Search NASA⌕ Search

SEARCH · Search NASA

Results for “community evolution”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Context-aided analysis of community evolution in networks

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Increasing bacterial tolerance and metabolism of the biofuel, n-butanol using community-level evolution and functional genomics

n-butanol is a strong biofuel candidate since it is more energy dense and less volatile than ethanol. The bottleneck for industrial production of biobutanol is the toxicity; most microbes cannot survive about 1.5% v/v. We propose to develop bioremediation capability along with the biofuel technology to mitigate future spills and their consequences. Two main approaches were used: community-level screening and functional genomics, both followed by directed evolution to increase biodegradation. For the first approach, a bacterial library from historic bioremediation projects was re-isolated and re-characterized, then mixed into artificial communities of ~20 strains (“master mixes”). These master mixes were screened for tolerance to butanol, and continuously exposed to increase tolerance. Promising communities were then tested for growth with butanol as the sole carbon source. Secondly, we looked for bacteria with alcohol dehydrogenase enzymes and looked to increase butanol metabolism. We found that the tolerance for n-butanol may be improved with repeated exposure, but it was difficult to switch from tolerance to metabolism. Bacterial community dynamics may be influenced by n-butanol concentration, and there was putative butanol metabolism found with both research approaches.

09 BIOMASS FUELS↗
DOE OSTI2024

Increasing Bacterial Tolerance and Metabolism of the Biofuel, N-Butanol Using Community-Level Evolution and Functional Genomics

Bioremediation capability should be developed along with biofuel technology to mitigate the potential damage of future spills. One biofuel that is being developed is biobutanol, since n-butanol is more energy dense and less volatile than ethanol. A bottleneck for industrial production of biobutanol is its toxicity; most microbes cannot survive about 1.5% v/v. Thus, microbial bioremediation of n-butanol would need microbes that can both tolerate and metabolize butanol. We used ecological and evolutionary biology approaches to find bacteria that could tolerate and metabolize butanol. We then tried to increase the metabolism of butanol by promising bacterial strains and communities. Ecological community-level assays and screenings were conducted followed by 16S amplicon sequencing to identify butanol-tolerant artificial bacterial communities. Promising communities were then tested for growth with butanol as the sole carbon source. Secondly, we looked for bacteria with alcohol dehydrogenase enzymes and looked to increase butanol metabolism. We found that the tolerance for n-butanol may be improved with repeated exposure, but it was difficult to switch from tolerance to metabolism. Bacterial community v dynamics may be influenced by n-butanol concentration, and there was putative butanol metabolism found with both research approaches.

09 BIOMASS FUELS↗
DOE OSTI2017

The Evolution of Community Engagement Through Ongoing Services Provided by the Fernald Preserve Visitors Center - 17402

The Fernald Preserve is located on the site of a former uranium-processing plant that underwent extensive environmental remediation followed by comprehensive ecological restoration. It is managed by the US Department of Energy Office of Legacy Management and is located approximately 29 kilometers (km) (18 miles) northwest of Cincinnati, Ohio. Remedial activities and subsequent ecological restoration have converted the site from an industrial production facility to an undeveloped park, encompassing a series of wetlands, prairies, and forested communities. Although the remediation of buildings and soil contamination was completed in 2006, site aquifer remediation is ongoing and remains a topic of interest to the community. Much of the site is open to the public, with approximately 11 km (7 miles) of year-round walking trails within the 425-hectare (1050-acre) site. The Fernald Preserve Visitors Center is open Wednesdays through Saturdays and site staff are available to accommodate program requests as needed. The Visitors Center was originally requested by stakeholders involved in developing recommendations for the site's final land use. Stakeholders requested a 'multi-use educational facility' that would recognize the site's history, serve as a source of site information, and recognize the contributions of energy workers during the Cold War. This building was converted from a warehouse built to support site cleanup and was ultimately awarded a Platinum rating by the U.S. Green Building Council. The Visitors Center was designed to accommodate site history exhibits, free meeting space for nonprofit groups, and public interpretive programs. It was opened to the public in 2008, and today's ongoing visitor services are continually adapted to meet the needs of current stakeholders. Over time, the interpretive services have evolved to include more diverse onsite cultural and natural history public programs, events, and activities. Today's community engagement efforts include public and private onsite programs and exhibits, public and private offsite programs and displays, meeting space, and regular open hours for the Visitors Center. This paper presents the various methods used to engage the greater Cincinnati community at the Fernald Preserve. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Model-based Analysis of Mixed Uranium(VI) Reduction by Biotic and Abiotic Pathways During in Situ Bioremediation

