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At least 19 records

MBIR: A cryo-ET 3D reconstruction method that effectively minimizes missing wedge artifacts and restores missing information

Cryo-Electron Tomography (cryo-ET) has become an essential technique in revealing cellular and macromolecular assembly structures in their native states. However, due to radiation damage and the limited tilt range, cryo-ET suffers from low contrast and missing wedge artifacts, which limits the tomograms to low resolution and hinders further biological interpretation. We applied the Model-Based Iterative Reconstruction (MBIR) method to obtain tomographic 3D reconstructions of experimental cryo-ET datasets and demonstrated the advantages of MBIR in contrast improvement, missing wedge artifacts reduction, missing information restoration, and subtomogram averaging compared with other reconstruction approaches. Considering the outstanding reconstruction quality, MBIR has a great potential in the determination of high resolution biological structures with cryo-ET.

59 BASIC BIOLOGICAL SCIENCES↗

DEPRECATED - Sharing the Sun Community Solar Project Data (Dec 2020, Revision)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top. The revision dataset included projects with a total installed capacity in 2020 under the state-level program but missing the project level details.

14 SOLAR ENERGY↗

Community Solar Project Database

This database represents a list of community solar projects identified through various sources as of Spring 2018. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the souces e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to Eric O'Shaughnessy at eric.oshaughnessy@nlr.gov.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (June 2019)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2019. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the souces e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (May 2020)

This data set is no longer current - The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of May 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (June 2020)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (December 2021)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (June 2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2022. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (December 2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2022. In addition, this dataset updated the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects, based on the most recent program data we collected as of July 2023. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - Sharing the Sun Community Solar Project Data (December 2023)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2023. In addition, this dataset updated the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects, based on the most recent program data we collected as of March 2024. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

Sharing the Sun Community Solar Project Data

This database represents a list of community solar projects, complete and pending, identified through various sources. The dataset is updated multiple times per year. The current version is the first file located below. Previous versions of the dataset published before June of 2024 can be found in the dataset below labeled “ARCHIVE_Sharing the Sun Community Solar Project Data_Before 06.24.“ The list has been reviewed but errors may exist, and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NLR invites input to improve the database including, to correct erroneous information, add missing projects, fill in missing information, and remove inactive projects. Updated information can be submitted to Sudha Kannan ( sudha.kannan@nlr.gov ).

14 SOLAR ENERGY↗

Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic

Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW.

54 ENVIRONMENTAL SCIENCES↗

Means of Meeting Chapter 15 Requirements for a Small Modular Reactor Cooled by Lead-Bismuth

The mission of the Department of Energy Office of Nuclear Energy (DOE-NE) is to advance nuclear power as a resource capable of meeting the nation’s energy, environmental, and national security needs by resolving technical, cost, safety, proliferation resistance, and security barriers through Research, Development, and Demonstration (RD&D). To assist in accomplishing this, DOE-NE has established the Gateway for Accelerated Innovation in Nuclear (GAIN) to provide the nuclear community with access to the technical, regulatory, and financial support necessary to move innovative nuclear energy technologies toward commercialization. As part of this program, a GAIN Voucher was awarded to the Columbia Basin Consulting Group (CBCG) to develop for the Lead-Bismuth Reactor (LBR): (1) a means of meeting Chapter 15 requirements and (2) to perform an assessment against a gap analysis developed by the U.S. Nuclear Regulatory Commission (NRC) for Chapter 15. These two assessments will determine to what extent the LBR can adapt to or meet the Chapter 15 Accident Analysis requirements, and determine gaps that may still exist and what additional information is needed in order to close them. The CBCG LBR design is being developed with a high level of passive safety. It has the advantages of the classical liquid metal reactor, i.e. small size and passive safety, while having the additional advantage of a chemically inert coolant. The CBCG and the Pacific Northwest National Laboratory (PNNL) collaborated to develop an initial licensing pathway for the CBCG Lead-Bismuth conceptual reactor design. It was found that the reactor is well along the licensing pathway and is licensable although some additional design information is needed to establish a complete licensing basis. The missing information is identified in this report and it is noted that much of the missing information can be obtained from the Fast Flux Test Facility (FFTF), a DOE liquid metal test reactor. It was also found that the traditional Chapter 15 requirements for assessing the response of a reactor to possible accident sequences can be adapted to the LBR concept. The Chapter 15 review examined several classes of initiating events and accidents that are normally covered in Chapter 15: reactivity initiated events, reduction in heat removal events, local fuel failure events, and events initiated on the steam supply side. Although some design information is lacking at present, the LBR can be expected to respond as required by Chapter 15 requirements to almost all of the first three events. For events initiated on the steam side, design information is lacking and so conclusions cannot be reached at present. On the other hand, it should be quite possible to develop the design information for the steam supply side and the impact of events initiated on this side can be assessed in a subsequent study.

