Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-awardee_organization
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Aggressive behaviors are influenced by poverty and stress. In the current study, the researchers seek to understand how youth aggression might be biologically advantageous in the context of both acute and chronic stress exposure. The investigators also consider potential gender differences in the biological benefits of aggression, as well as potential influences of this buffering effect on later risky behaviors. Understanding the functions of aggression, and its potential benefits for youth growing up in the context of adversity is critical to inform the development and implementation of effective, innovative interventions.<br/><br/>Recent evolutionary models of development argue that problematic behaviors, including aggression, are not necessarily “deficits,” but rather reflect adaptive responses to stressful, unstable or disadvantageous environments. However, despite evidence from animal models showing that aggression can reduce physiological stress, no research with humans has systematically evaluated aggression as a potential protective buffer against the effects of stress exposure on biological regulation. In this project, the researchers critically evaluate hypotheses with two rigorous and complementary study designs that build on an ongoing, NSF-funded longitudinal study of 250 sociodemographically diverse adolescents whose risk exposure, aggression, and biological functioning have been well characterized since early childhood (ages 4-14). Study 1 uses a longitudinal design to test relations between childhood risk exposure (ages 0-8) and a multisystem indicator of biological dysregulation at age 17 as buffered by aggression across early adolescence (ages 10-14). Study 2 uses a laboratory experiment to expose youth to stress, manipulate opportunities to aggress following the stressor, and test the effects of aggression on stress physiology. Together, these studies have the potential to dramatically revise extant models of aggression with significant implications for improving the effectiveness of contemporary interventions, which do not currently consider the positive biological function of aggression, particularly for high-risk youth.<br/><br/>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": "14128", "attributes": { "award_id": "2115429", "title": "A Model of Generalized Ingroup Recognition Advantage", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Build and Broaden" ], "program_reference_codes": [], "program_officials": [ { "id": 2516, "first_name": "Steven", "last_name": "Breckler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 431338, "principal_investigator": { "id": 30674, "first_name": "Arthur", "last_name": "Calanchini", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>People often have worse recognition memory for individuals who are not (versus are) in their racial group. This well-known cross-race effect can lead to racial disparities. As one example, in the US criminal justice system nearly a third of wrongful convictions that later are overturned were based on errors made in cross-race identification. The cross-race effect would seem to be specific to faces: A glance at a face quickly reveals information about race, gender, age, and other social categories. Yet many objects beyond a face, or even beyond a person, may signal group membership and cause a recognition bias. This research investigates the possibility that the ingroup recognition advantage exemplified by the cross-race effect is a general recognition bias. This research has the potential to transform our theoretical understanding of social influences on perception and cognition, including the basic psychological mechanisms that contribute to racial disparities. <br/><br/>This project builds upon research in social psychology, developmental psychology, cognitive neuroscience, and vision science to develop a comprehensive model of ingroup recognition and examine its social impact. The project is organized around three sets of experiments, all using a basic paradigm in which participants complete a learning task followed by a recognition task. The first set of experiments aims to differentiate two causes of an ingroup recognition advantage: People may better recognize information relevant to their own group because (1) they have more lifetime experiences with their group and thus have perceptual expertise, and/or (2) they are more interested and motivated to attend to their own group. Each cause can be tested with experiments in which participants form new groups and are exposed to objects with which they have no prior experience but are related to their group or another group. A second set of experiments examines whether an ingroup recognition advantage occurs to the same extent for different groups to which one belongs, and a third set examines how the generalized ingroup recognition advantage can lead to stereotypic judgments of others. Under conditions of tight experimental control, this research tests a novel model that accounts for: (1) the mechanisms and boundary conditions of ingroup recognition; (2) the qualitative nature of the cognitive processes underlying the generalized ingroup recognition advantage; and (3) the implications of the generalized ingroup recognition advantage. The interdisciplinary and integrative nature of this research has implications that are relevant to social psychologists, policymakers, legal scholars, law enforcement, and the general public, and it can inform interventions to reduce stereotyped judgments. The project also provides specialized training to students at a minority-serving institution (MSI) that ranks highly in terms of diversity, social mobility, and graduation rates.