of the different versions of the device. The sensor showing the best performance, is subsequently used for the Cas13a-powered proof-of-concept measurement of two miRNAs (miRNA-19b and miRNA-20a) from the miRNA-17∼92 cluster, which is dysregulated in the blood of pediatric medulloblastoma patients. Quantification of the latter, alongside simultaneous negative control measurements are accomplished on the same device. We thereby confirm the applicability of our platform to the challenge of amplification-free, parallel detection of multiple nucleic acids.
The Fast Average Current Mode control methodology is a novel method for the implementation of a current compensator in a switched-mode power supply. It does not require compensation against sub-harmonic instability and is inductor independent. In this work, the digital implementation of this topology is compared against an analog implementation using simulation. Additionally, a hardware prototype is created to validate the digital simulation's results. In a Simulink environment, parameters of the digital implementation, such as the digital-to-analog converter resolutions and the delay counter frequency are varied to research their impact on system performance. The simulations show that a digital current compensator has similar performance as an analog implementation when designed tailored to the application. When evaluating the whole control loop the digital system is inferior due to added delays caused by digital to analog conversion. By operating the Buck converter hardware implementation as a current source, the functionality of the current mode control implementation in a FPGA was proven. Voltage control cannot be validated due to hardware issues. Due to the successful simulation of the source code with a mixed signal model of the converter, it can be assumed that it is functional. Apart from performance, a digital implementation shows many benefits compared to an analog solution, such as configurability of control parameters and easy compensation of component variations and aging.
The spatial redistribution of Japanese direct investment in the United Kingdom between 1991 and 2010
(2013)
The goal of this paper is to design a low-loss 1 x 32 Y-branch optical splitter for optical transmission systems, using two different design tools employing Beam Propagation Method. As a first step, a conventional 1 x 32 Y-branch splitter was designed and simulated in two-dimensional environment of OptiBPM photonic tool. The simulated optical properties feature high loss, high asymmetric splitting ratio and a large size of the designed structure, too. In the second step of this work we propose an optimization of the conventional splitter design leading to suppression of the asymmetric splitting ratio to one-third of its initial value and to the improvement of the losses by nearly 2 dB. In addition, 50% size reduction of the designed structure was also achieved. This length-optimized low-loss splitter was then modelled in a three-dimensional environment of RSoft photonic tool and the simulated results confirm the strong improvement of the optical properties.
Design of low loss 1x64 y-branch splitter having symmetric splitting ratio and small footprint
(2014)
Learning together
(2019)