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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.
Power plant operators increasingly rely on predictive models to diagnose and monitor their systems. Data-driven prediction models are generally simple and can have high precision, making them superior to physics-based or knowledge-based models, especially for complex systems like thermal power plants. However, the accuracy of data-driven predictions depends on (1) the quality of the dataset, (2) a suitable selection of sensor signals, and (3) an appropriate selection of the training period. In some instances, redundancies and irrelevant sensors may even reduce the prediction quality.
We investigate ideal configurations for predicting the live steam production of a solid fuel-burning thermal power plant in the pulp and paper industry for different modes of operation. To this end, we benchmark four machine learning algorithms on two feature sets and two training sets to predict steam production. Our results indicate that with the best possible configuration, a coefficient of determination of R^2 = 0.95 and a mean absolute error of MAE=1.2 t/h with an average steam production of 35.1 t/h is reached. On average, using a dynamic dataset for training lowers MAE by 32% compared to a static dataset for training. A feature set based on expert knowledge lowers MAE by an additional 32 %, compared to a simple feature set representing the fuel inputs. We can conclude that based on the static training set and the basic feature set, machine learning algorithms can identify long-term changes. When using a dynamic dataset the performance parameters of thermal power plants are predicted with high accuracy and allow for detecting short-term problems.
This thesis aims to support the product development process. Therefore, an approach is developed, implemented as a prototype and evaluated, for automated solution space exploration of formally predefined design automation tasks holding the product knowledge of engineers. For this reason, a classification of product development tasks related to the representation of the mathematical model is evaluated based on the parameters defined in this thesis. In a second step, the mathematical model should be solved. A Solver is identified able to handle the given problem class.
Due to the context of this work, System Modelling Language (SysML) is chosen for the product knowledge formalisation. In the next step the given SysML model has to be translated into an object-oriented model. This translation is implemented by extracting information of a ".xml"-file using the XML Metadata Interchanging (XMI) standard. The information contained in the file is structured using the Unified Modelling Language (UML) profile for SysML. Afterwards a mathematical model in MiniZinc language is generated. MiniZinc is a mathematical modelling language interpretable by many different Solvers. The generated mathematical model is classified related to the Variable Type and Linearity of the Constraints and Objective of the generated mathematical model. The output is stored in a ".txt"-file.
To evaluate the functionality of the prototype, time consumption of the different performed procedures is measured. This data shows that models containing Continuous Variables need a longer time to be classified and optimised. Another observation shows that the transformation into an object-oriented model and the translation of this model into a mathematical representation are dependent on the number of SysML model elements. Using MiniZinc resulted in the restriction that models which use non-linear functions and Boolean Expressions cannot be solved. This is because the implementation of non-linear Solvers at MiniZinc is still in the development phase. An investigation of the optimally of the results, provided by the Solvers, was left for further work.
The Digital Factory Vorarlberg is the youngest Research Center of Vorarlberg University of Applied Sciences. In the lab of the research center a research and learning factory has been established for educating students and employees of industrial partners. Showcases and best practice scenarios for various topics of digitalization in the manufacturing industry are demonstrated. In addition, novel methods and technologies for digital production, cloud-based manufacturing, data analytics, IT- and OT-security or digital twins are being developed. The factory comprises only a minimum core of logistics and fabrication processes to guarantee manageability within an academic setup. As a product, fidget spinners are being fabricated. A webshop allows customers to individually design their products and directly place orders in the factory. A centralized SCADA-System is the core data hub for the factory. Various data analytic tools and methods and a novel database for IoT-applications are connected to the SCADA-System. As an alternative to on premise manufacturing, orders can be pushed into a cloud-based manufacturing platform, which has been developed at the Digital Factory. A broker system allows fabrication in distributed facilities and offers various optimization services. Concepts, such as outsourcing product configuration to customers or new types of engineering services in cloud-based manufacturing can be explored and demonstrated. In this paper, we present the basic concept of the Digital Factory Vorarlberg, as well as some of the newly developed topics.
Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
A novel calorimetric technique for the analysis of gas-releasing endothermic dissociation reactions
(2020)
In engineering design, optimization methods are frequently used to improve the initial design of a product. However, the selection of an appropriate method is challenging since many
methods exist, especially for the case of simulation-based optimization. This paper proposes a systematic procedure to support this selection process. Building upon quality function deployment, end-user and design use case requirements can be systematically taken into account via a decision
matrix. The design and construction of the decision matrix are explained in detail. The proposed
procedure is validated by two engineering optimization problems arising within the design of box-type boom cranes. For each problem, the problem statement and the respectively applied optimization methods are explained in detail. The results obtained by optimization validate the use
of optimization approaches within the design process. The application of the decision matrix shows the successful incorporation of customer requirements to the algorithm selection.
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.
A rapid change to remote work during the beginning of the Covid-19 pandemic allowed many organizations to roll out new collaboration platforms to rapidly digitalize their workflows and processes in order to continue operation. This sudden shift to remote work revealed to employees the potential benefits of working remotely in the form of additional flexibility and also showed the challenges and barriers organizations could face by introducing such a strategy. This thesis aims to uncover the key considerations that the organizations of the industrial sector in Vorarlberg need to consider establishing a remote work strategy. According to the results from the research, the Covid-19 pandemic was as a paradigm change for the interviewed decision makers about how they thought about remote work and how they transformed their respective organizations too continue to operate. After the initial phase of Covid-19 restrictions organizations started to experiment with a remote work strategy of their own, based on their past experiences. For now, most of the interviewed organizations use already different remote work concepts and evaluate which one suits best their needs. The main considerations as to why an organization introduced a remote work strategy are to be an attractive employer and to stay ahead in the search for new talent. Further by introducing a remote work strategy, organizations need to change their rules of collaboration, adapt their core values to fit a remote workplace and to introduce collaboration platforms which are designed to support a remote workforce.
Creating a schedule to perform certain actions in a realworld environment typically involves multiple types of uncertainties. To create a plan which is robust towards uncertainties, it must stay flexible while attempting to be reliable and as close to optimal as possible. A plan is reliable if an adjustment to accommodate for a new requirement causes only a few disruptions. The system needs to be able to adapt to the schedule if unforeseen circumstances make planned actions impossible, or if an unlikely event would enable the system to follow a better path. To handle uncertainties, the used methods need to be dynamic and adaptive. The planning algorithms must be able to re-schedule planned actions and need to adapt the previously created plan to accommodate new requirements without causing critical disruptions to other required actions.
Scrum has been a prominent project management framework for managing software development projects. The scrum team embodies values such as commitment, focus, respect, courage, and openness to develop trust, which serves as the foundation of the scrum framework. However, in recent years, scrum teams are shifting towards a work-from-home environment which is relatively new to most of them and known to present various challenges. Looking at the benefits of adhering to scrum values, this study aims to investigate the challenges scrum teams experience in adhering to scrum values while operating virtually, as well as to explore practical strategies to overcome the identified challenges, particularly during the storming stage of team development. This research employed a qualitative methodology using semi-structured interviews with scrum team members who have experience working in a virtual environment. Through qualitative content analysis of semi-structured interviews, this research identifies significant challenges within five main categories: communication, collaboration, interpersonal dynamics, the virtual work environment, and personal workspace issues. However, beyond the challenges, the study reveals practical strategies as well for successful team dynamics and higher efficiency. The strategies derived from team members' experiences are categorized into six categories: enhanced meeting management, leveraging in-person engagements, optimizing tools & technology, effective communication strategies, team-building, and nurturing a positive work culture.
Traditional power grids are mainly based on centralized power generation and subsequent distribution. The increasing penetration of distributed renewable energy sources and the growing number of electrical loads is creating difficulties in balancing supply and demand and threatens the secure and efficient operation of power grids. At the same time, households hold an increasing amount of flexibility, which can be exploited by demand-side management to decrease customer cost and support grid operation. Compared to the collection of individual flexibilities, aggregation reduces optimization complexity, protects households’ privacy, and lowers the communication effort. In mathematical terms, each flexibility is modeled by a set of power profiles, and the aggregated flexibility is modeled by the Minkowski sum of individual flexibilities. As the exact Minkowski sum calculation is generally computationally prohibitive, various approximations can be found in the literature. The main contribution of this paper is a comparative evaluation of several approximation algorithms in terms of novel quality criteria, computational complexity, and communication effort using realistic data. Furthermore, we investigate the dependence of selected comparison criteria on the time horizon length and on the number of households. Our results indicate that none of the algorithms perform satisfactorily in all categories. Hence, we provide guidelines on the application-dependent algorithm choice. Moreover, we demonstrate a major drawback of some inner approximations, namely that they may lead to situations in which not using the flexibility is impossible, which may be suboptimal in certain situations.
