Results for “data sustainability” — Page 12

DOE OSTI2022

Towards a robust out-of-the-box neural network model for genomic data

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity. Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study. While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Kick Signatures Through Multiphase Data

Oral presentation on NETL research delivered at the 2022 Resource Sustainability Meeting. This effort concerns advancing a concept for early kick detection through the development of synthetic (simulation and experiment) multiphase acoustic data corresponding to well characterized kick events. The simulation activities are the focus of the talk.

Carney, Janine↗

DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗

DOE OSTI2026

Roadmap for transforming heterogeneous catalysis with artificial intelligence

Artificial intelligence (AI) is poised to transform heterogeneous catalysis, opening avenues for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental and chemical sectors. This promise, however, hinges on overcoming fundamental barriers, including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. Furthermore, we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately autonomous laboratories to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration.

Computational methods↗

DOE OSTI2020

Post Conference Activities for the NCSE 2019 Annual Conference: Sustainable Infrastructure & Resilience

The National Council for Science and the Environment (NCSE) held its 19th Annual Conference from January 7-10, 2019. The NCSE 2019 Annual Conference: Sustainable Infrastructure & Resilience explored how systems thinking that includes tools, data, and synthesis effectively supports the increasingly urgent need to advance a more resilient, sustainable society. Nearly 600 participants from a range of scientific disciplines and sectors including natural and social science, humanities, engineering, government, business, and civil society attended the conference. Post-conference activities planned through this project were planned to extend the impact of the Annual Conference and were made possible through support from the Department of Energy. These efforts included: 1) developing and producing a research pilot on Energy Systems and Resilient Infrastructure, which advances research focused on the better alignment of education and workforce needs with respect to energy resilience; 2) facilitating dialogue and in-person convenings with the Energy Education Community of Practice across the broader NCSE research community; and 3) designing and hosting a webinar series focused on the role of science in infrastructure and resilience. This report summarizes the outcomes of the proposed activities and additional work made possible by support from the Department of Energy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Secure and Privacy Aware Data Sharing Approach for Smart Electric Vehicles

The integration of smart electric vehicles (SEVs) into smart cities marks a significant step toward creating efficient, sustainable, and connected urban spaces. However, secure and private data sharing is a major challenge as SEVs connect with smart city systems. The interaction between SEVs and consumer electronic devices (CEDs) raises serious concerns about data security and privacy. Here, to address these challenges, this article presents how blockchain technology and federated learning (FL) can address these issues. The proposed approach provides a secure and privacy-aware framework for data exchange between SEVs and CEDs in smart cities. The experiment results demonstrate the effectiveness of the proposed framework for secure data sharing and maintaining system reliability in smart city environments. It also enables trust and promotes the widespread adoption of interconnected urban technologies.

Das, Debashis [Meharry Medical College, Nashville,↗

DOE OSTI2021

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. We develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. Here we design a generalized preconditioned Crank-Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank-based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two-dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

CoREC: Scalable and Resilient In-memory Data Staging for In-situ Workflows

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean time between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed, and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this article, we present CoREC, a scalable and resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article presents optimizations for load balancing and conflict-avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on leadership class computing machines and present an experimental evaluation in the article. Here, the experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Effect of zeolite type, temperature, and pH on Stage III glass alteration behavior for two nuclear waste glasses

We report long-term laboratory scale static alteration tests (up to 1500 d) were conducted on two low-activity waste glasses (LAWA44 and IDF18-A161) at S/V = 2000 m –1 . Tests were seeded with either analcime, clinoptilolite, zeolite P1, or zeolite P2 to induce Stage III glass alteration behavior. The effect of several parameters on glass Stage III behavior was studied with consideration of several variables, including temperature (22 °C – 90 °C) and pH (floating or initially set using KOH at values from 9.5 to 11.5). Temporal solution data and solids characterization at the end of the experiments are reported. Solution data demonstrated the occurrence of a sustained rate acceleration at temperatures down to 40 °C and the activation energy for these Stage III rates was determined for both glasses. Three major secondary phases were identified at the end of the experiments for both glasses: zeolite P1, zeolite P2, and/or analcime (along with minor phases: zeolites, clays, carbonates). The identification of the crystalline phases at the end of the experiment often differed from the zeolite seed phase. In addition, the use of the Avrami equation showed unimpeded zeolite growth (3-dimensional) in certain conditions, suggesting that in certain conditions the glass dissolution rate is controlled by zeolite formation kinetics whereas in other conditions the glass corrosion process impeded zeolite formation kinetics. The morphological evolution of the samples revealed the growth of the zeolites which completely fill the intergranular space in the powder bed. These results are discussed with regards to previous seeded and unseeded Stage III dissolution rate experiments performed on low- and high-activity nuclear waste glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Multicriteria Measures to Assess the Sustainability of Diets: A Systematic Review

Abstract Context Assessing the overall sustainability of a diet is a challenging undertaking requiring a holistic approach capable of addressing the multicriteria nature of this concept. Objective The aim was to identify and summarize the multicriteria measures used to assess the sustainability characteristics of diets reported at the individual level by healthy adults. Data Sources Articles were identified via PubMed, Scopus, and Web of Science. The search strategy consisted of key words and MeSH terms, and was concluded in September 2022, covering references in English, Spanish, and Portuguese. Data Extraction This systematic review followed the PRISMA guidelines. The search identified 5663 references, from which 1794 were duplicates. Two reviewers independently screened the titles and abstracts of each of the 3869 records and the full-text of the 144 references selected. Of these, 7 studies met the inclusion criteria. Data Analysis A total of 6 multicriteria measures were identified: 3 different Sustainable Diet Indices, the Quality Environmental Costs of Diet, the Quality Financial Costs of Diet, and the Environmental Impact of Diet. All of these incorporated a health/nutrition dimension, while the environmental and economic dimensions were the second and the third most integrated, respectively. A sociocultural sustainability dimension was included in only 1 of the measures. Conclusion Despite some methodological concerns in the development and validation process of the identified measures, their inclusion is considered indispensable in assessing the transition towards sustainable diets in future studies. Systematic Review Registration PROSPERO registration no. CRD42022358824.

Rei, Mariana (ORCID:0000000189453708)↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