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In this paper, we propose and simulate a new type of three-dimensional (3D) optical splitter based on multimode interference (MMI) for the wavelength of 1550 nm. The splitter was proposed on the square basis with the width of 20 x 20 µm2 using the IP-Dip polymer as a standard material for 3D laser lithography. We present the optical field distribution in the proposed MMI splitter and its integration possibility on optical fiber. The design is aimed to the possible fabrication process using the 3D laser lithography for forthcoming experiments.
Purpose: The purpose of this qualitative phenomenological study is to explore the of self-initiated expatriates prior to and during acculturation to life in a smaller periphery region such as Vorarlberg, Austria. By providing insights into their lived experience this research aims to fill in the gaps of missing information on motivators, success factors to adjustment, issues, and stressors, and more that SIEs experience when adjusting. Specifically, what items promote adjustment and what items hinder adjustment.
Findings: Developed a better understanding of how and what motivational factors lead to expatriation. Furthermore, that opportunities arise by chance. During acculturation, language factors (dialect), cultural differences act as stressors. While social support, and organizational support, learning of the language act as promoters of acculturation.
Further Research could be done including ethnicities, SIEs moving from developed to developing countries, adjustment in regions with dialect vs no dialect.
Key words: self-initiated expatriates, expatriation, acculturation, adjustment, promoting acculturation, hindering acculturation.
With the digitalisation, and the increased connectivity between manufacturing systems emerging in this context, manufacturing is shifting towards decentralised, distributed concepts. Still, for manufacturing scenarios manual input or augmentation of data is required at system boundaries. Especially in distributed manufacturing environments, like Cloud Manufacturing (CMfg) systems, constant changes to the available manufacturing resources and products pose challenges for establishing connections between them. We propose a feature-oriented representation of concepts, especially from the manufacturing domain, which serves as the basis for (semi-) automatically linking, e.g., manufacturing resources and products. This linking methodologies, as well as knowledge inferred using it, is then used to support distributed manufacturing, especially in CMfg environments, and enhance product development. The concepts and methodologies are to be evaluated in a real world learning factory.
In the context of this master thesis, general tensions within the relationship between headquarters and their subsidiaries are examined using the practical example of a Swiss company with its subsid-iary in Kenya. Thereby, the influence of cultural aspects and the associated different expectations on management and leadership are emphasized. In doing so, two countries are compared which have not yet been considered in the same context. The objective of this master thesis is to develop a framework that enables the headquarter in the German speaking area of Switzerland and the sub-sidiary in the Bantu speaking area of Kenya to overcome cultural barriers and to increase mutual understanding in the business context. This will facilitate the identification of potentially dysfunc-tional aspects in the working relationship and provide a basis for optimizing the existing business relationship between the Swiss headquarter and the Kenyan subsidiary.
This thesis addresses the overarching question of what the two business entities need to know about each other in terms of cultural characteristics and emerging differences in business practices (in terms of management/leadership) in order to improve the overall cooperation and working rela-tionship between the headquarter and its subsidiary. Thus, the following topics are emphasized with-in this thesis: tensions within the headquarter/subsidiary relationship, concise country profiles of Switzerland and Kenya including a cultural overview of both countries, cultural concepts including organizational culture, common leadership theories related to the situational leadership approach, and finally, an evaluation of the current status quo in the working relationship between the Swiss headquarter and the Kenyan subsidiary based on interviews.
The increasing digitalisation of daily routines confronts people with frequent privacy decisions. However, obscure data processing often leads to tedious decision-making and results in unreflective choices that unduly compromise privacy. Serious Games could be applied to encourage teenagers and young adults to make more thoughtful privacy decisions. Creating a Serious Game (SG) that promotes privacy awareness while maintaining an engaging gameplay requires, however, a carefully balanced game concept. This study explores the benefits of an online role-playing boardgame as a co-designing activity for creating SGs about privacy. In a between-subjects trial, student groups and educator/researcher groups were taking the roles of player, teacher, researcher and designer to co-design a balanced privacy SG concept. Using predefined design proposal cards or creating their own, students and educators played the online boardgame during a video conference session to generate game ideas, resolve potential conflicts and balance the different SG aspects. The comparative results of the present study indicate that students and educators alike perceive support from role-playing when ideating and balancing SG concepts and are happy with their playfully co-designed game concepts. Implications for supporting SG design with role-playing in remote collaboration scenarios are conclusively synthesised.