Alleviating the curse of dimensionality in minkowski sum approximations of storage flexibility
(2023)
Many real-world applications require the joint optimization of a large number of flexible devices over some time horizon. The flexibility of multiple batteries, thermostatically controlled loads, or electric vehicles, e.g., can be used to support grid operations and to reduce operation costs. Using piecewise constant power values, the flexibility of each device over d time periods can be described as a polytopic subset in power space. The aggregated flexibility is given by the Minkowski sum of these polytopes. As the computation of Minkowski sums is in general demanding, several approximations have been proposed in the literature. Yet, their application potential is often objective-dependent and limited by the curse of dimensionality. In this paper, we show that up to 2d vertices of each polytope can be computed efficiently and that the convex hull of their sums provides a computationally efficient inner approximation of the Minkowski sum. Via an extensive simulation study, we illustrate that our approach outperforms ten state-of-the-art inner approximations in terms of computational complexity and accuracy for different objectives. Moreover, we propose an efficient disaggregation method applicable to any vertex-based approximation. The proposed methods provide an efficient means to aggregate and to disaggregate typical battery storages in quarter-hourly periods over an entire day with reasonable accuracy for aggregated cost and for peak power optimization.
Bubble column humidifiers (BCHs) are frequently used for the humidification of air in various water treatment applications. A potential but not yet profoundly investigated application of such devices is the treatment of oily wastewater. To evaluate this application, the accumulation of an oil-water emulsion using a BCH is experimentally analyzed. The amount of evaporating water vapor can be evaluated by measuring the humidity ratio of the outlet air. However, humidity measurements are difficult in close to saturated conditions, as the formation of liquid droplets on the sensor impacts the measurement accuracy. We use a heating section after the humidifier, such that no liquid droplets are formed on the sensor. This enables us a more accurate humidity measurement. Two batch measurement runs are conducted with (1) tap water and (2) an oil-water emulsion as the respective liquid phase. The humidity measurement in high humidity conditions is highly accurate with an error margin of below 3 % and can be used to predict the oil concentration of the remaining liquid during operation. The measured humidity ratio corresponds with the removed amount of water vapor for both tap water and the accumulation of an oil-water emulsion. Our measurements show that the residual water content
in the oil-water emulsion is below 4 %.
Vast amounts of oily wastewater are byproducts of the petrochemical and the shipping industry and to this day frequently discharged into water bodies either without or after insufficient treatment. To alleviate the resulting pollution, water treatment processes are in great demand. Bubble column humidifiers (BCHs) as part of humidification–dehumidification systems are predestined for such a task, since they are insensitive to different feed liquids, simple in design and have low maintenance requirements. While humidification in a bubble column has been investigated plentiful for desalination, a systematic investigation of oily wastewater treatment is missing in literature. We filled this gap by analyzing the treatment of an oil–water emulsion experimentally to derive recommendations for future design and operation of BCHs. Our humidity measurements indicate that the air stream is always saturated after humidification for a liquid height of only 10 cm. A residual water mass fraction of 3.5 wt% is measured after a batch run of six hours. Furthermore, continuous measurements show that an increase in oil mass fraction leads to a decrease in system productivity especially for high oil mass fractions. This decrease is caused by the heterogeneity of the liquid temperature profile. A lower liquid height mitigates this heterogeneity, therefore decreasing the heat demand and improving the overall efficiency. The oil content of the produced condensate is below 15 ppm, allowing discharge into various water bodies. The results of our systematic investigation prove suitability and indicate a strong future potential for the use of BCHs in oily wastewater treatment.
An implementation approach of the gap navigation tree using the TurtleBot 3 Burger and ROS Kinetic
(2020)
The creation of a spatial model of the environment is an important task to allow the planning of routes through the environment. Depending on the number of sensor inputs different ways of creating a spatial environment model are possible. This thesis introduces an implementation approach of the Gap Navigation Tree which is aimed for usage with robots that have a limited amount of sensors. The Gap Navigation Tree is a tree structure based on depth discontinuities constructed from the data of a laser scanner. Using the simulated TurtleBot 3 Burger and ROS kinetic a framework is created that implements the theory of the Gap Navigation Tree. The framework is structured in a way that allows using different robots with different sensor types by separating the detection of depth discontinuities from the building and updating of the Gap Navigation Tree.
