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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A statistical study of radio-source structure effects on astrometric very long baseline interferometry observations

Errors from a number of sources in astrometric very long baseline interferometry (VLBI) have been reduced in recent years through a variety of methods of calibration and modeling. Such reductions have led to a situation in which the extended structure of the natural radio sources used in VLBI is a significant error source in the effort to improve the accuracy of the radio reference frame. In the past, work has been done on individual radio sources to establish the magnitude of the errors caused by their particular structures. The results of calculations on 26 radio sources are reported in which an effort is made to determine the typical delay and delay-rate errors for a number of sources having different types of structure. It is found that for single observations of the types of radio sources present in astrometric catalogs, group-delay and phase-delay scatter in the 50 to 100 psec range due to source structure can be expected at 8.4 GHz on the intercontinental baselines available in the Deep Space Network (DSN). Delay-rate scatter of approx. 5 x 10(exp -15) sec sec(exp -1) (or approx. 0.002 mm sec (exp -1) is also expected. If such errors mapped directly into source position errors, they would correspond to position uncertainties of approx. 2 to 5 nrad, similar to the best position determinations in the current JPL VLBI catalog. With the advent of wider bandwidth VLBI systems on the large DSN antennas, the system noise will be low enough so that the structure-induced errors will be a significant part of the error budget. Several possibilities for reducing the structure errors are discussed briefly, although it is likely that considerable effort will have to be devoted to the structure problem in order to reduce the typical error by a factor of two or more.

Ulvestad, J. S.↗
DOE OSTI2019

Learning nonlinear level sets for dimensionality reduction in function approximation

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators) with fast-to-evaluate predictive machine learning models, so as to accelerate the engineering design processes. There are two major challenges in constructing such predictive models: (a) high-dimensional inputs (e.g., many independent design parameters) and (b) small training data, generated by running extremely time-consuming simulations. Thus, reducing the input dimension is critical to alleviate the over-fitting issue caused by data insufficiency. Existing methods, including sliced inverse regression and active subspace approaches, reduce the input dimension by learning a linear coordinate transformation; our main contribution is to extend the transformation approach to a nonlinear regime. Specifically, we exploit reversible networks (RevNets) to learn nonlinear level sets of a high-dimensional function and parameterize its level sets in low-dimensional spaces. A new loss function was designed to utilize samples of the target functions' gradient to encourage the transformed function to be sensitive to only a few transformed coordinates. The NLL approach is demonstrated by applying it to three 2D functions and two 20D functions for showing the improved approximation accuracy with the use of nonlinear transformation, as well as to an 8D composite material design problem for optimizing the buckling-resistance performance of composite shells of rocket inter-stages.

Zhang, Guannan↗
DOE OSTI2017

Evaluating opportunities to improve material and energy impacts in commodity supply chains

When evaluated at the scale of individual processes, next-generation technologies may be more energy and emissions intensive than current technology. Furthermore, many advanced technologies have the potential to reduce material and energy consumption in upstream or downstream processing stages. In order to fully understand the benefits and consequences of technology deployment, next-generation technologies should be evaluated in context, as part of a supply chain. This work presents the Materials Flow through Industry (MFI) supply chain modeling tool. The MFI tool is a cradle-to-gate linear network model of the US industrial sector that can model a wide range of manufacturing scenarios, including changes in production technology and increases in industrial energy efficiency. The MFI tool was developed to perform supply chain scale analyses in order to quantify the impacts and benefits of next-generation technologies and materials at that scale. For the analysis presented in this paper, the MFI tool is utilized to explore a case study comparing three lightweight vehicle supply chains to the supply chain of a conventional, standard weight vehicle. Several of the lightweight vehicle supply chains are evaluated under manufacturing scenarios that include next-generation production technologies and next-generation materials. Results indicate that producing lightweight vehicles is more energy and emission intensive than producing the non-lightweight vehicle, but the fuel saved during vehicle use offsets this increase. In this case study, greater reductions in supply chain energy and emissions were achieved through the application of the next-generation technologies than from application of energy efficiency increases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Life cycle assessment and life cycle costs for pre-disaster waste management systems

