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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.
Modern portable electronic devices have seen component heat load increasing, while the space available for heat dissipation has decreased. This requires the thermal management system to be optimized to attain the high performance heat sink. Heat sinks plays a major role for dissipating heat in electronic devices. Phase change material (PCM) is used to enhance the heat dissipation in heat sink. This paper reports the results of an experimental investigation of the performance of Pin fin heat sinks filled with phase change materials for thermal management of electronic devices. The experimental set ups are prepared with the graphical programming language with Lab VIEW (Laboratory Virtual Instruments for Engineering Workbench. Three different types of Pin fin Heat sink with and without PCM are investigated based on different operational timings and the temperature is acquired with the help of Data Acquisition Card (DAQ). The results indicated that the inclusion of the PCM could stabilize the temperature for a longer period and reduce the heating rates and peak temperatures of heat sink with increasing the number of fins can enhance the thermal performance of electronic devices.
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.
Highly-sensitive single-step sensing of levodopa by swellable microneedle-mounted nanogap sensors
(2023)
Microneedle (MN) sensing of biomarkers in interstitial fluid (ISF) can overcome the challenges of self-diagnosis of diseases by a patient, such as blood sampling, handling, and measurement analysis. However, the MN sensing technologies still suffer from poor measurement accuracy due to the small amount of target molecules present in ISF, and require multiple steps of ISF extraction, ISF isolation from MN, and measurement with additional equipment. Here, we present a swellable MN-mounted nanogap sensor that can be inserted into the skin tissue, absorb ISF rapidly, and measure biomarkers in situ by amplifying the measurement signals by redox cycling in nanogap electrodes. We demonstrate that the MN-nanogap sensor measures levodopa (LDA), medication for Parkinson disease, down to 100 nM in an aqueous solution, and 1 μM in both the skin-mimicked gelatin phantom and porcine skin.
Organic acidurias (OAs), urea-cycle disorders (UCDs), and maple syrup urine disease (MSUD) belong to the category of intoxication-type inborn errors of metabolism (IT-IEM). Liver transplantation (LTx) is increasingly utilized in IT-IEM. However, its impact has been mainly focused on clinical outcome measures and rarely on health-related quality of life (HRQoL). Aim of the study was to investigate the impact of LTx on HrQoL in IT-IEMs. This single center prospective study involved 32 patients (15 OA, 11 UCD, 6 MSUD; median age at LTx 3.0 years, range 0.8–26.0). HRQoL was assessed pre/post transplantation by PedsQL-General Module 4.0 and by MetabQoL 1.0, a specifically designed tool for IT-IEM. PedsQL highlighted significant post-LTx improvements in total and physical functioning in both patients' and parents' scores. According to age at transplantation (≤3 vs. >3 years), younger patients showed higher post-LTx scores on Physical (p = 0.03), Social (p < 0.001), and Total (p =0.007) functioning. MetabQoL confirmed significant post-LTx changes in Total and Physical functioning in both patients and parents scores (p ≤ 0.009). Differently from PedsQL, MetabQoL Mental (patients p = 0.013, parents p = 0.03) and Social scores (patients p = 0.02, parents p = 0.012) were significantly higher post-LTx. Significant improvements (p = 0.001–0.04) were also detected both in self- and proxy-reports for almost all MetabQoL subscales. This study shows the importance of assessing the impact of transplantation on HrQoL, a meaningful outcome reflecting patients' wellbeing. LTx is associated with significant improvements of HrQol in both self- and parentreports. The comparison between PedsQL-GM and MetabQoL highlighted that MetabQoL demonstrated higher sensitivity in the assessment of diseasespecific domains than the generic PedsQL tool.
Long-Term outcome of infantile onset pompe disease patients treated with enzyme replacement therapy
(2024)
Background: Enzyme replacement therapy (ERT) with recombinant human alglucosidase alfa (rhGAA) was approved in Europe in 2006. Nevertheless, data on the long-term outcome of infantile onset Pompe disease (IOPD) patients at school age is still limited.
Objective: We analyzed in detail cardiac, respiratory, motor, and cognitive function of 15 German-speaking patients aged 7 and older who started ERT at a median age of 5 months.
Results: Starting dose was 20 mg/kg biweekly in 12 patients, 20 mg/kg weekly in 2, and 40 mg/kg weekly in one patient. CRIM-status was positive in 13 patients (86.7%) and negative or unknown in one patient each (6.7%). Three patients (20%) received immunomodulation. Median age at last assessment was 9.1 (7.0–19.5) years. At last follow-up 1 patient (6.7%) had mild cardiac hypertrophy, 6 (42.9%) had cardiac arrhythmias, and 7 (46.7%) required assisted ventilation. Seven patients (46.7%) achieved the ability to walk independently and 5 (33.3%) were still ambulatory at last follow-up. Six patients (40%) were able to sit without support, while the remaining 4 (26.7%) were tetraplegic. Eleven patients underwent cognitive testing (Culture Fair Intelligence Test), while 4 were unable to meet the requirements for cognitive testing. Intelligence quotients (IQs) ranged from normal (IQ 117, 102, 96, 94) in 4 patients (36.4%) to mild developmental delay (IQ 81) in one patient (9.1%) to intellectual disability (IQ 69, 63, 61, 3x < 55) in 6 patients (54.5%). White matter abnormalities were present in 10 out of 12 cerebral MRIs from 7 patients.
Measuring what matters
(2023)
Patient reported outcomes (PROs) are generally defined as ‘any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else’. A broader definition of PRO also includes ‘any information on the outcomes of health care obtained directly from patients without modification by clinicians or other health care professionals’. Following this approach, PROs encompass subjective perceptions of patients on how they function or feel not only in relation to a health condition but also to its treatment as well as concepts such as health-related quality of life (HrQoL), information on the functional status of a patient, signs and symptoms and symptom burden. PRO measurement instruments (PROMs) are mostly questionnaires and inform about what patients can do and how they feel. PROs and PROMs have not yet found unconditional acceptance and wide use in the field of inborn errors of metabolism. This review summarises the importance and usefulness of PROs in research, drug legislation and clinical care and informs about quality standards, development, and potential methodological shortfalls of PROMs. Inclusion of PROs measured with high-quality, well-selected PROMs into clinical care, drug legislation, and research helps to identify unmet needs, improve quality of care, and define outcomes that are meaningful to patients. The field of IEM should open to new methodological approaches such as the definition of core sets of variables including PROs to be systematically assessed in specific metabolic conditions and new collaborations with PRO experts, such as psychologists to facilitate the systematic collection of meaningful data.
X-ray microtomography is a nondestructive, three-dimensional inspection technique applied across a vast range of fields and disciplines, ranging from research to industrial, encompassing engineering, biology, and medical research. Phasecontrast imaging extends the domain of application of x-ray microtomography to classes of samples that exhibit weak attenuation, thus appearing with poor contrast in standard x-ray imaging. Notable examples are low-atomic-number materials, like carbon-fiber composites, soft matter, and biological soft tissues.We report on a compact and cost-effective system for x-ray phase-contrast microtomography. The system features high sensitivity to phase gradients and high resolution, requires a low-power sealed x-ray tube, a single optical element, and fits in a small footprint. It is compatible with standard x-ray detector technologies: in our experiments, we have observed that single-photon counting offered higher angular sensitivity, whereas flat panels provided a larger field of view. The system is benchmarked against knownmaterial phantoms, and its potential for soft-tissue three-dimensional imaging is demonstrated on small-animal organs: a piglet esophagus and a rat heart.We believe that the simplicity of the setupwe are proposing, combined with its robustness and sensitivity, will facilitate accessing quantitative x-ray phase-contrast microtomography as a research tool across disciplines, including tissue engineering, materials science, and nondestructive testing in general.
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.
Grey Box models provide an important approach for control analysis in the Heating, Ventilation and Air Conditioning (HVAC) sector. Grey Box models consist of physical models where parameters are estimated from data. Due to the vast amount of component models that can be found in literature, the question arises, which component models perform best on a given system or dataset? This question is investigated systematically using a test case system with real operational data. The test case system consists of a HVAC system containing an energy recovery unit (ER), a heating coil (HC) and a cooling coil (CC). For each component, several suitable model variants from the literature are adapted appropriately and implemented. Four model variants are implemented for the ER and five model variants each for the HC and CC. Further, three global optimization algorithms and four local optimization algorithms to solve the nonlinear least squares system identification are implemented, leading to a total of 700 combinations. The comparison of all variants shows that the global optimization algorithms do not provide significantly better solutions. Their runtimes are significantly higher. Analysis of the models shows a dependency of the model accuracy on the number of total parameters.
The production of liquid-gas mixtures with desired properties still places high demands on process technology and is usually realized in bubble columns. The physical calculation models used have individual dimensionless factors which, depending on the application, are only valid for small ranges consisting of flow velocity, nozzle geometry and test setup. An iterative but time-consuming design of such dispersion processes is used in industry for producing a liquid-gas mixture according to desired requirements. In the present investigation, we accelerate the necessary design loops by setting up a physical model, which consists of several subsystems that are enriched by dedicated experiments to realize liquid-gas dispersions with low volume fraction and small air bubble diameters in oil. Our approach allows the extraction of individual dimensionless factors from maps of the introduced subsystems. These maps allow for targeted corrective measures of a production process for keeping the quality. The calculation-based approach avoids the need for performing iterative design loops. Overall, this approach supports the controlled generation of liquid-gas mixtures.
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.
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.
A model is presented that allows for the calculation of the success probability by which a vanilla Evolution Strategy converges to the global optimizer of the Rastrigin test function. As a result a population size scaling formula will be derived that allows for an estimation of the population size needed to ensure a high convergence security depending on the search space dimensionality.