Skiing is one of the most popular winter sports in the world and especially in the alps. As the skiers enjoy their time on the slopes the most annoying thing that could happen is long waiting times at a lift. Unfortunately, because of climate changes, this happens more regularly because smaller skiing areas at lower altitudes have to close and the number of good skiing days decreases as well. This leads to a increase in the number of skiers in the skiing areas which inevitably leads to longer waiting times and dissatisfied skiers. To prevent this from happening, the carriers of the skiing areas have to manage the skiers flow and distribution and what better way to analyse the current situation and possible changes then by simulating the whole area. A simulation has the advantage of being flexible with regards to time as well as configuration. Be it simulating a skiing day and look into detail of the behaviour of a single skier and how it moves in the area by simulating in real time or setting the focus to the whole area and find out when and where queues are forming throughout the whole day by speeding up the time and simulate the day in only seconds, everything is possible. Even simulating a scenario where some part of the area is closed and the skiers cannot take specific lifts due to some technical error or some slopes because of to less snow. By simulating and analysing all these scenarios not only does the experts of the skiing area gain valuable statistical information about the area but can also simulate changes to the system like a crowd fl ow control or an increase or decrease in capacity of a lift. The simulation built in context with this work for the skiing area of Mellau shows all those applications but can also be used as a basis for further improvements of the skiing area or be expanded to other areas like Damüls. The simulation was implemented using the Anylogic simulation environment and the statistical evaluation was also performed in this program.
This master’s thesis provides an overview of a more efficient, future-oriented living concept in Dornbirn, Austria. The use of a combined heat and power unit (CHP), in combination with a thermal storage, as a heating system is specifically investigated. In order to make this heating system more attractive for the consumer, the sale of the generated electricity from the CHP is considered. The more efficient use of energy for heating increases the attractiveness by a minimisation of the living space. This master’s thesis aims to draw attention to the issue and to achieve a rethinking in the planning of future living space. For the research and elaboration of this thesis, statistics and trustworthy literature were used, and physical modelling was applied. This Master’s thesis can be assigned to the fields of energy technology, mechatronics, architecture and civil engineering. It contributes for students, researchers, and other interested person in these sectors.
Activation of heat pump flexibilities is a viable solution to support balancing the grid via Demand Side Management measures and fulfill the need for flexibility options. Aggregators as interface between prosumers, distribution system operators and balance responsible parties face the challenge due to data privacy and technical restrictions to transform prosumer information into aggregated available flexibility to enable trading thereof. Thereby, literature lacks a generic, applicable and widely accepted flexibility estimation method for heat pumps,which incorporates reduced sensor and system information, system- and demand-dependent behaviour. In this paper, we adapt and extend a method from literature, by incorporating domain knowledge to overcome reduced sensor and system information. We apply data of five real-world heat pump systems, distinguish operation modes, estimate power and energy flexibility of each single heat pump system, proof transferability of the method, and aggregate the flexibilities available to showcase a small HP pool as a proof of concept.
The demand for managing data across multiple domains for product creation is steadily increasing. Model-Driven Systems Engineering (MDSE) is a solution for this problem. With MDSE, domain-specific data is formalized inside a model with a custom language, for example, the Unified Modelling Language (UML). These models can be created with custom editors, and specialized domains can be integrated with extensions to UML, e.g., the Systems Modeling Language (SysML). The most dominant editor in the open-source sector is Eclipse Papyrus SysML 1.6 (Papyrus), an editor to create SysML diagrams for MDSE.
In the pursuit of creating a model and diagrams, the editor does not support the user appropriately or even hinders them. Therefore, paradigms from the diagram modelling and Human Computer Interaction (HCI) domains, as well as perceptual and design theory, are applied to create an editor prototype from scratch. The changes fall into the categories of hierarchy, aid in the diagram composition, and navigation. The prototype is compared with Papyrus in a user test to determine if the changes have the effect of improving usability.
