Infants and toddlers with developmental disabilities or delays use early intervention (EI) for rehabilitation services. Yet, EI quality is compromised for racially and ethnically diverse and socially disadvantaged families. A key lever to improve EI quality is family-centered care, an evidence-based approach that is grounded in family engagement for shared decision-making. This project is motivated by the need to give families a smart and connected option for engaging in the design of the EI service plan for their child. This effort will develop and evaluate an upgraded Participation and Environment Measure (PEM), an evidence-based electronic option for directing equitable family-centered EI service design. PEM upgrades will (a) increase content relevance for racially and ethnically diverse families, and (b) leverage modern artificial intelligence solutions to personalize the PEM user experience to a broader range of EI enrolled families. This upgraded PEM electronic option will be evaluated in a population of racially and ethnically diverse EI families, to assess for its capacity to improve EI quality and to appraise supports and barriers to its longer-term implementation within the broader EI service system. This project builds evidence for the first customized, culturally relevant electronic option to direct family-centered care during EI service design. The approaches and technologies developed may be applicable to similar service contexts. Additionally, this project increases opportunities for conducting interdisciplinary research at the intersection of computer science and rehabilitation science, building interprofessional capacity for research engagement among EI service providers and students training for pediatric rehabilitation careers, and sponsoring students from historically underrepresented groups in diverse research labs that value inclusive excellence.
This proposal develops key innovations to family-centered EI in two ways. First, for the PEM electronic option, the project will (a) increase content relevance for racially and ethnically diverse families, and (b) personalize the PEM user experience to a broader range of EI enrolled families. For the former, the project will establish cultural equivalencies of the original PEM assessment and critically examine its intervention content to ensure that families can voice concerns about racial climate and collect and share goal attainment strategies using community-preferred communication channels. For the latter, the team will incorporate an adaptive conversational agent into the PEM intervention to improve caregiver navigation and guidance, and we will develop methods to automatically customize its strategy exchange feature to individual caregiver needs. These innovations will result in fundamental advances to natural language processing research through the investigation of adaptive dialogue policies for task-oriented or mixed-initiative dialogue systems, generalized dialogue act schema, and lexicon-informed meaning representations. We will evaluate the upgraded PEM electronic option with racially and ethnically diverse and socially disadvantaged EI enrolled families, to assess for its capacity to improve caregiver and provider perceptions of family-centered EI service quality, improve parent engagement in EI service plan implementation, and increase the availability and relevance of participation-focused EI service plans. We will engage EI stakeholders to appraise supports and barriers to its longer-term implementation in EI. These advances will yield evidence for a customized, culturally-relevant electronic option to foster family-centered care in EI.
Disasters disproportionately impact older adults who experience increased fatality rates; such individuals often live in age-friendly communities and senior health facilities (SHFs). During a crisis, older adults are often unable to shelter safely in place or self-evacuate due to a range of physical conditions (need for life-sustaining equipment, impaired mobility) and cognitive afflictions (e.g. dementia, Alzheimer?s). First responders assisting older adults could benefit from seamless, real-time access to critical life-saving information about the living facilities (e.g., floor plans, operational status, number of residents) and about individual residents (e.g., health conditions such as need for dialysis, oxygen, personal objects to reduce anxiety). Such information, siloed within organizational logs or held by caregivers, is inaccessible and/or unavailable at the time of response. This interdisciplinary project brings together IT, geriatrics and resilience experts with disaster-response agencies and SHF providers to create information preparedness and transform disaster resilience for older adults.