Effective lead management
(2023)
In the last few years the global interest on lead management has increased. This classic topic for marketing and sales departments is aimed at converting potential customers into sales. The following thesis identifies the challenges and solutions for marketing and sales departments in order to process effective lead management. Using data from a literature review and qualitative empirical research, conducted with representatives of marketing and sales departments, the results showed overall and task specific challenges and solutions. The research indicates that overall challenges and solutions regarding the gap between marketing and sales, new processes and data management including data quality, software and silos emerge. In addition task specific challenges and solutions concerning lead generation including purchased leads, lead qualification, lead nurturing and sales specific challenges and solutions conclusively the focus on existing customers, time famine and lead routing were identified. This thesis provides a framework for further studies regarding the challenges and solutions for marketing and sales departments processing lead management.
This study aims to address the research gap surrounding the role of leadership in the formation of high-performance teams within startup companies. While there is existing research on high-performing teams, limited attention has been given to leadership in this environment. To bridge this gap, the study combines a literature review and qualitative analysis through semi-structured interviews with diverse stakeholders in startups, with the goal of providing practical guidance for startup executives based on the research findings. The study uncovers key aspects of leadership in high-performance teams, emphasizing the importance of skills such as motivation and support for team members, fostering psychological safety and trust, and effectively managing uncertainty. In addition to resource constraints and high expectations, the study sheds light on the challenges faced by leaders in startup and high-performance team environments, particularly the blurring of traditional leadership roles as team members seek autonomy and decision-making authority. These findings present opportunities for future research to explore this progressive leadership style. Overall, this study contributes to our understanding of leadership dynamics within high-performance teams operating in the context of startups. It offers valuable insights that can help startup executives navigate the complexities of leadership and foster the development of successful and high-performing teams.
In an oversaturated market, companies are required to use innovative and, above all, creative advertising methods to capture their customers’ attention, and thus differentiate themselves from rival businesses. To this end, companies have been increasingly relying on the use of humor, a phenomenon that remains highly subjective and is perceived differently by each individual. This master’s thesis, which was completed as part of the International Marketing and Sales program at the FH Vorarlberg, focuses on this phenomenon of humor as well as its impact on advertising perception. With the aid of three different theories, the term “humor” is defined. Furthermore, this study explains and researches the so-called vampire effect, wherein various factors (in this case humor) draw attention away from the actual advertising message. In addition, this thesis takes a closer look at involvement, as a person’s involvement or interest in a brand or product can influence brand and product recall and recognition. An online survey was conducted to determine whether the vampire effect caused by humor is able to influence brand and product recall. In other words, this concerns whether the viewer can still remember the brand and product afterward or whether the humor employed triggers the vampire effect. Furthermore, this thesis explored whether the vampire effect caused by humor is able to influence brand and product recognition. Recall is the retrieval of information from memory without direct cues, whereas recognition refers to the recognition of information when it is presented again. Furthermore, within this context, it was discovered that brand and product recall varies with low and high involvement viewers of the advertisement. In other words, this means that the strength of the vampire effect caused by humor changes depending on the strength of the viewer’s involvement. During the course of this research, it was further observed that the humor employed significantly affects the perception of the advertising message, thus confirming the existence of the vampire effect. This effect also influences both brand as well as product recall and recognition. In both cases, participants in the survey were less able to remember the product and brand in the humorous advertising. Furthermore, it was proven that people with low involvement in the advertised product group are more heavily affected by the vampire effect. As such, they are more likely to not remember the product or brand after seeing the advertisement.
The presented master thesis of the study subject International Management and Leadership at the University of Applied Science Vorarlberg in Dornbirn handles the potential future influence of the EU Corporate Sustainability Due diligence on SMEs. First this thesis introduces the most important regulations that might come into place with this Due Diligence Act and gives a theoretical input when and how it will come into place, and also who it will affect directly and who will be affected indirectly. The empirical data resulted of several qualitative expert interviews and a following quantitative research. The expert interviews are split in two different groups, first the topic experts from institutions like chamber of commerce or chamber of labour and second experts from highly successful Austrian companies which are already handling the topic and the future challenges. Expected outcome of the qualitative interviews was a better view on the actual situation especially the impact on small and medium enterprises. On the basis of this results the quantitative survey was produced. In the quantitative survey the goal was to see, how much entrepreneurs and companies in the small and medium sector already are aware of the upcoming legal challenges throughout the supply chain. With all this collected data the practical outcome of this thesis is the Checklist, which helps entrepreneurs to find out if and how much they will be affected by the Act. And finally, the most important part is the Guideline, which introduces first risk assessment tools, that will help companies to prepare for future legislation and bring undoubtedly a certain advantage for the upcoming challenges.
Although workplace climate has been already extensively studied, the research has not led to firm conclusions regarding leadership trainings referring to the awareness of psychological safety in a company and its influence on existing teams and the general work climate. The author used the already existing model of Carr, Schmidt, Ford, & DeShon (2003) and adjusted it with psychological safety as 4th climate item to develop hypothesen which can also be seen as a path analytic model. The model posied that climate affects individual level outcomes through its impact on cognitive and affective states. Therefore, the author wants to show the correlation between the 4 higher order facets of climate affect the individual levels of job performance, psychological well-being and withdrawal through their impact on orangizational commitment and job saitsfaction (Carr, Schmidt, Ford, & DeShon, 2003).
This thesis investigates the role of leadership behaviours of C-level executives in the context of post-M&A integration processes. The primary focus is on understanding the impact of specific leadership behaviours on inspiring desirable follower effects and facilitating emotional acceptance during organizational change. Drawing on the frameworks presented in “Six- Dimension Integrative Model of Leadership” and "The Six Domains of Leadership" developed by Sitkin et al., the study conducts expert interviews with managers from middle management who have recently experienced M&A integration. The answers are analysed in depth to identify the most effective leadership behaviours, highlighting those mentioned most frequently and those capable of triggering multiple follower effects simultaneously. The result is a list of behaviours that can serve as a guideline for C-level executives who want to foster desirable follower effects throughout the M&A integration journey.
The control measures for the COVID-19 pandemic, early 2020, caused a chain reaction that eventually led to a shortage of components in the electronic manufacturing industry. A lack of components meant that the production and sales were interrupted or even stopped. For many electronic manufacturing firms, this was seen as a crisis. A crisis is mostly divided into three phases called the pre-crisis phase, crisis management and post-crisis phase. The pre-crisis phase involves an environmental assessment and setting up of crisis management teams, and plan. The crisis management phase has to do with the collection and interpretation of information and the mitigation of the crisis. The post-crisis phase looks at learnings from the crisis. In this paper it was investigated how the electronic manufacturing firms in Vorarlberg managed the crisis in the period between 2020 and 2022. The overall aim was to get a full understanding of how it affected the operations regarding the respective crisis teams and which factors were considered most important for setting up the teams. Two basic criteria which had to be over-come was the uncertainty and lack of time. It was seen that even though the fundamental structure did not change, crisis teams were added in the form of a crisis management team and task forces. The task forces played a major role in getting an understanding of the problem and the effect it has on the business. The crisis management team, which includes high level managers from all affected functional areas, had to re-evaluate the high level strategy and decide what needs to be done, and who will be doing it. In order to do so, they needed to understand what the priorities are regarding components and products and then decide on the priorities regarding affected business. The new strategy was then handed down to the task forces for implementation. A major focus of this paper was also on decision making and how everything contributed to making decisions that had the right effect in resolving the financial crisis for the organizations.
Supply shortages faced in products and resources from semiconductors to natural gas in recent years have had impact massive on global economy, but such challenges are not new for supply chain professionals. Many major events in the past have disrupted supply chains: 9/11 attack in New York, Tsunami in Japan to name a few, but COVID19 have had the biggest and widespread impact in the modern times. Even though supply chain resilience being a term coined in early 2000’s, its usage and importance has increased since then. With the curiosity of assessing the current state of sup-ply chain resilience literature and finding a resilience measurement method which is a one-fit for all supply chains in the manufacturing industry of Vorarlberg, the following research project was undertaken. Research is carried out with mixed methods, using a systematic literature review followed by expert interviews. In the conclusion of the research the author argues that there is a significant difference in the understanding of the term resilience within industry, there is a lack on the need for a meas-ure for resilience. The ways in which the structure of an organization impacts the level of resilience, foreseen benefits of digitalization and technologies for resilience are also dis-cussed. A comparative analysis on the SCR measurement methods discovered in literature, resulted in recommending Resilience index for on-time delivery proposed by Carvalho et al for the mentioned industry.
Having autonomy in the workplace can have a positive impact on employees’ performance, which in turn can benefit the organization’s competitive advantages. While previous researches have primarily focused on the psychological effects of job autonomy on employee performance and has been limited to certain domains, the relationship between job autonomy and organizational design is an important area of study for organizations seeking to improve their competitiveness. This thesis proposes a conceptual model for designing an organization structure that promotes employee performance in manufacturing companies by removing obstacles towards obtaining job autonomy. The focus is on ambitious employees who seek growth and development opportunities within their organization. The model is based on a review of existing literature on job autonomy and organizational design. Exploratory qualitative research was conducted with selected ambitious employees from different industries by means of one-on-one semi-structured interviews. Overall, the proposed model has practical implications for manufacturing companies looking to motivate their employees, as well as for researchers seeking to advance their understanding of organizational design in our times.
