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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Data-driven integration of genome-scale regulatory and metabolic network models

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

The Deep Impact Network Experiment Operations Center Monitor and Control System

The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.

Delay/Disruption Tolerant Network (DTN)↗
NASA NTRS2016

Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

Space Networking↗
NASA NTRS2016

Toward a Unified Routing Framework for Delay-Tolerant Networking

Routing in Delay-/Disruption-Tolerant Networking (DTN) has long been recognized as a challenging research topic. The difficulty lies in the fact that link intermittency and network partitioning, possibly coupled with long delays, prevent the use of Internet solutions based on an up-to-date comprehensive knowledge of network topology, as communicated by routing protocols. In the literature on DTN routing, there is a dichotomy between solutions designed for deterministic (e.g., space flight) networks, such as Contact Graph Routing (CGR), and the wide variety of protocols designed for opportunistic terrestrial networks. After a discussion of the origin and motivations of this duality, the paper presents an opportunistic extension of CGR (OCGR). The aim is to try to resolve the DTN routing dichotomy by providing a unified approach suitable for all DTN environments.

Routing↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Delta size and plant patchiness as controls on channel network organization in experimental deltas

Understanding the feedbacks between water, sediment, and vegetation in deltas is an important part of understanding deltas as ecomorphodynamic systems. Here, we conducted a set of laboratory experiments using alfalfa (Medicago sativa) as a proxy for delta vegetation to investigate (1) the effects of plants on delta growth and channel network formation and (2) the timescales controlling delta evolution in the presence of plants. Experiments were conducted with fluctuating discharge (i.e. flood and base flow periods) and variable seeding densities. We found that when deltas were small, channels had no memory across flood cycles, as floods could completely fill the incised channel network. When deltas were large, the larger channel volume could remain underfilled to keep channel memory. Plant patches also helped to increase the number of channels and make a more distributive network. Patchiness increased over time to continually aid in bifurcation, but as vegetation cover and patch sizes increased, patches began to merge. Larger patches blocked the flow to enhance topset deposition and channel filling, even for the case of large deltas with a high channel volume. We conclude that both plant patchiness and delta size affect the development of the channel network, and we hypothesize that their influences are manifested through two competing timescales. The first timescale, T v , defines the time when the delta is large enough for channels to have memory (i.e., remain underfilled), and the second, T p , defines the time when vegetation patches merge, amplifying deposition and blocking channels. Finally, when run time is between these two timescales, the delta can develop a persistent distributary network of channels aided by bifurcation around plant patches, but once T p is reached, the channel network can again be destroyed by vegetation.

58 GEOSCIENCES↗
DOE OSTI2024

Shortest path network interdiction with asymmetric uncertainty

Abstract This paper considers an extension of the shortest path network interdiction problem that incorporates robustness to account for parameter uncertainty. The shortest path interdiction problem is a game of two players with conflicting agendas and capabilities: an evader, who traverses the arcs of a network from a source node to a sink node using a path of shortest length, and an interdictor, who maximizes the length of the evader's shortest path by interdicting arcs on the network. It is usually assumed that the parameters defining the network are known exactly by both players. We consider the situation where the evader assumes the nominal parameter values while the interdictor uses robust optimization techniques to account for parameter uncertainty or sensor degradation. We formulate this problem as a nonlinear mixed‐integer semi‐infinite bilevel program and show that it can be converted into a mixed‐integer linear program with a second order cone constraint. We use random geometric networks and transportation networks to perform computational studies and demonstrate the unique decision strategies that our variant produces. Solving the shortest path interdiction problem with asymmetric uncertainty protects the interdictor from investing in a strategy that hinges on key interdictions performing as promised. It also provides an alternate strategy that mitigates the risk of these worst‐case possibilities.

Punla‐Green, She'ifa Z.↗
DOE OSTI2019

Making social networks more human: A topological approach

A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5 or greater. Furthermore, after we remove the roughly 15 million violator-violator edges from the 1.2-billion-edge Twitter-2010 follower graph, 34% of the violator nodes experience a factor-of-two decrease in PageRank. PageRank is a key component of many graph algorithms such as node/edge ranking and graph sparsification. Thus, this artificial inflation would bias algorithmic output, and result in some incorrect decisions based on this output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Opinion Dynamics on Networks under Correlated Disordered External Perturbations

