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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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This presentation will describe recent efforts to visualize muon collider detector geometries and col- lision events with modern 3D game development tools, particularly Unreal Engine 5. The workflow from HEP-specific tools to industry standard formats will be described, and an interactive demo will be available.
Validation and verification of engineering models is important to understand potential weaknesses and issues in the model. This is accomplished through the application of constraint logic to the model. These models and the constraints put upon them can be represented through a graph structure. Here we give a visualization system to aid users understanding, locating, and fixing constraint violations in their systems. We give users several ways to narrow down on the specific errors and parts of the graph they’re interested in. Users have the opportunity to choose the types of errors that will be shown in the graph. Clustering is applied to the graph to help users narrow down their searches. Several other graph interactions are given to support discovery of constraint violations.
The goal of this report is to provide insight to the development of vt-tv, a C++ HPC visualization tool designed for insightful analysis of load-balancing metrics in the DARMA toolkit. In particular, it delves into its modular data model and diverse usage scenarios, emphasizing adaptability and efficiency.
Accurate modeling of gas hydrate reservoir productivity and geomechanical risks associated with subsurface dissociation of natural gas hydrates (NGH) requires the determination of model parameters through physical testing on natural hydrate-bearing sediments (HBS). This involves investigating the hydro-mechanical behavior of undisturbed hydrate samples from nature under in situ conditions using pressure core characterization and analysis, which provides a unique opportunity for research. By employing state-of-the-art micro computed tomography imagery on cryogenically preserved, hydrate-bearing sediment samples, we can determine hydrate saturation as well as permeability with and without the presence of hydrates in the sediment. Furthermore, utilizing a machine learning based image segmentation technique, it is possible to extract pore space and grain information. Subsections of the entire image volume were used to determine anisotropic permeabilities using a finite-difference method Stokes solver (FDMSS). Additionally, permeability measurements on whole pressure and temperature preserved hydrate-bearing core were analyzed by utilizing the National Energy Technology Laboratory’s (NETL) Pressure Core Characterization and X-ray CT Visualization Tool (PCXT) to manipulate, cut, and analyze pressure preserved sediment. Permeabilities were measured under a broad range of vertical stress states to simulate expected pressure changes during production scenarios, and the results show that permeabilities derived from images are in agreement with those from traditional core derived experiments. The collected stress-dependent permeability, permeability anisotropy, and corresponding gas hydrate saturations provide valuable input into numerical simulations of reservoir productivity. These properties have been proven to be key parameters determining a long-term reservoir response under depressurization.
Geant4 offers versatile visualization tools to study detector geometry, particle trajectories, and hits. These tools support both interactive and batch modes (e.g., vis.mac). With a unified interface, Geant4 integrates various graphics systems such as G4VisExecutive, OpenGL, Qt, HepRApp, DAWN, VTK, and ToolsSG. Advanced examples, including the ICRP145 Human Phantoms and the gMocren tool, highlight its applications in medical research and simulation and as well as radiation protection and shielding studies. These features make Geant4 a robust framework for exploring particle interactions and supporting domain-specific use cases.
U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.
In this work, we adapt the Convolutional Visual Network (CVN) approach [1] to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph[8]. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentations. We then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This poster presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.
This Core Model Proposal (CMP) comprehensively restructures and updates the macroeconomic and socioeconomic modules in gcamdata. It includes visualizations of key data inputs, accounting identities, and data flows in the context of GCAM-Macro-KLEM. Major improvements include: (1) updating the Penn World Table (PWT) to version 10 and incorporating a new source, the Global Macro Database (GMD); (2) updating the SSP socioeconomics database from version 3.0.1 to 3.2; (3) introducing SSP-specific differentiation of employment and labor force data; (4) improving data integration between national accounts (from PWT, GMD, and GTAP) and GDP/population data from external sources; and (5) general data cleaning and structural refinements. We document the data sources and key assumptions used throughout the processing. These updates establish the foundation for the forthcoming KLEAM version of GCAM-macro.
This map poster uses Oak Ridge National Laboratory’s Existing Hydropower Assets Plant Dataset FY20 to visualize the geospatial distribution and characteristics of existing hydropower plants in the United States.