Highlights: • Disaster waste management should include disaster waste response. • Environmental and economic disaster waste management system must be constructed. • Temporary incinerators have environmental and economic disadvantages. • Regional air pollution reduction is vital in disaster waste management planning. - Abstract: This study develops a method of environmental and economic evaluation of an integrated disaster waste management system that considers the spatial scale of removal, transport, and treatment of disaster waste. A case study was conducted on combustibles, which is a type of disaster waste derived from dwellings, in Mie Prefecture, Japan. First, we calculated the quantity and the spatial distribution of disaster waste derived from dwellings and tsunami debris produced as a result of a large-scale earthquake. The quantity of disaster waste was estimated as 7,178,000 t with functioning flood-preventing facilities and 11,956,000 t without functioning flood prevention facilities. Ensuring resilience in the face of earthquakes and tsunamis by renovating flood-preventing facilities is extremely important in decreasing the production of wastes, especially in coastal regions. Next, the transportation network for transporting combustibles in disaster waste to temporary storage sites, incineration plants, and landfill was constructed using an optimization model. The results showed that if flood-preventing facilities do not function properly, the installation of temporary incineration facilities becomes essential. Life-cycle emissions of CO{sub 2}, SO{sub x}, NO{sub x}, and PM and the costs of removal, storage, and treatment of combustibles were calculated as 258,000 t, 618 t, 1705 t, 7.9 t, and 246 million USD, respectively, in the case of functioning flood-preventing facilities. If flood-preventing facilities do not function, the quantity of environmentally unfriendly emissions and the costs increase. This result suggested the significance of renovation in order to maintain the conditions of flood-preventing facilities to decrease the environmental burden and costs as well as keep the production of disaster waste at a minimum.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Active, tailorable adhesives for dissimilar material bonding, repair and reassembly

Joining of materials and components is inevitable as it allows versatility in assembly and repair along with reduction in time and cost of manufacturing. However, joints are mostly considered the ‘weak-links’ of the structure due to the complex phenomena and interactions of several elements of either similar or dissimilar materials. These complexities combined with the need for lightweight structures and increased safety require better understanding and development of robust dissimilar material joints. The proposed technique uses ‘active adhesives’ and inherits all the advantages of bonded joints, such as lightweight, elimination of holes and associated stress-concentrations, and overcomes its shortcomings of disassembly and repair. Thermoplastic adhesives modified by the incorporation of electrically conductive graphene nanoplatelets at a concentration above the percolation point provide a unique synergy of mechanical, thermal and electrical properties. While the choice of the thermoplastic is governed by the desired application, the addition of the graphene nanoplatelets allows energy to be deposited primarily in the adhesive. The percolated network of graphene particles in the adhesive can quickly couple to microwave (MW) radiation via non-contact methods and increase the adhesive temperature to above the required processing temperatures. The adhesive melts and flows over the adherends and upon cooling forms a structural adhesive bond. Furthermore, the process can be used to disassemble the adhesive joint if repair or reworking is required. The dissimilar material joints in this study include fiber reinforced polymer composites (FRP) made of glass and carbon fibers being joined with metals such as advanced high strength steel (AHSS) and aluminum (Al). The global objectives of the proposed work were achieved through: a) processing, material development and optimization of the active adhesive, b) lab-scale evaluation and detailed material characterization and, c) design, testing and applications. An integrated experimental and numerical approach that used novel non-destructive evaluation techniques to validate the simulations were used. Such experimentally validated simulations could be used to predict designs beyond the experimental matrix. Overall, this project showed the feasibility and robustness of ‘active’, ‘reversible’ thermoplastic adhesives and their use in large-scale multi-material joints.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Lidar-Radiometer Inversion Code (LIRIC) for the Retrieval of Vertical Aerosol Properties from Combined Lidar Radiometer Data: Development and Distribution in EARLINET

This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code)algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar radiometric input data we use measurements from European Aerosol Re-search Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data by the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height-dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Inter-comparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLNET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.