The study involved 10 participants with different knowledge levels of UML, ranging from beginners to experts. Each participant was tested on a navigation and modelling task in both the newly created editor, named Modelling Studio, and Papyrus. The study was evaluated through a questionnaire and analysis of the diagrams produced by the tasks.
The findings are that Modelling Studio’s changes to the hierarchical elements improved their rating. Furthermore, aid for diagram composition could be reinforced by changes to the alignment helper tool and adjustments to the default arrow behaviour of a diagram. Lastly, model navigation adjustments improve a link’s visibility and rating of a specialized link (best practice). The introduction of breadcrumbs had limited success in bettering navigation usability. The prototype deployed a broad spectrum of changes that found improvement already, which can, however, be further improved and tested more thoroughly.
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.
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?
Companies develop and implement strategies with the aim to address the needs of their customers. Acquisition is one market expansion strategy that companies can use to acquire new market access, technologies and/or to grow organically. In recent years, Chinese companies have been active in acquiring companies all over the globe to develop their strategic position. This caused certain contra reaction in Europe and as well in the Swiss media against cross-border acquisitions of Swiss companies.
Swiss companies and particularly the Swiss-MEM (Machinery, Electrical and Mechanical) industry is highly export oriented and their value proposition builds on attributes like knowledge, technology, and differentiating products. Among them are many “hidden champions” and niche players who successfully dominate the market segment.
As observed with Chinese companies, Indian companies also started to become more active outside of their domestic markets by increasing their foreign direct investments into Europe, Asia and North America, over the last decades. The lasting and good relationship of India and Switzerland might trigger the wish for Indian companies to acquire Swiss and particularly Swiss-MEM companies for acquisitions.
This Master’s Thesis assesses how often Indian investments into public and privately owned Swiss-MEM companies by acquisition happen, how are the attempts of acquisitions perceived by the stakeholders and what measures Swiss and Swiss-MEM companies can take, to protect themselves from being acquired. To access the research topic, several sub-questions will be analysed with the aid of primary and secondary research to assess the situation.
The research topic is of particular interested to the author since he spent over 20 years working in the Swiss-MEM industry, involved in international affairs and in recent years specifically with India. The observation of Chinese acquisition activities and insight into the size and potential of India were the drivers for researching whether India might follow China’s example.
In conclusion, Indian companies are not explicitly targeting Swiss and Swiss-MEM companies, but there are reasons to believe that it would make sense for Indian companies to look into the acquisition of Swiss and Swiss-MEM companies. The perception of such acquisitions varies, but there are arguments for and against them. Companies must take strategic and organisational measures in order to prevent themselves from becoming the target of an acquisition. However, it is known that the state should not interfere in the market and a discussion at a political level, planning how to deal with cross-border acquisition, is needed.
Further areas for research based on this Master’s Thesis could be the review of how the targeting of Swiss and Swiss-MEM companies by Indian companies would look, and also the topic of the succession planning in Swiss secondary sector in conjunction with Indian targeting for acquisitions. A third area to research might be investigating the political aspects involved in the research questions.
The boom of information technology development created high demand for skilled labour force in IT occupations. IT professionals install, test, build, repair or maintain hardware and software and can do the job from any location in the world.
Demand for the workforce significantly outstrips the global supply. In a situation of staff shortage employers have to compete on local and global labour markets. The ability of a firm to attract and retain the best talent would become a source of its sustainable competitive advantage.
Aim of the study is to understand what influences perception of employment attractiveness by IT professionals the most. This study intends to expend the existing knowledge about employees´ needs and “psychological contract” concept.
The research was conducted with the participation of 4 IT and 4 HR English-speaking experts who live and work in Austria. In the study the grounded theory approach and the descriptive qualitative methods were applied.
The research findings explain which factors influence the decision of IT professionals to join, stay or leave an employer. The results are discussed in relation to talent attraction and retention practices of Austrian employers.
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.