Uranium bioremediation has emerged as a potential strategy of cleanup of radionuclear contamination worldwide. An integrated geochemical & microbial community model is a promising approach to predict and provide insights into the bioremediation of a complicated natural subsurface. In this study, an integrated column-scale model of uranium bioremediation was developed, taking into account long-term interactions between biotic and abiotic processes. It is also combined with a comprehensive thermodynamic analysis to track the fate and cycling of biogenic species. As compared with other bioremediation models, the model increases the resolution of the connection of microbial community to geochemistry and establishes direct quantitative correlation between overall community evolution and geochemical variation, thereby accurately predicting the community dynamics under different sedimentary conditions. The thermodynamic analysis examined a recently identified homogeneous reduction of U(VI) by Fe(II) under dynamic sedimentary conditions across time and space. It shows that the biogenic Fe(II) from Geobacter metabolism can be removed rapidly by the biogenic sulphide from sulfate reducer metabolism, hence constituting one of the reasons that make the abiotic U(VI) reduction thermodynamically infeasible in the subsurface. Further analysis indicates that much higher influent concentrations of both Fe(II) and U(VI) than normal are required to for abiotic U(VI) reduction to be thermodynamically feasible, suggesting that the abiotic reduction cannot be an alternative to the biotic reduction in the remediation of uranium contaminated groundwater.

bioremediation↗
DOE OSTI2021

Community Fabric: Visualizing communities and structure in dynamic networks

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Final Report on Community Solar Design Models for Consumer, Industry, & Utility Success

Since the inception of the first community solar program in 2006, a number of different utility, solar industry and consumer driven community solar program models have emerged across the country with varying regulatory and solar market frameworks behind them. There is little consistency in how programs are administered, participant benefits are assigned, and customers are charged for participation. Further, while it is regularly speculated that community solar programs meet the needs of customers, no such consumer research has been completed against different community solar program models. This collaborative research effort addressed three primary issues: Program design models are numerous and evolving, with varying degrees of applicability across different state policy, utility regulatory, and solar market types. The agents, transaction methods, and accruing costs and benefits are highly variable and largely built on anecdotally successful model evolution. ‘Community solar’ (shared/group solar, solar gardens, etc.) has become a catch-all concept that means different things to different stakeholders, limiting constructive program development and growth. Everything from green pricing programs to remote net metering to metering aggregation has been framed as community solar. Consumer research on individual concepts and program models has been done, but no comprehensive and comparative market research on the broader design models and attributes has been performed. SEPA’s collaborative research effort on Community Solar Design Models for Consumer, Industry, & Utility Success is premised on the principle that greater standardization of community solar models and better understanding of consumer interests are key underpinnings of increased community solar deployment. Utilizing a cross-sectional multi-stakeholder working group, as well as focus groups and surveys, webinars, targeted outreach, and other tools, this project aims to expand the community solar knowledge base, facilitate information exchange, identify opportunities for standardization and improvement, determine what consumers’ interests are and how best to message to them, promote the research results within the solar and utility industries, and advance key opportunities for strategic deployment plans for community solar. A summary of these key takeaways/lessons learned during the grant period include: Siting of system should be evaluated up-front in the process Engage stakeholders collaboratively and early to help ensure the best chance for a successful program IT/billing departments must have a seat at the design table Establish upfront target objectives with respect to residential and commercial program access levels helps ensure program success Balance trade-offs to manage utility and developer risk with customer participation carefully to optimize pricing and sizing the program offering Over the course of the 42 month grant period, SEPA and its project team conducted a series of activities all designed to accelerate the number and size of community solar programs across the country. These activities included: Community Solar Program Design Models (2 reports) – SEPA, in collaboration with its projects team, the Coalition for Community Solar Access, and over 60 administrators of actual community solar programs, developed two reports providing hard data on the developments of community solar. These reports touched on program performance, quantity and type of programs, and program design steps, among other aspects. The reports collectively received over 2000 unique downloads. Market Research Report – SEPA, in collaboration with The Shelton Group, conducted a series of national surveys and focus groups to identify what customers were looking for in a community solar offering. The resulting report, “What the Community Solar Customer Wants”, received over 1,000 unique downloads. Technical Assistance Projects – SEPA supported 8 organizations who were pursuing community solar programs. Based on this support, over 5.7 MWs of new solar capacity have been developed. Technical Assistance Case Studies – SEPA developed two case studies detailing the technical assistance work in Fremont, NE Department of Utilities and the public power arm of Minster Ohio. These two reports have received 1,002 unique downloads. Webinar – SEPA hosted two webinars on community solar which collectively received 743 participants. The webinars focused on disseminating the findings of the research conducted under this project, as well as the research conducted in separate solar market pathways projects. Community Solar Design Workshops – 120 registrants learned about the trends in community solar and participated a business case exercise to design their own programs.

