# data[].attributes.title
RII Track-4: The Integration of Plasmonic Nanoantenna and Super-hydrophobic Surface for Ultrasensitive Fluorescence CRISPR Biosensing

# data[].attributes.abstract
Early diagnosis provides significant and unprecedented benefits since patients diagnosed at an early stage of diseases often have a good chance for cure and functional outcomes. In addition, rapid testing is crucial to combat the pandemic as exemplified by the ongoing COVID-19 pandemic. This project aims to design a direct Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) based point of care diagnostic system without pre-amplification of viral genomes. It will allow ultralow and ultrasensitive detection of many diseases (e.g., cardiovascular diseases, cancer, and infectious diseases) at an early stage when the concentration of viral genomes in body fluids (i.e., urine, blood, saliva) is still very low and not sufficient to be detected by existing technologies. This NSF EPSCoR RII Track-4 fellowship provides the opportunity to collaborate with a renowned expert in point-of-care diagnostics for infectious diseases in the Department of Biomedical Engineering at the University of Connecticut to achieve this goal. The successful completion of this project will lead to noninvasive, inexpensive, mass-producible systems for early detection, treatment outcome evaluation of diseases, greatly improving patient morbidity and reducing healthcare cost, particularly important to Nevada, which consistently ranks near the bottom in terms of higher rates of the 12 leading causes of death.The objectives of the project are to (1) integrate nanoantenna with super-hydrophobic surfaces for enhancing the CRISPR/Cas12a detection sensitivity without pre-amplification; (2) integrate the designed enhanced CRISPR/Cas12a fluorescence detection module with microfluidics for viral detection in the blood sample. Although CRISPR based point of care diagnostic system has emerged as a popular technology and a powerful tool for rapid screening due to its simplicity and flexibility, it still has many limitations such as low stability in complex biological samples. One promising solution is to explore the nanoantenna technique to trigger the enhanced Localized surface plasmon. However, the nanoantenna technique is still far from being routinely implemented in biomedical fields due to a major obstacle not from plasmonics but from the mass transport: Most nanoantennas typically rely on diffusion to capture target molecules, which makes the detection time impractically long. This project integrates the nanoantenna with the superhydrophobic surface to address this diffusion limit. Droplets over super-hydrophobic surfaces maintain quasi spheres during evaporation and do not wet the surface. Therefore, the droplet evaporation replaces the diffusion and concentrates molecules onto the sensitive regions of the nanoantenna, becoming the dominant mechanism of mass transfer. The droplet evaporation time is not only much shorter than the diffusion time but also can be actively controlled, which is an additional benefit. The combination of plasmonics and super-hydrophobic surfaces offers a unique solution to the aforementioned key challenge and holds the promising for ultralow and ultrasensitive biosensing platforms enabled by CRISPR. The training and research experience provided by this RII Track-4 fellowship will allow the PI to successfully transition from the background of material science and engineering to a biomedical researcher.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.

# data[].attributes.title
Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting

# data[].attributes.funder_divisions[]
Computer and Information Science and Engineering (CISE)

# data[].attributes.principal_investigator.affiliations[].name
University of North Carolina at Charlotte

# data[].attributes.awardee_organization.name
University of North Carolina at Charlotte

# data[].attributes.abstract
This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.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.

# data[].attributes.title
SBIR Phase II: Advanced Artificial Intelligence for Robotic E-Commerce Pick-and-Pack Automation

# data[].attributes.funder_divisions[]
Technology, Innovation and Partnerships (TIP)

# data[].attributes.abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the resiliency of the supply chain by implementing flexible robotic systems for materials handling.  The robotic systems that are controlled by artificial intelligence. E-commerce sales are increasing 20% year over year. During the COVID-19 pandemic additional retail volume shifted online and many customers became accustomed to sourcing essentials using e-commerce. This shift has put a greater burden on asupply chain infrastructure that has traditionally relied on human labor to pick, sort, pack, and process items for delivery. These manual processes are monotonous, error-prone, and sometimes dangerous, have extremely high worker turnover. The automation of these processes elevates worker roles and brings greater consistency to the processes. The innovation developed during this Phase II project may enable broader automation of complex materials handling processes by creating novel training systems for artificial intelligence-enabled robotic systems that are configured specifically for individual customer needs. This innovation may increase US supply chain resilience, enabling citizens to rapidly and reliably obtain necessities such as food, medicine, and health supplies without needing to leave their homes. The commercial opportunity is large, with over $20B spent on US pick and pack wages annually.This Small Business Innovation Research (SBIR) Phase II project seeks to develop new methods for rapidly training artificial intelligence (AI)-enabled robotic systems built for object identification and manipulation. Warehouse object manipulation tasks are variable and automating them often requires custom solutions for each customer and facility. These custom solutions are often prohibitively expensive. To solve these problems, an industrial operating system that can be deployed across many configurations of materials handling processes is required. This project aims to develop modules critical to scaling commercial deployments, such as quality control vision systems, automated assessments of item pickability, and enhanced AI systems for robotic picking. The anticipated result of this project is an industrial AI-enabled robotic operating system that allows rapid configuration of robotic systems to implement highly-optimized processes for picking and packing individual items in e-commerce logistics.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.

