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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The genetics of aerotolerant growth in an alphaproteobacterium with a naturally reduced genome

Reduced genome bacteria are genetically simplified systems that facilitate biological study and industrial use. The free-living alphaproteobacterium Zymomonas mobilis has a naturally reduced genome containing fewer than 2,000 protein-coding genes. Despite its small genome, Z. mobilis thrives in diverse conditions including the presence or absence of atmospheric oxygen. However, insufficient characterization of essential and conditionally essential genes has limited broader adoption of Z. mobilis as a model alphaproteobacterium. Here, we use genome-scale CRISPRi-seq (clustered regularly interspaced short palindromic repeats interference sequencing) to systematically identify and characterize Z. mobilis genes that are conditionally essential for aerotolerant or anaerobic growth or are generally essential across both conditions. Comparative genomics revealed that the essentiality of most “generally essential” genes was shared between Z. mobilis and other Alphaproteobacteria, validating Z. mobilis as a reduced genome model. Among conditionally essential genes, we found that the DNA repair gene, recJ, was critical only for aerobic growth but reduced the mutation rate under both conditions. Further, we show that genes encoding the F1FO ATP synthase and Rhodobacter nitrogen fixation (Rnf) respiratory complex are required for the anaerobic growth of Z. mobilis. Combining CRISPRi partial knockdowns with metabolomics and membrane potential measurements, we determined that the ATP synthase generates membrane potential that is consumed by Rnf to power downstream processes. Rnf knockdown strains accumulated isoprenoid biosynthesis intermediates, suggesting a key role for Rnf in powering essential biosynthetic reactions. Our work establishes Z. mobilis as a streamlined model for alphaproteobacterial genetics, has broad implications in bacterial energy coupling, and informs Z. mobilis genome manipulation for optimized production of valuable isoprenoid-based bioproducts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Partnership For Edge Physics Simulation

In this effort, we will extend our prior work as part of CPES (i.e., DART and DataSpaces) to support in-situ tight coupling between application codes that exploits data locality and core-level parallelism to maximize on-chip data exchange and reuse. This will be accomplished by mapping coupled simulations so that the data exchanges are more localized within the nodes. Coupled simulation workflows can more effectively utilize the resources available on emerging HEC platforms if they can be mapped and executed to exploit data locality as well as the communication patterns between application components. Scheduling and running such workflows requires an extended framework that should provide a unified hybrid abstraction to enable coordination and data sharing across computation tasks that run on the heterogeneous multi-core-based systems, and develop a data-locality based dynamic tasks scheduling approach to increase on-chip or intra-node data exchanges and in-situ execution. This effort will extend our prior work as part of CPES (i.e., DART and DataSpaces), which provided a simple virtual shared-space abstraction hosted at the staging nodes, to support application coordination, data sharing and active data processing services. Moreover, it will transparently manage the low-level operations associated with the inter-application data exchange, such as data redistributions, and will enable running coupled simulation workflow on multi-cores computing platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ChargeX OCPI Recommendations

The Open Charge Point Interface (OCPI) is an open protocol that enables electric vehicle (EV) charging systems to work together across networks. It supports communication and data sharing between Charge Point Operators (CPOs), who manage charging stations, and e-Mobility Service Providers (eMSPs), who provide charging services to EV drivers. OCPI facilitates functions like user authorization, remote charge point control, charging session data exchange, and billing through Charge Detail Records (CDRs). This allows EV roaming, so drivers can charge at different networks without multiple accounts. As the EV market grows due to increased adoption and technological advancements, OCPI faces higher demands. This has revealed issues with CDR format consistency, timestamp standardization across regions, transmission of EV-side error codes for troubleshooting, and support for new use cases. These challenges can affect operations and user experience, particularly as the industry starts considering Vehicle-to-Grid (V2G) systems, where EVs supply energy to the grid, and Vehicle-to-Everything (V2X) technologies for broader energy interactions. Using feedback from the ChargeX Diagnostics taskforce discussions, industry 1-on-1 meetings, technical standards, and OCPI’s evolution through versions (e.g., OCPI 2.1.1, 2.2, and 2.2.1), this report identifies these issues and suggests practical recommendations. These aim to improve interoperability, streamline operations, and prepare OCPI for future trends in the EV charging ecosystem.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2018

TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems

We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4× performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multimodal representation learning for predicting molecule–disease relations

Motivation: Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Retro-Commissioning Sensor Suitcase for Energy Efficiency

This project will enable Pacific Northwest National Laboratory (PNNL) and Lawrence Berkeley National Laboratory (LBNL) to work with industry partner, GreenPath Energy Solutions, joint developers of the Suitcase, to enhance the capabilities and usability of the Retro-Commissioning Sensor Suitcase (hereinafter the Sensor Suitcase or Suitcase), assisting GreenPath to take the Suitcase to market and rapidly expand their market share. The proposed project will specifically focus on 1) adding sensors types to extend the data collection capability and support more building and equipment performance metrics and identification of even more energy saving opportunities, 2) developing algorithms to identify recommendations for the new energy savings opportunities from item 1 and to prototype software modifications implementing them, 3) validating the use of vibration sensors to detect the operating state of a broader set of packaged HVAC equipment (additional capacities, different numbers of stages, etc.) than tested in initial development and modifying the state algorithm and software code, as needed, 4) improving cost-effectiveness in manufacturing the Sensor Suitcase, and 5) additional field testing of the technology in real buildings to more comprehensively validate Suitcase performance and to guide refinement of its capabilities. Project results will position GreenPath, and potential future licensees, to implement the new capabilities developed in this project in GreenPath’s RCx Building Suitcase, increasing its functionality and the savings resulting from its use. These enhancements will increase the value of the Suitcase to users and increase the potential market for its use and impacts in that impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

Effects and Responses to Spaceflight in the Mouse Retina

Several stress environmental factors are combined in a unique fashion during spaceflight, affecting living beings widely across their physiological systems. Recently, attention has been placed on vision changes in astronauts returning from long duration missions. Alterations include hyperoptic shift, globe flattening, choroidal folds and optic disc edema, which are probably associated with increased intracranial pressure. These observations justify a better characterization of the ocular health risks associated with spaceflight. This study investigates the impact of spaceflight on the biology of the mouse retina. Within a successful tissue sharing effort, eyes from albino Balb/cJ mice aboard STS-133 were collected for histological analysis and gene expression profiling of the retina at 1 and 7 days after landing. Both vivarium and AEM (Animal Enclosure Module) mice were used as ground controls. Oxidative stress-induced DNA damage was higher in the flight samples compared to controls on R+1, and decreased on R+7. A trend toward higher oxidative and cellular stress response gene expression was also observed on R+1 compared to AEM controls, and these levels decreased on R+7. Several genes coding for key antioxidant enzymes, namely, heme-oxygenase-1, peroxiredoxin, and catalase, were among those upregulated after flight. Likewise, NF B and TGFbeta1, were upregulated in one flight specimen that overall showed the most elevated oxidative stress markers on R+1. In addition, retinas from vivarium control mice evidenced higher oxidative stress markers, NF B and TGFbeta1, likely due to the more intense illumination in vivarium cages versus the AEM. These preliminary data suggest that spaceflight represents a source of environmental stress that translates into oxidative and cellular stress in the retina, which is partially reversible upon return to Earth. Further work is needed to dissect the contribution of the various spaceflight factors (microgravity, radiation) and to evaluate the impact of the stress response on retinal health.

