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The utilization of lasers in dentistry expands greatly in recent years. For instance, fs-lasers are effective for both drilling and caries prevention, while cw-lasers are useful for adhesive hardening. A cutting-edge application of lasers in dentistry is the debonding of veneers. While there are pre-existing tools for this purpose, there is still potential for improvement. Initial efforts to investigate laser assisted debonding mechanisms with measurements of the optical and mechanical properties of teeth and prosthetic ceramics are presented. Preliminary tests conducted with a laser system used for debonding that is commercially available showed differences in the output power set at the systems console to that at specified distances from the handpiece. Furthermore, the optical properties of the samples (human teeth and ceramics) were characterised. The optical properties of the ceramics should closely resemble those of teeth in terms of look and feel, but they also influence the laser assisted debonding technique and thus must be taken into account. In addition first attempts were performed to investigate the mechanical properties of the samples by means of pump-probe-elastography under a microscope. By analyzing the sample surface up to 20 ns after a fs-laser pulse impact, pressure and shock waves could be detected, which can be utilized to determine the elastic constants of specific materials. Together such investigations are needed to shape the basis for a purely optical approach of debonding of veneers utilizing acoustic waves.
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.