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Institute
Soft magnetic Fe-Al alloys have been a subject of research in the past. However, they never saw the same reception in technical applications as the Fe-Si or Fe-Ni alloys, which is, to some extent, due to a low ductility level and difficulties in manufacturing. Additive manufacturing (AM) technology could be a way to avoid issues in conventional manufacturing and produce soft magnetic components from these alloys, as has already been shown with similarly brittle Fe-Si alloys. While AM has already been applied to certain Fe-Al alloys, no magnetic properties of AM Fe-Al alloys have been reported in the literature so far. Therefore, in this work, a Fe-12Al alloy was additively manufactured through laser powder bed fusion (L-PBF) and characterized regarding its microstructure and magnetic properties. A comparison was made with the materials produced by casting and rolling, prepared from melts with an identical chemical composition. In order to improve the magnetic properties, a heat treatment at a higher temperature (1300 °C) than typically applied for conventionally manufactured materials (850–1150 °C) is proposed for the AM material. The specially heat-treated AM material reached values (HC: 11.3 A/m; µmax: 13.1 × 103) that were close to the heat-treated cast material (HC: 12.4 A/m; µmax: 20.3 × 103). While the DC magnetic values of hot- and cold-rolled materials (HC: 3.2 to 4.1 A/m; µmax: 36.6 to 40.4 × 103) were not met, the AM material actually showed fewer losses than the rolled material under AC conditions. One explanation for this effect can be domain refinement effects. This study shows that it is possible to additively manufacture Fe-Al alloys with good soft magnetic behavior. With optimized manufacturing and post-processing, further improvements of the magnetic properties of AM L-PBF Fe-12Al may still be possible.
Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.
In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.
In the fast-growing but also highly competitive market of battery-powered power tools, cell-pack-cooling systems are of high importance, as they guarantee safety and short charging times. A simulation model of an 18 V power tool battery pack was developed to be able to evaluate four different pack-cooling systems (two heat-conductive polymers, one phase change material, and non-convective air as reference) in an application scenario of practical relevance (the intensive use of a power tool followed by cooling down and charging steps). The simulation comprises battery models of 21700 cells that are commercially available as well as heat transfer models. The study highlights the performance of the different cooling materials and their effect on the maximum pack temperature and total charging cycle time. Key material parameters and their influence on the battery pack temperature and temperature homogeneity are discussed. Using phase change materials and heat-conductive polymers, a significantly lower maximum temperature during discharge (up to 26%) and a high shortening potential of the use/charging cycle (up to 32%) were shown. In addition to the cooling material sweep, a parameter sweep was performed, varying the external temperature and air movement. The high importance of the conditions of use on the cooling system’s performance was illustrated.
Transformations in the work–nonwork interface highlight the importance of effectively managing the boundaries between life domains. However, do the ways individuals manage the boundaries between work and nonwork life change from one day to the next? If so, which antecedents may explain these intra-individual fluctuations in boundary management? Drawing on boundary management, spillover, and resource theories, we investigate daily changes in segmentation preferences and integration enactments as a function of experiencing strain in work and nonwork life. Assuming that changes in segmentation preferences reflect an individual’s strategy to regulate negative cross-role spillover, we suppose that strain increases individuals’ segmentation preferences; at the same time, however, it could force individuals to enact more integration.
Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
VR-V&V
(2023)
To build quality into a software (SW) system necessitates supporting quality-related lifecycle activities during the software development. In software engineering, software Verification and Validation (V&V) processes constitute an inherent part of Software Quality Assurance (SQA) processes. A subset of the V&V activities involved are: 1) bidirectional traceability analysis of requirements to design model elements, and 2) software testing. Yet the complex nature of large SW systems and the dependencies involved in both design models and testing present a challenge to current V&V tools and methods regarding support for trace analysis. One of software’s essential challenges remains its invisibility, which also affects V&V activities. This paper contributes VR-V&V, a Virtual Reality (VR) solution concept towards supporting immersive V&V activities. By visualizing requirements, models, and testing artifacts with dependencies and trace relations immersively, they are intuitively accessible to a larger stakeholder audience such as SQA personnel while supporting digital cognition. Our prototype realization shows the feasibility of supporting immersive bidirectional traceability as well as immersive software test coverage and analysis. The evaluation results are based on a case study demonstrating its capabilities, in particular traceability support was performed with ReqIF, ArchiMate models, test results, test coverage, and test source to test target dependencies.
Software design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations.
