Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1390&sort=-id
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This project, Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for accelerated Impact (STRAIT I3), addresses fundamental gaps between the science and the engineering that is preventing the effective use of AI models with medical imaging data. The project will leverage the large number of open dataset efforts available for medical imaging, including imaging resources for COVID-19. Thousands of AI models are also published in the scientific literature each year for such data. Yet, these resources are not consistently accessible at scale nor are they able to be validated for clinical application. This project includes three thrust areas to address this problem. Thrust Area 1 democratizes access to data sets through traceable data annotation. Thrust Area 2 transforms the assessment and peer review process for data, to ensure fair and consistent evaluation of technologies. Thrust Area 3 targets reproducible execution and comparison of models to facilitate translation to practice. In Phase I, this Convergence Accelerator project will create direct public health and technology benefits by enhancing the radiological assessment of COVID-19 pneumonia. In Phase II, it will extend these benefits into a medical imaging ecosystem spanning multiple medical imaging domains. Broader impacts will be achieved by engaging various identified communities through professionally led studios, consented A/B testing studies, and structured outreach. All project thrusts utilized open software and commodity hardware, wherever possible, so that the innovations from this project on scalable image data validation will enhance other related efforts in open source software, open science, reproducible science, and findable science.This project works towards achieving a fundamental rethinking in how model-centric AI could be validated and translated in medical imaging, algorithm design, and medical science. The intellectual activities are organized around three research thrusts, each addressing an essential challenge that currently confronts the development and translation of AI-based medical imaging tools. One research thrust is on creating a lightweight data provenance and annotation interface compatible with both clinical imaging and research studies. The second is on facilitating rapid innovation in AI architectures while creating an enhanced validation/peer review process to avoid irreproducible implementations and overtraining of models. The third thrust is the integration of these efforts into a novel Model Zoo to provide robust capabilities for validation, assessment, and translation. This research effort will utilize core scientific innovations from the collaborative team consisting of members from a university (Vanderbilt), a medical center (Vanderbilt Medical Center), two industry partners (MD.ai, Kaggle), and a professional society (SIIM), alongside widely used, open source platforms. In Phase 1 of the Convergence Accelerator, the project will focus on newly created public and private datasets for COVID-19. Phase II will scale this approach to different medical imaging modalities, including dermatology and ophthalmology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "620", "attributes": { "award_id": "2014829", "title": "SBIR Phase I Machine Learning for Screening Acute Respiratory Distress Syndrome in General and COVID-19 Patient Populations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1386, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-01-31", "award_amount": 225000, "principal_investigator": { "id": 1387, "first_name": "Ritankar", "last_name": "Das", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 330, "ror": "", "name": "Dascena", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 330, "ror": "", "name": "Dascena", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to improve early and accurate acute respiratory distress syndrome (ARDS) detection. ARDS detection is vital due to the recent COVID-19 outbreak and the propensity for individuals testing positive for COVID-19 to develop ARDS as a serious complication, as well as the 140,000 patients per year in the United States admitted with ARDS. The ARDS diagnostic market in the US was an estimated $154 million in 2018. This project will advance a machine-learning algorithm to accurately predict ARDS onset in the COVID-19 patient population. These systems will monitor patient electronic health records and automatically provide ARDS prediction alerts for both general and COVID-19 patient populations, thereby enabling appropriate intervention and prevention methods in advance of ARDS onset to improve patient outcomes. This Small Business Innovation Research (SBIR) Phase I project will use semi-supervised machine learning (SSL) to develop and validate an ARDS prediction screening tool. The goals and anticipated technical results are as follows: Aim 1 will employ semi-supervised deep learning to develop a model for the prediction of ARDS up to 48 hours prior to onset. Because SSL will improve generalized performance, the tool can be applied in settings where many clinical features are not available, including a lack of radiographic data. Aim 2 will validate and optimize the semi-supervised model across external datasets. Validation on external datasets will evaluate the algorithm across a variety of hospital-specific measurement frequencies, demographics, and care practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "619", "attributes": { "award_id": "1952140", "title": "SCC-PG: Toward Disease-Resistant School Communities by Reinventing the Interfaces among Built Environments, Occupants, and Microbiomes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1381, "first_name": "Michal", "last_name": "Ziv-El", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2022-04-30", "award_amount": 