# data[].attributes.title
SCC-IRG Track 1: Serving Households in AReas with food Insecurity with a Network for Good: SHARING

# data[].attributes.funder_divisions[]
Computer and Information Science and Engineering (CISE)

# data[].attributes.other_investigators[].affiliations[].name
North Carolina Agricultural & Technical State University

# data[].attributes.abstract
This project seeks to address hunger relief in the US by maximizing equitable access to safe food, while considering the food preferences of food-insecure households, and simultaneously addressing redistribution of usable food that would otherwise be wasted. In the US, 10.5% of the households were food insecure in 2019, and that number increased by 29% in 2020 with the spread of COVID-19, according to the Feeding America Covid-19 impact assessment. Yet, it is estimated that 30–40% of food supply is wasted in the US. Working with two food banks in North Carolina and one in Alabama, each serving a range of counties—together with their associated networks of food-insecure households, food-secure households, other nonprofit organizations, and local businesses such as growers, supermarkets, restaurants, and other businesses in the service regions—the project team of academic and community partners will co-develop a community-based socially intelligent nonprofit food rescue and distribution infrastructure and platform to use community resources to equitably serve food-insecure households.This project unites three aims to develop a socially intelligent nonprofit food rescue and distribution infrastructure to equitably serve food-insecure households by continually learning their preferences with feedback to upstream stages of the supply chain. Aim 1, Smart Sociotechnical Information Capturer and Predictor: Understand the behavior of donors, beneficiaries, and volunteers by creating a socially intelligent infrastructure that records data in real-time and learns evolving stakeholders’ and end users’ needs, preferences, and utilization over time. Aim 2, Tactical Supply Chain Planner: Design and optimize the community food sharing network in response to stakeholder behaviors by constructing a technology and data-driven supply chain framework that adapts to evolving stakeholder behaviors to best serve the hunger needs of food-insecure households within the community. Aim 3, Real-Time, Logistics Operations Optimizer: Satisfy beneficiary needs through communal self-renewal by connecting food-insecure households to community-based supply options in real-time, and optimizing real-time pickup and delivery logistics while adhering to food safety time windows. The proposed infrastructure will facilitate more effective food distribution aimed at reducing hunger while simultaneously enhancing sustainability.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.

# data[].attributes.title
SBIR Phase I:  Ace2 decoy as a pan-coronavirus therapeutic (COVID-19)

# data[].attributes.funder_divisions[]
Technology, Innovation and Partnerships (TIP)

# data[].attributes.abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the advancement of a patient-friendly and cost-effective way to prevent and treat COVID-19 infection arising from SARS-CoV-2 and its variants. The inability to quickly stop the spread of respiratory infectious pathogens can have devastating global consequences, resulting in millions of deaths and creating an enormous economic burden. This project will prove the viability of an aerosolized pan-coronavirus neutralizing agent that can be delivered directly to the lungs, either as an early-stage, post-infection treatment or as a prophylactic. An inhalable therapeutic has a stronger commercial potential than the currently approved monoclonal antibodies which require intravenous delivery, and this drug will be more likely to retain potency against future variants. The COVID-19 virus is expected to persist in the human population, and novel variants thereof will continue to emerge. Therefore, this technology could be crucial in addressing these ongoing medical needs.This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate in vivo efficacy of an inhalable decoy receptor that would effectively inhibit SARS-CoV-2 interaction with its endogenous cellular target and thus prevent infection of the host. The mechanism of SARS-CoV-2 viral entry into respiratory epithelial cells depends on the binding of viral Spike trimer to the host Ace2 receptor. The decoy receptor approach would use a recombinant soluble version of the Ace2 receptor that would bind and coat the viral particle, competing for Spike interaction with endogenous Ace2 and thus prevent virus docking to the cell surface. Stabilizing mutations in the Ace2 protein could enable it to act as a decoy receptor and also have sufficient stability in an inhalable formulation, allowing it to be deployed directly to the respiratory tract via a nebulizer. The dependence on Ace2 receptor binding is a potential Achilles heel of coronaviruses, as it is unlikely that SARS-CoV-2 or similar coronaviruses can mutate around the requirement to interact with this host protein.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.