The team will design, develop and deploy CareDEX, a novel community contributed data-exchange platform, that empowers SHFs to readily assimilate, ingest, store and exchange information, both apriori and in real-time, with response agencies to care for older adults in extreme events. The CareDEX information pipeline enables SHFs to capture individual information about changing health conditions and personalized needs and share them with responders to help improve response. Information co-produced with civic partners will identify and refine resident-specific data via tools for proactive collection/update. Given the sensitive nature of personal information, e.g., health-profiles, CareDEX will incorporate policy-based information sharing mechanisms that balance needs for individual privacy with authorized information release. CareDEX?s hybrid-cloud architecture seamlessly enables data to be securely stored on-premise (at SHF) and in the cloud for remote access by responders and temporary caregivers. Relocation of older adults requires regional information (e.g. road-conditions, facility status) - CareDEX will integrate GIS tools to provide first-responders with uptodate region-level situational awareness for dynamic decision-support. The prototype CareDEX platform will be co-developed with core civic partners, e.g. Front Porch (a nation-wide senior-care provider) and deployed at a SHF in Anaheim, CA. Collaborations with local response agencies (Los Angeles, Orange County, San Bernardino, San Diego) and national entities (FEMA, Red Cross, NFPA/FPRF) will mesh needs of emergency responders with caregivers. CareDEX will be evaluated using diverse scenarios - a wildfire event triggering relocation, wildfires coupled with a pandemic, and rapid onset earthquake events with small warning times and increased uncertainty.
The CIVIC Innovation Challenge is a collaboration with Department of Energy, Department of Homeland Security Science and Technology Directorate, Federal Emergency Management Agency (FEMA), and the National Science Foundation
In the era of data sharing, it is still challenging, insecure, and time-consuming for scientists to share lessons learned from agricultural data collection and data processing. The focus of this project is to mitigate such challenges by intersecting expertise in plant science, secure networked systems, software engineering, and geospatial science. The proposed cyber-physical system will be evaluated in the laboratory and deployed on real crop farms in Missouri, Illinois, and Tennessee. All results will be shared with international organizations whose goal is to increase food security and improve human health and nutrition.
The proposed system will securely orchestrate data gathered using sensors, such as hyperspectral and thermal cameras to collect imagery on soybean, sorghum, and other crops. Preprocessed plant datasets will be then offered to scientists and farmers in different formats via a web-based system, ready to be processed by deep learning algorithms or consumed by thin clients. Data collected from different crop farms will be used to train distributed deep learning systems, using novel architectures that optimize privacy and training time. Such machine learning systems will be used to predict plant stress and detect pathogens. Finally, the cyber-physical system will integrate novel data processing software with existing NSF-funded hardware platforms, introducing novel algorithmic contributions in edge computing and giving feedback to farmers, closing the loop. The results of this project will impact research on high-value crops with significant levels of automation, such as those in protected agriculture and fish crop hydroponics systems in desert farming. Planned outreach activities will impact solutions for smallholder farmers that collaborators at the International Center for Agricultural Research in the Dry Areas (ICARDA) support. Although this work will focus on enabling data science for farming applications, the work will also inform management of other IoT applications, e.g., smart and connected healthcare or other cyber-human systems.
Modern vehicle fleets are equipped with increasing levels of sensor instrumentation that generate large quantities of data on asset conditions and operational awareness. In recent years, there has been a growing literature on methods that harness this data to provide predictive insights for operations. Taken individually, these methods provide limited improvements to fleet-level decision making. There are significant and dynamic interdependencies in large-scale vehicle fleets that include (i) continuous asset-to-asset interactions in degradation and failure risks, (ii) interactions across vehicles related to operational coordination and use of shared resources (e.g. mission requirements and spare part resources), and (iii) interactions between spare part logistics, maintenance and operations. Additional layers of challenges are introduced through stringent requirements for data residency, privacy, and computational scalability. This NSF project provides a unified predictive-prescriptive framework for vehicle fleet management that integrates (i) sensor-driven predictions on dynamically evolving asset failure probabilities and operational risks, with (ii) adaptive robust optimization models for fleet-level operations, maintenance and respond logistics. Intellectual merits of the project include formulation of sensor-driven risks within decentralized and differentially private mixed integer optimization models; and a parallel development of tailored solution methods. Broader impacts of the project include dissemination of research findings through publications, coursework, conferences and workshops. The project will support summer internships and undergraduate research opportunities, specifically for students from underrepresented communities, to educate the next-generation of engineers for vehicle fleet management.