In today’s world, companies feel the urge to disguise from competitors and to connect emotionally with consumers in order to foster a meaningful and long-lasting relationship. Simultaneously, stakeholders demand an increase of companies’ social responsibility. Cause-related marketing (CRM) is a marketing tool that addresses the change in societal values and the rising expectations from stakeholder groups. The increasing number of companies that choose to partner with a non-profit organization highlights that linking a charitable cause to the company's brand is an effective marketing tool. Authors illustrate that CRM, as a form of showing corporate social responsibility, will become even more important in the future. This master thesis examines the relationship between CRM, emotions, and culture. The research goal is to identify if CRM programs are effective in evoking emotions in consumers and if the cultural background of a consumer influences the evocation of certain emotions. The empirical findings outline that CRM programs are effective in evoking emotions. Other-focused emotions evoked by CRM programs are stronger expe-rienced by members of collectivistic countries than by members of individualistic countries. Likewise other-focused emotions evoked by CRM programs are stronger experienced by high interdependent selves than by low interdependent selves.
A concept for a recommender system for the information portal swissmom is designed in this work. The challenges posed by the cold start problem and the pregnancy-related temporal interest changes need to be considered in the concept. A state-of-the-art research on recommender systems is conducted to evaluate suitable models for solving both challenges. The explorative data analysis shows that the article's month of pregnancy is an important indicator of how relevant an article is to a user. Neither collaborative filtering, content-based filtering, hybrid models, nor context-aware recommender systems are applicable because the user's pregnancy phase is unknown in the available data. Therefore, the proposed recommender system concept is a case-based model that recommends articles which belong to the same gestation phase as the currently viewed article.
This recommender system requires that the month of pregnancy, in which an article is relevant, is known for each article. However, this information is only available for 31% of all articles about pregnancy. Consequently, this work looks for an approach to predict the month of gestation based on the article text. The challenges with this are that only few training data are available, and the article texts of the various months of pregnancy often contain the same terms, considering all articles are about pregnancy. A keyword-based approach using the TF-IDF model is compared with a context-based approach using the BERT model. The results show that the context-based approach outperforms the keyword-based approach.
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 purpose of an energy model is to predict the energy consumption of a real system and to use this information to address challenges such as rising energy costs, emission reduction or variable energy availability. Industrial robots account for an important share of electrical energy consumption in production, which makes the creating of energy models for industrial robots desirable. Currently, energy modeling methods for industrial robots are often based on physical modeling methods. However, due to the increased availability of data and improved computing capabilities, data-driven modeling methods are also increasingly used in areas such as modeling and system identification of dynamic systems. This work investigates the use of current data-driven modeling methods for the creation of energy models focusing on the energy consumption of industrial robots.
For this purpose, a robotic system is excited with various trajectories to obtain meaningful data about the system behavior. This data is used to train different artificial neural network (ANN) structures, where the structures used can be categorized into (i) Long Short Term Memory Neural Network (LSTM) with manual feature engineering, where meaningful features are extracted using deeper insights into the system under consideration, and (ii) LSTM with Convolutional layers for automatic feature extraction. The results show that models with automatic feature extraction are competitive with those using manually extracted features. In addition to the performance comparison, the learned filter kernels were further investigated, whereby similarities between the manually and automatically extracted features could be observed. Finally, to determine the usefulness of the derived models, the best-performing model was selected for demonstrating its performance on a real use case.