# data[].attributes.title
RAPID: Collaborative Research: Metapopulation Modeling to Develop Strategies to Reduce COVID-19 Transmission in Public Spaces

# data[].attributes.abstract
The COVID-19 pandemic presents an unprecedented challenge to public and private institutions to safely reopen public spaces, including workspaces and schools. However, we have little guidance on how to manage the use of shared spaces in light of a highly transmissible, but invisible, pathogen. The fundamental aim of this project is to better understand how SARS-CoV-2 spreads in built environments. Predictions generated by mathematical modeling will be experimentally tested using a surrogate non-pathogenic virus. This project presents a new paradigm where the likelihood of infected individuals being present, the amount and manner of viral shedding, the locations of viruses over time, and the usage-needs of a location provide for a major advancement in the assessment of public space occupancy and usage. The ultimate goal is to develop practices capable of limiting virus transmission and meeting the current worldwide challenge to public health. Recommendations will resemble established building and fire codes, which regulate how space is allotted per occupant based upon design and usage requirements; our analyses will generate a “COVID Code” that can be generalized for use during future outbreaks. This research will also provide training opportunities for students and postdoctoral scholars.  A recently developed computational model (the Ephemeral Island Metapopulation Model (EIMM)) that applies metapopulation theory to explain how pathogens persist in hospital environments will be revised to address the spatial spread of SARS-CoV-2 within built environments. The EIMM defines aspects of the built environment as distinct habitable zones of occupancy (“demes”) in much the same manner as human hosts are considered, but these demes have their own biological parameters relevant to the survival and transmission of SARS-CoV-2. The number and size of both living and non-living demes, instead of human hosts alone, are used to model size and location of pathogen populations using ecologically relevant parameters, such as growth rate, population size, and carrying capacity. An enveloped bacteriophage phi6 will be used to validate model expectations as well as test control strategies in real environments such as classrooms. The goal is to test which interventions suggested by the EIMM minimize opportunities for phage phi6 spread in shared spaces, and this information can be adapted to provide estimates of how various interventions would affect SARS-CoV-2 persistence and transmission.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

# data[].attributes.title
RAPID/Collaborative Research:  Examining Household Movements and Evacuation Decision-Making in a Compounding Risk Event

# data[].attributes.abstract
Selecting protective actions in response to hurricane threats can be a challenging process for households. Adding to the complexity of this process are the risks stemming from the ongoing COVID-19 pandemic and the compounding risks posed by lingering utility outages, high heat, and the remnants of Hurricane Nicholas in the weeks following Hurricane Ida. While many studies have examined protective action decisions of households in response to natural and human-induced risks, less is known about this process in instances where households face simultaneous disasters and compounding risks. The purpose of this grant for Rapid Response Research (RAPID) collaborative project is to examine household protective action decisions during two simultaneous events: Hurricane Ida and the ongoing COVID-19 pandemic. Time-sensitive data gathered through online, phone, and mail surveys and supplemented with GPS/mobile phone data will be used to examine household protective action decision-making and mobility patterns before, during, and after Hurricane Ida. The findings from this project are expected to save lives and minimize stress during evacuations and return trips. Additionally, the findings of the research will benefit the emergency management community and society as new knowledge related to protective action decisions during simultaneous hazard events can help maximize safety and efficiency in coordinating and managing the movements of displaced residents.Decisions pertaining to whether one evacuates or shelters, where to evacuate to, and when to return after a disaster all entail consideration of multiple factors related to risks, warning messages, household socio-demographic characteristics, and available resources. While numerous studies examine these decisions in the context of a single hazard scenario, very little is known as to how protective action decisions are selected during simultaneous disaster events. Furthermore, the nature of tradeoffs and their impact on decisions during dual risk scenarios are not well understood. The goal of this study is to collect ephemeral data to examine household decision-making during Hurricane Ida and the ongoing COVID-19 pandemic. By advancing the Protective Action Decision Model, this study will gather household data using surveys and GPS mobile phone data that will allow the understanding of three primary research questions: 1. How do households make evacuation, sheltering, post-event relocation and return-entry decisions during simultaneous disasters? 2. What factors influence evacuation and return-entry timing, the selection of evacuation destinations, and shelter-in-place decisions? 3. What is the nature of the multiple movements undertaken by households throughout the response and short-term recovery process? This project will advance theory pertaining to protective action decision-making during simultaneous disaster events and provide important insights into how risk tradeoffs inform these decisions. Specifically, new knowledge will be generated through: 1) gathering ephemeral data of household movements and protective action decisions undertaken during Hurricane Ida and the ongoing COVID-19 pandemic in order to advance understanding of this dynamic process; 2) documenting locations household move to during the disaster response and short-term recovery phases and why these locations were selected, and 3) providing new insights into how households perceive the tradeoffs in risks when making decisions during simultaneous disasters. Findings from this study will offer a more nuanced understanding of household decisions and movements in response to disasters and provide better insight into the experiences of displaced households during and after disasters.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.