VR-SysML+Traceability
(2023)
As systems grow in complexity, the interdisciplinary nature of systems engineering makes the visualization and comprehension of the underlying system models challenging for the various stakeholders. This, in turn, can affect validation and realization correctness. Furthermore, stakeholder collaboration is often hindered due to the lack of a common medium to access and convey these models, which are often partitioned across multiple 2D diagrams. This paper contributes VR-SysML, a solution concept for visualizing and interacting with Systems Modeling Language (SysML) models in Virtual Reality (VR). Our prototype realization shows its feasibility, and our evaluation results based on a case study shows its support for the various SysML diagram types in VR, cross-diagram element recognition via our Backplane Followers concept, and depicting further related (SysML and non-SysML) models side-by-side in VR.
VR-GitCity
(2023)
The increasing demand for software functionality necessitates an increasing amount of program source code that is retained and managed in version control systems, such as Git. As the number, size, and complexity of Git repositories increases, so does the number of collaborating developers, maintainers, and other stakeholders over a repository’s lifetime. In particular, visual limitations of command line or two- dimensional graphical Git tooling can hamper repository comprehension, analysis, and collaboration across one or multiple repositories when a larger stakeholder spectrum is involved. This is especially true for depicting repository evolution over time. This paper contributes VR-GitCity, a Virtual Reality (VR) solution concept for visualizing and interacting with Git repositories in VR. The evolution of the code base is depicted via a 3D treemap utilizing a city metaphor, while the commit history is visualized as vertical planes. Our prototype realization shows its feasibility, and our evaluation results based on a case study show its depiction, comprehension, analysis, and collaboration capabilities for evolution, branch, commit, and multi-repository analysis scenarios.
VR-EDStream+EDA
(2023)
With increasing digitalization, the importance of data and events, which comprise its most fundamental level, cannot be overemphasized. All types of organizations, including enterprises, business, government, manufacturing, and the supporting IT, are dependent on these fundamental building blocks. Thus, evidence-based comprehension and analysis of the underlying data and events, their stream processing, and correlation with enterprise events and activities becomes vital for an increasing set of (grassroot or citizen) stakeholders. Thus, further investigation of accessible alternatives to visually support analysis of data and events is needed. This paper contributes VR-EDStream+EDA, a solution for immersively visualizing and interacting with data and event streams or pipelines and generically visualizing Event-Driven Architecture (EDA) in Virtual Reality (VR). Our realization shows its feasibility, and a case-based evaluation provides insights into its capabilities.
The surface topography of biodegradable polymer foils is modified by mechanical imprinting on a submillimeter length scale. The created patterns strongly influence the wetting behavior and allow the preparation of hydrophobic surfaces with controlled solid-liquid interaction. A detailed analysis of anisotropic surface patterns reveals that the observed effect arises from a combination of topographical and compositional changes that are introduced to the surface. As a main result it is found that an individual combination of material and structure is required for the production of water-repellent biopolymer foils that are highly attractive for packaging applications.
Laser melting manufacturing of large elements of lunar regolith simulant for paving on the Moon
(2023)
The next steps for the expansion of the human presence in the solar system will be taken on the Moon. However, due to the low lunar gravity, the suspended dust generated when lunar rovers move across the lunar soil is a significant risk for lunar missions as it can affect the systems of the exploration vehicles. One solution to mitigate this problem is the construction of roads and landing pads on the Moon. In addition, to increase the sustainability of future lunar missions, in-situ resource utilization (ISRU) techniques must be developed. In this paper, the use of concentrated light for paving on the Moon by melting the lunar regolith is investigated. As a substitute of the concentrated sunlight, a high-power CO2 laser is used in the experiments. With this set-up, a maximum laser spot diameter of 100 mm can be achieved, which translates in high thicknesses of the consolidated layers. Furthermore, the lunar regolith simulant EAC-1A is used as a substitute of the actual lunar soil. At the end of the study, large samples (approximately 250 × 250 mm) with interlocking capabilities were fabricated by melting the lunar simulant with the laser directly on the powder bed. Large areas of lunar soil can be covered with these samples and serve as roads and landing pads, decreasing the propagation of lunar dust. These manufactured samples were analysed regarding their mineralogical composition, internal structure and mechanical properties.
