Refine
Year of publication
- 2020 (69) (remove)
Document Type
- Master's Thesis (29)
- Article (21)
- Conference Proceeding (11)
- Part of a Book (4)
- Doctoral Thesis (2)
- Other (1)
- Report (1)
Institute
- Forschungszentrum Mikrotechnik (15)
- Forschungszentrum Business Informatics (10)
- Technik | Engineering & Technology (6)
- Department of Computer Science (Ende 2021 aufgelöst; Integration in die übergeordnete OE Technik) (5)
- Soziales & Gesundheit (5)
- Forschungszentrum Energie (4)
- Wirtschaft (2)
- Department of Engineering (Ende 2021 aufgelöst; Integration in die übergeordnete OE Technik) (1)
- Forschung (1)
- Forschungszentrum Digital Factory Vorarlberg (1)
Language
- English (69) (remove)
Keywords
- Optical coherence tomography (3)
- Arrayed waveguide gratings (2)
- Human needs (2)
- International business (2)
- OCT (2)
- ROS (2)
- SME (2)
- Y-branch splitter (2)
- A priori & a posteriori segmentation (1)
- AI on the Edge (1)
Over the last years, polymers have gained great attention as substrate material, because of the possibility to produce low-cost sensors in a high-throughput manner or for rapid prototyping and the wide variety of polymeric materials available with different features (like transparency, flexibility, stretchability, etc.). For almost all biosensing applications, the interaction between biomolecules (for example, antibodies, proteins or enzymes) and the employed substrate surface is highly important. In order to realize an effective biomolecule immobilization on polymers, different surface activation techniques, including chemical and physical methods, exist. Among them, plasma treatment offers an easy, fast and effective activation of the surfaces by micro/nanotexturing and generating functional groups (including carboxylic acids, amines, esters, aldehydes or hydroxyl groups). Hence, here we present a systematic and comprehensive plasma activation study of various polymeric surfaces by optimizing different parameters, including power, time, substrate temperature and gas composition. Thereby, the highest immobilization efficiency along with a homogenous biomolecule distribution is achieved with a 5-min plasma treatment under a gas composition of 50% oxygen and nitrogen, at a power of 1000 W and a substrate temperature of 80 C. These results are also confirmed by different surface characterization methods, including SEM, XPS and contact angle measurements.
Although pilot projects are an accepted means of entry into prospects, research on the object of startups selling SaaS and use pilots to enter and to further scale within their prospect’s organization is limited. The reader can expect a collection of key practices of SaaS startups in the field of Decision Support Software. These combine the main sales-oriented elements within pilot projects that are reflected on by Customer Success Management, Change Management as well as cultural dimensions. Explorative interviews, mainly with stakeholders in Decision Support Software startups, were conducted to further gain an understanding of the research object. Results indicate that pilots are strategically used in the sales of such startups to simultaneously deal with their customer’s uncertainties and as a means for the startups to get commitment and increase their value proposition through the additional service that they offer in order to acquire an internal support basis. Customer Success Management as well as Change Management are furthermore advantageous in quickly achieving measurable results that leverage buyers and seller’s justification for further sales.
Cultural Due Diligence
(2020)
Much research has been conducted in recent years to discover the reasons for the high failure rate of M&As, whereas one frequently cited reason is the incompatibility of the corporate cultures. In order to minimize this risk and to be able to react to these differences already at an early stage, Cultural Due Diligence offers itself as part of the due diligence process. Unlike existing, more general research, I emphasize the cultural challenges companies face when investing transnationally with this thesis. Using the results of a single case study with inductive character, I answer the question how to conduct Cultural Due Diligence in cross-border M&As and propose an appropriate model. The findings reveal that especially in cross-border M&As, cultural incompatibility poses a risk for failure. I was able to find out that companies that seek to grow internationally with M&As deal with similar issues in terms of corporate culture as pointed out in existing Cultural Due Diligence methods. The present study, however, shows that national culture has a great influence on corporate culture, which is why it is essential to include it in the cultural assessment in cross-border acquisitions. This provides information about why there are differences, besides the fact that they exist. Only this understanding puts a company in the appropriate starting position to recognize differences, understand them, assess whether these differ-ences are acceptable, as well as to develop appropriate strategies to address them in the integration phase.