The advent of autonomous and self-driving cranes represents a significant advancement in industrial automation. One critical prerequisites for achieving this long-term goal is the accurate and reliable detection of tools guided by ropes in real-world environments. Since the tool is suspended by ropes, the tool pose cannot be controlled directly. This master’s thesis addresses the challenges of pose estimation for rope-guided tools using point cloud measurements. The proposed algorithm utilizes constraints imposed by the crane kinematics and information extracted during the segmentation process to efficiently infer the pose of the hook, therefore enabling the use of the pose for decision making in real-time critical applications. RANSAC (Random Sample and Consensus) is deployed in the segmentation process to extract geometric primitives from the point cloud which represent the ropes and distinctive parts of the tool. Since the point cloud is often to sparse for feature matching a bounding box is used to estimate the initial position of the tool. Two different methods are presented to improve the initial pose. A computationally expensive method with a high level of confidence, integrating the ICP (Iterative Closest Point) algorithm is used as a benchmark. A linear Kalman filter is used in the second method which is real-time capable. The benchmark is then used to evaluate the real-time capable approach. The core contributions of this research lie in the innovative utilization of bounding boxes for pose estimation. The findings and methodologies presented herein constitute an advancement towards the realization of autonomous and self-driving cranes.
The Fast Average Current Mode control methodology is a novel method for the implementation of a current compensator in a switched-mode power supply. It does not require compensation against sub-harmonic instability and is inductor independent. In this work, the digital implementation of this topology is compared against an analog implementation using simulation. Additionally, a hardware prototype is created to validate the digital simulation's results. In a Simulink environment, parameters of the digital implementation, such as the digital-to-analog converter resolutions and the delay counter frequency are varied to research their impact on system performance. The simulations show that a digital current compensator has similar performance as an analog implementation when designed tailored to the application. When evaluating the whole control loop the digital system is inferior due to added delays caused by digital to analog conversion. By operating the Buck converter hardware implementation as a current source, the functionality of the current mode control implementation in a FPGA was proven. Voltage control cannot be validated due to hardware issues. Due to the successful simulation of the source code with a mixed signal model of the converter, it can be assumed that it is functional. Apart from performance, a digital implementation shows many benefits compared to an analog solution, such as configurability of control parameters and easy compensation of component variations and aging.
Recent years have been commanded by a cascade of unpredictable incidents, that have redefined new standards in our private, but also in our professional life. Events like the financial crisis, the COVID-19 pandemic, the energy crisis in Europe, resource scarcity and so forth have caused instability, forcing companies towards flexibility, constantly adapting their operative structures according to the needs of the moment. The effective adaptation to this environment is the key for reacting the dynamism of the market, and for guaranteeing future success. However, the introduction of these crucial changes on a stable company organisation is challenging. Furthermore, due to digitalisation, boundaries between countries have been removed, and the daily cooperation with co-workers and customers all around the globe became the new standard. The establishment of a good corporate culture where diverse people can work in harmony and, is a difficulty that comes ahead.
This master thesis developed from a professional perspective. The topics of change management and corporate culture where combined, and the relationship between these two concepts was studied. This master thesis aims utilising corporate culture as an instrument in managements favour, to implement strategical changes easily and successfully in a more efficient way. The relation between corporate culture and the resistance to change, focusing on the initiation of the change process, was the main area of study. Research questions and hypothesis, formulated with a solid theoretical background, are to be answered based firstly on literature, and secondly on the results of empirical quantitative re-search. To conclude, a set of recommendations for corporates were suggested with the intention of guiding companies how to use corporate culture as an instrument for change management.
This thesis focuses on implementing and testing communication over a private 5G standalone network in an industrial environment, with a specific emphasis on communication between two articulated robots. The main objective is to examine machine-to-machine communication behavior in various test scenarios. Initially, the 5G core and radio access network components are described, along with their associated interfaces, to establish foundational knowledge. Subsequently, a use case involving two articulated robots is implemented, and essential metrics are defined for testing, including round-trip time, packet and inter-packet delay, and packet error rate. The tests investigate the impact of 5G quality of service, packet size, and transmission interval on communication between the robots, focusing on the effects of network traffic. The results highlight the significance of prioritizing network resources based on the assigned quality of service identifier (5QI), demonstrate the influence of packet sizes on communication performance, and underscore the importance of transmission intervals for automation purposes. Additionally, the study examines how network disturbances influence the movements of a robot controlled via 5G, establishing a direct relationship between network metrics and the resulting deviations in the robot’s trajectory. The work concludes that while machine-to-machine communication can be successfully implemented with 5G SA, tradeoffs must be carefully considered, especially concerning packet error rate, and emphasizes the importance of understanding the required resources before implementation to ensure feasibility. Future research directions include investigating network slicing, secure remote control of robots, and exploring the use of higher frequency bands. The study highlights the significance of aligning theoretical standards with practical implementation options in the evolving landscape of 5G Networks.
Lack of transparency and traceability of products and their raw materials means that most products can only be thrown away or not properly recycled due to a lack of relevant data. This conflicts with the circular economy principles, which are demanded by several initiatives, including the European Union. The aim of this master thesis is to analyze this conflict and to propose a technical solution based on Distributed Ledger Technology that enables transparency and traceability of products and their materials. Therefore, the thesis addresses two central research questions: 1. How can traceability and transparency be enabled by integrating a DLT solution? 2. How would a prototype with the integration of smart contracts and DLT look like? To answer these questions, a blockchain solution is implemented using Hyperledger Fabric. The solution uses the immutability and decentralized nature of DLT to record and track the movement of products and their materials throughout their life cycle in the Circular Economy. Furthermore, with private data collections, confidentiality, and privacy are granted while ensuring transparency. The thesis contributes to the Circular Economy field by exploring the principles, models, and challenges of the Circular Economy and the circularity goals of a Digital Product Passport to develop a suitable technical solution. The chosen blockchain framework, Hyperledger Fabric, is presented, and its key components and features are highlighted. The thesis also delves into the design decisions and considerations behind the Digital Product Passport platform, explaining the architecture and transaction flow together with the prototype implementation and demonstration to showcase the functionality of the solution. Results and analysis provide insights into the challenges of the Circular Economy, sustainable resource management, and the Digital Product Passport, resulting in recommendations for future improvements and enhancements. Overall, this thesis offers a practical solution utilizing DLT to enable transparency and traceability in the Circular Economy, contributing to the realization of sustainable and efficient resource management practices to ultimately contribute to the set Circular Economy initiatives.
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.
This thesis evaluates the feasibility of conducting visual inspection tests on power industry constructions using object detection techniques. The introduction provides an overview of this field’s state-of-the-art technologies and approaches. For the implementation, a case study is then conducted, which is done in collaboration with the partner company OMICRON Electronics GmbH, focusing on power transformers as an example. The objective is to develop an inspection test using photographs to identify power transformers and their subcomponents and detect existing rust spots and oil leaks within these components. Three object detection models are trained: one for power transformers and sub-components, one for rust detection, and one for oil leak detection. The training process utilizes the implementation of the YOLOv5 algorithm on a Linux-based workstation with an NVIDIA Quadro RTX 4000 GPU. The power transformer model is trained on a dataset provided by the partner company, while open-source datasets are used for rust and oil leak detection. The study highlights the need for a more powerful GPU to enhance training experiments and utilizes an Azure DevOps Pipeline to optimize the workflow. The performance of the power transformer detection model is satisfactory but influenced by image angles and an imbalance of certain sub-components in the dataset. Multi-angle video footage is a proposed solution for the inspection test to address this limitation and increase the size of the dataset, focusing on reducing the imbalance. The models trained on open-source datasets demonstrate the potential for rust and oil leak detection but lack accuracy due to their generic nature. Therefore, the datasets must be adjusted with case-specific data to achieve the desired accuracy for reliable visual inspection tests. The results of the case study have been well-received by the partner company’s management, indicating future development opportunities. This case study will likely be a foundation for implementing visual inspection tests as a product.
Programmable Logic Controller (PLC) modules are used in industrial settings to control and monitor various manufacturing processes. Detecting these modules can be helpful during installation and maintenance. However, the limited availability of real annotated images to train an object detector poses a challenge. This thesis aims to research object detection of these modules on real images by using synthetic data during training. The synthetic images are generated from CAD models and improved with Generative Adversarial Networks (GANs). The CAD models are rendered in different scenes, and perfectly annotated images are automatically saved. A technique called domain randomization is applied during rendering. It renders the modules in different poses with constantly changing backgrounds. As the CAD models do not visually resemble the real modules, it is necessary to improve the synthetic images. This project researches StarGAN and CycleGAN for the task of image-to-image translation. A GAN is trained with real and synthetic images and can then translate between these domains. YOLOv8 and Faster R-CNN are tested for object detection. The best mean Average Precision (mAP) is achieved when training with a synthetic dataset where 50% of the images were improved with StarGAN. When trained with YOLOv8 and evaluated on a real dataset, it achieves a mAP of 84.4%. Overall, the accuracy depends on the quality of the CAD models. Using a GAN improves the detection rate for all modules, but especially for unrealistic CAD models.
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.