Future lunar exploration will be based on in-situ resource utilization (ISRU) techniques. The most abundant raw material on the Moon is lunar regolith, which, however, is very scarce on Earth, making the study of simulants a necessity. The objective of this study is to characterize and investigate the sintering behavior of EAC-1A lunar regolith simulant. The characterization of the simulant included the determination of the phase assemblage, characteristic temperatures determination and water content analysis. The results are discussed in the context of sintering experiments of EAC-1A simulant, which showed that the material can be sintered to a relative density close to 90%, but only within a very narrow range of temperatures (20–30 °C). Sintering experiments were performed for sieved and unsieved, as well as for dried and non-dried specimens of EAC-1A. In addition, an analysis of the densification and mechanical properties of the sintered specimens was done. The sintering experiments at different temperatures showed that the finest fraction of sieved simulant can reach a higher maximum sintering temperature, and consequently a higher densification and biaxial strength. The non-dried powder exhibited higher densification and biaxial strength after sintering compared to the dried specimen. This difference was explained with a higher green density of the non-dried powder during pressing, rather than due to an actual influence on the sintering mechanism. Nevertheless, drying the powder prior to sintering is important to avoid the overestimation of the strength of specimens to be fabricated on the Moon.
The volume of program source code created, reused, and maintained worldwide is rapidly increasing, yet code comprehension remains a limiting productivity factor. For developers and maintainers, well known common software design patterns and the abstractions they offer can help support program comprehension. However, manual pattern documentation techniques in code and code-related assets such as comments, documents, or models are not necessarily consistent or dependable and are cost-prohibitive. To address this situation, we propose the Hybrid Design Pattern Detection (HyDPD), a generalized approach for detecting patterns that is programming-language-agnostic and combines graph analysis (GA) and Machine Learning (ML) to automate the detection of design patterns via source code analysis. Our realization demonstrates its feasibility. An evaluation compared each technique and their combination for three common patterns across a set of 75 single pattern Java and C# public sample pattern projects. The GA component was also used to detect the 23 Gang of Four design patterns across 258 sample C# and Java projects as well as in a large Java project. Performance and scalability were measured. The results show the advantages and potential of a hybrid approach for combining GA with artificial neural networks (ANN) for automated design pattern detection, providing compensating advantages such as reduced false negatives and improved F1 scores.
Today’s Industry 4.0 Smart Factories involve complicated and highly automated processes. Nevertheless, certain crucial activities such as machine maintenance remain that require human involvement. For such activities, many factors have to be taken into account, like worker safety or worker qualification. This adds to the complexity of selection and assignment of optimal human resources to the processes and overall coordination. Contemporary Business Process Management (BPM) Systems only provide limited facilities regarding activity resource assignment. To overcome these, this contribution pro- poses a BPM-integrated approach that applies fuzzy sets and rule processing for activity assignment. Our findings suggest that our approach has the potential for improved work distribution and cost savings for Industry 4.0 production processes. Furthermore, the scalability of the approach provides efficient performance even with a large number of concurrent activity assignment requests and can be applied to complex production scenarios with minimal effort.
Although production processes in Industry 4.0 set- tings are highly automated, many complicated tasks, such as machine maintenance, continue to be executed by human workers. While smart factories can provide these workers with some digitalization support via Augmented Reality (AR) devices, these AR tasks depend on many contextual factors, such as live data feeds from machines in view, or current work safety conditions. Although currently feasible, these localized contextual factors are mostly not well-integrated into the global production process, which can result in various problems such as suboptimal task assignment, over-exposure of workers to hazards such as noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) were not particularly designed to consider and integrate context-aware factors during planning and execution. This paper describes the AR-Process Framework (ARPF) for extending a BPMS to support context-integrated modeling and execution of processes with AR tasks in industrial use cases. Our realization shows how the ARPF can be easily integrated with prevalent BPMS. Our evaluation findings from a simulation scenario indicate that ARPF can improve Industry 4.0 processes with regard to AR task execution quality and cost savings.
With the increasing pressure to deliver additional software functionality, software engineers and developers are often confronted with the dilemma of sufficient software testing. One aspect to avoid is test redundancy, and measuring test (or code or statement) coverage can help focus test development on those areas that are not yet sufficiently tested. As software projects grow, it can be difficult to visualize both the software product and the software testing area and their dependencies. This paper contributes VR-TestCoverage, a Virtual Reality (VR) solution concept for visualizing and interacting with test coverage, test results, and test dependency data in VR. Our VR implementation shows its feasibility. The evaluation results based on a case study show its support for three testing-related scenarios.
The increasing demand for software functionality necessitates an increasing amount of program source code that is retained and managed in version control systems, such as Git. As the number, size, and complexity of Git repositories increases, so does the number of collaborating developers, maintainers, and other stakeholders over a repository’s lifetime. In particular, visual limitations of Git tooling hampers repository comprehension, analysis, and collaboration across one or multiple repositories with a larger stakeholder spectrum. This paper contributes VR-Git, a Virtual Reality (VR) solution concept for visualizing and interacting with Git repositories in VR. Our prototype realization shows its feasibility, and our evaluation results based on a case study show its support for repository comprehension, analysis, and collaboration via branch, commit, and multi-repository scenarios.