Wootan, David W.↗

SU-E-I-08: An Inpaint-Based Interpolation Technique to Recover Blocked Information for Cone Beam CT with a Synchronized Moving Grid (SMOG)

Purpose: Synchronized moving grid (SMOG) is a promising technique to reduce scatter and ghost artifacts in cone beam computed tomography (CBCT). However, the grid blocks part of image information in each projection, and multiple projections at the same gantry angle have to been taken to obtain full information. Because of the continuity of a patient's anatomy in the projection, the blocked information may be estimated by interpolation. This study aims to evaluate an inpainting-based interpolation approach to recover the missing information for CBCT reconstruction. Method: We used a simple region-based inpainting approach to interpolate the missing information. For a pixel to be interpolated, we divided the nearby regions having image information into 6 sub-regions: up-left, up-middle, up-right, down-left, down-middle, and down-right, each with 9 pixels. The average pixel value of each sub-region was calculated. These average values, along with the pixel location, were used to determine the interpolated pixel value. We compared our approach with the Criminisi Exemplar (CE) and total variation (TV) based inpainting techniques. Projection images of Catphan and a head phantom were used for the comparison. The SMOG was simulated by erasing the information (filling with “0”) of the areas in each projection corresponding to the grid. Results: For the Catphan, the processing time was 178, 45 and 0.98 minutes for CE, TV and our approach, respectively. The signal to noise ratio (SNR) was 19.4, 18.5 and 26.4 db, correspondingly. For the head phantom, the processing time was 222, 45 and 0.93 minutes for CE, TV and our approach, respectively. The SNR was 24.6, 20.2 and 26.2db correspondingly. Conclusion: We have developed a simple inpainting based interpolation approach, which can recover some of the image information for the SMOG-based CBCT imaging. This study is supported by NIH/NCI grant 1R01CA166948-01.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

WE-EF-207-08: Improve Cone Beam CT Using a Synchronized Moving Grid, An Inter-Projection Sensor Fusion and a Probability Total Variation Reconstruction

Purpose: To present a cone beam computed tomography (CBCT) system, which uses a synchronized moving grid (SMOG) to reduce and correct scatter, an inter-projection sensor fusion (IPSF) algorithm to estimate the missing information blocked by the grid, and a probability total variation (pTV) algorithm to reconstruct the CBCT image. Methods: A prototype SMOG-equipped CBCT system was developed, and was used to acquire gridded projections with complimentary grid patterns in two neighboring projections. Scatter was reduced by the grid, and the remaining scatter was corrected by measuring it under the grid. An IPSF algorithm was used to estimate the missing information in a projection from data in its 2 neighboring projections. Feldkamp-Davis-Kress (FDK) algorithm was used to reconstruct the initial CBCT image using projections after IPSF processing for pTV. A probability map was generated depending on the confidence of estimation in IPSF for the regions of missing data and penumbra. pTV was finally used to reconstruct the CBCT image for a Catphan, and was compared to conventional CBCT image without using SMOG, images without using IPSF (SMOG + FDK and SMOG + mask-TV), and image without using pTV (SMOG + IPSF + FDK). Results: The conventional CBCT without using SMOG shows apparent scatter-induced cup artifacts. The approaches with SMOG but without IPSF show severe (SMOG + FDK) or additional (SMOG + TV) artifacts, possibly due to using projections of missing data. The 2 approaches with SMOG + IPSF removes the cup artifacts, and the pTV approach is superior than the FDK by substantially reducing the noise. Using the SMOG also reduces half of the imaging dose. Conclusion: The proposed technique is promising in improving CBCT image quality while reducing imaging dose.

60 APPLIED LIFE SCIENCES↗