Open Access

We present a new concept of 3D polymer-based 1 × 4 beam splitter for wavelength splitting around 1550 nm. The beam splitter consists of IP-Dip polymer as a core and polydimethylsiloxane (PDMS) Sylgard 184 as a cladding. The splitter was designed and simulated with two different photonics tools and the results show high splitting ratio for single-mode and multi-mode operation with low losses. Based on the simulations, a 3D beam splitter was designed and realized using direct laser writing (DLW) process with adaptation to coupling to standard single-mode fiber. With respect to the technological limits, the multi-mode splitter having core of (4 × 4) μm 2 was designed and fabricated together with supporting stable mechanical construction. Splitting properties were investigated by intensity monitoring of splitter outputs using optical microscopy and near-field scanning optical microscopy. In the development phase, the optical performance of fabricated beam splitter was examined by splitting of short visible wavelengths using red light emitting diode. Finally, the splitting of 1550 nm laser light was studied in detail by near-field measurements and compared with the simulated results. The nearly single-mode operation was observed and the shape of propagating mode and mode field diameter was well recognized.

With Cloud Computing and multi-core CPUs parallel computing resources are becoming more and more affordable and commonly available. Parallel programming should as well be easily accessible for everyone. Unfortunately, existing frameworks and systems are powerful but often very complex to use for anyone who lacks the knowledge about underlying concepts. This paper introduces a software framework and execution environment whose objective is to provide a system which should be easily usable for everyone who could benefit from parallel computing. Some real-world examples are presented with an explanation of all the steps that are necessary for computing in a parallel and distributed manner.

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.

How people perceive stigmatization at work in connection with mental health problems and what role this stigmatization fulfils in the DACH-Region, means Germany-Austria-Switzerland, has so far received no greater attention from scientists. Although the stigma of mental illness has been extensively researched among the general population, little is known about its consequences of the stigma of mental health in the workplace. This study seeks to bridge the gap in this area. As the purpose of this thesis is to illustrate the dynamics of stigmatization rather than to explain its mere quantitative relevance, I have chosen to investigate how the complex systemic interdependencies according to Forrester (1968) manifest in the reflection of the subjects. On the background of socio-cultural aspects in the DACH-Region regarding mental health problems and forms and natures of stigma while following the question what role stigmatization plays in this German-speaking area DACH, I conducted a qualitative social research study with affected persons (employees from various German companies) to investigate this issue. Hereby I focus on people working in the industry sector. The present thesis begins by exploring the question of intercultural and sociocultural differences in the DACH region according to Hofstede’s Dimensions, as well as their possible relevance for answering the research question. Definitions and theoretical interpretations regarding the backgrounds about mental health, mental health problems and their appearance will be mentioned. Based on Goffman’s (1963) research on stigma, I investigate why mental health issues have the potential to stigmatize especially at the workplace. Goffman’s ideas on stigma illustrate how by providing important insights into understanding the situation of affected persons. The connection between stereotypes, stigmatization, and discriminatory behaviour according to Major & O’Brien (2005) is hereby necessary to be noticed. Through personal interviews I explore how, what way, people at work perceive stigmatization surrounding mental health problems and how stigmas interact. The findings conducted in this study give a cue towards the systemic approach of stigmatization. That is why a new hypothesis on the ways of stigmatization in German-speaking countries is drawn up. Stigmatization is under investigation as a systemic instrument for maintaining management and group power to affect single employees and restore group identity, consciously or unconsciously. I discuss the theoretical and practical implications of these findings for management behaviour and leadership development in organizations.