Repeatable processes are fundamental for describing how enterprises and organizations operate, for production, for Industry 4.0, etc. As digitalization and automation progresses across all organizations and industries, including enterprises, business, government, manufacturing, and IT, evidence-based comprehension and analysis of the processes involved, including their variations, anomalies, and performance, becomes vital for an increasing set of stakeholders. Process Mining (PM) relies on logs or processes (as such evidence-based) to provide process-centric analysis data, yet insights are not necessarily visually accessible for a larger set of stakeholders (who may not be process or data analysts). Towards addressing certain challenges described in the Process Mining Manifesto, this paper contributes VR-ProcessMine, a solution for visualizing and interacting with PM results in Virtual Reality (VR). Our realization shows its feasibility, and a case-based evaluation provides insights into its capabilities.
As systems grow in complexity, the interdisciplinary nature of systems engineering makes the visualization and comprehension of the underlying system models challenging for the various stakeholders. This, in turn, can affect validation and realization correctness. Furthermore, stakeholder collaboration is often hindered due to the lack of a common medium to access and convey these models, which are often partitioned across multiple 2D diagrams. This paper contributes VR-SysML, a solution concept for visualizing and interacting with SysML models in Virtual Reality (VR). Our prototype realization shows its feasibility, and our evaluation results based on a case study shows its support for the various SysML diagram types in VR, cross-diagram element recognition via our backplane followers concept, and depicting further related (SysML and non-SysML) models side-by-side in VR.
Strategy development is one of the crucial factors for a firm's performance. For it
to be developed, a strategic analysis has to be conducted first. It enables
companies to gain a deeper understanding of their internal and external
environment. In the present work, the specialty coffee market is closely analyzed through a strategic analysis. The focus of this study is the young company Tikuna, a coffee producer that aims to enter the German market. In this context, Tikuna's possible entry into the German market and the companies competitive capacities are analyzed. In order to implement the different tools of the analysis, extensive literature research, as well as one expert interview and a survey were conducted.
It was found that Tikuna possesses all characteristics to enter the German
market. However, due to the lack of a differentiation factor in Tikuna's value
proposition, its competitive capacity is limited to a short period of time. In this
sense, different recommendations are given in order to ensure long term success
in the market. The central one being that Tikuna has to use its main strength and
bring innovation to the market.
Leveraging Augmented Reality to Support Context-Aware Tasks in Alignment with Business Processes
(2021)
The seamless inclusion of Augmented Reality (AR) with Business Process Management Systems (BPMSs) for Smart Factory and Industry 4.0 processes remains a challenge. Towards this end, this paper contributes an approach integrating context-aware AR into intelligent business processes to support and guide manufacturing personnel tasks and enable live task assignment optimization and support task execution quality. Our realization extends two BPMSs (Camunda and AristaFlow) and various AR devices. Various AR capabilities are demonstrated via a simulated industrial case study.
Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.
Purpose: The aim is to be able to advise patients on the choice of sports and exercises regarding the effects on the intraocular pressure.
Methods: The search engines Google Scholar and PubMed were used to search for suitable studies. The studies were summarized, and the most important data were collected in one table for each study. The effect on the IOP was extracted or, if not given in the article, calculated by the difference of means of the IOP after or during exercise, and the baseline IOP before, whenever these values were available.
Findings: A total of 47 studies out of the years 1990 to 2020 that investigated the influence on the IOP of the most popular sports actively practiced in Germany were reviewed and summarized: twelve for running, sixteen for fitness/ weight training, one for swimming/diving, twelve for cycling, four for hiking, and two for yoga.
Conclusions: Throughout all studies and sports it was seen that physical fitness stabilized the IOP. Higher
intensity of exercise led to higher fluctuations of the IOP. Moderate endurance training keeps the IOP fluctuations low and may lead to a lower baseline IOP if practiced on a regular base. Fitness and weight training lead to fluctuations of the IOP in a pronounced manner when performed at moderate and high intensity. Therefore, only a moderate training can be recommended if there is need to keep the IOP stable. Isometric exercise is not recommended as it provokes a rise of the IOP even when performed with light loads. The Valsalva Maneuver should always be avoided as it leads to additional fluctuations of the IOP. Also, the IOP behaved more stable during resistance training when higher fitness was present.