<br/><br/>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": "14415", "attributes": { "award_id": "2121760", "title": "Collaborative Research: How roots, regolith, rock and climate interact over decades to centuries - the R3-C Frontier", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "FRES-Frontier Rsrch Earth Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 12641, "first_name": "Richard", "last_name": "Yuretich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 532558, "principal_investigator": { "id": 31029, "first_name": "Hoori", "last_name": "Ajami", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31028, "first_name": "Daniel R", "last_name": "Hirmas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>This project will examine how the interaction of climate, the physical and chemical characteristics of the bedrock, and the action of vegetation, control the movement and storage of water and carbon on Earth’s surface. These processes in turn influence climate by altering important factors such as greenhouse gas concentrations like H2O and CO2. Human activities can change these pathways, and this research will enable the forecasting of the possible impacts upon the Earth-surface environment. To achieve this goal requires synthesizing existing datasets, collecting new data, and training teams of people in the fields of water science, geochemistry, soil science, geophysics, ecology, and Earth system modeling. The project will include 28 undergraduate students, four graduate students, and three postdoctoral scholars across seven universities to collectively explore how the interaction of plant roots and bedrock regulate water and carbon movement between the land and atmosphere. The project will also train 45 educators to develop discovery-based learning approaches in their classes, the products of which will be publicly accessible on available web platforms.<br/><br/>This project will investigate when and to what degree bedrock exerts more control than roots on water and carbon fluxes. Using an interdisciplinary approach that incorporates new data collection, data harvesting, machine learning, and numerical modeling, this research will determine the mechanisms by which bedrock and fracture distributions govern the development of preferential flow paths. It will also examine depth, degree, and timing of coupling between the subsurface and atmosphere and its impact on water storage and fluxes. The project will explore how plant roots interact with bedrock to shape the subsurface structure, associated carbon storage, and transpiration rates. Methods will include 3D geophysical surveys and structural soil pore analyses to determine the occurrence of changes in the subsurface and how they govern root water uptake. Global in situ and remotely sensed data will be integrated via machine learning to discern emergent patterns in subsurface structure on larger scales. The project will leverage existing datasets and collect new data from the NSF Critical Zone Cluster Networks (CZCNs), National Ecological Observatory Network (NEON), and Long-Term Ecological Research (LTER) programs. The ultimate outcome will be a comprehensive framework of hydro-biogeochemical linkages to forecast how climatic conditions and subsurface structure regulate hydrological flow and the carbon cycle.<br/><br/>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": "14846", "attributes": { "award_id": "2409903", "title": "Development of novel numerical methods for forward and inverse problems in mean field games", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 298862, "principal_investigator": { "id": 31525, "first_name": "Yat Tin", "last_name": "Chow", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Mean field games is the study of strategic decision making in large populations where individual players interact through a certain quantity in the mean field. Mean field games have strong descriptive power in socioeconomics and biology, e.g. in the understanding of social cooperation, stock markets, trading and economics, biological systems, election dynamics, population games, robotic control, machine learning, dynamics of multiple populations, pandemic modeling and control as well as vaccination distribution. It is therefore essential to develop accurate numerical methods for large-scale mean field games and their model recovery. However, current computational approaches for the recovery problem are impractical in high dimensions. This project will comprehensively study new computational methods for both large-scale mean field games and their model recovery. The comprehensive plans will cover algorithmic development, theoretical analysis, numerical implementation and practical applications. The project will also involve research on speeding up the forward and inverse problem computations to speed up the computation for mean field game modeling and turn real life mean field game model recovery problems from computationally unaffordable to affordable. The research team will disseminate results through publications, professional presentations, the training of graduate students at the University of California, Riverside as well as through public outreach events that involve public talks and engagement with high school math fairs. The goals of these outreach events are to increase public literacy and public engagement in mathematics, improve STEM education and educator development, and broaden participation of women and underrepresented minorities.