149998, "principal_investigator": { "id": 1385, "first_name": "Shuai", "last_name": "Li", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1382, "first_name": "Qiang", "last_name": "He", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1383, "first_name": "Xueping", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1384, "first_name": "Tami H", "last_name": "Wyatt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Schools are hubs connecting the constituent groups of communities. This very nature of schools combined with the high concentration of vulnerable populations make schools hotbeds for the transmission of pathogens. In addition to the staggering death toll and burden on healthcare systems, infectious diseases such as seasonal flu and coronavirus disease 2019 (COVID-19) also lead to prolonged and repeated school closures, causing massive loss of education and productivity in communities. There is an urgent and critical need to build resilience for schools and connected communities against diseases. This project leverages the NSF big idea “Harnessing the Data Revolution” and proposes a transformative paradigm “Disease-Resistant School Communities”. Intelligent technologies will be created to reinvent the interactions among school environments, occupants, and microbiomes to control pathogen transmissions and reduce infection risks. In addition, stakeholders will be engaged to develop management strategies to make schools healthier, smarter, safer, and more sustainable for education and community well-being. If successful, this project could enhance the resilience of the 130,000 public and private schools used by 55 million K-12 students and 7 million adults in the nation against infectious diseases, reduce the enormous societal costs that would result from school closures, and significantly improve public health and economic prosperity. In addition, this project will also improve public scientific literacy by engaging community stakeholders in research, and raise community awareness for effective practices to prevent disease transmission.The ultimate goal of this planning grant is to develop intelligent technologies to model and monitor the environment-occupant-microbiome interactions in school communities, and exploit the unprecedented information for school management to reduce the risks of spreading infectious diseases. This project will explore: 1) disease-resistant designs based on the prediction of microbiome colonization and succession; 2) disease-resistant operations based on the monitoring of interactions among environments, occupants, and microbiomes; and 3) more effective hygiene practices and interventions to reduce disease transmission based on smart and connected informatics. By linking the microbial contamination patterns and transmission pathways with quantifiable design attributes and controllable operation paradigms, this research will lay a computational foundation for parametric design and operation control for reduced exposure to pathogens in schools. The information needs and effective information communication venues among different stakeholders will be identified to develop smart interfaces for connected decisions and actions to reduce contamination and transmission risks. This project will also lead to a novel community engagement model, through which students, teachers, school administrators, parents, healthcare providers, scientists, and engineers are all involved in the development of intelligent technologies and discovery of new knowledge to establish citizen-centric living laboratories for idea generation, data collection, prototype validation, and solution evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "618", "attributes": { "award_id": "2038967", "title": "CPS: Medium: Bio-socially Adaptive Control of Robotics-Augmented Building-Human Systems for Infection Prevention by Cybernation of Pathogen Transmission", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1375, "first_name": "Sankar", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-12-31", "award_amount": 1199130, "principal_investigator": { "id": 1380, "first_name": "Shuai", "last_name": "Li", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1376, "first_name": "Jindong", "last_name": "Tan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1378, "first_name": "Qiang", "last_name": "He", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1379, "first_name": "Mingzhou", "last_name": "Jin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Microbial pathogen transmission in buildings is an urgent public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in public buildings with minimal disruptions in building functions. With the ultimate goal to develop healthy buildings that minimize risks of infectious diseases, this project will develop smart control strategies for buildings and assistive robots to mitigate pathogen transmission and occupant exposure. New techniques will be developed to monitor and predict pathogen spreading, automate building ventilation, enable intelligent recognition of contaminated objects, and perform precision disinfection to reduce pathogen transmission through air circulation and surface contacts. Findings from this project will also guide occupants and facility managers to develop and implement effective behavioral interventions and hygiene practices. If successful, this research will revolutionize the control of built environments to enable protection against infectious diseases, which will have vast public health and economic benefits to the nation. This project will also create new and unique opportunities to stimulate the academic interests of students and support the development of next-generation workforce adequately equipped with interdisciplinary computing and engineering skills needed to address challenges facing the nation.The objectives of this