Novel myopia control spectacle lenses induce peripheral contrast reduction via optical diffusion. It is suggested, that the contrast reduction alters retinal processes in the low-level neural circuity, leading to an inhibition of eye growth. The purpose of this thesis is to evaluate the influence of full-field contrast reduction on low-level neural processing of the retina, described by the edge contrast sensitivity.
This research project is of particular importance since there is a lack of adequate data on pediatric eye and vision disorders in Russia, particularly in the Volga region. In the present study, we estimate the prevalence of vision disorders among school-aged children who participated in a vision screening program in Samara, Russia. The relationship between learning-related visual dysfunctions is explored in depth, such to illustrate the connection between vision and learning. Hence, a key feature in this study is the inclusion of binocular vision disorders among the conditions tested.
The direct ophthalmoscope is a retinal screening tool that has been in existence and development for more than 150 years, yet, the rapid influence of technological evolution in screening tools, has left the direct ophthalmoscope untouched. The main purpose of this master thesis is to determine if the direct ophthalmoscope has reached its maximum potential of development and, additionally, to determine if a further development, including a more electronic input, would be feasible.
Forming complex parts out of high and ultra-high strength aluminium alloys has proved to be more challenging in comparison to the currently used deep drawing steels. Nevertheless, aluminium alloys show a limited formability in contrast with, for example, deep drawing steels. Novel processes like Warm-forming, W-Temper or Hotforming, offer the potential to produce light and highly integrated one-piece components from such aluminium alloys at elevated temperatures. When considering aluminium alloys of the 7000 group, which can reach strength values (UTS) of about 600 MPa, crash components such as side impact bars would offer a suitable field of application.
Forming at elevated temperatures, in particular with the Hotforming process, offers high potential in the production of complex structural components on the one hand and in the use of existing press hardening equipment on the other. To date, the material behaviour of aluminium alloys in the 7000 group, applied in such processes and in the later final state after forming, is not sufficiently known.
Therefore, in this study, systematic investigations on the formability and the final strength during and after forming at elevated temperature of the EN AW-7075 aluminium wrought alloy from different suppliers are conducted. In general, material- and damage/ failure models were created and implemented into simulation in order to make predictions. Characterisation of the plastic material properties on the basis of various tensile specimens as shear-, notched-, tensile- and Erichsen tests are carried out to adapt the complex material- and failure models such as Barlat YLD2000 and GISSMO to the experimental values using a parameter optimisation. These were made for the material conditions during forming, i.e. after solution heat treatment, the final condition after artificial ageing at 180°C for 20 minutes, which corresponds to the cathodic dip coating, and the T6 condition, which is the highest strength condition.
To evaluate a suitable friction coefficient for high temperature forming processes, anti-friction agents are screened, and the potential applicability evaluated by strip-drawing tests. Thereby, using an analytical relationship, friction coefficients are determined at room temperature and 180°C, which are used as corresponding friction model for the finite element forming simulation.
Crash simulations using the nonlinear finite element method (FEM) of side impact protection beams are used to demonstrate the weight saving potential of high and ultra-high strength aluminium alloys compared to a beam made of press hardened steel. A weight saving of about 20 % could be achieved with the same crash performance. This can be significantly increased to around 30 % - 40 % by using local reinforcements such as CFRP or GFRP (carbon/ glass fibre reinforced plastic) patch. For this reason, a novel process was developed which is based on the conventional Hotforming process with an integrated thermal direct joining step called “Extended Hotforming”.
Subsequently, a heatable forming tool for the production of a serial like sheet metal side impact beam was developed to validate the finite element simulation and to demonstrate the potential of the forming processes at elevated temperatures for aluminium sheet metal components.
As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.
DEKXTROSE: An Education 4.0 Mobile Learning Approach and Object-Aware App Based on a Knowledge Nexus
(2020)
The exponential growth in knowledge coupled with the decreasing knowledge half-life creates a challenging situation for educational programs - particularly those preparing software engineers for their very dynamic high-technology field. Teachers in high technology education areas are challenged in selecting and making relevant knowledge intuitively accessible to students, especially with regard the highly dynamic digital and software technologies. This paper contributes a knowledge nexus-based multimedia approach aligned with Higher Education 4.0 for creating learning apps on mobile devices that support multiple didactic models, leverage intrinsic curiosity and motivation, support gamification, and enable digital collaboration. Object recognition is used to trigger learning paths, and various didactic methods are supported via workflow-like learning flows to support group or team-based learning. A prototype app was realized to demonstrate its feasibility and an empirical evaluation in software engineering shows the didactic potential and advantages of the approach, which can be readily generalized and applied to the arts, sciences, etc.
