The 10 most recently published documents
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-EvoEA+BP
(2023)
Enterprise digitalization results in an evolving and dynamic IT landscape of digital elements, relations, knowledge, content, activities, and business processes (BPs), which are spread across disparate enterprise IT systems, repositories, and tools. To be relevant, useful, and actionable, Enterprise Architecture (EA) relies on comprehensive documentation based on underlying information corresponding to reality. Yet current diagram-centric 2D visualizations for EA and BP models are too limited in scope to express reality (intentionally simplifying), are typically static (and not kept up-to-date), and cannot express and integrate the changing complexities of the enterprise context. This misalignment with reality and a changing enterprise misinforms and constrains the context-awareness and perception of EA and BP for stakeholders, impeding analyses, management, and holistic insights into the enterprise digital reality. This paper contributes our nexus-based Virtual Reality (VR) solution concept VR-EvoEA+BP to support comprehensive enterprise context visualization in conjunction with EA and model evolution and BP mining and analysis. Portraying an organic, evolving, and dynamic enterprise while supplementing static enterprise structure depictions, our implementation demonstrates its feasibility. A case study based on enterprise analysis and BP scenarios exhibits its potential.
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-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.
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.
EMDR-Therapie im Vergleich zu EMDR-Therapie, erweitert durch taktilen und auditorischen Stimulus
(2023)
EMDR (Eye Movement Desensitization and Reprocessing) ist ein neurobiologisch orientierter psychotherapeutischer Ansatz zu Behandlung von posttraumatischen Belastungsstörungen (PTBS) und anderen belastenden Lebensereignissen. Dabei werden Augenbewegungen eingesetzt, um die Verarbeitung und Integration traumatischer Erinnerungen zu erleichtern. Diese nichtinterventionelle Anwendungsbeobachtung untersucht die Bedeutung von Augenbewegungen in EMDR und potenzielle Vorteile der Einbeziehung auditiver und taktiler Reize in die Therapie.
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.
Das Ziel der vorliegenden Arbeit ist die Ermittlung der Genauigkeit und Reproduzierbarkeit des Smartphone-gestützten, mobilen Refraktionssystems EyeNetra im Vergleich zur herkömmlichen Refraktion mit dem DNEye Scanner und dem Phoropter. Zudem wird die Messgenauigkeit von Netra mit einem auf einem ähnlichen Messprinzip basierenden Gerät namens EyeQue verglichen.