research are: 1) Advance understanding of linkages among physical, biological, and social processes that drive the dynamics of human-pathogen interactions in building environments; and 2) Develop a novel cyber-physical system of integrated monitoring, building control, robot adaptation, and human-in-the-loop interactions to reduce the transmission of infectious pathogens. This research pioneers a novel digital-twinning approach that integrates building information modeling, privacy-preserving internet of things sensing, spatiotemporal molecular and metagenomic sequencing, and machine learning to map building-human-pathogen interactions for predicting contamination and infection risks at multiple spatiotemporal scales. New methods will be developed to connect and manage the buildings, occupants, and robots to reduce pathogen burdens. These methods include: model-based and dynamic-data-enabled optimal control of building ventilation; learning algorithms for robotic identification of contaminated spots; adaptive disinfection processes with behavioral considerations; and co-optimization of building operations and robotic disinfection with real-time sensing. In addition, user-centric systems will be developed to analyze contextual information and recommend hygiene practices, organizational operations, and crowd management to prevent disease spreading and maintain functionalities within buildings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "617", "attributes": { "award_id": "2041364", "title": "I-Corps: Development of a rapid point-of-care test for coronavirus (COVID-19) and antibody testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1373, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-07-31", "award_amount": 50000, "principal_investigator": { "id": 1374, "first_name": "Jin K", "last_name": "Montclare", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a rapid point-of-care (POC) diagnostic test for COVID-19 based on a lateral flow assay (LFA). The goal is to decrease community spread of infection and help mitigate the negative economic impacts of world-wide pandemics. The proposed diagnostic may allow for universal access to testing of both coronavirus infection and immunity via protein-protein interactions with virions and antibodies at once as compared with using a polymerase chain reaction (PCR)-based test that relies on time-consuming reverse transcription of RNA to detect the virus only. The consensus among public health experts is that safely emerging from a lockdown will require regular testing of millions of Americans. Current testing technologies are limited due to a large percentage of false negatives (up to 20%). Additionally, access to coronavirus and antibody testing is limited in low resource settings that lack reliable electrical services, running water, and diagnostic devices and personnel to operate them. Although testing capabilities are increasing among currently employed technologies such as at-home nasopharyngeal sampling, the creation of a rapid, simple POC test has not been addressed. The proposed technology may decrease community spread of infection and help mitigate the negative economic impacts of the COVID-19 pandemic. This I-Corps project is based on the translation of a lateral flow assay (LFA) diagnostic COVID-19 test, akin to a pregnancy test, that will report the presence of both the virus and its antibody in patient samples. Because LFAs employ capillary force on a polymeric strip with detection zones, they are easy-to-use, eliminate the need for specialized equipment, and may be carried out as a single step, reducing the amount of sample handling. Using commercially available computational software, detector proteins with high sensitivity and high specificity have been designed. Preliminary experimental results confirm the high sensitivity and computational results confirm high specificity. DNA constructs are concurrently under development with optimization of LFA test strips. For precise, rapid and large-scale loading, the requirements for aa multiplex lateral flow test strip (MLFTS) process design from a commercially-available, drop-on-demand printer that will accommodate the protein designs and facilitate scale-up and manufacturing will be evaluated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "616", "attributes": { "award_id": "2025819", "title": "SBIR Phase II (COVID-19): Highly Potent Nanozeolite-based Silver Antimicrobials", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1371, "first_name": "Benaiah", "last_name": "Schrag", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-15", "end_date": "2022-08-31", "award_amount": 1200000, "principal_investigator": { "id": 1372, "first_name": "Bo", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 329, "ror": "", "name": "ZeoVation, Inc.", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 329, "ror": "", "name": "ZeoVation, Inc.", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to provide a long-lasting antimicrobial and antiviral solution for medical, military and consumer applications. Antibiotic resistance has increased the prevalence of hospital-acquired infections, and the novel coronavirus SARS-CoV-2 has caused a worldwide COVID-19 pandemic. This project seeks to mitigate these problems by developing a technology to effectively decrease the transfer of bacteria and viruses via contaminated surfaces. This antimicrobial surface coating technology kills pathogens and minimizes their transport; it can be used in medical and healthcare institutions to support contagion mitigation efforts. This Small Business Innovation Research (SBIR) Phase II project will scale up manufacturing of a silver/zinc ion nanozeolite via a method to produce a spray product. Formulations that can be easily and uniformly applied on soft and hard surfaces by optimal choice of additives will be developed. Two unique features of the spray are increased adhesion of the active nanozeolite to a surface as well as the ability to predict when the activity of the nanozeolite is diminished by visual observations, prompting the reapplication process. Uniform spreading of the nanozeolite on a surface will be controlled by the viscosity of the spray and the design of the pump sprayer. This project will demonstrate efficacy against gram-negative, gram-positive bacteria, pathogenic fungi and coronaviruses, as well as developing application protocols.