Chaikovsky, A.↗
DOE OSTI2018

DIRECT SIMULTANEOUS MEASUREMENT OF PARTICULATE MATTER AND AMMONIA STORAGE ON COMBINED SELECTIVE CATALYTIC REDUCTION FILTER SYSTEMS USING RADIO FREQUENCY SENSORS

Tightening global emissions regulations are motivating interest in the development and implementation of Selective Catalytic Reduction + Filtration (SCRF) systems, which are designed to reduce the concentration of tailpipe particulate matter (PM) and NO x emissions. These systems allow designers to combine the NO x reduction capability of an SCR with the filtration capability of a particulate filter on a single unit. Practical implementation of these systems requires reliable measurement and diagnosis of their state – both with respect to trapped particulate matter as well as adsorbed ammonia. Currently, these systems rely on a variety of gas sensors, mounted upstream or downstream of the system, that only provide an indirect inference of the operation state. In this study, a single radio frequency (RF) sensor was used to perform simultaneous measurements of soot loading and ammonia inventory on an SCRF. Several SCRF core samples were tested at varying soot and ash loads in a catalyst reactor bench. Soot levels were measured by monitoring changes in the bulk dielectric properties within the catalyst using the sensor, while ammonia levels were determined by feeding selected regions of the RF spectrum into a pretrained generalized regression neural network model. Results show the RF sensor is able to directly measure the instantaneous ammonia inventory, while simultaneously providing soot loading measurements within 0.5 g/L. These results confirm that simultaneous measurements of both the PM and ammonia loading state of an SCRF are possible using a single RF sensor via analysis of specific features in the full RF spectrum. The results indicate significant potential to remove the control barriers typically associated with the implementation of advanced SCRF systems.

Ragaller, Paul↗
DOE OSTI2023

Ammonia Electrosynthesis from Nitrate Using a Ruthenium–Copper Cocatalyst System: A Full Concentration Range Study

Electrochemical synthesis of ammonia via the nitrate reduction reaction (NO3RR) has been intensively researched as an alternative to the traditional Haber–Bosch process. Most research focuses on the low concentration range representative of the nitrate level in wastewater, leaving the high concentration range, which exists in nuclear and fertilizer wastes, unexplored. The use of a concentrated electrolyte (≥1 M) for higher rate production is hampered by poor hydrogen transfer kinetics. Herein, we demonstrate that a cocatalytic system of Ru/Cu 2 O catalyst enables NO3RR at 10.0 A in 1 M nitrate electrolyte in a 16 cm 2 flow electrolyzer, with 100% faradaic efficiency toward ammonia. Detailed mechanistic studies by deuterium labeling and operando Fourier transform infrared (FTIR) spectroscopy allow us to probe the hydrogen transfer rate and intermediate species on Ru/Cu 2 O. Ab initio molecular dynamics (AIMD) simulations reveal that adsorbed hydroxide on Ru nanoparticles increases the density of the hydrogen-bonded water network near the Cu 2 O surface, which promotes the hydrogen transfer rate. Finally, our work highlights the importance of engineering synergistic interactions in cocatalysts for addressing the kinetic bottleneck in electrosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

In this work we develop and present a machine learning based downscaling approach using generative adversarial networks (GANs). GANs learn to distinguish the relationships between low-resolution and high-resolution simulations and generate accurate high-resolution output from low-resolution input (Stengel, Glaws, Hettinger, & King, 2020). Low-resolution numerical weather prediction (NWP) simulations at 9-km spatial and 60-minute temporal resolution were executed over Southeast Asia to provide input to the GANs model. GANs for wind, temperature, and pressure were trained on coarsened WIND Toolkit data with a diverse sampling of terrain and meteorological conditions. After training, the NWP simulations over Southeast Asia were enhanced by 3x along each horizontal spatial dimension and 4x along the temporal dimension. This novel downscaling approach generated 15-year high-resolution wind, temperature, and pressure data from January 2007 through December 2021 at multiple hub heights over Southeast Asia at 3-km spatial and 15-minute temporal resolution with a 16x reduction in compute time over standard dynamical downscaling.