Harnessing the true value of sensor data in a fleet management application, requires an integrated and detailed modeling of fleet level interactions, along with a seamless integration of sensor-driven sensing, and decision-making capabilities. To address this challenge, this proposal aims to develop a decentralized and differentially private framework for sensor-driven fleet management. In particular, the proposed project (i) integrates sensor-driven asset remaining life distributions within a joint decision optimization model to identify optimal operations, maintenance and spare part logistics schedule, (ii) dynamically models the perceived asset remaining life, failure risks and other operational uncertainties within an adaptive robust reformulation of the fleet management model, and (iii) reformulates the decision model within a decentralized and differentially private coordination mechanism. Significant computational challenges will be addressed through decentralized solution algorithms that leverage on the structure of the proposed decision optimization models.
A sudden surge in demand in traffic networks disrupts the equilibrium conditions upon which these networks are planned and operated. Lack of understanding of the population's strategic choices under extreme demand may result in paradoxical outcomes, such as evacuations aiming to save lives instead resulting in mass casualties on the road or opening up of new roads increasing rather than decreasing travel time. This project will devise systems and procedures for managing the strategic choices of populations (e.g., whether to evacuate or shelter in place, which escape routes to take) and the actions of the authorities (e.g., which zones to evacuate and in which sequence, where to route the traffic, whether to close some roads or open extra lanes in a given direction). The tools resulting from this project will enable better response systems to assist local authorities in managing extreme demand, such as when entire counties have to be evacuated to protect the residents from a wildfire. The project will develop a modeling and simulation tool chain to predict traffic bottleneck locations and their severity together with expected travel times and delays, thus determining the spectrum of outcomes, identifying worst cases, and enabling the authorities to make informed decisions.
The technical approach is rooted in population games, which model the dynamics of strategic noncooperative interactions among large populations of agents competing for resources. The project, however, will depart from the equilibrium focus of the existing theory and will offer transient analysis tools that account for not only the strategy revisions of the agents, but also a host of cyber and physical dynamics, such as queueing dynamics in traffic flow, responsive signal control at intersections, information dissemination to agents, and evolution of hazards, such as fire propagation. The research tasks to enable the project's vision of a "cyber-physical population game theory" include characterizing transient behavior with system-theoretic methods, accounting for uncertainty in strategy revision models, extending the theory to a continuum of user preferences, rethinking the stochastic processes underlying the dynamical models, modifying the theory for short-term horizons for time-critical operations, learning dynamical models from data, and formulating extensive form games between a population and a single agent, motivated by the population response to evacuation orders. In addition, the project will identify control actions (such as responsive signal policies, road closures, disabling certain turns) to close the data-decision-action loop and steer the dynamics towards desirable outcomes and avoiding unsafe ones.
With the advancements in sensing technologies, agricultural farm management has transformed into a data-enabled process. Data collected at farms enabled artificial intelligence (AI) frameworks to develop models capable of predicting traits such as crop yields and health conditions, allowing for data-informed decision-making. However, in the current state of practice, these smart farms are siloed, developing AI models solely based on data obtained from a farm, ignoring the data generated in other farms. This lack of collaboration among farms results in limited generalization capability of models and directly impacts farm management decisions. While pooling data from a network of farms into a centralized server to generate more robust models is possible, most farmers are reluctant to share their data due to data privacy concerns. Therefore, this project aims to develop a novel holistic framework that allows for collaboration between farms, preserves privacy, and encourages simultaneous collaboration and personalization in the data-driven modeling of agricultural farms. The constructed models are used in farm decision-making and management. This framework will alleviate farmers? data privacy concerns, resulting in further adoption of smart farming technologies. Therefore, the project may result in the more prevalent use of digital tools by farms, improving management decisions and increasing farm productivity. Eventually, the acceptance and use of digital solutions will enhance food quality and decrease the environmental footprint. Several educational and outreach efforts for the integration of research into undergraduate and graduate courses and broadening the participation of underrepresented groups are envisioned.