Viskores is a scientific visualization library that is the primary deployment of such algorithms to the parallel accelerated processors of modern DOE supercomputers. In this paper, we review the capabilities provided by Viskores and how these capabilities are leveraged by other software in the high-performance computing ecosystem. We discuss the Viskores data representation and pay particular attention to array management. Through this array management we describe how data is adapted between Viskores and other software along with strategies for converting dynamic, polymorphic objects to static representations better suited to GPU processing. We conclude with several examples of Viskores integrating with high-performance software that is used in production today.
To improve clinical care practice, it is important to understand the variability of clinical pathways executed in different contexts (e.g., pathways in different geographical locations, demographics, and phenotypic groups). A common way of representing clinical pathways is through network-based representations that capture trajectories of treatment steps. However, first-order networks, which are based on the Markovian property and the de facto standard model to represent transitions between steps, often fail to capture real trajectories. This paper introduces a visual analytic tool to explore and compare pathways represented in higher-order networks. Because each higher node in the network is a subtrajectory (i.e., partial or full history of treatment steps), the tool can display true sequences of treatment steps and compute the similarity of the two networks in a space of higher-order nodes. The tool also highlights areas in which the two networks are similar and dissimilar and how a certain subtrajectory is realized differently in different pathways. The paper demonstrates the tool's usefulness by applying it to multiple antidepressant pharmacotherapy pathways for veterans diagnosed with major depressive disorder and by illustrating heterogeneity in prescription patterns across pathways.
Interactive visualizations are invaluable tools for building intuition and supporting rapid exploration of datasets and models. Numerous libraries in Python support interactivity, and workflows that combine Jupyter and IPyWidgets in particular make it straightforward to build data analysis tools on the fly. However, the field is missing the ability to arbitrarily overlay widgets and plots on top of others to support more flexible details-on-demand techniques. This work discusses some limitations of the base IPyWidgets library, explains the benefits of IPyVuetify and how it addresses these limitations, and finally presents a new open-source solution that builds on IPyVuetify to provide easily integrated widget overlays in Jupyter.
Background Akkermansia muciniphila is a member of the gut microbiome, using mucin as sources of carbon, nitrogen, and energy. Since the first discovery of this unique bacterium in 2004, A. muciniphila has been extensively studied. It is considered a promising “next-generation beneficial microbe.” The purpose of this paper is to sort out the research status and summarize the hotspots through bibliometric analysis of the publications of A. muciniphila. Methods The publications about A. muciniphila from January 2004 to February 2022 were obtained from the Web of Science Core Collection. Visualization analyses were performed using three bibliometric tools and GraphPad Prism. Results A total of 1,478 published documents were analyzed. Annual publication number grew from 1 in 2004 to 336 in 2021, with China being the leading producer (33.36%). De Vos, Willem M was the most productive author with the highest H-index (documents = 56, H-index = 37), followed by Cani, Patrice D (documents = 35, H-index = 25). And Scientific Reports published the most papers. PNAS was the keystone taxa in this field, with high betweenness centrality (0.11) and high frequency. The keywords with high frequency in recent years include: oxidative stress, diet, metformin, fecal microbiota transplantation, short-chain fatty acids, polyphenols, microbiota metabolites and so on. The keyword “oxidative stress” was observed to be increasing in frequency recently. Conclusion Over time, the scope of the research on the clinical uses of A. muciniphila has gradually increased, and was gradually deepened and developed toward a more precise level. A. muciniphila is likely to remain a research hotspot in the foreseeable future and may contribute to human health.
