Translational computer science at the scientific computing and imaging institute
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Computer Science↗
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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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Abstract for CRADA between National Energy Technology Laboratory and American Institute of Chemical Engineers dba RAPID Manufacturing Institute
NETL and RAPID Manufacturing will develop advanced electromagnetic (EM) reactors and catalysts to produce light olefins from alternative feedstocks using CO2 soft oxidation. Light olefins are the building blocks for many plastic products, in addition to synthetic fibers, rubbers, and polymers. This project has the potential to produce high-value light olefins from alternative feedstocks with reduced energy consumption (50-70%), carbon intensity (50-70%), and production cost (15-25%) compared to the traditional olefin production processes.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award
Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.
59 BASIC BIOLOGICAL SCIENCES↗
Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins
Abstract Water resources systems models enable valuable inferences on consequential system stressors by representing both the geophysical processes determining the movement of water and the human elements distributing it to its various competing uses. This study contributes a diagnostic evaluation framework that pairs exploratory modeling with global sensitivity analysis to enhance our ability to make inferences on water scarcity vulnerabilities in institutionally complex river basins. Diagnostic evaluation of models representing institutionally complex river basins with many stakeholders poses significant challenges. First, it needs to exploit a large and diverse suite of simulations to capture important human‐natural system interactions as well as institutionally aware behavioral mechanisms. Second, it needs to have performance metrics that are consequential and draw on decision‐relevant model outputs that adequately capture the multisector concerns that emerge from diverse basin stakeholders. We demonstrate the proposed model diagnostic framework by evaluating how potential interactions between changing hydrologic conditions and human demands influence the frequencies and durations of water shortages of varying magnitudes experienced by hundreds of users in a subbasin of the Colorado River. We show that the dominant factors shaping these effects vary both across users and, for an individual user, across percentiles of shortage magnitude. These differences hold even for users sharing diversion locations, demand levels or water right seniority. Our findings underline the importance of detailed institutional representation for such basins, as institutions strongly shape how dominant factors of stakeholder vulnerabilities propagate through the complex network of users.
Hadjimichael, Antonia↗
Creating the National Renewable Energy Laboratory: Implications for New National Energy Research Institutes
The history of the design and development of the National Renewable Energy Laboratory (NREL) in the United States illustrates how the plans for a new institute can be realized and evolve over four decades. This history summarizes the process of planning and developing NREL as a new research institute to meet its mandated mission, including creating a research agenda and plans for R&D management, human resources, financial sustainability, and facilities. The NREL origin story, informed by the development of similar institutions around the world, also reveals generalized principles useful for decision makers across the globe who are designing their own research institute. These principles are described in this publication.
energy institute↗
Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization
Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.
Zhou, Doudou↗
New Mexico Cybersecurity Center Institute 2020 Experience
The New Mexico Cybersecurity Center 2020 Summer Institute with Sandia National Laboratories was the rst ever for the New Mexico Cybersecurity Centers. Due to the occurrence of the COVID-19 global pandemic during the summer of 2020 some unforeseen challenges had to be faced in running the institute, such as the fact that all students had to work remotely. This document details the students involved in the institute and their contributions to Sandia projects over the summer of 2020, as well as an analysis on how to successfully run a summer institute via distance.
97 MATHEMATICS AND COMPUTING↗
RAPID Manufacturing Institute Final Report
The Rapid Advancement of Process Intensification Deployment (RAPID) Manufacturing Institute, founded in 2017, is a public/private partnership between the U.S. Department of Energy and the American Institute of Chemical Engineers (AIChE). RAPID promotes the development, deployment and commercialization of Process Intensification (PI) and Modular Chemical Process Intensification (MCPI) technologies, enabling U.S. manufacturing to reduce energy consumption, improve process efficiencies and lower investment and operating costs. This mission was carried out through parallel work breakdown structure elements including the establishment of committees to guide the operations and technical direction of RAPID, the establishment of management practices and institute processes, education and workforce development (EWD), and six technical focus areas for the development of technologies to advance PI and MCPI. Throughout the initial six-year cooperative agreement, RAPID worked to meet performance metrics which focused on the operation and sustainment of the institute, education and workforce development and the development of PI and MCPI for the advancement of U.S. manufacturing. All these metrics were successfully met through a total of 43 projects which leveraged $\$$70M Federal with $\$$90M cost share. As a result of these efforts, 84 private and public organizations were brought together by RAPID as members to co-invest in R&D, commercialization and deployment of innovative technologies. In the research portfolio, 82% of the 38 projects achieved > 20% energy efficiency improvement. A RAPID Test Network was developed with 51 testbed facilities to enable access to resources, facilities, tools, and expertise. Eight EWD programs were also developed with over 13,000 impressions. RAPID’s efforts to research, develop, demonstrate, and deploy high-impact PI and modular process technology solutions have enabled reduced energy use, increased sustainability, and improved profitability for U.S. manufacturing.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
“Frameworks, Algorithms and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute” (Final Technical Report)
SMU personnel formed a portion of the overall “Time Integration” team within the FASTMath SciDAC-5 Institute, and we interacted very closely with team members from collaborating institutions. The major goals of our team within the FASTMath institute may be categorized into two groups: the development of advanced and application-aware time integration methods and software, and close interactions with DOE application scientists to facilitate their use of these new methods and software. The two goals are intimately linked, since our research and development of novel tools is informed by the needs of our application partners, who in turn benefit from subsequent mathematical and software advances. While the SMU personnel collaborated with the rest of the Time Integration FASTMath team on most of our shared deliverables within the larger FASTMath institute, SMU personnel primarily contributed to the following subset of those goals: ● expanding capabilities for higher-order and solve-decoupled multirate methods, ● enhancing support for temporal adaptivity within multirate methods, and ● adding structure-aware time integration methods. Each of these goals focused both on intellectual contributions through journal articles or research presentations, and on enhancements to the open-source SUNDIALS library of time integrators and nonlinear solvers, of which the SMU PI Reynolds is a core developer.