Purpose: In this thesis the viable system model (VSM) is used as a framework to develop a model for the management of a business alliance that contains the necessary and sufficient conditions for maintaining synergy of its constituent organisations and for adapting to a changing environment so that it can remain a long-term viable alliance. In addition, a model is developed that makes explicit the inherent link between the VSM and the core elements of knowledge management theory. Based then on the alliance management model and the link established between the VSM and knowledge management, an application framework is developed to guide practitioners in defining necessary alliance management functions and relationships, the knowledge required by that management to fulfill those functions, and the processes that need to be in place to manage that knowledge. Design/strategy: The research has been divided into four phases: theoretical construction, refinement with practitioners, real-world application, and evaluation of test case and toolset. The researcher has worked closely with practitioners actively involved in the formation of a new international alliance to develop a VSM model and application framework for the alliance management. Formally, the research strategy has been defined as an action research and the research philosophy as one of pragmatism. Findings/limitations: The developed application framework, has been successfully used to identify absent and incomplete roles, actions, and interactions within the management of the specific alliance test case. This has helped to demonstrate how the application framework and VSM model can be used to diagnose and, most importantly, to articulate and visualise management deficiencies to facilitate clear and unambiguous discussions. The timing of this cross-sectional research did not allow the application framework to be utilised from the outset of the alliance formation as an organisational planning tool and also not to its full extent to support the development of knowledge processes for the alliance management. However, the step-by-step approach used in developing the toolset and then explaining its application will allow the reader to judge its credability and generalisability for other practical applications. Practical implications: The developed toolset consists of a VSM for an alliance management, job descriptions for that management (responsibilities, interfaces, and core competencies), a visual model illustrating the link between the VSM and knowledge management, and an application framework to guide the filling of the alliance management job descriptions in phases of recruitment, onboarding, and development (of interfaces and activities processes). Overall, one could say that the conditions prescribed by the VSM are rather obvious and yet, as seen by the specific alliance test case, many of these conditions have been completely overlooked by a management that was more than capable, willing, and empowered to enact those conditions. This gives a good indication that the toolset which has been compiled in a visual and tabular systematic fashion may well be useful to practitioners for the organisational planning of an alliance management. The visual representation of a management role in the VSM as a set of knowledge episodes put forward by this research is significant. It forces the express recognition that knowledge management is an integral part of every interaction that takes place and every action performed that, according to the VSM, are necessary and altogether are sufficient for viability. It means that knowledge management cannot be considered as some abstract topic or unnecessary overhead or afterthought – it is entirely necessary, practical and forms a natural course of events during design of action/interaction processes. In other words, if an organisation is viable then, by definition, it does knowledge management whether or not it is formally recognised as such. The VSM, by defining necessary and sufficient actions and interactions for its roles, therefore provides a focus for relevant knowledge and serves as a tool for structured knowledge management. Originality/value: This research addresses a general academic call for hands-on insights of VSM applications by sharing real-world insights, artifacts and reflections generated by a practical and relevant organisational management application. It also addresses the potential, recognised by academics, for VSM as a framework for knowledge management by developing an intuitive model linking those theories and then using that model as part of a framework to guide its application. The introduction to aspects of knowledge management theory relevant to the model developed as well as the meticulousness and comprehensive explanation of the VSM provides a solid theoretical foundation for practitioners. The developed toolset is based on existing theories from multiple fields of research that have been logically linked and extended in an original and novel manner with a strong focus on practical application. This researcher’s hope is that this will stimulate interest for future research and practical application from academics and practitioners alike.
The implementation of direct-to-consumer (D2C) business models has become more important for companies trying to develop a competitive edge and improve consumer engagement in today's rapidly expanding e-commerce market. This master's thesis investigates the important success elements and problems of deploying D2C models in the e-commerce business. The research question focuses on identifying the factors that contribute to the successful transition to D2C models and the obstacles businesses encounter along the way. Through qualitative research using the Eisenhardt method and in-depth case studies with industry experts, this study provides valuable insights into key success factors for direct-to-consumer (D2C) business models in e-commerce.The findings highlight that businesses that effectively implement D2C models utilize key success factors such as a clear value proposition, customer engagement and relationship build- ing, seamless online experiences, targeted marketing and digital advertising, brand identity and storytelling, and flexibility and adaptability. However, they also face challenges related to operational adjustments, marketing and branding investments, competition, and market saturation. Based on these research outcomes, this thesis provides recommendations for businesses seeking to switch to or implement D2C models in e-commerce. These recommendations emphasize embracing a customer-centric mindset, developing digital capabilities, foster- ing strong leadership commitment, leveraging data and analytics, establishing direct customer relationships, optimizing operational processes, building brand trust and credibility, and allocating resources wisely. This master's thesis provides a comprehensive analysis of the key success factors and challenges associated with the transition to or implementation of D2C business models in the e-commerce industry. It provides advice to help companies successfully transition to D2C models.
This thesis aims to determine how banks can prepare for fulfilling and implementing the IFRS S1 requirements, which have been published by the International Sustainability Standard Board. It also examines the extent to which banks in Liechtenstein and Switzerland have already implemented the existing regulatory requirements in the area of sustainability transparency and integrated them into their financial reporting. The focus is to determine whether, and to what extent, these requirements enable banks to disclose relevant information on sustainability aspects in their financial reports. In order to answer the research question appropriately, a qualitative research method according to Mayring was used, which included conducting expert interviews. In this context, it is important to analyze the possibilities of IFRS S1 concerning the identification, assessment, and disclosure of sustainability risks and opportunities. The thesis also analyzes the impact of the regulatory requirements on banks, including the challenges of implementing IFRS S1 and the potential benefits and opportunities for banks of complying with the sustainability transparency requirements. The results are intended to develop a better understanding of how the regulatory requirements for sustainability transparency can be effectively used by banks to improve the quality and comparability of sustainability-related financial information under IFRS S1.
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.
Open tracing tools
(2023)
Background: Coping with the rapid growing complexity in contemporary software architecture, tracing has become an increasingly critical practice and been adopted widely by software engineers. By adopting tracing tools, practitioners are able to monitor, debug, and optimize distributed software architectures easily. However, with excessive number of valid candidates, researchers and practitioners have a hard time finding and selecting the suitable tracing tools by systematically considering their features and advantages. Objective: To such a purpose, this paper aims to provide an overview of popular Open tracing tools via comparison. Methods: Herein, we first identified 30 tools in an objective, systematic, and reproducible manner adopting the Systematic Multivocal Literature Review protocol. Then, we characterized each tool looking at the 1) measured features, 2) popularity both in peer-reviewed literature and online media, and 3) benefits and issues. We used topic modeling and sentiment analysis to extract and summarize the benefits and issues. Specially, we adopted ChatGPT to support the topic interpretation. Results: As a result, this paper presents a systematic comparison amongst the selected tracing tools in terms of their features, popularity, benefits and issues. Conclusion: The result mainly shows that each tracing tool provides a unique combination of features with also different pros and cons. The contribution of this paper is to provide the practitioners better understanding of the tracing tools facilitating their adoption.
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.
Immersive educational spaces
(2023)
"If only we had had such opportunities to grasp history like this when I was young" – words by an almost 80-year-old woman holding an iPad on which both, the buildings in the background and a tower in the form of a virtual 3D object, appear within reach. To "grasp" history - what an apt use of this action-oriented word for an augmented reality application built on considerations of thinking and acting in history. This telling image emerged during the first test run of the app i.appear which will be the focus of this article's considerations on the use of immersive learning environments. The application i.appear has been used in the city of Dornbirn (Austria) for a year now to teach historical content through location-based augmented reality and other interactive and multimedia technologies. After a brief description of the potential of such applications, the epistemological structure of the hosting app i.appear and its functionality will be outlined. This article will focus on the “Baroque Master Builders” tour of the hosting app that was created and tested as part of the current research.
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 previous studies of linear rotary systems with active magnetic bearings, parametric excitation was introduced as an open-loop control strategy. The parametric excitation was realized by a periodic, in-phase variation of the bearing stiffness. At the difference between two of the eigenfrequencies of the system, a stabilizing effect, called anti-resonance, was found numerically and validated in experiments. In this work, preliminary results of further exploration of the parametric excitation are shared. A Jeffcott rotor with two active magnetic bearings and a disk is investigated. Using Floquet theory, a deeper insight into the dynamic behavior of the system is obtained. Aiming at a further increase of stability, a phase difference between excitation terms is introduced.
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.
Industrial demand side management has shown significant potential to increase the efficiency of industrial energy systems via flexibility management by model-driven optimization methods. We propose a grey-box model of an industrial food processing plant. The model relies on physical and process knowledge and mass and energy balances. The model parameters are estimated using a predictive error method. Optimization methods are applied to separately reduce the total energy consumption, total energy costs and the peak electricity demand of the plant. A viable potential for demand side management in the plant is identified by increasing the energy efficiency, shifting cooling power to low price periods or by peak load reduction.
A trend from centralized to decentralized production is emerging in the manufacturing domain leading to new and innovative approaches for long-established production methods. A technology supporting this trend is Cloud Manufacturing, which adapts technologies and concepts known from cloud computing to the manufacturing domain. A core aspect of Cloud Manufacturing is representing knowledge about manufacturing, e.g., machine capabilities, in a suitable form. This knowledge representation should be flexible and adaptable so that it fits across various manufacturing domains, but, at the same time, should also be specific and exhaustive. We identify three core capabilities that such a platform has to support, i.e., the product, the process and the production.We propose representing this knowledge in semantically specified knowledge graphs, essentially creating three through features interconnected ontologies each representing a facet of manufacturing. Finally, we present an exemplary implementation of a Cloud Manufacturing platform using this representation and its advantages.
In the current international business environment employees are spending large amounts of their time in meetings. More than ever these meetings take place remotely and often have the problem that individuals in the meeting do not share information or opinions. Employees often stay in muted in meetings and allow one or two participants to drive the conversation. This habit is especially troublesome for problem solving meetings. Problem solving meetings invite individuals from different disciplines to share and brainstorm possible causes for issues related to poor company outcomes. Active and open contribution from all members is required to achieve the group goals. This study aims to find methods that will increase contribution amongst meeting participants in regular meetings as well as problem solving meetings. The study tested sixteen topics for their influence on contribution in meetings. This was done in a survey, that was distributed within a multination engineering corporation, and on LinkedIn. There was a total of 68 responses. These responses were then separated by above average and below average participation in problem solving meetings. Hypothesis testing was done on the total group and separately on the problem-solving group. Employee participation in decision making and psychological safety were found to correlate highly with Contribution in meetings for both groups. Psychological safety was found to be of even greater importance to problem solving group. This study demonstrates that to increase contribution in meetings, leaders should provide a psychologically safe climate where employees share in the decision making. Furthermore, a psychologically safe environment is critical in problem solving meetings where members of different disciplines with low familiarity take part.