We study an influence network of voters subjected to correlated disordered external perturbations, and solve the dynamical equations exactly for fully connected networks. The model has a critical phase transition between disordered unimodal and ordered bimodal distribution states, characterized by an increase in the vote-share variability of the equilibrium distributions. The fluctuations (variance and correlations) in the external perturbations are shown to reduce the impact of the external influence by increasing the critical threshold needed for the bimodal distribution of opinions to appear. The external fluctuations also have the surprising effect of driving voters towards biased opinions. Furthermore, the first and second moments of the external perturbations are shown to affect the first and second moments of the vote-share distribution. This is shown analytically in the mean field limit, and confirmed numerically for fully connected networks and other network topologies. Studying the dynamic response of complex systems to disordered external perturbations could help us understand the dynamics of a wide variety of networked systems, from social networks and financial markets to amorphous magnetic spins and population genetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Characterizing Reactive Transport Behavior in a Three-Dimensional Discrete Fracture Network

While several studies have linked network and in-fracture scale properties to conservative transport behavior in subsurface fractured media, studies on reactive transport cases remain relatively underdeveloped. In this study, we explore the behavior of an irreversible kinetic reaction during the interaction of two solute plumes, one consisting of species A and the other species B. When the plumes converge, these species react kinetically to form a new species C via A+B→kC. This reactive system is studied using a three-dimensional discrete fracture network (DFN) model coupled with reactive Lagrangian particle tracking. We find that the interplay of network topology and chemical properties of the reactive solutes controls reactive transport processes. The network topology drives species A and B together, and the chemical properties dictate whether and how quickly a reaction occurs. Results demonstrate that reactions are most likely to occur in high-velocity fractures that make up the network backbone. The interplay between species’ chemical properties and transport is characterized by a non-dimensional Damköhler (Da) number. We show that the spatial distribution of reactions is sensitive to Da, which subsequently influences late-time tailing behavior in outlet breakthrough time distributions. The results of this study provide initial insights into how an irreversible reaction occurs during transport in a fracture network, using a methodology that can be applied to study reactive transport in a wide range of fractured media environments and contexts.

58 GEOSCIENCES↗
DOE OSTI2019

Community detection and unveiling of hierarchy in networks: a density-based clustering approach

Abstract The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuits to cybersecurity. We present a statistical mechanics approach that uses a normalized Gaussian function which captures the impact of a node within its neighborhood and leads to a density-ranking of nodes by considering the distance between nodes as punishment. A hill-climbing procedure is applied to determine the density attractors and identify the unique parent (leader) of each member as well as the group leader. This organization of the nodes results in a tree-like network with multiple clusters, the community tree. The method is tested using synthetic networks generated by the LFR benchmarking algorithm for network sizes between 500 and 30,000 nodes and mixing parameter between 0.1 and 0.9 . Our results show a reasonable agreement with the LFR results for low to medium values of the mixing parameter and indicate a very mild dependence on the size of the network.

Felfli, Zineb (ORCID:0000000275294635)↗
DOE OSTI2022

How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations

Abstract Social network analysis (SNA) tools and concepts are essential for addressing many environmental management and sustainability issues. One method to gather SNA data is to scrape them from environmental organizations’ websites. Web-based research can provide important opportunities to understand environmental governance and policy networks while potentially reducing costs and time when compared to traditional survey and interview methods. A key parameter is ‘search depth,’ i.e., how many connected pages within a website to search for information. Existing research uses a variety of depths and no best practices exist, undermining research quality and case study comparability. We therefore analyze how search depth affects SNA data collection among environmental organizations, if results vary when organizations have different objectives, and how search depth affects social network structure. We find that scraping to a depth of three captures the majority of relevant network data regardless of an organization’s focus. Stakeholder identification (i.e., who is in the network) may require less scraping, but this might under-represent network structure (i.e., who is connected). We also discuss how scraping web-pages of local programs of larger organizations may lead to uncertain results and how our work can combine with mixed methods approaches.

Sayles, Jesse S. (ORCID:0000000218378920)↗
DOE OSTI2016

A Roadmap and Cost Implications of Establishing Comprehensive Cancer Care Using a Teleradiotherapy Network in a Group of Sub-Saharan African Countries With No Access to Radiation Therapy