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "615", "attributes": { "award_id": "2024689", "title": "Collaborative Research: NRI: INT: Transparent and intuitive teleoperation interfaces for the future nursing robots and workers", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1369, "first_name": "Wendy", "last_name": "Nilsen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 17536, "principal_investigator": { "id": 1370, "first_name": "Paula L", "last_name": "Bylaska-Davies", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 328, "ror": "https://ror.org/04j8de466", "name": "Worcester State University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 328, "ror": "https://ror.org/04j8de466", "name": "Worcester State University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The recent pandemic outbreaks, including Ebola, Zika and the 2019 Novel Coronavirus (2019-nCoV), urge tele-medicine to go beyond mere tele-presence, to achieve robots that perform real-world nursing assistance tasks that require the coordinated control of manipulation, locomotion, and active teleoperation. Remotely-controlled nursing robots provide a promising alternative for quarantine and remote patient care. However, the traditional and contemporary human-robot interfaces fundamentally limit the performance and user experience of nursing robot teleoperation, and may reinforce burden and safety concerns that discourage healthcare workers to adopt robots. To address this problem, this project will (1) develop an innovative integration of transparent and intuitive teleoperation interface, to support the freeform and coordinated motion control of the remote nursing robots, and (2) integrate this interface with the robot intelligence to enable nursing professionals to learn robot teleoperation with minimal training, and to reduce the physical and cognitive workload using shared autonomy. The proposed project will promote the progress of science in human-robot interfaces for robot teleoperation, and advance the quality, availability and sustainability of healthcare in the present and future pandemic crisis. This project will have significant impacts on the domain of nursing, which consists of 2.9 million registered nurses and 160,000 nurse practitioners across the U.S. It will revolutionize patient-care in quarantine, and has the potential to extend to in-home care, clinics, and hospitals given the upcoming shortage of nursing workforce. The fundamental research also generalizes to other worker domains with robot tele-operations, including warehouse, social service, and maintenance. The proposed research will forge substantial collaboration among faculty and students in robotics engineering, nursing and social science. This project consists of two research themes. Research Theme 1 will develop a soft-robot teleoperation interface architecture and systematic human-inspired motion mapping strategies, to support the intuitive and transparent mapping of the motion, force, and perception information between humans and robots. The proposed interface will enable transparent and legible robot behavior of reaching-to-grasp, loco-manipulation, and the control of active telepresence. Research Theme 2 will develop the intelligence of the interface, to enable interactive learning and mutual adaptation between humans and robots. Based on game-theoretic planning, it will develop adaptive shared autonomous strategies that use human-robot communication via haptic feedback. It will employ active tele-presence to enhance the training and reduce workload in tele-operation of the human operator. The integrated interface will be evaluated in comprehensive user studies with registered nurses, nursing faculty and nursing students. The evaluation will assess the performance and user experience, including human-robot teaming, using efficiency, workload and interface effort metrics. It will also evaluate the social impacts of the proposed human-robot interface on the acceptance and adoption of nursing robots by the current and future nursing workforce. This proposal was funded with the National Institute for Occupational Safety and Health (NIOSH) in the Center for Disease Control and Prevention (CDC).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "614", "attributes": { "award_id": "2027168", "title": "Collaborative Research: NSFGEO-NERC:Conjugate Experiment to Investigate Sources of High-Latitude Magnetic Perturbations in Coupled Solar Wind-Magnetosphere-Ionosphere-Ground System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1365, "first_name": "Lisa", "last_name": "Winter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2024-09-30", "award_amount": 794406, "principal_investigator": { "id": 1368, "first_name": "Joseph B", "last_name": "Baker", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1366, "first_name": "Calvin Robert", "last_name": "Clauer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1367, "first_name": "Zhonghua", "last_name": "Xu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "This is a project that is jointly funded by the National Science Foundation’s Directorate of Geosciences (NSF/GEO) and the National Environment Research Council (UKRI/NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award, each Agency funds the proportion of the budget and the investigators associated with its own investigators and component of the