14 SOLAR ENERGY↗
DOE OSTI2018

Microbial Interactions With Dissolved Organic Matter Drive Carbon Dynamics and Community Succession

Knowledge of dynamic interactions between natural organic matter (NOM) and microbial communities is critical not only to delineate the routes of NOM degradation/transformation and carbon (C) fluxes, but also to understand microbial community evolution and succession in ecosystems. Yet, these processes in subsurface environments are usually studied independently, and a comprehensive view has been elusive thus far. In this study, we fed sediment-derived dissolved organic matter (DOM) to groundwater microbes and continually analyzed microbial transformation of DOM over a 50-day incubation. To document fine-scale changes in DOM chemistry, we applied high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and soft X-ray absorption spectroscopy (sXAS). We also monitored the trajectory of microbial biomass, community structure and activity over this time period. Together, these analyses provided an unprecedented comprehensive view of interactions between sediment-derived DOM and indigenous subsurface groundwater microbes. Microbial decomposition of labile C in DOM was immediately evident from biomass increase and total organic carbon (TOC) decrease. The change of microbial composition was closely related to DOM turnover: microbial community in early stages of incubation was influenced by relatively labile tannin- and protein-like compounds; while in later stages the community composition evolved to be most correlated with less labile lipid- and lignin-like compounds. These changes in microbial community structure and function, coupled with the contribution of microbial products to DOM pool affected the further transformation of DOM, culminating in stark changes to DOM composition over time. Our study demonstrates a distinct response of microbial communities to biotransformation of DOM, which improves our understanding of coupled interactions between sediment-derived DOM, microbial processes, and community structure in subsurface groundwater.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Adaptive Evolution of Synthetic Cooperating Communities Improves Growth Performance