97 MATHEMATICS AND COMPUTING↗
Sustainable Enablers of Knowledge Management Strategies in a Higher Education Institution
By facilitating the capture, organization, and dissemination of knowledge within and beyond the institution, knowledge management (KM) in higher education institutions (HEIs) fuels innovation, enhances research impact, and strengthens collaboration, ultimately leading to the creation of new knowledge and its valuable exchange. However, there is still much to explore in terms of the enablers of knowledge creation, sharing, and transfer. Therefore, this paper aims to identify the enablers of effective KM in the Polytechnique University of Leiria, which serves as a benchmark for other higher education institutions due to its leadership role in RUN-EU, a consortium of European universities. To achieve this, a narrative analysis based on information from SCOPUS and the institute’s website, focusing on innovation, research, and development strategies, is proposed. The findings suggest that for KM initiatives to be successful, they need to be strategically designed, culturally supported, technologically enabled, and integrated into existing workflows.
Santos, Eleonora (ORCID:0000000346930804)↗
The Gulf Nuclear Energy Infrastructure Institute: A Multidisciplinary Educational Approach for Integrated Nuclear Energy Safety Security and Safeguards in the Middle East
The Gulf Nuclear Energy Infrastructure Institute (GNEII) at Khalifa University of Science and Technology was created as a regional institute offering education, research and technical services to support nuclear energy safety, security and safeguards (3S) objectives. A mixed methods approach—using the (1) Course Evaluation, (2) GNEH Alumni Survey, (3) Capstone Project and, (4) GNEII-Related Literature data sets—was used to evaluate the effect of implementing this multidisciplinary `3S' educational program and the broader impact of the associated `3S' multidisciplinary institute on nuclear energy human resource development. Data sets (1), (2) and (3) illustrate how well GNEII implemented this novel 3S curriculum and resulted in successful knowledge transfer. Data sets (2), (3) and (4) illustrate how well GNEII's impact has positively influenced professional workplace behaviors and the institute's broader reputation to support responsible nuclear energy program education. Furthermore, GNEII demonstrates one option for successfully providing a multidisciplinary, 3S curriculum to support broader nuclear infrastructure and human resource development aims.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Enhancing HEP research in predominantly undergraduate institutions and community colleges
The long-term success of HEP lies in expanding inclusiveness beyond national labs and academic research institutions to a vast community of predominantly undergraduate institutions (PUI) and community colleges (CC). Institutions such as PUIs and CCs offer an early starting point in the pipeline that can mitigate issues of lack of diversity and underrepresented participation of different groups in HEP. However, there are many underlying systemic, structural, and cultural challenges that need to be addressed collectively. Experimental collaborations are largely populated by national labs and research-focused academic institutions (non-PUIs). The faculty at PUIs and CCs have a high teaching load that is detrimental to their research participation. In addition, there is a lack of guidance, access, and tough competition for securing research funding. The students also suffer from a lack of research infrastructure and technical equipment that can only be found at national labs and larger universities. There are existing successful efforts to enhance the HEP research experience of students and faculty members. This paper discusses ways to leverage these to provide more research opportunities and establish a sustainable national program targeting specifically the issues faced by communities at PUIs and CCs. The need for research mentoring and skill building for faculty members is also laid out. The changes discussed in this paper would make a direct impact on the current spectrum of challenges.
Bellis, Matt↗
Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy
High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.
36 MATERIALS SCIENCE↗
A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.
36 MATERIALS SCIENCE↗
IACMI–The Composites Institute Efforts in Current and Post–COVID-19 Manufacturing Era—Innovations and Sustainability
Institute for Advanced Composites Manufacturing Innovation (IACMI)–The Composites Institute comprises 150 industry members making up the supply chain from material suppliers; Tiers 1, 2, and 3 original equipment manufacturer (OEM)s and fabricators; academia; and national laboratories. In this work, the institute is addressing the needs for medical systems support in response to the coronavirus 2019 (COVID-19) crisis and the recovery of the U.S. composites industry and connected manufacturing supply chains. The impact of the current and post-COVID response by IACMI and its partners extends across health care, automotive, transportation, construction, infrastructure, aerospace, sports, marine, and industrial sectors. Sustainability and circular economy of advanced materials and manufacturing play a key role in overall reduced embodied energy. This technical note provides a summary of IACMI efforts in response to COVID-19 and outlook for the post-COVID era with a focus on sustainable advanced materials and manufacturing.
36 MATERIALS SCIENCE↗
2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study
# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study
# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study
# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