17 WIND ENERGY↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
DOE OSTI2019

Solar soft cost ontology for multi-stakeholder knowledge base

Introduction: Solar soft costs can account for 60% of the cost of an installed solar system, thus cost reductions for solar hinge on reducing soft costs. Soft costs are most frequently defined as non-hardware costs. Defining something by what it isn’t makes it inherently difficult to identify what exactly it is, which in turn makes it difficult to study. Our goal is to develop an ontology that identifies 1) a consistent and defined soft cost vocabulary, 2) who creates, shares and acquires soft cost knowledge, and 3) the learning processes used to facilitate knowledge flows. Ultimately, identifying these aspects of solar soft costs will provide a pathway for public policy and market interventions to reduce soft costs. Methods: To build the solar soft cost ontology (SSCO) we built a corpus of solar soft cost literature (through keyword search), then coded the corpus for all concepts related to or influencing costs, barriers, or time. Finally, we organized the concepts into taxonomies, including soft costs and network actors, and developed the properties (i.e., attributes and relationships) that link the concepts. In phase two, we will code a set of solar installer case studies and complimentary solar sector (e.g., distributors, software providers, trade organizations) firm case studies to update the SSCO, documenting changes and additions resulting from the use of practitioner feedback. Results and Discussion: The resulting SSCO includes concepts, attributes, and relationships that provide a rich and deep knowledge base that merges academic and practitioner expertise to create a shared and exhaustive vocabulary related to soft costs, a network of actors that impact those soft costs, and the attributes and relationships that connect them. The applications of the SSCO extend to a diverse group of stakeholders, including researchers, policy makers, and practitioners, engaged in reducing solar soft costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Sparse coding of pathology slides compared to transfer learning with deep neural networks

Background Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varying sizes surrounded by large empty regions. The locations of abnormal or cancerous cells, which may constitute a small portion of any given tissue sample, are not annotated. Cancer image datasets are also extremely imbalanced, with most slides being associated with relatively common cancers. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding. Results We show that conventional transfer learning using a state-of-the-art deep learning architecture pre-trained on ImageNet (RESNET) and fine tuned for a binary tumor/no-tumor classification task achieved between 85% and 86% accuracy. However, when all layers up to the last convolutional layer in RESNET are replaced with a single feature map inferred via a sparse coding using a dictionary optimized for sparse reconstruction of unlabeled pathology slides, classification performance improves to over 93%, corresponding to a 54% error reduction. Conclusions We conclude that a feature dictionary optimized for biomedical imagery may in general support better classification performance than does conventional transfer learning using a dictionary pre-trained on natural images.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

Mechanotransductive Regulation of Gap-Junction Activity Between MLO-Y4 Osteocyte-Like and MC3T3-E1 Osteoblast-Like Cells in Three-Dimensional Co-Culture.

Cell and animal studies conducted onboard the International Space Station and formerly on Shuttle flights have provided groundbreaking data illuminating the deleterious biological response of bone to mechanical unloading. However the intercellular communicative mechanisms associated with the regulation of bone synthesis and bone resorption cells are still largely unknown. Connexin-43 (CX43), a gap junction protein, is hypothesized to play a significant role in osteoblast and osteocyte signaling. The purpose of this investigation was to evaluate within a novel three-dimensional microenvironment how the osteocyte-osteoblast gap-junction expression changes when cultures are exposed to exaggerated mechanical load. MLO-Y4 osteocyte-like cells were cultured on a 3D-Biotek polystyrene insert and placed in direct contact with an MC3T3-E1 pre-osteoblast co-cultured monolayer and exposed to 48 h of mechanical stimulation (pulsatile fluid flow (PFF) or monolayer cyclic stretch (MCS)) then evaluated for viability, proliferation, metabolism, and CX43 expression. Mono-cultured MLO-Y4 and MC3T3-E1 control experiments were conducted under PFF and MCS stimulation to observe how strain application stimuli (PFF cell membrane shear or MCS cell focal adhesion/attachment loading) initiates different signaling pathways or downstream regulatory controls. TotalLive cell count, viability and metabolic reduction (Trypan Blue, LIVEDead and Alamar Blue analysis respectively) indicate that mechanical activation of MC3T3-E1 cells inhibits proliferation while maintaining an average 1.04E4 reductioncell metabolic rate, *p0.05 n4. MLO-Y4s in monolayer culture increase in number when exposed to MCS loading but the percent of live cells within the population is low (46.3 total count, *p0.05 n4), these results may indicate an apoptotic signaling cascade. PFF stimulation of the three-dimensional co-cultures elicits a universal increase in CX43 in MLO-Y4 and MC3T3-E1 cells, illustrated by immunohistological observation. Increased CX43 expression is also observed with the three-dimensional co-cultures with MC3T3-E1 MCS stimulation but the increased gap-junction protein presence was limited to the osteoblast-osteocyte interface region. Previously reported PCR evaluation of osteogenic markers further corroborate that the co-cultured populations communicative networks play a role in translating mechanical signals to molecular messaging. These findings suggests an osteocyte-osteoblast gap-junction signaling feedback mechanism may regulate mechanotransduction of apoptosis initiation and transcription of cytokine signaling proteins responsible for stem cell niche recruitment much more directly than previously believed.