Symbiotic interactions between organisms are important for human health and biotechnological applications. Microbial mutualism is a widespread phenomenon and is important in maintaining natural microbial communities. Although cooperative interactions are prevalent in nature, little is known about the processes that allow their initial establishment, govern population dynamics and affect evolutionary processes. To investigate cooperative interactions between bacteria, we constructed, characterized, and adaptively evolved a synthetic community comprised of leucine and lysine Escherichia coli auxotrophs. The co-culture can grow in glucose minimal medium only if the two auxotrophs exchange essential metabolites — lysine and leucine (or its precursors). Our experiments showed that a viable co-culture using these two auxotrophs could be established and adaptively evolved to increase growth rates (by ~3 fold) and optical densities. While independently evolved co-cultures achieved similar improvements in growth, they took different evolutionary trajectories leading to different community compositions. Experiments with individual isolates from these evolved co-cultures showed that changes in both the leucine and lysine auxotrophs improved growth of the co-culture. Interestingly, while evolved isolates increased growth of co-cultures, they exhibited decreased growth in mono-culture (in the presence of leucine or lysine). A genome-scale metabolic model of the co-culture was also constructed and used to investigate the effects of amino acid (leucine or lysine) release and uptake rates on growth and composition of the co-culture. When the metabolic model was constrained by the estimated leucine and lysine release rates, the model predictions agreed well with experimental growth rates and composition measurements. While this study and others have focused on cooperative interactions amongst community members, the adaptive evolution of communities with other types of interactions (e.g., commensalism, ammensalism or parasitism) would also be of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Anticipating Technical Expertise and Capability Evolution in Research Communities Using Dynamic Graph Transformers

The ability to anticipate global technical expertise and capability evolution trends is essential for national and global security, especially in safety-critical domains such as nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). Here, in this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by: 1) forecasting heterogeneous (rather than homogeneous) nodes and edges; and 2) relying on both discrete- and continuous-time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30%–80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance when previously unseen nodes appear in the test data for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice versa in the AI domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Impact of CO 2 on the Evolution of Microbial Communities Exposed to Carbon Storage Conditions, Enhanced Oil Recovery, and CO 2 Leakage

Geologic carbon storage (GCS) is a crucial part of a proposed mitigation strategy to reduce the anthropogenic carbon dioxide (CO 2 ) emissions to the atmosphere. During this process, CO 2 is injected as super critical carbon dioxide (SC-CO 2 ) in confined deep subsurface storage units, such as saline aquifers and depleted oil reservoirs. The deposition of vast amounts of CO 2 in subsurface geologic formations could unintentionally lead to CO 2 leakage into overlying freshwater aquifers. Introduction of CO 2 into these subsurface environments will greatly increase the CO 2 concentration and will create CO 2 concentration gradients that drive changes in the microbial communities present. While it is expected that altered microbial communities will impact the biogeochemistry of the subsurface, there is no information available on how CO 2 gradients will impact these communities. The overarching goal of this project is to understand how CO 2 exposure will impact subsurface microbial communities at temperatures and pressures that are relevant to GCS and CO 2 leakage scenarios. To meet this goal, unfiltered, aqueous samples from a deep saline aquifer, a depleted oil reservoir, and a fresh water aquifer were exposed to varied concentrations of CO 2 at reservoir pressure and temperature. The microbial ecology of the samples was examined using molecular, DNA-based techniques. The results from these studies were also compared across the sites to determine any existing trends. Results reveal that increasing CO 2 leads to decreased DNA concentrations regardless of the site, suggesting that microbial processes will be significantly hindered or absent nearest the CO 2 injection/leakage plume where CO 2 concentrations are highest. At CO 2 exposures expected downgradient from the CO 2 plume, selected microorganisms emerged as dominant in the CO 2 exposed conditions. Results suggest that the altered microbial community was site specific and highly dependent on pH. The site-dependent results suggest a limited ability to predict the emerging dominant species for other CO 2 -exposed environments. This study improves the understanding of how a subsurface microbial community may respond to conditions expected from GCS and CO 2 leakage. This is the first step for understanding how a CO 2 -altered microbial community may impact injectivity, permanence of stored CO 2 , and subsurface water quality. Future work with microbial communities from new subsurface sites would increase the current understanding of this project. Additionally, incorporation of metagenomic methods would increase understanding of potential microbial processes that may be prevalent in CO 2 exposed environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution using the Community Ice Sheet Model