The project aims to develop a federated analytics framework for high-dimensional and big data common in smart agricultural farms. The project will design a novel federated robust tensor-based modeling paradigm that enables exploiting the spatiotemporal structure of smart farm datasets. When the proposed approach is used, each farm creates a local model that is then transmitted to an aggregator, which creates an aggregated model. The aggregated model is then broadcast to each farm to generate a personalized model that supports local decision-making. The low-dimensional embedding of the tensor model allows for reduced model communication between the farms and the aggregator. Differential privacy approaches will be investigated to enhance the privacy-preservation properties of the proposed framework. The developed AI-enhanced connected multi-farm system will be tested in citrus as a case study. The proposed framework can contribute to other areas, such as modeling and monitoring multi-farm renewable energy systems and multi-facility advanced manufacturing systems.
Cyber-physical applications often analyze collected sensor data using machine learning algorithms. Many existing sensing systems lack intelligence about the target and naively generate large-scale data, making communication and computation significantly costly. In many cases, however, the data generated by sensors only contain useful information for a small portion of the sensor activity. For example, machine learning algorithms continuously process the visual sensors used for environmental/security monitoring to detect sensitive activities. Still, these sensors only carry out useful information for a short time. On the other hand, biological sensors intelligently generate orders of magnitude less amount of data. This project develops machine learning algorithms that provide real-time feedback to sensors to ensure they only generate data needed for learning purposes. The approach is expected to provide up to four orders of magnitude data reduction from sensors. The results from this research will broadly impact many sensors used in internet-of-things applications, including infrastructure, mobile devices, autonomous systems, robotics, and healthcare. The project will also support underrepresented minority students through synergistic outreach plans and educational activities, including programs for K-12 students, undergraduate research opportunities, and new course development.
The research approaches introduced in this project aim to make fundamental changes to sensing systems in order to make future sensors intelligent for a wide range of cyber-physical applications. First, this project will develop novel brain-inspired learning algorithms that can provide fast and real-time feedback to the sensing module to intelligently control the rate of data generation from sensors. This feedback also makes sensors aware of the target task, enabling situational awareness. Second, the project will develop a novel framework that tightly integrates with a sensing circuit and brain-inspired algorithms to dynamically control the sensor functionality in a close-loop manner. The proposed hardware platform exploits the robustness of learning algorithms to design near-sensor computing platforms that are highly approximate, parallel, and efficient. Finally, this project aims to evaluate the effectiveness of the framework on multiple large-scale systems. The prototype will be fully released under an established open-source library for public dissemination.
This NSF CPS project aims to redesign the information structure utilized by system operators in today's electricity markets to accommodate technological advances in energy generation and consumption. The project will bring transformative change to power systems by incentivizing and facilitating the integration of non-conventional energy resources via a principled design of bidding, aggregation, and market mechanisms. Such integration will provide operators with the necessary flexibility to operate a network with high levels of renewable penetration. This will be achieved by a comprehensive bottom-down approach that will first identify the intrinsic cost of utilizing novel renewable resources and accommodate the operational ecosystem accordingly. The intellectual merits of the project include novel theories and algorithms for operating a vast number of distributed resources and testbed implementations of markets and controls. The project's broader impacts include K-12 and undergraduate programs, including in-class and extra-curricular STEM activities through, e.g., Hopkins in-class and extra-curricular STEM activities, and the Caltech WAVE summer research program.