Pooled data from published reports on infants with clinically diagnosed vitamin B12 (B12) deficiency were analyzed with the purpose of describing the presentation, diagnostic approaches, and risk factors for the condition to inform prevention strategies. An electronic (PubMed database) and manual literature search following the PRISMA approach was conducted (preregistration with the Open Science Framework, accessed on 15 February 2023). Data were described and analyzed using correlation analyses, Chi-square tests, ANOVAs, and regression analyses, and 102 publications (292 cases) were analyzed. The mean age at first symptoms (anemia, various neurological symptoms) was four months; the mean time to diagnosis was 2.6 months. Maternal B12 at diagnosis, exclusive breastfeeding, and a maternal diet low in B12 predicted infant B12, methylmalonic acid, and total homocysteine. Infant B12 deficiency is still not easily diagnosed. Methylmalonic acid and total homocysteine are useful diagnostic parameters in addition to B12 levels. Since maternal B12 status predicts infant B12 status, it would probably be advantageous to target women in early pregnancy or even preconceptionally to prevent infant B12 deficiency, rather than to rely on newborn screening that often does not reliably identify high-risk children.
With the rise of people wearing smartwatches and the ever-lasting issue of stress, there has been an interest in detecting stress with wearables in real-time. This allows for interventions that take place exactly when stress occurs. However, many situations require all of our attention, making them unsuitable for any interventions. Additionally, many approaches currently do not factor in this aspect, running the risk of offering users undesirable interventions.
This thesis examines how contextual user information can be incorporated into a stress intervention system to reduce undesirable intervention timings. The system is split into detecting stress using heart rate variability (HRV) metrics obtained from a photoplethysmography (PPG) signal, and inferring user context from available sensor data. It is evaluated with a simulation-based approach using daily schedules of created personas and randomly sampled stressors during daily life.
The results obtained indicate the benefit of adding contextual user information to a stress intervention system. Depending on the busyness of the schedule, it can greatly decrease the number of received interventions. However, as these findings are attained without performing a user testing, it is unclear how they compare to results from real-world usage.
The production of liquid-gas dispersions places high demands on the process technology, which requires knowledge of the bubble formation mechanisms, as well as the phase parameters of the media combinations used. To obtain the bubble sizes introduced to a flow not knowing the phase parameters, different process parameters are investigated. Their quality and applicability are evaluated. The results obtained make it possible to simplify long design processes of dispersion processes in manufacturing plants and to ensure the product quality of the products manufactured, by reducing waste.
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.
In recent years, much research has been done on medical laser applications inside the human body, as they are minimally invasive and therefore have fewer side effects and are less expensive than conventional therapies. In order to bring the laser light into the human body, a glass fibre with a diffuser is needed. The goal of this master thesis is the characterization and production of fibre optic diffusers that can be used for the three therapeutic applications: photodynamic therapy, laser-induced thermotherapy and endovenous laser therapy. For this purpose the following goals have to be achieved:
- Optimization of the efficiency and homogeneity of internally structured diffusers
- Examine damage thresholds of the diffusers in the tissue using a crash test
- Achieving a better understanding of the decouple mechanism with a simulation
Using an ultra-short pulse laser, modifications could be introduced into the fibre in this way that the radiation profile is homogeneous and the decoupling efficiency is 68.3 %. It was discovered that the radiation profile depends on the wavelength. Attempts have been made to improve the decoupling efficiency by mirroring the distal end of the fibre. The mirror reflects the remaining light back into the fibre, so that it is also decoupled lateral on the modifications. Vapor-deposited aluminum with physical vapor deposition is a promising approach. However, the adhesion of the coating must be improved or the coating must be protected by a mechanical cover, otherwise it will flake off too quickly.
In a crash test, it was shown that the glass fibre diffusers can withstand 20 W laser power for 300 s without visible change. In an ex vivo test, the coagulation zone in the tissue was examined and it was showed that the diffusers radiate radially homogeneously. Using a ray trace simulation, the course of the light rays in the fibre was examined and the correlation of modification width and length with the decoupling efficiency was investigated. It was discovered that there are helical light rays in the fibre, which cannot be decoupled by modifications in the fibre centre.
Bubble columns are recently used for the humidification of air in water treatment systems and fuel cells. They are well applicable due to their excellent heat and mass transfer and their low technical complexity. To design and operate such devices with high efficiency, the humidification process and the impact of the operating parameters need to be understood to a sufficient degree. To extend this knowledge, we use a refined and novel method to determine the volumetric air–liquid heat and mass transfer coefficients and the humidifier efficiency for various parametric settings. The volumetric transfer coefficients increase with both of the superficial air velocity and the liquid temperature. It is further shown that the decrease of vapor pressure with an increase of the salinity results in a corresponding decrease in the outlet humidity ratio. In contrast to previous studies, liquid heights smaller than 0.1 m are investigated and significant changes in the humidifier efficiency are seen in this range. We present the expected humidifier efficiency with respect to the superficial air velocity and the liquid height in an efficiency chart, such that optimal operating conditions can be determined. Based on this efficiency chart, recommendations for industrial applications as well as future scientific challenges are drawn.