The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual use of property-based testing. In this code testing method, model specifications and invariants are expressed directly in code and tested through automated and randomised test data generation. This paper expands on our previous work and explores how to use property-based testing on a technical level to encode and test specifications of ABS. As use case the simple agent-based SIR model is used, where it is shown how to test agent behaviour, transition probabilities and model invariants. The outcome are specifications expressed directly in code, which relate whole classes of random input to expected classes of output. During test execution, random test data is generated automatically, potentially covering the equivalent of thousands of unit tests, run within seconds on modern hardware. This makes property-based testing in the context of ABS strictly more powerful than unit testing, as it is a much more natural fit due to its stochastic nature.
Complementarities and synergies of quadruple helix innovation design in smart city development
(2020)
Increased urbanization trends are stimulating regional needs to support transitions from urban environments to smart cities, using its holistic perspective as a source to innovation. Strong relations between smart cities, urban and regional development, are getting increased attention both at policy and implementation level, providing fertile ground for execution of the new European policy frameworks that supports quadruple helix approaches to innovation. Smart specialization strategies (RIS3) encompass such initiatives, placing ICT and collaboration between academia, industry, government, and citizen at the center of urban innovation. However, there is still lack of research on effects of such approaches to innovation, involving both quadruple helix clusters and ICT in utilizing innovation potentials for developing smart cities. This study aims to increase the understanding on how quadruple helix urban innovation strengthens competitiveness of regions by improving its local smart areas – RIS3. We identified smart specialization patterns and applied comparative benchmark between nine smallmedium sized urban regions in Central Europe. Building on these results, the study provides an overview of the effects of RIS3 strategies implemented through quadruple helix innovation clusters on competitiveness of regions and Smart City development.
Investigation of non-uniformly emitting optical fiber diffusers on the light distribution in tissue
(2020)
Zeros can cause many issues in data analysis and dealing with them requires specialized procedures. We differentiate between rounded zeros, structural zeros and missing values. Rounded zeros occur when the true value of a variable is hidden because of a detection limit in whatever mechanism was used to acquire the data. Structural zeros are values which are truly zero, often coming about due to a hidden mechanism separate from the one which generates values greater than 0. Missing values are values that are completely missing for unknown or known reasons. This thesis outlines various methods for dealing with different kinds of zeros in different contexts. Many of these methods are very specific in their ideal usecase. They are separated based on which kind of zero they are intended for and if they are better suited for compositional or for standard data.
For rounded zeros we impute the zeros with an estimated value below the detection limit. The author describes multiplicative replacement, a simple procedure that imputes values at a fixed fraction of the detection limit. As a more advanced technique, the author describes Kaplan Meier smoothing spline replacement, which interpolates a spline on a Kaplan Meier curve and uses the spline below the detection limit to impute values in a more natural distribution. Rounded zeros cannot be imputed with the same techniques that would be used for regular missing values, since there is more information available on the true value of a rounded zero than there would be for a regular missing value.
Structural zeros cannot be imputed since they are a true zero. Imputing them would falsify their values and produce a value where there should be none. Because of this, we apply modelling techniques that can work around structural zeros and incorporate them. For standard data, the zero inflated Poisson model is presented. This model utilizes a mixture of a logistic and a Poisson distribution to accurately model data with a large amount of structural zeros. While the Poisson distribution is only applicable to count data, the zero inflation concept can be applied to different kinds of distributions. For compositional data, the zero adjusted Dirichlet model is introduced. This model mixes Dirichlet distributions for every pattern of zeros found within the data. Non-algorithmic techniques to reduce the amount of structural zeros present are also shown. These techniques being amalgamation, which combines columns with structural zeros into more broad descriptors and classification, which changes columns into categorical values based on a structural zero being present or not.
Missing values are values that are completely missing for various known or unknown reasons. Different imputation techniques are introduced. For standard data, MissForest imputation is introduced, which utilizes a RandomForest regression to impute mixed type missing values. Another imputation technique shown utilizes both a genetic algorithm and a neural network to impute values based on the genetic algorithm minimizing the error of an autoencoder neural network. In the case of compositional data, knn imputation is presented, which utilizes the knn concept also found in knn clustering to impute the values based on the closest samples with a value available.
All of these methods are explained and demonstrated to give readers a guide to finding the suitable methods to use in different scenarios.
The thesis also provides a general guide on dealing with zeros in data, with decision flowcharts and more detailed descriptions for both compositional and standard data being presented. General tips on getting better results when zeros are involved are also given and explained. This general guide was then applied to a dataset to show it in action.