The increasing prevalence of myopia throughout the industrialized world in recent decades has caused costs and problems for the eye health. Changed lifestyle and behavior are the main causes. For the pathogenesis of myopia, the amount of time spent outdoor and near activities play an important role. Various options for the treatment of myopia have been described as effective in the literature. Normal single vision glasses and contact lenses can only provide clear vision, but do not reduce myopia progression. Orthokeratology shows a slowing of axial growth, but has an increased risk of infectious keratitis. Low-dose atropine (0.01%) is currently the best pharmacological option. It proved safe, effective and showed the least rebound effect with negligible side effects. Other options for the treatment of myopia include special glasses, behavioral changes and prolonged outdoor exposure (to prevent the onset of myopia), as well as other methods. An increasingly important myopia management option is multifocal contact lenses, that provide a peripheral treatment zone producing myopic defocus. Such myopia control lenses are available as customized or as daily or monthly lenses. Children benefit from wearing contact lenses more than just having refractive error correction and myopia control, they have a better self-esteem and improved quality of life. The numerous findings on the safety and efficacy of soft multifocal distance center contact lenses in children to reduce the progression of myopia suggest that this modality should be considered as a main treatment option. Less, but similar to orthokeratology, when wearing soft lenses there is a risk of developing potentially serious complications such as microbial keratitis. The introduction of child-appropriate risk minimization strategies, and patient and parent education with regular monitoring is essential and leads to successful contact lens wear. This literature review summarized the actual knowledge about myopia management, prevalence, etiology and the visual and healthy consequences of myopia.
The three currently most important strategies for slowing the progression of myopia are soft multifocal distance center contact lenses, Orthokeratology and low-dose atropine ophthalmic drops.
Enterprise Architecture (EA) Frameworks (EAFs) have attempted to support comprehensive and cohesive modeling and documentation of the enterprise. However, these EAFs were not conceived for today’s rapidly digitalized enterprises and the associated IT complexity. A digitally-centric EAF is needed, freed from the past restrictive EAF paradigms and embracing the new potential in a data-centric world. This paper proposes an alternative EAF that is digital, holistic, and digitally sustainable - the Digital Diamond Framework. D2F is designed for responsive and agile enterprises, for aligning business plans and initiatives with the actual enterprise state, and addressing the needs of EA for digitized structure, order, modeling, and documentation. The feasibility of D2F is demonstrated with a prototype implementation of an EA tool that applies its principles, showing how the framework can be practically realized, while a case study based on ArchiSurance example and an initial performance and scalability characterization provide additional insights as to its viability.
Databases are becoming an ubiquitous and integral part of most software as the data era and the Internet of Everything unfolds. Alternative database types such as NoSQL grow in popularity and allow data to be stored and accessed more simply or in new ways. Thus, software developers, not just database specialists, are more likely to encounter and need to deal with databases. Virtual Reality (VR) technology has grown in popularity, yet its integration in the software development tool chain has been limited. One potential application area for VR technology that has not been sufficiently explored is database-model visualization. This paper describes Virtual Reality Immersion in Data Models (VRiDaM), a generic database-model approach for visualizing, navigating, and conveying database-model information interactively. It describes and explores both native VR and WebVR solution concepts, with prototypes showing the viability of the approach.
The present study deals with the topic how a town can use its cultural heritage or,
more precisely, its industrial culture as a means to market itself as an innovative
business location and to foster a more pronounced sense of civic cohesion among
residents. Economic theory suggests that, nowadays, traditional location factors
such as access to resources and a performant infrastructure are less important than
in the industrial age. Recently, factors like a city’s potential to generate and retain
human and creative capital have emerged. Accordingly, the economic and social
role of cities has shifted – from a place where workers lived and manufactured
goods towards a deeply interwoven ecosystem of knowledge-intense value creation.
The question at the root of the present study is how Heidenheim’s rich industrial
cultural heritage can be used as a future-pointing source of power for rebranding
the town. This rebranding concept has to be developed according to the town’s role
in past, present and future, thus creating actual economic and societal value.
Industrial culture bears branding potential and is closely related to various aspects
of modern life and work. The study examines possibilities to create awareness for
these relations connecting past, present and future. Their relevance shall be
emphasized in order to establish both points of orientation and authenticity of place
in times when macroeconomic and societal trends are difficult to predict. Ideally,
residents shall be given a sort of local identification to hold on to, and potential
investors and entrepreneurs shall be encouraged to sustainably experience the
innovation-based DNA of Heidenheim. Therefore, the study searches for a value
proposition that takes into account the points mentioned above on terms of an
innovative theoretic framework. As a result of this thesis, precise suggestions for
the implementation of a new branding strategy based on the conceptual guidelines
developed in this study will be proposed to the municipality of Heidenheim and, in
addition, an interface using principles of virtual and augmented reality will be
introduced.