Gas hydrates are usually synthesized by bringing together a pressurized gas and liquid or solid water. In both cases, the transport of gas or water to the hydrate growth site is hindered once an initial film of hydrate has grown at the water–gas interface. A seemingly forgotten gas-phase technique overcomes this problem by slowly depositing water vapor on a cold surface in the presence of the pressurized guest gas. Despite being used for the synthesis of low-formation-pressure hydrates, it has not yet been tested for hydrates of CO 2 and CH 4 . Moreover, the potential of the technique for the study of hydrate decomposition has not been recognized yet. We employ two advanced implementations of the condensation technique to form hydrates of CO 2 and CH 4 and demonstrate the applicability of the process for the study of hydrate decomposition and the phenomenon of self-preservation. Our results show that CO 2 and CH 4 hydrate samples deposited on graphite at 261–265 K are almost pure hydrates with an ice fraction of less than 8%. Rapid depressurization experiments with thin deposits (approx. 330 mm thickness) of CO 2 hydrate on an aluminum surface at 265 K yield identical dissociation curves when the deposition is done at identical pressure. However, hydrates deposited at 1 MPa almost completely withstand decomposition after rapid depressurization to 0.1 MPa, while samples deposited at 2 MPa decompose 7 times faster. Therefore, this synthesis technique is not only applicable for the study of hydrate decomposition but can also be used for the controlled deposition of a super-preserved hydrate.
In times of global climate change, it is increasingly important to investigate emissions and resource consumption of all machines and, if possible, to improve them. This includes within the transport sector car ferries.
In order to reduce the environmental impacts of car ferries, the electrification has penetrated into this sector, which has led to the world's first fully electric car ferry. One of the most important components to operate this ferry is the energy storage. Not only the battery storage of the ferry itself is needed, but also an onshore battery storage system is needed to support the electrical grid.
The present study examines how storage technologies and concepts can impact the environment considering the world's first all-electric car ferry, MF Ampere, which operates in Norway.
To examine this, the current onshore battery storage system is compared to a concrete sphere storage system. For this purpose, data from the first test run of this new storage technology, which was successfully carried out by the Fraunhofer Institute in 2016, is considered. Subsequently, a life cycle assessment of the two storage systems is carried out to compare the environmental impacts.
The concrete sphere storage system performs better for 15 of 17 impact categories compared to the existing onshore battery storage system. Depending on the impact category the impact reduction is about 2% to 8%.
Nevertheless, it is difficult to estimate how long the useful life and how good the efficiency of the concrete ball storage will be, since no system of this size has been tested yet. Also, the costs of the concrete sphere storage system have not been considered.
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.
This master thesis investigates a Computational Intelligence-based method for solving PDEs. The proposed strategy formulates the residual of a PDE as a fitness function. The solution is approximated by a finite sum of Gauss kernels. An appropriate optimisation technique, in this case JADE, is deployed that searches for the best fitting parameters for these kernels. This field is fairly young, a comprehensive literature research reveals several past papers that investigate similar techniques.
To evaluate the performance of the solver, a comprehensive testbed is defined. It consists of 11 different Poisson equations. The solving time, the memory consumption and the approximation quality are compared to the state of the art open-source Finite Element solver NGSolve. The first experiment tests a serial JADE. The results are not as good as comparable work in the literature. Further, a strange behaviour is observed, where the fitness and the quality do not match. The second experiment implements a parallel JADE, which allows to make use of parallel hardware. This significantly speeds up the solving time. The third experiment implements a parallel JADE with adaptive kernels. It starts with one kernel and introduce more kernels along the solving process. A significant improvement is observed on one PDE, that is purposely built to be solvable. On all other testbed PDEs the quality-difference is not conclusive. The last experiment investigates the discrepancy between the fitness and the quality. Therefore, a new kernel is defined. This kernel inherits all features of the Gauss kernel and extends it with a sine function. As a result, the observed inconsistency between fitness and quality is mitigated.