mechanical loading↗
NASA NTRS2016

Mechanotransductive Regulation of Gap-Junction Activity Between MLO-Y4 Osteocyte-Like and MC3T3-E1 Osteoblast-Like Cells in Three-Dimensional Co-Culture

Cell and animal studies conducted onboard the International Space Station and formerly on Shuttle flights have provided groundbreaking data illuminating the deleterious biological response of bone to mechanical unloading. However the intercellular communicative mechanisms associated with the regulation of bone synthesis and bone resorption cells are still largely unknown. Connexin-43 (CX43), a gap junction protein, is hypothesized to play a significant role in osteoblast and osteocyte signaling. The purpose of this investigation was to evaluate within a novel three-dimensional microenvironment how the osteocyte-osteoblast gap-junction expression changes when cultures are exposed to exaggerated mechanical load. MLO-Y4 osteocyte-like cells were cultured on a 3D-Biotek polystyrene insert and placed in direct contact with an MC3T3-E1 pre-osteoblast co-cultured monolayer and exposed to 48 h of mechanical stimulation (pulsatile fluid flow (PFF) or monolayer cyclic stretch (MCS)) then evaluated for viability, proliferation, metabolism, and CX43 expression. Mono-cultured MLO-Y4 and MC3T3-E1 control experiments were conducted under PFF and MCS stimulation to observe how strain application stimuli (PFF cell membrane shear or MCS cell focal adhesionattachment loading) initiates different signaling pathways or downstream regulatory controls. TotalLive cell count, viability and metabolic reduction (Trypan Blue, LIVEDead and Alamar Blue analysis respectively) indicate that mechanical activation of MC3T3-E1 cells inhibits proliferation while maintaining an average 1.04E4 reductioncell metabolic rate, *p0.05 n4. MLO-Y4s in monolayer culture increase in number when exposed to MCS loading but the percent of live cells within the population is low (46.3 total count, *p0.05 n4), these results may indicate an apoptotic signaling cascade. PFF stimulation of the three-dimensional co-cultures elicits a universal increase in CX43 in MLO-Y4 and MC3T3-E1 cells, illustrated by immunohistological observation. Increased CX43 expression is also observed with the three-dimensional co-cultures with MC3T3-E1 MCS stimulation but the increased gap-junction protein presence was limited to the osteoblast-osteocyte interface region. Previously reported PCR evaluation of osteogenic markers further corroborate that the co-cultured populations communicative networks play a role in translating mechanical signals to molecular messaging. These findings suggests an osteocyte-osteoblast gap-junction signaling feedback mechanism may regulate mechanotransduction of apoptosis initiation and transcription of cytokine signaling proteins responsible for stem cell niche recruitment much more directly than previously believed.

Juran, C. M.↗
NASA NTRS1991

Development of visual 3D virtual environment for control software

Virtual environments for software visualization may enable complex programs to be created and maintained. A typical application might be for control of regional electric power systems. As these encompass broader computer networks than ever, construction of such systems becomes very difficult. Conventional text-oriented environments are useful in programming individual processors. However, they are obviously insufficient to program a large and complicated system, that includes large numbers of computers connected to each other; such programming is called 'programming in the large.' As a solution for this problem, the authors are developing a graphic programming environment wherein one can visualize complicated software in virtual 3D world. One of the major features of the environment is the 3D representation of concurrent process. 3D representation is used to supply both network-wide interprocess programming capability (capability for 'programming in the large') and real-time programming capability. The authors' idea is to fuse both the block diagram (which is useful to check relationship among large number of processes or processors) and the time chart (which is useful to check precise timing for synchronization) into a single 3D space. The 3D representation gives us a capability for direct and intuitive planning or understanding of complicated relationship among many concurrent processes. To realize the 3D representation, a technology to enable easy handling of virtual 3D object is a definite necessity. Using a stereo display system and a gesture input device (VPL DataGlove), our prototype of the virtual workstation has been implemented. The workstation can supply the 'sensation' of the virtual 3D space to a programmer. Software for the 3D programming environment is implemented on the workstation. According to preliminary assessments, a 50 percent reduction of programming effort is achieved by using the virtual 3D environment. The authors expect that the 3D environment has considerable potential in the field of software engineering.