work. This project is to (1) operate, maintain, and expand a high-latitude array of autonomous instruments to support research of the wider geospace research community into the sources of inter-hemispheric asymmetries, (2) conduct focused science investigations to develop understanding of the sources of high-latitude magnetic perturbations in the multi-scale, global, solar wind - magnetosphere – ionosphere – ground (SWMIG) system, including during the 2021 solar eclipse and (3) conduct education and outreach to facilitate broader access to polar research efforts. These objectives will be achieved through an unsurpassed network of closely-spaced magnetically-conjugate magnetometers in Antarctica and in the Northern Hemisphere near the 40 degree magnetic meridian, most of which have already been deployed. This project expands an existing Virginia Tech/Technical University of Denmark partnership to include the British Antarctic Survey (BAS), Space Science Institute, and UCLA. Graduate and undergraduate students will be supported, including a special research program to engage students from minority-serving institutions.Measurements of surface magnetic field perturbations are important to remotely sense and characterize the SWMIG phenomena that affect technology – such as geomagnetically induced currents – and thereby to develop physical models and forecast space weather impacts. However, understanding the sources of magnetic perturbations in the coupled SWMIG system is challenging due to their simultaneous dependence on driving conditions, ionospheric conductivity and ground conductivity. We seek to address the following science questions, \"How do magnetosphere-ionosphere current systems couple to high-latitude ground magnetic perturbations? What roles do current system spatial scale, inhomogeneous ionospheric conductivity, and inhomogeneous ground conductivity play?\" By combining British Antarctic Survey, Technical University of Denmark, and NSF-supported magnetometers, a new combined array will provide unprecedented coverage throughout the auroral zone/cusp in both hemispheres simultaneously. These data enable novel experiments to isolate the respective contributions of driver spatial/temporal scale, ionospheric conductivity, and local ground conductivity in the generation of ground magnetic perturbations. This project includes field work in the Antarctic, supported by both the U.S. Antarctic Program (USAP) and the BAS. USAP and BAS have agreed to support maintenance visits to receiver site locations and to support the retrograde of equipment at the end of the program. BAS and USAP will work collaboratively to deploy an additional instrument to a logistically feasible location that best serves the project. The USAP and BAS have agreed to support this program logistically, with the first field deployment year to be determined after the uncertainties related to the coronavirus pandemic are resolved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "613", "attributes": { "award_id": "2027210", "title": "Collaborative Research: NSFGEO-NERC:Conjugate Experiment to Investigate Sources of High-Latitude Magnetic Perturbations in Coupled Solar Wind-Magnetosphere-Ionosphere-Ground System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1363, "first_name": "Lisa", "last_name": "Winter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2024-09-30", "award_amount": 295128, "principal_investigator": { "id": 1364, "first_name": "Michael", "last_name": "Hartinger", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 327, "ror": "https://ror.org/046a9q865", "name": "Space Science Institute", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 327, "ror": "https://ror.org/046a9q865", "name": "Space Science Institute", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This is a project that is jointly funded by the National Science Foundation’s Directorate of Geosciences (NSF/GEO) and the National Environment Research Council (UKRI/NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award, each Agency funds the proportion of the budget and the investigators associated with its own investigators and component of the work. This project is to (1) operate, maintain, and expand a high-latitude array of autonomous instruments to support research of the wider geospace research community into the sources of inter-hemispheric asymmetries, (2) conduct focused science investigations to develop understanding of the sources of high-latitude magnetic perturbations in the multi-scale, global, solar wind - magnetosphere – ionosphere – ground (SWMIG) system, including during the 2021 solar eclipse and (3) conduct education and outreach to facilitate broader access to polar research efforts. These objectives will be achieved through an unsurpassed network of closely-spaced magnetically-conjugate magnetometers in Antarctica and in the Northern Hemisphere near the 40 degree magnetic meridian, most of which have already been deployed. This project expands an existing Virginia Tech/Technical University of Denmark partnership to include the British Antarctic Survey (BAS), Space Science Institute, and UCLA. Graduate and undergraduate students will be supported, including a special research program to engage students from minority-serving institutions.Measurements of surface magnetic field perturbations are important to remotely sense and characterize the SWMIG phenomena that affect technology – such as geomagnetically induced currents – and thereby to develop physical models and forecast space weather impacts. However, understanding the sources of magnetic perturbations in the coupled SWMIG system is challenging due to their simultaneous dependence on driving conditions, ionospheric conductivity and ground conductivity. We seek to address the following science questions, \"How do magnetosphere-ionosphere current systems couple to high-latitude ground magnetic perturbations? What roles do current system spatial scale, inhomogeneous ionospheric conductivity, and inhomogeneous ground conductivity play?