This master thesis investigates drivers and barriers of innovation workshops for an intercultural participant group. Actively dealing with innovation management is considered vital for companies which are acting on competing markets. An innovation workshop is a useful tool in order to foster innovation ability, develop innovative ideas and drive innovation forward. Intercultural participant groups are not only a common challenge in today's business world but also entail several benefits as they incorporate diverse knowledge bases and perspectives and hence contribute to the ideation and innovation process. Within the master thesis a broad variety of barriers and drivers are evaluated. Main barriers of innovation workshops for an intercultural participant group are high conflict potential, miscommunication, language barriers, a lack of management support, no agreement on workshop objectives as well as poor workshop preparation, organization and facilitation. Main drivers of innovation workshops for an intercultural participant group are a heterogeneous group composition, intercultural competence of the facilitator, the opening up of mindset silos, an intensive workshop preparation and empathetic facilitation. These drivers and barriers build the basis for the determination of success factors and recommendations for action for organizers and facilitators of an innovation workshop for an intercultural participant group. In the further course of the paper an exemplary workshop design will be presented as a guideline and framework for managerial practice
Data is the new oil,” said British Mathematician and Tesco marketing mastermind Clive Humbly1. Data has also been described as the backbone of digital retail enterprises2 and the currency of the digital age. Whether these statements live up to be true is debatable, but what is certain is the fact that the internet age has contributed to the avalanche of data witnessed today. In a century dominated by predictive analysis and artificial intelligence, it is no surprise that by the end of the last decade, data companies Apple, Amazon and Microsoft closed as the world´s first trillion-dollar companies, with their revenues dwarfing economies of several countries across the globe.3The recognition of the importance of data in today´s economy bears with it the responsibility to protecting its owners. While this intricate balance has long been the subject of legal analysis the General Data Protection Regulation, 2018, is hailed as the world´s most comprehensive and strict data protection regime currently in force. In addition to protecting the personal data of persons from its member countries, the Regulation also seeks to ensure the same protection accompanies any data transferred out of the European Union to other countries. It is almost 5 years since the Regulation was passed and process of implementation into business operations an important topic of discussion. Of importance to this study are the Modernized Standard Contractual Clauses, a tool of data transfer to countries outside the EU, which replace the three sets of SCCs adopted by the now repealed Data Protection Directive 94/46. These Standard Contractual Clauses came into effect on 27th September 2021, and companies have until 27th September 2022 to rely on the old set of clauses. With this deadline coming up, how far have the clauses been integrated into operations by businesses in Austria and the EU?
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.
Fear of failure is a major factor influencing entrepreneurial actions. Since the female quota for startups and self-employment is still lower than for men, the aim is to determine the extent to which the fear of failure is incorporated into the entrepreneurial actions of women in Austria. The trailblazer and pioneer in female entrepreneurship America is used as an international benchmark for evaluation. A quantitative survey was conducted among women from Austria and America on their fears of failure related to self-employment and their aspirations to become self-employed. There were significant differences in the quantitative study between self-employed and non-self-employed women, irrespective of their country of origin. As a result, recommendations for action were created to reduce the influence of Fear of Failure on entrepreneurial actions of Austrian women.
The impact of organizational citizenship behavior for the environment on corporate sustainability
(2022)
Today, many businesses increasingly engage in pro-environmental activities to face environmental challenges such as pollution or climate change. In addition to formal management practices, employees are impacting environmental advances with voluntary pro-environmental activities, also known as Organizational Citizenship Behavior for the Environment. The purpose of this master thesis is to explore factors that could influence employees’ engagement in Organizational Citizenship Behavior for the Environment. For this aim, five semi-structured interviews were carried out with multinational corporations from the DACHL region. The results show that certain leadership styles, corporate culture, a sustainability-driven mindset, environmental concern, communication and motivation can influence employees’ engagement in Organizational Citizenship Behavior for the Environment. In addition, the cumulative effect of small initiatives seems to considerably impact environmental sustainability. In contrast to past research on this topic, this study takes a qualitative approach to explore different influencing factors of Organizational Citizenship Behavior for the Environment. In addition, the study focuses on businesses located in the DACHL region.
Projects, in which software products, services, systems and solutions are developed, all rely on the right requirements to be established. Software requirements are the expression of user wants or needs that have to be addressed, business objectives that have to be met, as well as capabilities and functionality that has to be developed. Meanwhile, practice shows that very often incorrect, unclear or incomplete requirements are established, which causes major problems for such projects. It could lead to budget overruns, missed deadlines and overall failure in worst-case scenarios.
The field of requirements engineering emerged as an answer to these shortcomings, aiming to systematize and streamline the process that
establishes requirements. Requirements elicitation is a key component of this process, and one of its starting points. The current thesis attempts to outline best practices in requirements elicitation, as well as what issues, obstacles and challenges are currently faced, and then present this through the lens of national culture. In this way its effects on the practice, if any, could be highlighted and studied further. The way this was achieved was by interviewing practitioners from two nations, which are shown to be
culturally different, and then comparing and contrasting the findings.
Meanwhile, the validity of those findings was enhanced by comparisons with existing literature.
Even though the findings were not compelling enough to form generalizations or concrete conclusions about the effects of national culture on requirements elicitation, these findings revealed patterns that could be worth exploring further. When it comes to requirements elicitation itself, it was observed to benefit from a structured and systematic approach, and be
most effective with one-on-one, instead of group interactions. The main pain points of the process stem from the complexity of communication, but are not always obvious. Practitioners are also advised to carefully plan the gathering of requirements, as the source may not have them readily available, and could even be unclear about what exactly is needed. Overall, this thesis research could be considered successful in its goal to shed a modicum of light on the issue at hand from a different, underexplored angle. By following a systematic and methodical approach, this research has also been made easier to expand or replicate.
Power cables play an important role in power grids. Insulation faults in cables can have adverse effects on the operating behaviour. These effects can be assessed through an AC withstand test by using a very-low frequency high voltage generator. This generator produces a sinusoidal voltage waveform at 0.1Hz with high voltage levels up to 65kV peak. During the quality assessment, the power cable is repeatedly charged and discharged. The discharging process is done by a discharging circuit where the energy is dissipated thermally. But to reuse the dissipated energy a novel extension in form of an energy storage system is presented. This thesis, therefore, describes the design process of an energy storage system that allows the temporary storage of the discharge energy. The developed system is composed of a bidirectional DC/DC converter and an aluminium electrolytic capacitor as storage type. Based on the maximum VLF system ratings the energy storage unit is dimensioned and sized. The effective power flow control between the storage system and the available discharge energy is done by a synchronous buck-boost converter. This bidirectional converter works in continuous conduction mode over the complete charging phase. Together with a theoretical analysis of the underlying problem and the use of converter analysis methods the selected synchronous buck-boost converter is dimensioned and sized. In addition, a state space AC modeling of the converter with its electrical uncertainties is conducted. With the converters AC model, the controller is designed. A closed-loop input converter current control scheme based on a proportional-integral controller is implemented. The system assessment is done by a model-based hardware implementation in Matlab Simulink and Plecs Blockset. The system is rated to store discharge energies up to 4.3kJ in a short charging period of 2.5s. The maximum peak power during the charging phase is 2.7kW. The digital proportional-integral controller is implemented through an emulation process of the designed analog controller. Based on a C-code implementation of the digital controller the gap between the real hardware is reduced. During the design process theoretical calculations are made and reveal that designing a capacitor storage unit has a direct impact on the peak system currents and also impose also limitations on permissible DC voltage ranges on electrical components. The developed energy storage system and its power flow control strategy were investigated through simulation studies. The results show proper charging of the energy storage medium. In addition, also a statement of the final technical feasibility is made. In total, this work summarizes a detailed design process of the energy storage system. This proof of concept is intended to further advance the system integration.
Development of a low pressure syringe pump for detecting cannabinoids through liquid chromatography
(2022)
The following thesis covers the miniaturization and characterization of a pneumatic syringe pump, which is used for applications in low-pressure liquid chromatography. For this purpose, the components of the prototype are dealt with in the first section. These include the membrane pump and the cylinder for pressure and force generation, the syringe used for sample preparation and the construction of the test column. Furthermore, the pressure preparation on the cylinder, the friction losses of the syringe and then the behavior of the syringe in various application scenarios are considered. In the second section, the focus is on the different behaviors when using water and ethanol as a solvent. Tests in normal applications, as well as with air pockets or leaking seals, show the different behavior and the resulting deviations in the test pressure of the column. In addition, the maximum forces that can be applied to the syringe are worked out in several tests and the different maximum pressures, which depend on the solvent contained, are evaluated. These different maximum pressures, which are due to the different sealing behavior in connection with the surface tension of the liquid, will be discussed in conclusion. An outlook follows, up to which test pressures the system can be used and how these can be achieved.
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.
The detection of glucose is an essential part of diabetes management and can help to prevent secondary diseases, that can occur as a result of diabetes. For this reason, it is important to improve the current glucose monitoring by developing novel sensors with high efficiency, low cost and compact design. The use of microelectrodes with interdigitated array (IDA) structures reduces the total detector size while providing benefits such as large currents, high sensitivity, and fast response. The aim of this thesis is to develop a novel sensor based on platinum interdigitated array (IDA) electrodes and to investigate which method is most effective for the detection of glucose. This work is divided into two parts. The first part is focused on the design and the fabrication of the sensor chips. The second part is concerned with the electrochemical characterisation of the sensors. Two distinct sensor designs are created, each consisting of a four-electrode system arranged as an interdigitated array. For the fabrication of the sensors, two different manufacturing processes are used. A lift-off process is used to fabricate the 2 μm-Gap sensor chips, whereas a lift-off free process is applied to produce the nanogap sensor chips. The electrochemical characterisation of both sensor chips is achieved by the immobilisation of the enzyme glucose oxidase (GOx) on the electrode surface. This thesis investigates the immobilisation of GOx by reduction of diazonium salts and the direct immobilisation of GOx by cyclic voltammetry. As a result of this work, it has been demonstrated that glucose detection by reduction of diazonium salts is error-prone due to modification with a multi-step procedure and is not suitable for our sensors based on platinum IDA electrodes. The direct immobilisation of GOx by cyclic voltammetry, by contrast, demonstrates the successful detection of glucose. In glucose solutions ranging from 5 mM to 20 mM, a direct correlation between the glucose concentration and the measured current is obtained. The reproducibility of direct immobilization is demonstrated by repeated performance with various sensors.