The thesis closes with a proposal for further investigations. The concepts here should be reconsidered by using better performing optimisation algorithms from the literature, like CMA-ES. Beyond that, an adaptive scheme for the collocation points could be tested. Finally, the fitness function should be further examined.
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.
The rapidly evolving nature of Industry 4.0 has confronted corporates with the challenge of being able to react rapidly and nimbly (Van Solingen, 2020). Hence, many corporates need to embark on a journey of adaptation toward becoming agile organisations (Schmitz, 2018). However, this adaptation can only be achieved if employees fully commit to changing to an agile posture, and the required commitment is simply not forthcoming without proper corporate initiatives (Neves & Caetano, 2009). As there is no holistic summary of corporate initiatives required to boost employees' commitment to change when approaching an agile transformation, this study supplements the current research. The initiatives are derived from the existing literature and from unique insights given into a European automotive supplier that is currently managing a global agile transformation. Employees’ perceptions of the transformation in Austria and China were recorded and conclusions regarding what drives employees’ commitment to change and what led to job terminations were determined.
This paper sought to identify and analyze what are the barriers towards women career’s development as business leaders in Brazil and Nicaragua when it comes to the country societal variables. In order to comprehend these barriers through women’s perception, qualitative data was chosen for this investigation, which was gathered through one-to-one interviews within businesswomen from Brazil and Nicaragua that have experience in leadership positions. The results of this research confirm that societal, economic, and political factors have great influence at gender equality and in how it affects women’s progress as business leaders. Thus, it can vary considerably between countries, even when they have similar culture backgrounds. Furthermore, it is imperative to comprehend these differences in order to close any gender gap in the field.
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.
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 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.
In this thesis the effect of dc voltage bias on the equivalent series resistance (ESR) of capacitors and especially ferroelectric dielectric ceramic capacitors (FDCC) is analysed. Further the influence of the dc biased ESR on the losses of capacitors is investigated. Also piezoelectric resonances (PR) occurring in FDCCs with applied dc bias and their influence on the losses are analysed.
Therefore a measurement circuit to measure the impedance and thus the ESR of capacitors in combination with a vector network analyser (VNA) is developed. Using the developed circuit the ESR of capacitors of different technologies is measured and their behaviour with dc bias is evaluated. The losses of an FDCC are measured in a power electronic (PE) circuit with a developed calorimetric measurement system (CMS). The influence of the PR is investigated by tuning the switching frequency of the PE system and thus the frequency of the capacitor current exactly into the PR. The measured losses are then compared to a calculation based on the capacitor current harmonics and the respective ESR.
The measurements show an increase of the ESR with dc bias for all measured FDCCs. The loss measurements show a significant increase of the losses in an FDCC if the current frequency matches the PR frequency. Consequently a decrease of the PE system's efficiency is measured. The loss calculations do not exactly match the measurements but there is a systematic deviation of the same order for all measurements.
This paper presents a project developed at the K.S.Rangasamy College of Technology (Tamilnadu,India) aimed at designing, implementing, and testing an autonomous multipurpose vehicle with safe, efficient, and economic operation. This autonomous vehicle moves through the crop lines of a Agricultural land and performs tasks that are tedious and/or hazardous to the farmers. First, it has been equipped for spraying, but other configurations have also been designed, such as: a seeding ,plug platform to reach the top part of the plants to perform different tasks (pruning, harvesting, etc.), and a trailer to transport the fruits, plants, and crop waste.
Design and optimization of 1x2N Y-branch optical splitters for telecommunication applications
(2020)
This paper presents the design and optimization of 1x2N Y-branch optical splitters for telecom applications. A waveguide channel profile, used in the splitter design, is based on a standard silica-on-silicon material platform. Except for the lengths of the used Y-branches, design parameters such as port pitch between the waveguides and simulation parameters for all splitters were considered fixed. For every Y-branch splitter, insertion loss, non-uniformity, and background crosstalk are calculated. According to the minimum insertion loss and minimum non-uniformity, the optimum length for each Y-branch is determined. Finally, the individual Y-branches are cascade joined to design various Y-branch optical splitters, from 1x2 to 1x64.