\" By combining British Antarctic Survey, Technical University of Denmark, and NSF-supported magnetometers, a new combined array will provide unprecedented coverage throughout the auroral zone/cusp in both hemispheres simultaneously. These data enable novel experiments to isolate the respective contributions of driver spatial/temporal scale, ionospheric conductivity, and local ground conductivity in the generation of ground magnetic perturbations. This project includes field work in the Antarctic, supported by both the U.S. Antarctic Program (USAP) and the BAS. USAP and BAS have agreed to support maintenance visits to receiver site locations and to support the retrograde of equipment at the end of the program. BAS and USAP will work collaboratively to deploy an additional instrument to a logistically feasible location that best serves the project. The USAP and BAS have agreed to support this program logistically, with the first field deployment year to be determined after the uncertainties related to the coronavirus pandemic are resolved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "612", "attributes": { "award_id": "2024802", "title": "Collaborative Research: NRI: INT: Transparent and Intuitive Teleoperation Interfaces for the Future Nursing Robots and Workers", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1357, "first_name": "Wendy", "last_name": "Nilsen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 490664, "principal_investigator": { "id": 1362, "first_name": "Zhi", "last_name": "Li", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 217, "ror": "https://ror.org/05ejpqr48", "name": "Worcester Polytechnic Institute", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1358, "first_name": "Jeanine L", "last_name": "Skorinko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1359, "first_name": "Yunus Dogan", "last_name": "Telliel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1360, "first_name": "Cagdas D", "last_name": "Onal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1361, "first_name": "Jie", "last_name": "Fu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 217, "ror": "https://ror.org/05ejpqr48", "name": "Worcester Polytechnic Institute", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The recent pandemic outbreaks, including Ebola, Zika and the 2019 Novel Coronavirus (2019-nCoV), urge tele-medicine to go beyond mere tele-presence, to achieve robots that perform real-world nursing assistance tasks that require the coordinated control of manipulation, locomotion, and active teleoperation. Remotely-controlled nursing robots provide a promising alternative for quarantine and remote patient care. However, the traditional and contemporary human-robot interfaces fundamentally limit the performance and user experience of nursing robot teleoperation, and may reinforce burden and safety concerns that discourage healthcare workers to adopt robots. To address this problem, this project will (1) develop an innovative integration of transparent and intuitive teleoperation interface, to support the freeform and coordinated motion control of the remote nursing robots, and (2) integrate this interface with the robot intelligence to enable nursing professionals to learn robot teleoperation with minimal training, and to reduce the physical and cognitive workload using shared autonomy. The proposed project will promote the progress of science in human-robot interfaces for robot teleoperation, and advance the quality, availability and sustainability of healthcare in the present and future pandemic crisis. This project will have significant impacts on the domain of nursing, which consists of 2.9 million registered nurses and 160,000 nurse practitioners across the U.S. It will revolutionize patient-care in quarantine, and has the potential to extend to in-home care, clinics, and hospitals given the upcoming shortage of nursing workforce. The fundamental research also generalizes to other worker domains with robot tele-operations, including warehouse, social service, and maintenance. The proposed research will forge substantial collaboration among faculty and students in robotics engineering, nursing and social science. This project consists of two research themes. Research Theme 1 will develop a soft-robot teleoperation interface architecture and systematic human-inspired motion mapping strategies, to support the intuitive and transparent mapping of the motion, force, and perception information between humans and robots. The proposed interface will enable transparent and legible robot behavior of reaching-to-grasp, loco-manipulation, and the control of active telepresence. Research Theme 2 will develop the intelligence of the interface, to enable interactive learning and mutual adaptation between humans and robots. Based on game-theoretic planning, it will develop adaptive shared autonomous strategies that use human-robot communication via haptic feedback. It will employ active tele-presence to enhance the training and reduce workload in tele-operation of the human operator. The integrated interface will be evaluated in comprehensive user studies with registered nurses, nursing faculty and nursing students. The evaluation will assess the performance and user experience, including human-robot teaming, using efficiency, workload and interface effort metrics. It will also evaluate the social impacts of the proposed human-robot interface on the acceptance and adoption of nursing robots by the current and future nursing workforce. This proposal was funded with the National Institute for Occupational Safety and Health (NIOSH) in the Center for Disease Control and Prevention (CDC).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1390, "pages": 1419, "count": 14184 } } }