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).

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.

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.

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.

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.

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.

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 award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

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.

Cancers are among the leading causes of death around the world, with an estimated annual mortality of close to 10 million. Despite significant efforts to develop effective cancer diagnosis and therapeutics, the ability to predict patient responses to anti-cancer therapeutic agents remains elusive. This is a critical milestone as getting the right choice of therapy early can mean superior anti-tumor outcomes and increased survival, while the wrong choice means tumor relapse, development of resistance, side effects without the desired benefit, and increased cost of treatment.