This research looks into the question of where and how Artificial Intelligence and Big Data can be usefully implemented into Affiliate Marketing. By consulting relevant literature and qualified experts, this work identifies 6 areas, where Artificial Intelligence can be beneficial. These areas were found to be Affiliate Recruitment, Affiliate Management, Product Data Feed Optimization, Tracking, Attribution and Forecasting.
The implementation of Artificial Intelligence in these areas revealed 3 advantages to the Affiliate Marketing channel: Saving of time, support of decision-making, and incentivizing of publishers. While a more detailed study of this research topic would be necessary for validating the results, the findings show that the implementation of Artificial Intelligence technology can help a business gain competitive advantage.
While Virtual Reality (VR) has been applied to various domains to provide new visualization and interaction capabilities, enabling programmers to utilize VR for their software development and maintenance tasks has been insufficiently explored. In this paper, we present the Hyper-Display Environment (HyDE) in the form of a mixed-reality (HyDE-MR) or virtual reality (HyDE-VR) variant respectively, which provides simultaneous multiple operating system window visualization with integrated keyboard/mouse viewing and interaction using MR or in pure VR via a virtual keyboard. This paper applies HyDE in a software development case study as an alternative to typical non-VR Integrated Development Environments (IDEs), supporting software engineering tasks with multiple live screens in VR as an augmented virtuality. The MR solution concept enables programmers to benefit from VR visualization and virtually unlimited information displays while supporting their more natural keyboard interaction for basic code-centric tasks. Thus, developers can leverage VR paradigms and capabilities while directly interacting with their favorite tools to develop and maintain program code. A prototype implementation is described, with a case study demonstrating its feasibility and an initial empirical study showing its potential.
Purpose: Recent studies found a reduction of myopia progression with multifocal contact lenses, however, with yet unclear mechanism. This raises the hypothesis that the addition zones of the multifocal contact lenses induce myopic defocus on the retina, which consequentially leads to choroidal thickening and therefore inhibited eye growth. We tested the effect of the optical design of multifocal contact lenses on choroidal thickness.
Methods: 18 myopic students wore four different contact lenses ((1) single-vision lens corrected for distance, (2) single-vision lens with +2.50 D full-field defocus, (3) “Multifocal center-distance” design, addition +2.50 D, (4) “Multifocal center-near” design, addition +2.50 D) for each 30 minutes on their right eye. Automated analysis of the macular choroidal thickness, vitreous chamber depth and eccentric photorefraction were performed before and after each contact lens.
Results: Choroidal thickness and vitreous chamber depth showed no significant differences to baseline with none of the contact lenses. Choroidal thickness increased the most with the “Multifocal center-distance” and the full-field defocus lens, followed by the “Multifocal center-near” and the single-vision contact lens (+2.1 ± 11.1 μm, +2.0 ± 11.1 μm, +1.6 ± 11.3 μm, +0.9 ± 11.2 μm, respectively). The “Multifocal center-distance” design showed an overall more myopic refractive profile than the other lenses. Changes of vitreous chamber depth occurred in anti-phase to these of choroidal thickness.
Conclusion: Multifocal contact lenses have no significant influence on choroidal thickness and after short-term wear. Therefore, it is assumed that it is not the main contributor to the protective effect of multifocal contact lenses in myopia control.
This paper describes an application analysis of one important topic of diversity
marketing – gender marketing. With the help of two surveys and content analyses
in two different media sectors – television and print media – the general trend of
gender representation in advertising could be located. While most survey
respondents are still using characteristics for males and females which were
shaped by traditional gender roles, most of them believe that the roles from the
1950s are outdated and that the media should adapt to the changes in societies in
regard to gender roles. However, the content analyses have shown that the
marketers have already adapted and are primarily presenting the viewers
contemporary images of men and women instead of the stereotyped ones from the
1950s. The only issue that has not changed yet is the color coding which starts to
differentiate between males and females since childhood. The findings of this
paper suggest that the perception and the reality do not always correspond with
each other and that, although the adoption of the change of gender roles is
advancing, it is still not completed yet.
Purpose
Automated scanpath comparison metrics should deliver an objective method to
evaluate the similarity of scanpaths. The aim of this thesis is an evaluation of
seven existing scanpath comparison metrics in static and dynamic tasks in order
to provide a guidline that helps to decide which algorithm has to be chosen for a
special kind of task.