Zanello, Susana B.↗
DOE OSTI2019

Genome of Alaskapox Virus, A Novel Orthopoxvirus Isolated from Alaska

Since the eradication of smallpox, there have been increases in poxvirus infections and the emergence of several novel poxviruses that can infect humans and domestic animals. In 2015, a novel poxvirus was isolated from a resident of Alaska. Diagnostic testing and limited sequence analysis suggested this isolate was a member of the Orthopoxvirus (OPXV) genus but was highly diverged from currently known species, including Akhmeta virus. Here, we present the complete 210,797 bp genome sequence of the Alaska poxvirus isolate, containing 206 predicted open reading frames. Phylogenetic analysis of the conserved central region of the genome suggested the Alaska isolate shares a common ancestor with Old World OPXVs and is diverged from New World OPXVs. We propose this isolate as a member of a new OPXV species, Alaskapox virus (AKPV). The AKPV genome contained host range and virulence genes typical of OPXVs but lacked homologs of C4L and B7R, and the hemagglutinin gene contained a unique 120 amino acid insertion. Seven predicted AKPV proteins were most similar to proteins in non-OPXV Murmansk or NY_014 poxviruses. Genomic analysis revealed evidence suggestive of recombination with Ectromelia virus in two putative regions that contain seven predicted coding sequences, including the A-type inclusion protein.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

The D3 Middleware Architecture

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid-dynamics) model executions. DARWIN captures, stores and indexes data; manages derived knowledge (such as visualizations across multiple datasets); and provides an environment for designers to collaborate in the analysis of test results. DARWIN is an interesting application because it supports high-volumes of data. integrates multiple modalities of data display (e.g., images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and views of data. Here we provide an overview of the architecture of D3, the third generation of DARWIN. Earlier versions of DARWIN were characterized by browser-based interfaces and a hodge-podge of server technologies: CGI scripts, applets, PERL, and so forth. But browsers proved difficult to control, and a proliferation of computational mechanisms proved inefficient and difficult to maintain. D3 substitutes a pure-Java approach for that medley: A Java client communicates (though RMI over HTTPS) with a Java-based application server. Code on the server accesses information from JDBC databases, distributed LDAP security services, and a collaborative information system. D3 is a three tier-architecture, but unlike 'E-commerce' applications, the data usage pattern suggests different strategies than traditional Enterprise Java Beans - we need to move volumes of related data together, considerable processing happens on the client, and the 'business logic' on the server-side is primarily data integration and collaboration. With D3, we are extending DARWIN to handle other data domains and to be a distributed system, where a single login allows a user transparent access to test results from multiple servers and authority domains.

Walton, Joan↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Collaboration Website for Muon Catalyzed Fusion and Muon Beam Production

This project sets up a website to support the nascent Muon Catalyzed Fusion collaboration including development of particle accelerators and transport beamlines for muon beams. The website is envisioned as having the general public information pages and private pages for collaboration members. Multimedia elements like images, text animations, and video lectures, covering a broad spectrum of topics will populate the educational site, covering muon facilities, to comprehensive explorations and seminal documents that define the science of Muon Catalyzed Fusion, Acceleration, Applications, Instrumentation, Beamline Design, and beam dynamics design codes. Ensuring compatibility across devices and operating systems, it also features integration with Google Docs for collaboration, a code repository (GitHub), a blog platform with comments (WordPress), the potential for ChatGPT integration and interactive graph plotting with Python Plotty to enhance data visualization. This project will maintain public and protected private pages, due to the proprietary nature of the work or research in progress. The public sections will be built to foster dissemination of information and highlight recent work within the NK Labs collaboration, including lectures, published papers, and regular blog posts with open commenting. The private section will support unpublished or nonpublic research, by facilitating collaborative efforts through integrated Google Docs and Python Plotty for shared graphing work. Ultimately, this project strives to make complex scientific knowledge more accessible to the public, foster enhanced collaboration, and serve as a platform for sharing cutting-edge research in Muon Catalyzed Fusion and Accelerators.

