Lung Digital Twin COVID-19 Infection: A Multiphysics - Multiscale HPC-Modeling Based on CFPD and Agent-Based Model Coupled Simulations

The present work is one of the three pieces (upper airways, lower conductive airways and respiratory zone) of a digital twin lung model developed by the Physical and Numerical Modelling research group from the CASE department in Barcelona Supercomputing Center (BSC). In particular, the study presents the solution of fluid flow and SARS-COV-2 particle transport in the lower conductive zone of the lungs, using a geometry based on patient specific images. The specific context of the current work is framed within the European Project: CREXDATA: Critical Action Planning over Extreme-Scale Data. Its general vision is to develop a generic platform for real-time critical situation management, including flexible action planning and agile decision-making over streaming data of extreme scale and complexity. One of the use cases of the project is the COVID-19 pandemic crisis, studying viral evolution in patients. To that end, the first step is to develop a mechanistic multiscale model to build a toolbox aimed at having a digital twin for the treatment of patients

Enhancing Insight Into Turbulent Lifted Hydrogen Jet Flames Using a Reynolds Stress, Stretched Flamelet Model

This study provides a numerical study of turbulent lifted hydrogen jet flames by employing a Reynolds stress, stretched flamelet model. Computations were conducted on turbulent lifted hydrogen jet flames using several pipe diameters with a range of jet exit velocities and different pipe exit shapes. The computed isotherms and the volumetric heat release rate contour lines are used to determine the thermal and reaction zone liftoff heights. By comparing computational results with experimental data, the limitations inherent in previous experiments concerning liftoff height are elucidated. The scrutinizing of the lifted flame base enhances the understanding of the dynamics governing hydrogen jet flames

Strategies for the Application of Quantum Computers in Computational Fluid Dynamics

A three-year DLR project entitled “Machine Learning and Quantum Computing – Digitalization of Aircraft Development 2.0”, was established in spring 2021 with the goal to investigate whether and how high-fidelity aerodynamic simulations can be carried out using innovative methods from the field of machine learning and in which way these methods can also be implemented on quantum computers

Scaffolding Bad Moral Agents

Recent work on ecological accounts of moral responsibility and agency have argued for the importance of social environments for moral reasons responsiveness. Moral audiences can scaffold individual agents’ sensitivity to moral reasons and their motivation to act on them, but they can also undermine it. In this paper, we look at two case studies of ‘scaffolding bad’, where moral agency is undermined by social environments: street gangs and online incel communities. In discussing these case studies, we draw both on recent situated cognition literature and on scaffolded responsibility theory. We show that the way individuals are embedded into a specific social environment changes the moral considerations they are sensitive to in systematic ways because of the way these environments scaffold affective and cognitive processes, specifically those that concern the perception and treatment of ingroups and outgroups. We argue that gangs undermine reasons responsiveness to a greater extent than incel communities because gang members are more thoroughly immersed in the gang environment

Image-to-Image Translation for Virtual Cresyl Violet Staining From 3D Polarized Light Imaging

Characterizing the structure of cortical networks in the brain requires complementary imaging techniques and the integration of different aspects such as fiber and cell body distributions. Ideally, different methods are applied to the same tissue for direct comparison. 3D polarized light imaging (3D-PLI) visualizes nerve fibers in brain tissue at high resolution based on optical properties alone. This enables subsequent staining of the same tissue for cell bodies after 3D-PLI measurement. However, this process is time-consuming, technically challenging, and requires nonlinear cross-modal registration to obtain pixel correspondence.Here we investigate image-to-image translation methods to predict the results of cell body staining directly from 3D-PLI, using generative adversarial networks (GANs) and neural style transfer (NST). We use 11 coronal sections of a vervet monkey brain for training, each imaged with 3D-PLI and subsequently stained with Cresyl violet for cell bodies. Since pixel-accurate registration of entire sections may be difficult and error-prone, we introduce an online registration head to linearly align model predictions for local image patches to the post-staining during network training. This exploits the fact that local deformations can be approximated by a linear model when a coarse pre-registration is available. The online alignment improves with predictions during training and ultimately converges to an accurate registration. We use a Fourier-based registration approach that is computationally efficient and GPU-parallelizable.We quantify model performance by comparing the predicted virtual staining to post-staining after 3D-PLI measurement. Our best model localizes the majority of larger cell instances (>100 µm² in-plane) segmented by a contour proposal network (CPN) with an F1 score of 63.1. The proposed online registration head significantly improves the performance of all investigated models, increasing F1 scores from 40.6 to 63.1 for NST and from 22.2 to 50.3 for a GAN.The applied virtual staining enables automatic localization of larger cell instances in unstained 3D-PLI images. Since the model predictions are pixel-aligned with 3D-PLI, they enable joint analysis of fiber tracts and cell bodies and may also serve as targets for registration of real post-staining. Future work will extend the training data to include more sections, brains and species, with potential applications to other imaging modalities

Sociotechnical Imaginaries of Locals on the Transformation in the German Mining Area "Rheinisches Revier"

The German mining region “Rheinisches Revier” is undergoing significant economic and social transformations. This transformation is driven by Germany's effort to achieve climate neutrality by 2045 through the phase-out of lignite. Our qualitative study takes a sociological perspective on this transformation and contributes to answering the question of how the structural change in the German mining region “Rheinisches Revier” is perceived by inhabitants of the “Rheinisches Revier” and which sociotechnical imaginaries its people have for the region's future. In addition, we examine the public's perception of various possible transformation paths by analysing which aspects are desirable or not desirable for the residents. We aim for a more in-depth understanding of the residents' ideas by taking a qualitative approach. Therefore, we interview residents of the region individually, considering various stakeholder groups. The data collection is conducted through individual interviews with the inhabitants. The interviews are analyzed using content analysis to identify patterns and themes in the data. The results of the study contribute to understand complex social processes regarding the German mining region “Rheinisches Revier” and indicate implications for political and economic decisionmakers