An Integrated Method for Determination of the Oswald Factor in a Multi-Fidelity Design Environment

Aircraft conceptual design often focuses on unconventional configurations like for example forward swept wings. Assessing the characteristics of these configurations usually requires the use of physic based analysis modules. This is due to the fact that for unconventional configurations no sucient database for historic based analysis modules is available. Nevertheless, physic based models require a lot of input data and their computational cost can be high. Generating input values in a trade study manually is work-intensive and error-prone. Conceptual design modules can be used to generate sucient input data for physic based models and their results can be re-integrated into the conceptual design phase. In this study a direct link between a conceptual design module and an aerodynamic design module is presented. Geometric information is generated by the conceptual design module and the physic based results, in form of the Oswald factor, are then fed back. Apart from the direct link, an equation for determination of the Oswald factor is derived via a Symbolic Regression Approach

Understanding User Experience of COVID-19 Maps through Remote Elicitation Interviews

During the coronavirus pandemic, visualizations gained a new level of popularity and meaning for a wider audience. People were bombarded with a wide set of public health visualizations ranging from simple graphs to complex interactive dashboards. In a pandemic setting, where large amounts of the world population are socially distancing themselves, it becomes an urgent need to refine existing user experience evaluation methods for remote settings to understand how people make sense out of COVID-19 related visualizations. When evaluating visualizations aimed towards the general public with vastly different socio-demographic backgrounds and varying levels of technical savviness and data literacy, it is important to understand user feedback beyond aspects such as speed, task accuracy, or usability problems. As a part of this wider evaluation perspective, micro-phenomenology has been used to evaluate static and narrative visualizations to reveal the lived experience in a detailed way. Building upon these studies, we conducted a user study to understand how to employ Elicitation (aka Micro-phenomenological) interviews in remote settings. In a case study, we investigated what experiences the participants had with map-based interactive visualizations. Our findings reveal positive and negative aspects of conducting Elicitation interviews remotely. Our results can inform the process of planning and executing remote Elicitation interviews to evaluate interactive visualizations. In addition, we share recommendations regarding visualization techniques and interaction design about public health data

MACE: Deliverable D7.6 - Report on user interface design and community experiments

This deliverable presents the progress of the user interface design and community building experiments within the MACE project. In Chapter 2 we generally present the interface of the MACE portal, which is a platform to discover and enrich architectural resources and, at the same time, to support the community formed around architectural topics. Besides the advanced search, the portal provides various visual tools for metadata based search and browsing, tailored to architectural needs (see Chapter 3). Different metadata widgets are used to visualize and access multiple dimensions of each resource, as presented in Chapter 4. These widgets not only establish meaningful cross–connections between resources, but also invite to add and edit metadata effortlessly. In order to generate a critical mass of metadata and ensure sustainability of projects’ outcomes, supporting community and fostering end user contributions are critical. In Chapter 5, we present the components deploied in this direction as well as an analytical framework for incentive mechanisms. Within the dissemination strategy, the MACE project has got a unique chance to raise its public awareness at La Biennale of architecture in Venice, 2008. In this context we designed an interactive installation, demonstrating, in an exhibition setting, the benefits of resource interconnection via metadata (see Chapter 6). Chapter 7 presents our preliminary conclusions and an overview of planned future activities

Unfolding -- A library for interactive maps

Visualizing data with geo-spatial properties has become more important and prevalent due to the wide spread dissemination of devices, sensors, databases, and services with references to the physical world. Yet, with existing tools it is often difficult to create interactive geovisualizations tailored for a particular domain or a specific dataset. We present Unfolding, a library for interactive maps and data visualization. Unfolding provides an API for designers to quickly create and customize geo-visualizations. In this paper, we describe the design criteria, the development process, and the functionalities of Unfolding. We demonstrate its versatility in use through a collection of examples. Results from a user survey suggests programmers find the library easy to learn and to use

AIRCRAFT CONFIGURATION ANALYSIS USING A LOW-FIDELITY, PHYSICS BASED AEROSPACE FRAMEWORK UNDER UNCERTAINTY CONSIDERATIONS

During the early stages of aircraft design, limited information is available to conduct decisions that base on the quality of aircraft configurations. In the present study, information on physical and statistical models is supplemented by the uncertainty that is inherent to the applied analysis modules and propagated through the complete design workflow. Using this method, the possibility arises to make a statement on the level of certainty with which one concept is preferred above another

Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging.Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei.Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future

native t1 and t2 provide distinctive signatures in hypertrophic cardiac conditions comparison of uremic hypertensive and hypertrophic cardiomyopathy

Abstract Aims Profound left ventricular (LV) hypertrophy with diastolic dysfunction and heart failure is the cardinal manifestation of heart remodelling in chronic kidney disease (CKD). Previous studies related increased T1 mapping values in CKD with diffuse fibrosis. Native T1 is a non-specific readout that may also relate to increased intramyocardial fluid. We examined concomitant T1 and T2 mapping signatures and undertook comparisons with other hypertrophic conditions. Methods In this prospective multicentre study, consecutive CKD patients (n = 154) undergoing routine clinical cardiac magnetic resonance (CMR) imaging were compared with patients with hypertensive (HTN, n = 163) and hypertrophic cardiomyopathy (HCM, n = 158), and normotensive controls (n = 133). Results Native T1 was significantly higher in all patient groups, whereas native T2 in CKD only (p  Conclusions Our findings reveal different CMR signatures of common hypertrophic cardiac phenotypes. Native T1 was raised in all conditions, indicating the presence of pathologic hypertrophic remodelling. Markedly raised native T2 was CKD-specific, suggesting a prominent role of intramyocardial fluid

Animated Edge Textures in Node-Link Diagrams: a Design Space and Initial Evaluation

International audienceNetwork edge data attributes are usually encoded using color, opacity, stroke thickness and stroke pattern, or some combination thereof. In addition to these static variables, it is also possible to animate dynamic particles flowing along the edges. This opens a larger design space of animated edge textures, featuring additional visual encodings that have potential not only in terms of visual mapping capacity but also playfulness and aesthetics. Such animated edge textures have been used in several commercial and design-oriented visualizations, but to our knowledge almost always in a relatively ad hoc manner. We introduce a design space and Web-based framework for generating animated edge textures, and report on an initial evaluation of particle properties – particle speed, pattern and frequency – in terms of visual perception

More is the Same; Phase Transitions and Mean Field Theories

This paper looks at the early theory of phase transitions. It considers a group of related concepts derived from condensed matter and statistical physics. The key technical ideas here go under the names of "singularity", "order parameter", "mean field theory", and "variational method". In a less technical vein, the question here is how can matter, ordinary matter, support a diversity of forms. We see this diversity each time we observe ice in contact with liquid water or see water vapor, "steam", come up from a pot of heated water. Different phases can be qualitatively different in that walking on ice is well within human capacity, but walking on liquid water is proverbially forbidden to ordinary humans. These differences have been apparent to humankind for millennia, but only brought within the domain of scientific understanding since the 1880s. A phase transition is a change from one behavior to another. A first order phase transition involves a discontinuous jump in a some statistical variable of the system. The discontinuous property is called the order parameter. Each phase transitions has its own order parameter that range over a tremendous variety of physical properties. These properties include the density of a liquid gas transition, the magnetization in a ferromagnet, the size of a connected cluster in a percolation transition, and a condensate wave function in a superfluid or superconductor. A continuous transition occurs when that jump approaches zero. This note is about statistical mechanics and the development of mean field theory as a basis for a partial understanding of this phenomenon.Comment: 25 pages, 6 figure