<br/><br/>The project will provide novel computational methods for both forward and inverse problems of mean field games. The team will (1) develop two new numerical methods for forward problems in mean field games, namely monotone inclusion with Benamou-Brenier's formulation and extragradient algorithm with moving anchoring; (2) develop three new numerical methods for inverse problems in mean field games with only boundary measurements, namely a three-operator splitting scheme, a semi-smooth Newton acceleration method, and a direct sampling method. Both theoretical analysis and practical implementations will be emphasized. In particular, numerical methods for inverse problems for mean field games, which is a main target of the project, will be designed to work with only boundary measurements. This represents a brand new field in inverse problems and optimization. The project will also seek the simultaneous reconstruction of coefficients in the severely ill-posed case when only noisy boundary measurements from one or two measurement events are available.<br/><br/>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": "15656", "attributes": { "award_id": "2502655", "title": "I-Corps: Translation potential of plant PYR1 biosensors for the rapid testing of environmental contaminants", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "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": "2025-01-01", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32164, "first_name": "Ian", "last_name": "Wheeldon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of sensors for a wide array of previously undetectable chemicals. Global industrialization has created advanced materials and chemicals that persist in the environment with lasting effects on human health. Current technologies that test for environmental contaminants using chromatographic methods and laboratory test kits are slow, expensive, and inaccessible to consumers. This chemical sensor technology may provide portable test strips (similar to those used to test for COVID-19) to test for small molecules characteristic of pharmaceuticals, pesticides, and per- and polyfluoroalkyl substances (or PFAS). This technology may make field-based and in-home testing of pesticides and PFAS possible for the first time, giving consumers and regulators a way to alleviate safety concerns about pollutants in drinking water and food. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of PYR1 biosensors, plant hormone receptors that, when mutated, may be used to identify a wide variety of chemicals, including environmental contaminants (e.g., organophosphate pesticides and PFAS). Ligand recognition occurs exclusively in the PYR1 subunit, not the HAB1 partner, which makes the system significantly easier to engineer for new ligands than previously developed methods. The efficacy of these sensors has been demonstrated in yeast, bacteria, plants, and in vitro to test for substances of abuse in blood, urine, and saliva. These sensors also have been stabilized for high temperature and used as sensors in living plants. To date, the sensors have been designed for hundreds of target molecules, and ongoing refinement of the pipeline methodology makes it possible to identify sensors for new targets in less than a week. 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": "333", "attributes": { "award_id": "2151970", "title": "Forced Displacement and Community Resilience: Housing Insecurity under COVID-19 in Inland Southern California", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 587, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 336050, "principal_investigator": { "id": 589, "first_name": "Qingfang", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 588, "first_name": "Wei", "last_name": "Kang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The unique nature of the COVID-19 pandemic created a disaster situation that highlights the importance of stable housing, particularly as recent evidence suggests that eviction increased the risk of COVID-19 infection and mortality. This study will improve knowledge of processes and consequences of evictions before and after the COVID-19 pandemic in Inland Southern California. The first goal is to analyze the demographic and socioeconomic profile of renters who recently experienced an eviction, as well as the relocation process and outcome. The second goal is to analyze how community resilience and neighborhood change, such as neighborhoods that are gentrifying or becoming more impoverished, are tied to outcomes for renters. The third goal is to evaluate whether and how these outcomes are changed by the pandemic. This study will advance our understanding of involuntary residential choices under an external shock like a pandemic. It contributes to resilience scholarship and helps us understand why the root cause of socioeconomic disadvantage is the primary source of vulnerability under disastrous events, and how housing security interacts with community resilience. As eviction is linked to social, economic, and health disparities, and urban poverty, effective eviction-prevention initiatives could go a long way toward addressing these enduring problems. This study provides evidence for policy interventions designed to address eviction and stem its consequences. It will also provide significant implications for practice and policy in housing markets and social welfare to alleviate social and spatial divides by race, ethnicity, and class that have been exacerbated by the pandemic disruption. This study investigates formal and informal eviction and neighborhood change before and after the COVID-19 pandemic in Inland Southern California using a multiscalar, comparative, and mixed-methods framework. Using both public and