Methods
The applicability of the algorithms for a static, visual search task and a dynamic,
interactive video game task as well as their constraints and limitations were tested.
Therefore, binocular gaze data were recorded by using the eye tracking system The
Eye Tribe (The Eye Tribe ApS, Copenhagen/ Denmark). Objective task performance
measures from 21 subjects were used in order to create scanpath groupings
for which a relevant effect of dissimilarity was to be expected. Objective task performance
measures such as task performance time were statistically evaluated and
compared to the results gained by the comparison metrics.
Results
Four of the algorithms being used successfully identified differences for static and
dynamic tasks: MultiMatch, iComp, SubsMatch and the Hidden Markov Model.
ScanMatch was very sensitive for the static task but not applicable to the dynamic
task whereas FuncSim was suitable for dynamic but not for static tasks. Eyenalysis
failed to detect any effect.
Conclusion
The applicability of scanpath comparison metrics depends on the state of the task,
respectively on the kind of experimental set up. In future, the application area for
eye tracking will expand and an improvement of automated scanpath comparison
metrics is therefore required.
Purpose: The purpose of this thesis is to provide a comprehensive literature review about albinism as an inherited metabolic disorder of melanin synthesis along with those related conditions impacting the visual system. As such, it addresses eye care emphasizing the visual consequences of albinism along with diagnostic and treatment options.
Methods: Background knowledge about ocular development is given as well as information about etiological biochemical and genetic processes. The current classification, clinical findings and their assessment and management options are presented based on recent results of research. In conclusion, two case reports are described as examples of visual care options.
Results: Melanin plays a big role in the retinal and chiasmal development. Melanin biosynthesis can be disrupted by different genes in various ways which leads to the current classification of albinism. Clinical findings include fundus hypopigmenta-tion, nystagmus, iris transillumination, photophobia, foveal hypoplasia, excessive chiasmal decussation, reduced visual acuity, high astigmatism (with-the-rule), strabismus and decreased stereopsis. Treatment options to improve visual acuity, fixation and binocularity are (tinted) prescription lenses and contact lenses, low vision aids, surgical procedures and vision therapy. Medication and supplementa-tion for increased pigmentation are currently being tested on mice.
Conclusions: Albinism is caused by genetic mutations resulting in ocular and cutaneous hypopigmentation. It establishes various phenotypes that require different therapy approaches in order to improve vision and therefore quality of life.
Purpose
To determine the stereo threshold and inherent variability with a monitor-based two-rod test at various eccentricities of the visual field. Additionally, to evaluate the duration of this procedure.
Subjects and methods
A pilot trial was conducted in five ophthalmologically normal subjects (2 male and 3 female) aged 21 – 23 years. Two black rods on white background, which appeared under an angle of 1° were presented in a viewing distance of 5.0 meters. The right rod was stationary, whilst the left rod appeared under a stereoscopic parallax, with an either proximal or distal displacement to the image plane. Threshold determination was assessed at seven eccentricities of the visual field by a staircase method. Eccentricities were 0° centrally, 5° to the right and left, 10° to the right and left and 15° to the right and left of the visual field. Proximal and distal displacement as well as the sequence of eccentricities were presented in random order. Stereo acuity was measured in two different sessions for four subjects and in five different sessions for one subject. For all sessions the duration was recorded. All sessions were separated by a time interval of at least 24 hours and no longer than 7 days. Evaluation was made by Wilcoxon test and Kruskal Wallis test at the 95% confidence level (CI) and the median was assessed for all thresholds.
Results
Stereo acuity declines with increasing eccentricities of the retina similar to visual acuity. While at 0° eccentricity thresholds were found to be lowest with (median) 5.0 seconds of arc (‘’) and the CI (0.5’’, 30.5’’) for all measurements, they increased to 112.2’’ at 15° eccentricity to the left in proximal displacement. Distal it was 19.9’’ centrally and 112.2’’ to the right at 15° eccentricity with CI (0.5’’, 30.5’’) for all measurements.
Repeatability of the threshold determination was found to be best at 0° eccentricity with proximal displacement showing the exact same result in the repetitive session and poorest repetition was found at 15° eccentricity to the left with distal displacement. Distal repeatability was worse than proximal. Median and CI of duration time was 5.3 (3.2, 8.3) minutes.
Conclusion
Stereo acuity thresholds are repeatable however increase with increasing eccentricity. Repetitions of the threshold determination do not vary considerably. The duration of these measurements indicates the monitor-based two-rod test as a fast procedure, that can be applied in future studies. The test program is limited by an imperfect algorithm and the stereoscopic images evoke cues, this should be reworked.