In today’s world, fiber optic networks for data transmission are an essential technology. This technology provides multiple advantages compared to conventional electrical data transmission. The simultaneous transmission of multiple optical signals in a single fiber is one of the main benefits of fiber optic cable. This is accomplished by directing the different optical signals into a single fibre and splitting them up after the transmission in order to obtain the individual signals. Arrayed Waveguide Gratings (AWGs) are used for this purpose in modern optical networks. Design and evaluation process are two components of AWG development. During the evaluation of several simulated and already manufactured AWGs for telecommunication applications, it was discovered that the channel spacing parameter does not conform telecommunication standards. The correct shift of the geometric parameter ”separation of the output waveguides” leads to the standard-conform channel spacing.
According to the current state of the art, no commercial tool is available which calculates the shift of this parameter correctly. The aim of this thesis is the development of a software tool to calculate the accurate shifting of the geometric parameter ”separation of the output waveguides” of an AWG. This tool operates as an interface between the design and evaluation processes and must be able to import the data format of the evaluation process and returns the data in a suitable data format for the design process. The Vorarlberg University of Applied Sciences uses three different methods for the shifting of the geometric parameter ”separation of the output waveguides”. These methods are evaluated and optimised as part of this thesis. Additionally, it has been determined that the shift of the geometric parameter ”separation of the output waveguides” has no significant impact on the performance of the AWG.
Erosion due to cavitation is a common problem for any kind of water turbine. Most of the currently used techniques to detect cavitation are using an Acoustic Emission (AE) sensor and highspeed cameras during operation. For the pelton wheel which is subject of this thesis it is impossible to take pictures during operation, because of the splashing water and the mist. Therefore this thesis aims to explore possibilities in detecting erosion on the buckets of the pelton wheel on images taken during manual inspections. Since the provided images are snapshots taken with a mobile phone camera without a tripod, a lot of effort was invested in the preprocessing of the images. For the main task, the classification of the erosion, two methods were evaluated: Local Binary Patterns (LBP) + kN-earest neighbor classification and the classification with a Convolutional Neural Network (CNN). The given 2405 images, contained 4810 buckets on which the erosion was graded from zero to four. This means the baseline for the classification accuracy is 20%. LBP + kNearest neighbor classification scored 32.03%. The chosen CNN model, a light version of the Xception architecture outperformed the LBP + kNearest classification with 58,29%. The biggest issue found during research is the variance of the erosion grading by the maintainance personnel. Reasons for this are: no objective grading critera like the area of erosion in mm2, classification by different employees, a shift in grading from overall bucket condition to erosion from cavitation and too many classes for grading. The mentioned reasons were confirmed by the manual classification experiment were an IllwerkeVKW employee had to perform the grading on images of the dataset. The contestants accuracy score was 36% for this task. The result of 58,29% classification accuracy indicates that an automated grading of erosion by cavitation is feasible.
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.
Systems are constantly increasing in complexity. This poses challenges to managing and using system knowledge. The Systems Modeling Language (SysML) is a modeling language specifically for systems, while Machine Learning (ML) is a tool to tackle complex problems. Currently, no bridge between systems modelled in SysML and ML regarding said systems has been proposed in literature. This thesis presents an approach that uses Model-driven Software Engineering (MDSE) and Template-based Code Generation (TBCG) to generate a ML IPython Notebook (IPYNB) from a SysML model. A mapping configuration using JavaScript Object Notation (JSON) allows the definition of mappings between SysML elements and template variables, enabling configuration and user-supplied templates. To test the approach, a SysML model describing ML to predict the weather based on data is created. Python ML templates are supplied and template variables mapped with the JSON mapping configuration are proposed in the thesis. The outcome is an executable IPYNB that contains all information from the SysML model and follows the modelled workflow. The findings of the work show that model-driven ML using SysML as a modeling language is beneficial due to the representation of ML knowledge in a general-purpose modeling language and the reusability of SysML model elements. It further shows that TBCG and a mapping configuration allow for more flexible code generation without changing the source implementation.
Offline speech to text engine for delimited context in combination with an offline speech assistant
(2022)
The inatura museum in Dornbirn had planned an interactive speech assistant-like exhibit. The concept was that visitors could ask the exhibit several questions that they would like to ask a flower. Solution requirements regarding the functionalities were formulated, such as the capacity to run offline because of privacy reasons. Due to the similarity of the exhibit, open-source offline Speech To Text (STT) engines and speech assistants were examined. Proprietary cloud-based STT engines associated with the corresponding speech assistants were also researched. The aim behind this was to evaluate the hypothesis of whether an open-source offline STT engine can compete with a proprietary cloud-based STT engine. Additionally, a suitable STT engine or speech assistant would need to be evaluated. Furthermore, analysis regarding the adaption possibilities of the STT models took place. After the technical analysis, the decision in favour of the STT engines called "Vosk" was made. This analysis was followed by attempts to adapt the model of Vosk. Vosk was compared to proprietary cloud-based Google Cloud Speech to Text to evaluate the hypothesis. The comparison resulted in not much of a significant difference between Vosk and Google Cloud Speech to Text. Due to this result, a recommendation to use Vosk for the exhibit was given. Due to the lack of intent parsing functionality, two algorithms called "text matching algorithm" and "text and keyword matching algorithm" were implemented and tested. This test proved that the text and keyword matching algorithm performed better, with an average success rate of 83.93 %. Consequently, this algorithm was recommended for the intent parsing of the exhibit. In the end, potential adaption possibilities for the algorithms were given, such as using a different string matching library. Some improvements regarding the exhibit were also presented.
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.
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 alarming degradation of the natural environment is leading many consumers to increasingly demand sustainable products. Since 2017, the global purchase intention of such products has increased by 63%. To respond to the increasing demand, more and more companies have started producing products from sustainable materials such as recycled products. However, purchase intention does not always result in actual behavior and can vary due to different products and in country-specific contexts. Hence, it is the purpose of this study to determine which factors influence the purchase intention of recycled products and whether these factors differ between a developed country such as Germany and a developing country such as South Africa. Furthermore, the study aims to discover whether there are differences in purchase intention with regard to different product categories, whether there is an intention-behavior gap, and whether there are country-specific differences. Finally, target groups of the corresponding countries will be derived. To answer the research questions, a quantitative study was conducted using an online questionnaire in Germany (n = 603) and South Africa (n = 692). The findings demonstrate that the purchase intention for recycled products is significantly higher in South Africa than in Germany, but no significant difference in the factors influencing the purchase intention could be found. However, the factors differ in terms of the extent of their influence. Thus, the factor “Attitude / Environmental Concern” has the strongest influence in South Africa, while the factor “Value / Accessibility” has the strongest influence in Germany. Likewise, a difference could be found concerning the products, with the purchase intention for mobile phones generally smaller than for t-shirts and toilet paper. In a country-specific comparison, however, purchase intention for t-shirts is significantly higher in South Africa than in Germany. An intention-behavior gap was identified for the sample, and it was found that the age groups 25 to 49 have the strongest purchase intentions and that the purchase intention increases significantly with increasing education level.
Today, industrial B2B manufacturers face a rapidly changing environment, exacerbated by increasing globalization and associated shifts in the competitive landscape. Digital transformation and the emergence of new innovations and technologies are forcing companies to rethink their business models and offerings to integrate digital services to strengthen competitive advantages. Suppliers are becoming more deeply involved in customer processes through digital after-sales services, with the aim of exploiting efficiencies. Following the servitization transformation, companies intend to change their purpose from focusing on the pure physical product to becoming a service provider with emphasis on value creation and the capture of the customer.
To investigate how customer and supplier perspectives agree and what requirements each side has for digital after-sales services, exploratory qualitative research was conducted with customers and suppliers by means of one-on-one interviews. The thesis aimed to assess the status, progress, and future possibilities of implementing digital after-sales services and business models based on them. Research shows that the far-reaching establishment of product-accompanying services with strong connectivity and customer-centricity is primarily relevant. Disruptive business models still require a mind-shift and organizational readiness on the part of both customers and suppliers. In principle, digitization in after-sales interaction is beneficial and should be steadily advanced to make customer processes as well as further developments at the supplier level more efficient and well-founded through the analysis of real data. Overall, this thesis outlines important aspects that need to be considered while developing digital service innovations to deal with customer demands appropriately.
Keywords: Servitization, Digital After-Sales Services, Predictive Maintenance, Remote Monitoring, Digital Interaction, Digital Service Innovation, Digital Service Innovation Process
The presented master thesis of the study subject International Marketing and Sales at the Fachhochschule Vorarlberg in Dornbirn deals with the influence of emotions on the attitude toward hydrogen cars and their purchase intention. For this purpose, an empirical analysis with a correlation analysis was conducted in order to be able to determine the correlations of the individual parameters.
At the beginning of the thesis the hydrogen technology was presented in more detail and by means of suitable criteria it was shown that the hydrogen car represents a certain potential, however, in comparison to the combustion cars and electric cars, the hydrogen car is currently in third place. The relatively long range, the fast-refueling and the sustainability were identified as advantages, while the current high price and the poorly developed refueling station network are currently the biggest ob-stacles to a hydrogen car. It can be seen that research and development of hydrogen cars is being driven forward in many countries around the world, including by the gov-ernment side through the provision of various subsidies. For this reason, the future development of the driving technology remains exciting and simultaneously uncertain.