restrictive datasets, this study will model the prevalence of eviction and the threat of it at both the household and neighborhood levels. As residential mobility shapes the future life course of evicted households and neighborhood dynamics, the team will model the residential choice of evicted renters and neighborhood dynamics. Further, the project conducts in-depth interviews with tenants, landlords, real estate agents and housing developers, non-profit organizations, and government officials to examine the pathways through which individual characteristics, neighborhood environment, and institutional forces contribute to eviction. The multiscalar, mixed-methods and comparative framework will advance knowledge on the process of eviction at the household level, as well as neighborhood dynamics, policy interventions, power relations, and the coping process of local communities during a pandemic-like disruption. Findings from this study will not only directly benefit policymaking and practice in this region, but also contribute to knowledge in the field for national audiences.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": "499", "attributes": { "award_id": "2030049", "title": "Collaborative Research: RAPID--Urban Air Quality during the Coronavirus (COVID-19) Shelter-In-Place Orders", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1015, "first_name": "Sylvia", "last_name": "Edgerton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 101072, "principal_investigator": { "id": 1016, "first_name": "Kelley", "last_name": "Barsanti", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In this RAPID project, a collaborative PI team intends to collect time sensitive atmospheric samples in the Los Angeles, CA, area, where historically high pollutant levels of ozone (O3) and fine particulate matter (PM2.5) have plagued public health. By taking advantage of significant reductions in atmospheric emissions associated with current COVID-19 shelter-in-place orders, a natural experiment has presented itself that allows for observations to be made under uniquely useful conditions. Results will help constrain predictive models of pollutant concentrations and guide regulatory agencies in best strategies to mitigate poor air quality.Gaseous and particulate samples will be collected during and after the lifting of COVID-19 by co-locating sampling devices on Caltech’s established roof-top sampling platform, where continuous monitoring of essential parameters, including NOx, O3, and PM2.5, is ongoing. Focus in this study is on the detailed chemical speciation of the important precursor group of compounds denoted as volatile to intermediate volatility organic compounds (I/VOCs) containing 1 to 15 carbon atoms (C1-C15). These compounds are emitted through a number of different sources, including from fossil fuel production and burning, use of chemical products, and biological productivity. Their ill-defined sources and reactivities have been attributed to an existing gap in knowledge that could describe higher-than-expected O3 levels in megacities where precursor emissions have seen a general decrease in past decades. Here, I/VOC sources and source markers will be determined during a period when transportation associated emissions to VOCs and NOx are low. State-of-the-art analyses of collected samples at PIs’ laboratories include two-dimensional gas chromatography (GC×GC) with time-of-flight mass spectrometry (TOFMS) and a multi-column/detector GC system with 5 different types of separation and detection combinations. Results will (i) provide new insight into the intricate mechanisms of O3 and PM2.5 production under uniquely low NOx conditions and a changing mix of VOCs, and (ii) help constrain predictive models of atmospheric chemistry and air quality.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": "596", "attributes": { "award_id": "2031942", "title": "Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1306, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-15", "end_date": "2023-11-30", "award_amount": 498086, "principal_investigator": { "id": 1307, "first_name": "Cassandra", "last_name": "Guarino", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving undergraduate computer science education. It will do so by completing a research study that can reveal potential systemic limitations in access to computer science education by all students. This research study will examine ten-years of undergraduate student application, admissions, and retention data from four institutions. Analysis of these data will describe how students of varying demographics and pre-college preparation are present throughout the computer science talent pipeline. This study will fill an important research gap about factors that affect the flow of students into and through the computer science major. It is well documented that the demographic characteristics of computer science students are highly skewed toward males versus females and have skewed racial/ethnic distributions. What is not yet understood is at what point in the talent pipeline these imbalances are greatest and the degree to which they change as students progress through computer science undergraduate programs. In addition, the current educational disruption caused by COVID-19 provides the important and unique opportunity to determine what effect, if any, the resulting educational changes have had on participation of different groups of students in computer science. Students from underrepresented groups appear to have encountered greater difficulty accessing distance learning and being connected to the full range of educational opportunities presented by these unique circumstances, which