Hirose, Michitaka↗
NASA NTRS1993

On-board closed-loop congestion control for satellite based packet switching networks

NASA LeRC is currently investigating a satellite architecture that incorporates on-board packet switching capability. Because of the statistical nature of packet switching, arrival traffic may fluctuate and thus it is necessary to integrate congestion control mechanism as part of the on-board processing unit. This study focuses on the closed-loop reactive control. We investigate the impact of the long propagation delay on the performance and propose a scheme to overcome the problem. The scheme uses a global feedback signal to regulate the packet arrival rate of ground stations. In this scheme, the satellite continuously broadcasts the status of its output buffer and the ground stations respond by selectively discarding packets or by tagging the excessive packets as low-priority. The two schemes are evaluated by theoretical queuing analysis and simulation. The former is used to analyze the simplified model and to determine the basic trends and bounds, and the later is used to assess the performance of a more realistic system and to evaluate the effectiveness of more sophisticated control schemes. The results show that the long propagation delay makes the closed-loop congestion control less responsive. The broadcasted information can only be used to extract statistical information. The discarding scheme needs carefully-chosen status information and reduction function, and normally requires a significant amount of ground discarding to reduce the on-board packet loss probability. The tagging scheme is more effective since it tolerates more uncertainties and allows a larger margin of error in status information. It can protect the high-priority packets from excessive loss and fully utilize the downlink bandwidth at the same time.

Chu, Pong P.↗
DOE OSTI2017

Comparative analysis of TCDD-induced AhR-mediated gene expression in human, mouse and rat primary B cells

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental pollutant that activates the aryl hydrocarbon receptor (AhR) resulting in altered gene expression. In vivo, in vitro, and ex vivo studies have demonstrated that B cells are directly impaired by TCDD, and are a sensitive target as evidenced by suppression of antibody responses. The window of sensitivity to TCDD-induced suppression of IgM secretion among mouse, rat and human B cells is similar. Specifically, TCDD must be present within the initial 12 h post B cell stimulation, indicating that TCDD disrupts early signaling network(s) necessary for B lymphocyte activation and differentiation. Therefore, we hypothesized that TCDD treatment across three different species (mouse, rat and human) triggers a conserved, B cell-specific mechanism that is involved in TCDD-induced immunosuppression. RNA sequencing (RNA-Seq) was used to identify B cell-specific orthologous genes that are differentially expressed in response to TCDD in primary mouse, rat and human B cells. Time course studies identified TCDD-elicited differential expression of 515 human, 2371 mouse and 712 rat orthologous genes over the 24-h period. 28 orthologs were differentially expressed in response to TCDD in all three species. Overrepresented pathways enriched in all three species included cytokine-cytokine receptor interaction, ECM-receptor interaction, focal adhesion, regulation of actin cytoskeleton and pathways in cancer. Differentially expressed genes functionally associated with cell-cell signaling in humans, immune response in mice, and oxidation reduction in rats. Overall, these results suggest that despite the conservation of the AhR and its signaling mechanism, TCDD elicits species-specific gene expression changes. - Highlights: • Kovalova TAAP Highlights Nov. 2016 • RNA-Seq identified TCDD-induced gene expression in PWM-activated primary B cells. • TCDD elicited differential expression of 515 human, 2371 mouse and 712 rat orthologs. • 28 orthologs were differentially expressed in response to TCDD in all three species. • TCDD elicits mostly species-specific gene expression changes in activated B cells.

60 APPLIED LIFE SCIENCES↗