Abstract. The future retreat rate for marine-based regions of the Antarctic Ice Sheet is one of the largest uncertainties in sea-level projections. The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) aims to improve projections and quantify uncertainties by running an ensemble of ice sheet models with atmosphere and ocean forcing derived from global climate models. Here, the Community Ice Sheet Model (CISM) is used to run ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution. Using multiple combinations of sub-ice-shelf melt parameterizations and calibrations, CISM is spun up to steady state over many millennia. During the spin-up, basal friction parameters and basin-scale thermal forcing corrections are adjusted to optimize agreement with the observed ice thickness. The model is then run forward for 550 years, from 1950–2500, applying ocean thermal forcing anomalies from six climate models. In all simulations, the ocean forcing triggers long-term retreat of the West Antarctic Ice Sheet, especially in the Filchner–Ronne and Ross sectors. Mass loss accelerates late in the 21st century and then rises steadily for several centuries without leveling off. The resulting ocean-forced sea-level rise at year 2500 varies from about 150 to 1300 mm, depending on the melt scheme and ocean forcing. Further experiments show relatively high sensitivity to the basal friction law, moderate sensitivity to grid resolution and the prescribed collapse of small ice shelves, and low sensitivity to the stress-balance approximation. The Amundsen sector exhibits threshold behavior, with modest retreat under many parameter settings but complete collapse under some combinations of low basal friction and high thermal forcing anomalies. Large uncertainties remain, as a result of parameterized sub-shelf melt rates, simplified treatments of calving and basal friction, and the lack of ice–ocean coupling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Impact of CO 2 on the Evolution of Microbial Communities Exposed to Carbon Storage Conditions, Enhanced Oil Recovery, and CO 2 Leakage

Geologic carbon storage (GCS) is a crucial part of a proposed mitigation strategy to reduce the anthropogenic carbon dioxide (CO 2 ) emissions to the atmosphere. During this process, CO 2 is injected as super critical carbon dioxide (SC-CO 2 ) in confined deep subsurface storage units, such as saline aquifers and depleted oil reservoirs. The deposition of vast amounts of CO 2 in subsurface geologic formations could unintentionally lead to CO 2 leakage into overlying freshwater aquifers. Introduction of CO 2 into these subsurface environments will greatly increase the CO 2 2 concentration and will create CO 2 concentration gradients that drive changes in the microbial communities present. While it is expected that altered microbial communities will impact the biogeochemistry of the subsurface, there is no information available on how CO 2 gradients will impact these communities. The overarching goal of this project is to understand how CO 2 exposure will impact subsurface microbial communities at temperatures and pressures that are relevant to GCS and CO 2 leakage scenarios. To meet this goal, unfiltered, aqueous samples from a deep saline aquifer, a depleted oil reservoir, and a fresh water aquifer were exposed to varied concentrations of CO 2 at reservoir pressure and temperature. The microbial ecology of the samples was examined using molecular, DNA-based techniques. The results from these studies were also compared across the sites to determine any existing trends. Results reveal that increasing CO 2 leads to decreased DNA concentrations regardless of the site, suggesting that microbial processes will be significantly hindered or absent nearest the CO 2 injection/leakage plume where CO 2 concentrations are highest. At CO 2 exposures expected downgradient from the CO 2 plume, selected microorganisms emerged as dominant in the CO 2 exposed conditions. Results suggest that the altered microbial community was site specific and highly dependent on pH. The site-dependent results suggest a limited ability to predict the emerging dominant species for other CO 2 exposed environments. This study improves the understanding of how a subsurface microbial community may respond to conditions expected from GCS and CO 2 leakage. This is the first step for understanding how a CO 2 -altered microbial community may impact injectivity, permanence of stored CO 2 , and subsurface water quality. Future work with microbial communities from new subsurface sites would increase the current understanding of this project. Additionally, incorporation of metagenomic methods would increase understanding of potential microbial processes that may be prevalent in CO 2 exposed environments.

54 ENVIRONMENTAL SCIENCES↗