are very strongly related to technological know-how. There is legitimate cause for concern that the pandemic will further divide the advantaged from the disadvantaged, further marginalizing the underrepresented groups that the project is studying from opportunities to advance into computer science majors and progress successfully through them. Computer science is an area of critical strategic importance for the nation, and a field in which cultivating domestic talent can have enormous impact. Thus, examining pre- and post- pandemic patterns of participation in computer science have the potential to help the nation meet its growing needs for talent in computer science and related fields, such as cybersecurity and artificial intelligence. This study will use a large, rich data set compiled from ten years of undergraduate application, admissions, and course-level data from four institutions: Loyola Marymount University, Cal State University Long Beach, the University of California Riverside, and the University of California San Diego. Analysis of these longitudinal data will improve understanding of who has access, who applies, who is admitted, and who succeeds in computer science. Using classical statistical approaches and modern machine learning based approaches to analysis of large data sets, the study seeks to understand how to improve the inclusion of all students in computer science. It will supplement this large-scale quantitative analysis with qualitative analysis of results from targeted focus-groups and interviews. The qualitative analysis, coupled with the quantitative analysis of longitudinal data from four institutions with different student demographics and other characteristics, will provide a deeper analysis of access to and success in computer science than any previous study. The resulting extension of knowledge has the potential to lay the foundation for achieving equitable access to computer science education for all students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.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": "1003", "attributes": { "award_id": "2040301", "title": "Collaborative Research: Interactions of Airborne Engineered Nanoparticles with Lung Surfactant Films", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 2446, "first_name": "Nora", "last_name": "Savage", "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": 241356, "principal_investigator": { "id": 2447, "first_name": "Younjin", "last_name": "Min", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Rapid developments in nanotechnology have led to the production and use of various types of engineered nanoparticles. It is inevitable that airborne nanoparticles may be released to the environment and may cause potential consequences for human health, in particular, in the respiratory tract including the lung (alveolar region) once inhaled. The lung surfactant, which covers the alveoli as a thin liquid film, represents the first line of defense against such airborne nanoparticles at the air-liquid interface. This collaborative project, involving a synergistic combination of experimental and computational studies, seeks to study the effects of physicochemical and structural properties of engineered nanoparticles on interfacial flow behaviors and stability of surfactant films. This research activity also is aimed at obtaining a better fundamental understanding of the molecular interactions arising between surfactant films and potentially hazardous engineered nanoparticles in realistically imitated physiological conditions. Fundamental knowledge gained through this project is, therefore, expected to provide new insights into the subsequent retention, translocation, and clearance of inhaled nanoparticles and the sequential processes associated with engineered nanoparticle toxicity overall. The project results will also advance the basic knowledge of the fate of biological nanoparticles, such as coronavirus virions, in the lungs that may have practical implications in medicine. Educational and mentoring aspects of this project include training graduate students in advanced surface science tools and computational techniques, mentoring underrepresented undergraduate students in research, and developing teaching modules and subjects relevant to nanoparticle interactions in biological systems. As the production and use of engineered nanoparticles increases day by day, it is inevitable that these nanoparticles will be released to the environment. Therefore, the occurrence and fate of engineered nanoparticles in the environment, and the potential consequences on human health have been increasingly recognized as issues of critical importance. In particular, airborne nanoparticles can result in a much greater likelihood and extent of exposure to the environment and thus living beings. This collaborative project will focus on improving our fundamental understanding of the molecular interactions between engineered nanoparticles and lung surfactant films at multiple-length scales. The project will evaluate the distribution and fate of inhaled airborne nanoparticles in the respiratory tract. This research project is structured around two specific objectives. First, this project aims to determine the effects of physicochemical and structural properties of inhaled engineered nanoparticles on the viscoelastic responses and interfacial stability of lung surfactant films. Second, the project will generate fundamental data concerning the molecular mechanisms of interfacial interactions in lung surfactant monolayers and multilayers in the absence and presence of engineered nanoparticles at multiple length scales in physiological environments. To