# data[].attributes.title
RAPID: Agenda Generality and Behavior in Social Network Interactions about COVID-19

# data[].attributes.abstract
The same types of social networks that transmit the COVID-19 disease may be leveraged to spread healthy norms and positive behaviors. This research gathers important, time-sensitive data to understand the conditions under which digital social networks can influence health behaviors relevant to the COVID-19 pandemic and how to reduce negative social influences in digital environments. At a time when people spend unprecedented amounts of time on digital networks, public health strategies deployed in these networks may shape the health and social outcomes of Americans in the next 12 months. This research advances understanding of these public health strategies.The project’s theory is that discussing either general or specific issues (e.g., curbing COVID-19 disease or wearing a mask) can have important consequences on the spread of risky attitudes and behaviors through a network. The research entails (a) an ecological study of Twitter and Instagram networks and (b) experiments manipulating the mix of healthy and risky behaviors promoted in the network and the focus of the discussion on either general or specific issues. The project generates public health recommendations and algorithms to improve health discussions on social media. The investigators use a dynamic panel data model to predict individual behavior from the individual’s own attitudes and own past behaviors as well as the behaviors of other members of their network. The research team uses graph convolutional networks both to capture richer network aspects and to model sparse networks.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.

# data[].attributes.title
RAPID/Collaborative Research:  Examining Household Movements and Evacuation Decision-Making in a Compounding Risk Event

# data[].attributes.abstract
Selecting protective actions in response to hurricane threats can be a challenging process for households. Adding to the complexity of this process are the risks stemming from the ongoing COVID-19 pandemic and the compounding risks posed by lingering utility outages, high heat, and the remnants of Hurricane Nicholas in the weeks following Hurricane Ida. While many studies have examined protective action decisions of households in response to natural and human-induced risks, less is known about this process in instances where households face simultaneous disasters and compounding risks. The purpose of this Grants for Rapid Response Research (RAPID) collaborative project is to examine household protective action decisions during two simultaneous events: Hurricane Ida and the ongoing COVID-19 pandemic. Time-sensitive data gathered through online, phone, and mail surveys and supplemented with GPS/mobile phone data will be used to examine household protective action decision-making and mobility patterns before, during, and after Hurricane Ida. The findings from this project are expected to save lives and minimize stress during evacuations and return trips. Additionally, the findings of the research will benefit the emergency management community and society as new knowledge related to protective action decisions during simultaneous hazard events can help maximize safety and efficiency in coordinating and managing the movements of displaced residents.Decisions pertaining to whether one evacuates or shelters, where to evacuate to, and when to return after a disaster all entail consideration of multiple factors related to risks, warning messages, household socio-demographic characteristics, and available resources. While numerous studies examine these decisions in the context of a single hazard scenario, very little is known as to how protective action decisions are selected during simultaneous disaster events. Furthermore, the nature of tradeoffs and their impact on decisions during dual risk scenarios are not well understood. The goal of this study is to collect ephemeral data to examine household decision-making during Hurricane Ida and the ongoing COVID-19 pandemic. By advancing the Protective Action Decision Model, this study will gather household data using surveys and GPS mobile phone data that will allow the understanding of three primary research questions: 1. How do households make evacuation, sheltering, post-event relocation and return-entry decisions during simultaneous disasters? 2. What factors influence evacuation and return-entry timing and the selection of evacuation destinations? Similarly, what factors influence shelter-in-place decisions? 3. What is the nature of the multiple movements undertaken by households throughout the response and short-term recovery process? This project will advance theory pertaining to protective action decision-making during simultaneous disaster events and provide important insights into how risk tradeoffs inform these decisions. Specifically, new knowledge will be generated through: 1) gathering ephemeral data of household movements and protective action decisions undertaken during Hurricane Ida and the ongoing COVID-19 pandemic in order to advance understanding of this dynamic process; 2) documenting locations household move to during the disaster response and short-term recovery phases and why these locations were selected, and 3) providing new insights into how households perceive the tradeoffs in risks when making decisions during simultaneous disasters. Findings from this study will offer a more nuanced understanding of household decisions and movements in response to disasters and provide better insight into the experiences of displaced households during and after disasters.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.