We report that Several bacteria have long been known to interact intimately with fungi, but molecular approaches have only recently uncovered how cosmopolitan these interactions are in nature. Currently, bacterial–fungal interactions (BFI) are inferred based on patterns of co-occurrence in amplicon sequencing investigations. However, determining the nature of these interactions, whether the bacteria are internally or externally associated, remains a grand challenge in BFI research. Fluorescence in situhybridization (FISH) is a robust method that targets unique sequences of interest which can be employed for visualizing intra-hyphal targets, such as mitochondrial organelles or, as in this study, bacteria. We evaluate the challenges and employable strategies to resolve intra-hyphal BFI to address pertinent criteria in BFI research, such as culturing media, spatial distribution of bacteria, and abundance of bacterial 16S rRNA copies for fluorescent labeling. While these experimental factors influence labeling and detection of endobacteria, we demonstrate how to overcome these challenges thorough permeabilization, appropriate media choice, and targeted amplification using hybridization chain reaction FISH. Such microscopy imaging approaches can now be utilized by the broader research community to complement sequence-based investigations and provide more conclusive evidence on the nature of specific bacterial–fungal relationships.
Moving the current linear economy toward circularity is expected to have environmental, economic, and social impacts. Various modeling methods, including economic input-output modeling, life cycle assessment, agent-based modeling, and system dynamics, have been used to examine circular supply chains and analyze their impacts. This work describes the newly developed Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework, which is designed to model how the impacts of supply chains might change as circularity increases. We first establish the framework with a discussion of modeling capabilities that are needed to capture circularity transitions; these capabilities are based on the fact that supply chains moving toward circularity are dynamic and therefore not at steady state, may encompass multiple industrial sectors or other interdependent supply chains and occupy a large spatial area. To demonstrate the capabilities of CELAVI, we present a case study on end-of-life wind turbine blades in the U.S. state of Texas. Our findings show that depending on exact process costs and transportation distances, mechanical recycling could lead to 69% or more of end-of-life turbine blade mass being kept in circulation rather than being landfilled, with only a 7.1% increase in global warming potential over the linear supply chain. We discuss next steps for framework development.
Basketball is a sport where shooting is one of the most important factors that determines the success or failure of the teams. Therefore, basketball players need to shoot the ball accurately. The present study aimed to evaluate whether different shooting conditions influence the visual behavior of athletes. For this, the Tobbi Pro Glasses 3 equipment was used to evaluate the time of the first fixation, the number of fixations, and the total time of fixations in the basket, during the shooting movement. Different distances to the basket, simulated gym audience noise, and shooting with opposition were considered. A group of 18 athletes with an average age of 22 ± 3.72 years and an average basketball practice experience of 12.5 ± 4.52 years performed 10 valid shots from different distances and different shooting angles, with different restrictions, namely, shooting with simulated opposition and gym audience noise. Statistically significant differences between shooting with opposition and baseline, in terms of the number of fixations and the total time of fixation, were found. Regarding shooting with gym audience noise, differences only occur for the total time of fixations in one position. Despite this, in all variables and positions, the values were lower in shooting with constraints compared to the baseline.
Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.
We present a revision to the visual orbit of the young, directly imaged exoplanet 51 Eridani b using four years of observations with the Gemini Planet Imager. The relative astrometry is consistent with an eccentric (e=0.53{sub -0.13}{sup +0.09}) orbit at an intermediate inclination (i=136{sub -11}{sup +10} °), although circular orbits cannot be excluded due to the complex shape of the multidimensional posterior distribution. We find a semimajor axis of 11.1{sub -1.3}{sup +4.2} au and a period of 28.1{sub -4.9}{sup +17.2} yr, assuming a mass of 1.75 M{sub ⊙} for the host star. We find consistent values with a recent analysis of VLT/SPHERE data covering a similar baseline. We investigate the potential of using the absolute astrometry of the host star to obtain a dynamical mass constraint for the planet. The astrometric acceleration of 51 Eri derived from a comparison of the Hipparcos and Gaia catalogs was found to be inconsistent at the 2σ–3σ level with the predicted reflex motion induced by the orbiting planet. Potential sources of this inconsistency include a combination of random and systematic errors between the two astrometric catalogs and the signature of an additional companion within the system interior to current detection limits. We also explored the potential of using Gaia astrometry alone for a dynamical mass measurement of the planet by simulating Gaia measurements of the motion of the photocenter of the system over the course of the extended 8 yr mission. We find that such a measurement is only possible (>98% probability) given the most optimistic predictions for the Gaia scan astrometric uncertainties for bright stars and a high mass for the planet (≳3.6 M {sub Jup}).