Purpose: To propose a roadmap and explore the cost implications of establishing a teleradiotherapy network to provide comprehensive cancer care and capacity building in countries without access to radiation therapy. Methods and Materials: Ten low-income sub-Saharan countries with no current radiation therapy facilities were evaluated. A basic/secondary radiation therapy center (SRTC) with 2 teletherapy, 1 brachytherapy, 1 simulator, and a treatment planning facility was envisaged at a cost of 5 million US dollars (USD 5M). This could be networked with 1 to 4 primary radiation therapy centers (PRTC) with 1 teletherapy unit, each costing USD 2M. The numbers of PRTCs and SRTCs for each country were computed on the basis of cancer incidence, assuming that a PRTC and SRTC could respectively treat 450 and 900 patients annually. Results: An estimated 71,215 patients in these countries will need radiation therapy in 2020. Stepwise establishment of a network with 99 PRTCs and 28 SRTCs would result in 155 teletherapy units and 96% access to radiation therapy. A total of 310 radiation oncologists, 155 medical physicists, and 465 radiation therapy technologists would be needed. Capacity building could be undertaken through telementoring by networking to various international institutions and professional societies. Total infrastructure costs would be approximately USD 860.88M, only 0.94% of the average annual gross domestic product of these 10 countries. A total of 1.04 million patients could receive radiation therapy during the 15-year lifespan of a teletherapy unit for an investment of USD 826.69 per patient. For the entire population of 218.32 million, this equates to USD 4.11 per inhabitant. Conclusion: A teleradiotherapy network could be a cost-contained innovative health care strategy to provide effective comprehensive cancer care through resource sharing and capacity building. The network could also be expanded to include other allied specialties. The proposal calls for active coordination between all national and international organizations backed up by strong geopolitical commitment and action from all stakeholders.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Resiliency-based restoration optimization for dependent network systems against cascading failures

Due to the increasing importance of large-scale and complex network systems and the potential for massive cascading failures in these real-world systems, modeling of system resiliency and optimization of restoration strategies to mitigate system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. Although society has experienced many incidents that demonstrate the influence of cascading failures aggravated by dependencies inside network systems, existing resiliency-based restoration optimization research rarely if ever jointly considers the impact of system dependencies on cascading failures. In this paper, different restoration prioritization strategies are applied to network systems subject to cascading failures that take into account system dependencies. By conducting case studies on synthetic networks and the U.S. airport network system, the effects of restoration strategies are evaluated using a system resiliency metric and two system performance measurements. Furthermore, the influence of system dependency characteristics and the interplay between them and restoration strategies on system resiliency regarding cascading failures are also investigated. This work demonstrates the distinct effects of restoration prioritization actions against cascading failures to mitigate the performance loss in network systems with different properties. It also provides insights about restoration improvement by considering dependency impacts to effectively reduce the intensity and extent of cascading failures.

42 ENGINEERING↗
DOE OSTI2015

Actin-myosin network is required for proper assembly of influenza virus particles

Actin filaments are known to play a central role in cellular dynamics. After polymerization of actin, various actin-crosslinking proteins including non-muscle myosin II facilitate the formation of spatially organized actin filament networks. The actin-myosin network is highly expanded beneath plasma membrane. The genome of influenza virus (vRNA) replicates in the cell nucleus. Then, newly synthesized vRNAs are nuclear-exported to the cytoplasm as ribonucleoprotein complexes (vRNPs), followed by transport to the beneath plasma membrane where virus particles assemble. Here, we found that, by inhibiting actin-myosin network formation, the virus titer tends to be reduced and HA viral spike protein is aggregated on the plasma membrane. These results indicate that the actin-myosin network plays an important role in the virus formation. - Highlights: • Actin-myosin network is important for the influenza virus production. • HA forms aggregations at the plasma membrane in the presence of blebbistatin. • M1 is recruited to the budding site through the actin-myosin network.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Network modeling for reverse flows of end-of-life vehicles

Highlights: • We developed a network model for reverse flows of end-of-life vehicles. • The model considers all recovery operations for end-of-life vehicles. • A scenario-based model is used for uncertainty to improve real case applications. • The model is adequate to real case applications for end-of-life vehicles recovery. • Considerable insights are gained from the model by sensitivity analyses. - Abstract: Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is a need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles’ recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Reprocessable and Recyclable Polymer Network Electrolytes via Incorporation of Dynamic Covalent Bonds

Mechanically durable polymer network electrolytes are desirable for batteries; however, covalent cross-linking adversely affects their large-scale manufacturing, and they are difficult to recycle post-lifespan. In this work, we address the electrolyte reprocessability and recyclability challenges through the use of dynamic covalent bonds for polymer network solid electrolytes. Specifically, the associative dynamic vinylogous urethane motif is incorporated into a poly(ethylene oxide) network electrolyte containing lithium bis(fluorosulfonyl)imide (LiFSI) salt. The resulting dynamic covalent network electrolyte possesses a modest ion conductivity (~10–5 S/cm at room temperature) and good reprocessability. Stress–relaxation studies indicate that the LiFSI salt can catalyze the bond exchange and enhance the network dynamics. Furthermore, the reprocessability associated with bond exchange can be tuned by simply adjusting the formulation of reagent compositions, in which increasing the amount of excess amine or using a shorter chain of PEO prepolymer can further enhance the network dynamics and corresponding reprocessability.

25 ENERGY STORAGE↗