# data[].attributes.title
Keeping Shelters in Place: Understanding the Impacts of Residential Landlord Decision-Making on Post-Disaster Housing Stability

# data[].attributes.abstract
This research is responding to the local threats to rental housing security that emerged during the COVID-19 pandemic. Rental housing occupies a significant portion of the housing stock in US metropolitan areas, yet researchers know very little about the specific characteristics of the institutional and non-institutional entities that hold titles to those properties and determine housing supply, rents, and the conditions of both buildings and units. To further complicate this scenario, regulatory environments and rental housing market dynamics vary greatly across space, both within and between metropolitan regions. Resiliency, the ability to withstand and recover from a disaster or a shock, is shaped by the conditions of the local housing market and the associated regulatory environment. However, it is also shaped by the behaviors of the landlord population operating within that milieu. In the absence of existing knowledge about landlord characteristics, behaviors, and needs, cities and policy makers responding to disasters are left guessing how to stabilize their rental markets, keep renters housed, deliver meaningful assistance to property owners, and plan for an effective post-disaster recovery. This study contributes to the progress of science by investigating rental property owner characteristics and identifying meaningful rental owner categories as they relate to disaster and post-disaster decision-making. It contributes to the national health, prosperity and welfare by linking that knowledge to disaster-related rental housing outcomes in specific places. The COVID-19 pandemic has demonstrated that the security of the rental housing market is intertwined with a landlord’s ability to tackle financial challenges. The decisions that landlords make in the midst of a disaster affect not only their tenants’ ability to remain housed, but the ability of the city to respond to and recover from the event and ensure future housing stability.The central hypothesis of this study is that when responding to disasters, non-institutional rental property owners make property and investment decisions that accelerate ownership consolidation and reduce post-disaster housing security within communities. The research is structured as a longitudinal study using an innovative, convergent approach that brings together social science and data science in order to create new datasets and tools for data analysis. It fills a major gap in existing knowledge by investigating landlord decision-making across the stages of the disaster management cycle and identifying meaningful categories of non-institutional rental property owners based on landlord characteristics. This study sets out to answer not just who landlords are, but how they respond to disasters and how disaster-induced changes in the landlord population might continue to affect the built environment of cities and communities into the future. There are two nested research efforts within this proposal: to understand landlord characteristics and decision-making within the context of the post-pandemic recovery and potential future shocks or disasters; and, through data science approaches, to identify and characterize the landlord population and the potential value of better data utilization for promoting rental housing security during local recovery from hazard-related shocks and stresses. The overall project goals to improve housing outcomes within local disaster recovery efforts draw from the domain of social science including planning, sociology, economics, and finance. The project goals to create tools that improve the local institutional capacity for identifying and communicating with landlords rely on the domain of data science research. This project’s integrative methodology strengthens the capacity of each domain, generating an innovative approach where social science research is able to resolve the enduring problem of landlord invisibility and the data science techniques are refined through their application to real world problems. The unit of analysis for this study is the rental property owner, specifically non-institutional investors, in nine mid-sized US cities. These cites, though of similar size, have varied housing stocks, socioeconomic characteristics, and political orientations. They also provided unique state and local responses to the COVID housing crisis. Five of the cities are located in states that are part of the Gulf Coast region. All have either been recently affected by hazard-related disasters or are at high risk of experiencing a disaster. This chronological range of disaster experience and recovery will allow the tracking of landlord perspectives across the disaster management cycle.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.