Introducing distributed energy resources (DERs) at a large scale requires rethinking power grid operations to account for increased uncertainty and new operational constraints. The proposed research undertakes this task by overhauling the information structure that markets and grid controls utilize. We seek to characterize and shape how information is exchanged and used to manage the grid to improve efficiency, stability, and incentive alignment. The research is organized into three thrusts. Thrust 1 emphasizes the role of information in coordination. It seeks to characterize DER costs and constraints, designing bidding strategies tailored to convey information about the atypical characteristics of DER costs. Thrust 2 aims to develop aggregation strategies that efficiently manage resources by accounting for their cost and constraints, integrating DERs via an aggregate bid that protects sensitive user information and is robust to market manipulation. Finally, Thrust 3 characterizes the overall impact of DERs on operations. We will examine how user incentives that span across markets implicitly couple market outcomes and develop design mechanisms to mitigate inter-market price manipulation. We will also design pricing schemes that provide efficient DER allocation while preserving real-time operational constraints such as frequency regulation.
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
This project aims to strengthen dependability and robustness of the electric power grid by improving the capability to aggregated power system state estimation (PSSE) methods to monitor and assess the health of a power grid. The electric power grid is a cyber-physical systems, essential for modern daily life that is always available on-demand nearly anywhere at any time. The grid is arguably the largest global engineered structure. The goals of this project are to (1) understand vulnerabilities intrinsic to traditional PSSE methods, and (2) improve the dependability and robustness of PSSE algorithms to potentially disruptive conditions. This project extends recently developed power system optimization techniques to enable better situation awareness of the operations of the overall power system. The project will work with the Arkansas State University?s outreach program ?P-20 Educational Innovation Center? to share this research and encourage careers in STEM fields.
This project extends convex relaxation-based techniques for large-scale cyber-physical power system in order to improve the monitoring, analysis, and controllability of these systems. The project presents an efficient convex relaxation-based approach to the PSSE problem. The project leverages semidefinite programming relaxation and extreme point approaches for large-scale alternating current (AC) power systems to provide tighter bounds on flow analyses than traditional PSSE. This project also presents an analysis showing the infeasibility of transitioning between certain operating regions in the power system that can be used to provide safety and security guarantees before initiating state transitions. This approach can identify specific types of sparse false data, that might arise from corrupted sensors by nature or design, and go undetected by current power systems' bad data detection algorithms. The proposed tighter relaxation schemes will be broadly applicable to a wide variety of nonconvex and nonlinear optimization problems in large flow-system models and in complex optimization problems.
The electric grid critical infrastructure is undergoing a major transformation from concentrated carbon-intensive legacy generation options to renewables in the form of distributed energy resources. The goal of this NSF workshop is to bring together a wide-spread collaboration among researchers from different scientific disciplines, such as data analytics, computational sciences, atmospheric sciences, and social and behavioral sciences ? addressing the engineering of complex systems will enable the convergent science needed to address these emerging challenges. The objective is to continue the discussion from the prior NSF-sponsored Workshops held in the USA in 2020 and 2021, and jointly with the European partners in Europe in 2022, attended by over 100 researchers from well over 50 US/European academic, government and industry organizations to further grid resilience discoveries and strategies through the proposed workshop with the participation of a wider scientific community.
The workshop will engage researchers from different scientific disciplines and a variety of application domains to address scientific, engineering, social, and economic challenges of interdependencies among critical infrastructures and how to inform the policy and regulatory process of the needed changes. The scientific merit is in the convergent scientific discussion among researchers and practitioners that will explore five scientific areas: (1) Data and physics-based modeling discovering new fundamentals in deep-learning approaches, (2) Transformational electric grid distributed control strategies laying the foundation for a resilient net-zero grid of the future, (3) Synergies between social and behavioral sciences to assess a human aspect of grid modernization leading to new models for electricity markets and incentives, and (4) Scalability of cybersecurity and privacy requirements across millions of internet-of-things consumers, and (5) Cross-dependency between electric grid infrastructure and other critical infrastructures. The workshop will also address how to develop partnerships that will work closely on informing and engaging the public and enhancing the STEM education and training of K-12 students and early-carrier professionals. The international experience of interacting with peers from 15 universities from a dozen leading European countries will be invaluable for the USA participants in forming a broader social, cultural, and political understanding of grid modernization.