43 PARTICLE ACCELERATORS↗
NASA NTRS1992

Cockpit weather information needs

The primary objective is to develop an advanced pilot weather interface for the flight deck and to measure its utilization and effectiveness in pilot reroute decision processes, weather situation awareness, and weather monitoring. Identical graphical weather displays for the dispatcher, air traffic control (ATC), and pilot crew should also enhance the dialogue capabilities for reroute decisions. By utilizing a broadcast data link for surface observations, forecasts, radar summaries, lightning strikes, and weather alerts, onboard weather computing facilities construct graphical displays, historical weather displays, color textual displays, and other tools to assist the pilot crew. Since the weather data is continually being received and stored by the airborne system, the pilot crew has instantaneous access to the latest information. This information is color coded to distinguish degrees of category for surface observations, ceiling and visibilities, and ground radar summaries. Automatic weather monitoring and pilot crew alerting is accomplished by the airborne computing facilities. When a new weather information is received, the displays are instantaneously changed to reflect the new information. Also, when a new surface or special observation for the intended destination is received, the pilot crew is informed so that information can be studied at the pilot's discretion. The pilot crew is also immediately alerted when a severe weather notice, AIRMET or SIGMET, is received. The cockpit weather display shares a multicolor eight inch cathode ray tube and overlaid touch panel with a pilot crew data link interface. Touch sensitive buttons and areas are used for pilot selection of graphical and data link displays. Time critical ATC messages are presented in a small window that overlays other displays so that immediate pilot alerting and action can be taken. Predeparture and reroute clearances are displayed on the graphical weather system so pilot review of weather along the route can be accomplished prior to pilot acceptance of the clearance. An ongoing multiphase test series is planned for testing and modifying the graphical weather system. Preliminary data shows that the nine test subjects considered the graphical presentation to be much better than their current weather information source for situation awareness, flight safety, and reroute decision making.

Scanlon, Charles H.↗
DOE OSTI2023

PERKS: a Locality-Optimized Execution Model for Iterative Memory-bound GPU Applications

Iterative memory-bound solvers commonly occur in HPC codes. Typical GPU implementations have a loop on the host side that invokes the GPU kernel as much as time/algorithm steps there are. The termination of each kernel implicitly acts the barrier required after advancing the solution every time step. We propose an execution model for running memory-bound iterative GPU kernels: PERsistent KernelS (PERKS). In this model, the time loop is moved inside persistent kernel, and device-wide barriers are used for synchronization. We then reduce the traffic to device memory by caching subset of the output in each time step in the unused registers and shared memory. PERKS can be generalized to any iterative solver: they largely independent of the solver's implementation. We explain the design principle of PERKS and demonstrate effectiveness of PERKS for a wide range of iterative 2D/3D stencil benchmarks (geomean speedup of 2.12x for 2D stencils and 1.24x for 3D stencils over state-of-art libraries), and a Krylov subspace conjugate gradient solver (geomean speedup of 4.86x in smaller SpMV datasets from SuiteSparse and 1.43x in larger SpMV datasets over a state-of-art library). All PERKS-based implementations available at: https://github.com/neozhang307/PERKS.

Zhang, Lingqi↗
NASA NTRS2003

Software Helps Retrieve Information Relevant to the User

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

Mathe, Natalie↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

Science & Technology - Other Topics↗
DOE OSTI2024

Berkeley Lab Finite Element Framework (BELFEM) v0.1

The software program, referred to as BELFEM, is a specialized finite element code designed for the magnetodynamic modeling of high-temperature superconducting (HTS) tapes. It incorporates novel mixed finite element formulations, particularly the h-ϕ-formulation with thin-shell simplification, to efficiently simulate larger geometries. This methodology is extremely promising for predicting the electrodynamic performance of HTS tapes used in superconducting cables and magnets, offering the benefit of reduced computational cost. Compared to similar technologies like COMSOL Multiphysics and GetDP, BELFEM's performance benchmarking indicates superior efficiency in its thin-shell implementation. In the future, it will also support features like thermal coupling and inter-tape current sharing, enhancing its utility in research and development, particularly in nuclear fusion applications. The intent is to develop BELFEM as a robust and efficient tool for the HTS community, contributing to the analysis and design of superconducting cables and magnets.

Messe, Christian↗