achieve these goals, a synergistic combination between experimental and computational approaches will be employed. Experimental advances include a specially modified Langmuir trough, a quartz crystal microbalance with dissipation coupled with a custom-made particle generator unit, and a highly sophisticated surface forces apparatus. The computational component is based on a new coarse-grained computational framework for investigations of nanoscale interfacial processes at air-liquid interfaces, including dissipative particle dynamics models to predict the composition dependent surface tension, elasticity, viscosity, and stability of lung surfactant monolayer and bilayers with doped engineered nanoparticles. These collaborative experimental and computational studies of nanoscale interfacial phenomena are expected to provide qualitative and quantitative information on the viscoelastic properties of lung surfactant films and the attendant response to shear stresses upon breathing affected by adhered/piercing engineered nanoparticles. In addition, the principal investigators will generate systematic information on molecular interactions of engineered nanoparticles with the lung surfactant system, especially in terms of adhesion and fusion behaviors that are related to the structural integrity of lung surfactant films. The findings gained through this project will improve mechanistic understanding of the adhesion and translocation of nanoparticulate matter (e.g., coronavirus virions) across other general cell membranes. Educational components of this project involve training graduate students and mentoring undergraduate students from underrepresented groups in engineering through various programs offered at Rutgers University and the University of California, Riverside.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": "1144", "attributes": { "award_id": "2141203", "title": "RAPID/Collaborative Research: Linking Household and Infrastructure Data to Understand the Impacts of Winter Storm Uri in Texas", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 2891, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2022-08-31", "award_amount": 25000, "principal_investigator": { "id": 2892, "first_name": "Amir-Hamed", "last_name": "Mohsenian-Rad", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In February 2021, Winter-Storm Uri, impacted 25 states and more than 150 million Americans leading to extended power and water outages. Among the states impacted by the storm, Texas was the hardest hit. The impact of Uri on the state of Texas was far beyond expectations leading to several deaths, a significant halt in the distribution and administering of COVID-19 vaccines, and an estimated $90 billion economic loss. Most significantly, the independent electric grid managed by the Eclectic Reliability Council of Texas, came very close to a complete failure. Consequently, local electric utilities had to significantly reduce power consumption resulting in millions of households and businesses, experiencing significant and extended periods without power. There were also cascading failures of local water systems, as pumping stations failed. It was widely reported that the power outages disproportionately impacted some populations more than others, and yet there was little in the way of any systematic evidence establishing these observations. There is a need to scientifically document the nature of these failures, any potential disparate impacts, and establish contributing factors using appropriate data. Such data collection can provide opportunities to learn what went wrong at various levels, including technical and infrastructure maintenance issues, precautionary measures, planning, and execution. Findings from this research can be employed to address environmental justice issues and broader infrastructure resilience planning. As such, this project aims to perform ground truth validation by collecting detailed data from multiple fronts including perishable data from households and power/infrastructure sysetms. The findings of this research will advance knowledge for building more disaster resilient infrastructure systems which are critical for promoting the health, prosperity, and welfare of our nation.This research lies at the nexus of engineering and social sciences aiming to ultimately explore potential relationships between engineering infrastructure, operational decisions and household experiences. More specifically, this RAPID project will collect data on two fronts. The first is household data including demographic, socioeconomic statuses together with their experiences throughout the storm and outages. These data will be collected a hybrid data collection method including an online survey of households, interviews with community-based organizations (CMOs), and secondary data collections from various social media outlets. While online data collection helps with uncovering general trends, interviews with CMOs and impacted stakeholders within a diverse set of neighborhoods will help address digital divide issues within lower-income neighborhoods. Finally, content analysis of social media posts provides another layer of information will be used to fill the remaining data gaps and cross-examine data trends. The second data collection activity focuses on the collection of grid and utility data related to the locations, timing, and duration of outages during and after the storm. These data will complement household/neighborhood data to provide both top-down and bottom-up insights on the unraveling of the event and its associated decision making.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": 1391, "pages": 1424, "count": 14236 } } }