In the second step of the work, emotions were examined in more detail. The aim was to find out which emotions exist and which of them are predominant when buying a car, and then to find out what influence emotions have on the cognitive process, the attitude, and the purchase intention. It turned out that the majority of the population is highly involved in the purchase of a car and therefore tries to make rational decisions, which makes the influence of emotions more difficult, but not impossible.
By presenting suitable marketing tools for measuring emotions, it was shown that measuring emotions is a difficult undertaking. Measurement is often difficult or expen-sive and involves a great deal of effort. For this reason, beside the presentation of marketing tools, the strategic approach for a marketing campaign was also presented.
Based on the conducted empirical analysis, the influence of emotions on attitude and purchase intention could not be significantly confirmed but it could be proven that the knowledge about hydrogen cars is currently low. One inside is that an increase in awareness increases the purchase intention of hydrogen cars. Furthermore, a signifi-cant correlation between the sustainable attitude and the purchase intention could be proven. In addition, people who like to follow new trends are more likely to buy a hy-drogen car than others. This paper concludes with a brief summary of the findings and an outlook on the potential for improvement of the hydrogen car market.
Keywords: Hydrogen Cars, Sustainability, Emotions in Marketing, Purchase Inten-tion, Attitude, Marketing Tool
In recent years, more and more companies have become aware that a brand also has a social dimension, and with the advent of social media platforms, brand communities have experienced a shift from a traditional offline to more of an online presence. Brands of innovative consumer durables have also recognized social media brand communities as a very significant marketing strategy. It is therefore important to understand the influence of these communities on members' purchase intentions.
This master’s thesis has the goal of demonstrating to enterprises what aspects of a social media brand community will influence the purchase intention of its members and what should be considered in order to enhance it. This will ultimately lead to the following research question: How do social media brand communities influence the diffusion process of innovative consumer durables in the DACH region?
To answer this, a quantitative study has been conducted that has targeted people who are participating in a social media brand community of innovative consumer durables in the DACH region. This involved testing various criteria of a social media brand community and their impact on the diffusion process (purchase intention).
The findings of the study revealed that increased positive electronic word-of-mouth leads to enhanced purchase intention of members. Furthermore, the research has shown that higher identification with a social media brand community, greater engagement, increased entertainment value, faster corporate responsiveness, and reduced occurrence of social spam, do not have a positive effect on the diffusion process of innovative consumer durables.
The research focused on the wood pellet market for private consumers in Germany and has the objective to understand the factors affecting the purchase decisions of wood pellets buy-ers. The aims of the research were achieved by applying the Engel, Kollat, Blackwell model of consumer behaviour, by conducting in-depth interviews and deriving the grounded theory. The application of EKB model revealed that the following groups of factors may influence the con-sumers: cultural (culture and social class), social (family influence), personal (age and lifecycle stage), psychological (believes, motives, attitudes) and unexpected circumstances. In-depth interviews with the buyers of wood pellets helped specify the influencing factors to finally an-swer the research question. The analysis showed that the buyers of wood pellets are influ-enced by family, personal believes and attitudes, and the unexpected circumstances. The most important factors for the buyers are the quality of the pellets and reliability of the supplier. If they are satisfied with the quality of product and service, they will be reluctant to look for an alternative. And on the opposite side, in case the quality of the product or the service is unsat-isfactory this is a limiting factor and will stop the buyer from purchasing this product. Price is an important factor for the buyers of pellets in bags. However, in the situation of volatile mar-ket and exploding prices, the price factor will play a bigger role. Thus, the strategic factors for marketing of pellets is to concentrate on quality and price communication, and to focus on the quality of pellet delivery service.
Nowadays, online marketing is becoming increasingly important not only in the B2C but also in the B2B sector, as evidenced by marketing budget expenditures. Companies pursue overarching goals involving visibility and attention from prospective customers in order to raise brand awareness and as a result, outperform increasing market competition. This begs the question of whether online marketing is appropriate for increasing brand awareness.
The master´s thesis topic developed from both a personal as well as a professional perspective. Private research increased the author´s interest in the topic of online marketing. Furthermore, brands, whose level of awareness needs to be improved, are becoming a more common topic of professional debates. In this way, the research of the current master´s thesis was created.
The aim of this master's thesis was to discover how online marketing can be used to increase awareness of a brand. This will be analyzed by using the brand turn to zero, which offers consulting services for B2B customers in the sustainability industry. In this context, suitable and visible online marketing channels for increasing brand awareness are to be identified. In addition to this, suitable content for the company´s own as well as paid online marketing channels need to be collected. Furthermore, the influence that online marketing has on creating brand image awareness is to be presented.
Research questions are defined in order to achieve the described objectives. Within the scope of the master thesis, one main research question, as well as three sub-research questions, are to be answered based on the literature as well as the generated output resulting from the empirical part. Eight existing B2B customers of turn to zero, originating from com-mercial and industrial sectors, were interviewed in the empirical part. The interview findings were evaluated by using qualitative content analysis according to Mayring.
Research results showed that targeted combinations of online marketing channels are contributing to increase brand awareness. In addition, the research succeeded in determining suitable communication content for various online marketing channels. Furthermore, the in-fluence of online marketing could be investigated more closely in terms of brand image.
Startups usually have high growth ambitions but only limited resources. Therefore, they are looking for efficient and effective methods to grow their business. However, if they go interna-tional, challenging changes will likely be made if the company is not focused on the global market. The aim of this work is therefore to support startups in the internationalization of their business and to provide guidance from the beginning by using strategic marketing elements to facilitate this process. To achieve this, a qualitative method of analysis was chosen. First, a literature review was conducted on the relevant topics of a startup, strategic marketing, and internationalization. Five success factors emerged from the literature: commitment, strategy, research, marketing mix adaptation, and organization and network. These were then ana-lyzed in more detail in expert interviews. The analysis of the interview results shows that for the internationalization of startups, the commitment to internationalization and a global mindset is of great importance from the very beginning. This is because they influence all supporting and strategic marketing elements.
The number of electric vehicles will increase rapidly in the coming years. Studies suggest that most owners prefer to charge their electric vehicle at home, which will fuel the need for charging stations in residential complexes where vehicles can be charged overnight. Currently, there already are over 100 such residential complexes, with another 70 added every year in Vorarlberg alone. In most existing residential complexes, however, the grid connections are not sufficient to charge all vehicles at the same time with maximum power. In addition, it is also desirable for grid operators and electricity producers that the power demand be as smooth and predictable as possible. To achieve this, ways to manage flexible loads need to be found, which can operate within the technical constraints. Therefore, the most common scenarios how the load can be made grid-friendly with the help of optional battery storage and/or photovoltaics using optimization methods of linear and stochastic programming were examined. At the same time, the needs of the vehicle owners for charging comfort - namely to find their vehicles reliably charged at the time of their respective departure - were addressed by combining both objectives using suitable weights. The algorithms determined were verified in practice on an existing Vlotte prototype installation. For this purpose, the necessary programs were implemented in Python, so that the data obtained during the test operation, which lasted one month, could be subjected to a well-founded analysis. In addition, simulation studies helped to further reveal the influence of PV and BESS sizing on the achievable optimums and confirm that advanced optimization algorithms such as the ones discussed are a vital contribution in reducing the charging stations’ peak load while at the same time maintaining high satisfaction levels.
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 %.
Grid-scale electrical energy storage (EES) is a key component in cost-effective transition scenarios to renewable energy sources. The requirement of scalability favors EES approaches such as pumped-storage hydroelectricity (PSH) or compressed-air energy storage (CAES), which utilize the cheap and abundant storage materials water and air, respectively. To overcome the site restriction and low volumetric energy densities attributed to PSH and CAES, liquid-air energy storage (LAES) has been devised; however, it suffers from a rather small round-trip efficiency (RTE) and challenging storage conditions. Aiming to overcome these drawbacks, a novel system for EES is developed using solidified air (i.e., clathrate hydrate of air) as the storable phase of air. A reference plant for solidified-air energy storage (SAES) is conceptualized and modeled thermodynamically using the software CoolProp for water and air as well as empirical data and first-order approximations for the solidified air (SA). The reference plant exhibits a RTE of 52% and a volumetric storage density of 47 kWh per m3 of SA. While this energy density relates to only one half of that in LAES plants, the modeled RTE of SAES is comparable already. Since improved thermal management and the use of thermodynamic promoters can further increase the RTEs in SAES, the technical potential of SAES is in place already. Yet, for a successful implementation of the concept - in addition to economic aspects - questions regarding the stability of SA must be first clarified and challenges related to the processing of SA resolved.
The impact of global warming and climate change has forced countries to introduce strict policies and decarbonization goals toward sustainable development. To achieve the decarbonization of the economy, a substantial increase of renewable energy sources is required to meed energy demand and to transition away from fossil fuels. However, renewables are sensitive to environmental conditions, which may lead to imbalances between energy supply and demand. Battery energy storage systems are gaining more attention for balancing energy systems in existing grid networks at various levels such as bulk power management, transmission and distribution, and for end-users. Integrating battery energy storage systems with renewables can also solve reliability issues related to transient energy production and be used as a buffer source for electrical vehicle fast charging. Despite these advantages, batteries are still expensive and typically built for a single application – either for an energy- or power-dense application – which limits economic feasibility and flexibility. This paper presents a theoretical approach of a hybrid energy storage system that utilizes both energy- and power-dense batteries serving multiple grid applications. The proposed system will employ second use electrical vehicle batteries in order to maximise the potential of battery waste. The approach is based on a survey of battery modelling techniques and control methods. It was found that equivalent circuit models as well as unified control methods are best suited for modelling hybrid energy storages for grid applications. This approach for hybrid modelling is intended to help accelerate the renewable energy transition by providing reliable energy storage.
Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.
Violation-mitigation-based method for PV hosting capacity quantification in low voltage grids
(2022)
Hosting capacity knowledge is of great importance for distribution utilities to assess the amount of PV capacity possible to accommodate without troubling the operation of the grid. In this paper, a novel method to quantify the hosting capacity of low voltage grids is presented. The method starts considering a state of fully exploited building rooftop solar potential. A downward process is proposed - from the starting state with expected violations on the grid operation to a state with no violations. In this process, the installed PV capacity is progressively reduced. The reductions are made sequentially and selectively aiming to mitigate specific violations: nodes overvoltage, lines overcurrent and transformer overloading. Evaluated on real data of fourteen low voltage grids from Austria, the method proposed exhibits benefits in terms of higher hosting capacities and lower computational costs compared to stochastic methods. Furthermore, it also quantifies hosting capacity expansions achievable by overcoming the effect of the violations. The usage of a potential different from solar rooftops is also presented, demonstrating that a user-defined potential allows to quantify the hosting capacity in a more general setting with the method proposed.
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.
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.
Observing the ratios of the rail usage in terms of passenger travelled per km and per capita, we see that there are huge differences between countries, so some railway systems are performing better in catching passengers than others. By analysing the factors that make the railways attractive for users, and setting standard values for these factors, we can analyse how well a system is performing. This paper has investigated those factors and developed an assessment tool that will inform about the required improvements, so in a later stage specific strategies can be developed to increase the performance in order to attract more passengers. Spain will be used as case study, since the country has specially low passenger rail usage compared to other countries even though the large investments in high speed lines the country undertook in the last decades.
The e-commerce market has been growing for years and this trend seems to be continuing. Online stores for clothing are very successful. It seems that hardly any company can afford not to have a digital presence. This goes hand in hand with the fact that the range of products on offer to customers is getting bigger and bigger. But it's not just the range that's getting bigger, it's also the effort customers have to make to find the right product. For this reason, many successful online stores are already relying on AI. In doing so, companies are creating opportunities for customers that an employee could hardly manage. Implemented on the website, AI can check inventory, update it in real time, predict trends and evaluate customer or user data and make suitable recommendations. This is important for the customer because with the huge choice available, for one thing, personalization is increasingly important and being presented with a relevant selection. A central question is whether the recommendations are trustworthy and whether they can be equated with a real salesperson advising the customer. After all, trust is relevant in longterm customer relationships in that it leads to loyalty and satisfaction, which in turn increases the intention to repurchase. The recommendation tools mentioned are also of particular interest for another reason. On the one hand, they help customers to get a relevant selection of the offer and thus to get faster to the desired one. On the other hand, they are relevant for companies not only because of customer satisfaction, but also because of the chance to reduce returns. The large online stores for clothing offer their customers very generous opportunities to return the goods free of charge. In doing so, the companies have responded to customer wishes, because hardly anything is more important to them when it comes to online shopping: free returns. In this way, customers have minimized the risk of having to keep goods that do not fit or please them. This thesis examines whether recommendation tools can help customers to better assess the sizes and properties of clothing, so that they receive more suitable clothing and do not even feel the need to order several sizes of the same item of clothing. It can therefore be assumed that trust in the recommendations of the AI tools reduces uncertainty, which in turn should reduce the intention to return goods. Another assumption to be tested is that of the perceived usefulness of the recommendation tools. As a prerequisite to get an assessment of these assumptions is the usage of the tools. Therefore, a survey was initiated in the DACH region to assess the extent to which usage influences the factors mentioned. It was found by means of a regression analysis that the frequency of online purchases, mediated by perceived usefulness, explains the influence on trust.
In recent years, numerous studies around the world have examined the environmental potential of biochar to determine whether it can help address climate challenges. Several of these studies have used the Life Cycle Assessment (LCA) method to evaluate the environmental impacts of biochar systems. However, studies focus mainly on biochar obtained from pyrolysis, while the number of studies on biochar from gasification is small.
To contribute to the current state of LCA research on biochar from gasification, LCA was performed for biochar, electricity, and heat from a wood gasification plant in Vorarlberg, Austria. Woodchips from local woods are used as biomass feedstock to produce energy, i.e., electricity and heat. Thereby, biochar is obtained as a side product from gasification. The production of syngas and biochar takes place in a floating fixed-bed gasifier. Eventually, the syngas is converted to electricity in a gas engine and fed to the power grid. Throughout different stages within the gasification process, heat is obtained and fed into local heat grid to be delivered to customers. The biochar produced complies with the European Biochar Industry (EBI) guidelines and is used on a nearby farm for manure treatment and eventually for soil application. Thereby, the effect of biochar used for manure treatment is considered to reduce emissions occurring from manure, i.e., nitrogen monoxide (N2O). Further, the CO2 sequestration potential of biochar, i.e., removal of CO2 from the atmosphere and long-term storage, is considered. Several constructions, such as the construction of the gasification system and the heating grid, are included in the evaluation.
As input related reference flow, 1 kg of woodchips with water content of 40 % is used. Three functionals units are eventually obtained, i.e., 0.17 kg of biochar applied to soil, 4.47 MJ of heat and 2.82 MJ of electricity, each per reference flow. The results for Global Warming Potential (GWP) for biochar is – 274.7*10 - 3 kg CO2eq per functional unit, which corresponds to – 1.6 kg CO2eq per 1 kg biochar applied to soil. The GWP for heat results in 17.1*10 - 3 CO2eq per functional unit, which corresponds to 3.6*10 - 3 kg CO2eq per 1 MJ. For electricity, a GWP of 38.1*10 - 3 kg CO2eq per functional unit is obtained, which is equivalent to 13.5*10 - 3 kg CO2eq per 1 MJ.
The calculation was performed using SimaPro Version 9.1 and the ReCiPe method with hierarchist perspective.
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.
If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.
In recent years, ultrashort pulsed lasers have increased their applicability for industrial requirements, as reliable femtosecond and picosecond laser sources with high output power are available on the market. Compared to conventional laser sources, high quality processing of a large number of material classes with different mechanical and optical properties is possible. In the field of laser cutting, these properties enable the cutting of multilayer substrates with changing material properties. In this work, the femtosecond laser cutting of phosphor sheets is demonstrated. The substrate contains a 230 micrometer thick silicone layer filled with phosphor, which is embedded between two glass plates. Due to the softness and thermal sensitivity of the silicone layer in combination with the hard and brittle dielectric material, the separation of such a material combination is challenging for both mechanical separation processes and cutting with conventional laser sources. In our work, we show that the femtosecond laser is suitable to cut the substrate with a high cutting edge quality. In addition to the experimental results of the laser dicing process, we present a universal model that allows predicting the final cutting edge geometry of a multilayer substrate.
Entangled photon generation at 1550 nm in the telecom C-band is of critical importance as it enables the realization of quantum communication protocols over long distance using deployed telecommunication infrastructure. InAs epitaxial quantum dots have recently enabled on-demand generation of entangled photons in this wavelength range. However, time-dependent state evolution, caused by the fine-structure splitting, currently limits the fidelity to a specific entangled state. Here, we show fine-structure suppression for InAs quantum dots using micromachined piezoelectric actuators and demonstrate generation of highly entangled photons at 1550 nm. At the lowest fine-structure setting, we obtain a maximum fidelity of 90.0 ± 2.7% (concurrence of 87.5 ± 3.1%). The concurrence remains high also for moderate (weak) temporal filtering, with values close to 80% (50%), corresponding to 30% (80%) of collected photons, respectively. The presented fine-structure control opens the way for exploiting entangled photons from quantum dots in fiber-based quantum communication protocols.
Today, optics and photonics is widely regarded as one of the most important key technologies for this century. Many experts even anticipate that the 21st century will be century of photon much as the 20th century was the century of electron. Optics and photonics technologies affect almost all areas of our life and cover a wide range of applications in science and industry, e.g. in information and communication technology, in medicine, life science engineering as well as in energy and environmental technology. However even so attractive, the photonics is not well known by most people. To motivate especially young generation for optics and photonics we worked out a lecture related to the “light” for children aged eight to twelve years. We have prepared many experiments to explain the nature of light and its applications in our everyday life. Finally, we focused on the optical data transmission, i.e. how modern communication over optical networks works. To reach many children at home we recorded this lecture and offered it as a video online in the frame of children’s university at Vorarlberg University of Applied Sciences. By combining the hands-on teaching with having a fun while learning about the basic optics concepts we aroused interest of many children with a very positive feedback.
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.
Debugging errors in software applications can be a major challenge. It is not enough to know that a specific error exists, but the cause of it must be found in order to be able to fix it. Finding the source of an error can be time and cost intensive. The general approach is to analyse and debug the presumably erroneous part of the software. The analysis can be accompanied by instrumentation to gather additional information during the program execution. The analysis is made more difficult by the existence of different errors categories. Each category may need to be handled individually. Especially in embedded software applications, which commonly lack features like process or memory isolation, error detection and prevention can be even more challenging. This is the kind of problem this thesis tackles. This thesis tries to support developers during debugging and troubleshooting. The main focus is on errors related to memory management and concurrency. Specific features and properties of Arm Cortex-M processors are used to try to detect errors as well as their causes. For example, the memory protection unit is used to isolate the stack memories of different tasks running in a RTOS. The thesis tries to provide as much information as possible to the developer when reporting errors of any kind. The solution developed in this thesis also contains a custom memory allocator, which can be used to track down errors related to dynamic memory management. Furthermore, a Eclipse plugin has been developed which provides assertions for array accesses to detect and prevent out-of-bound accesses. The resulting solution has been implemented in commercial embedded software applications. This ensures that the developed solution is not only suitable for newly developed applications, but also for the integration into already existing products.
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.