Funded Projects

2023-24

Climate Resilience

Stimulating Behavior Change to Enhance Climate Resilience Policy and Action through a Serious Game Approach

This proposal focuses on climate resilience with an emphasis on behavior change. Previous research has shown that behavior change is essential to addressing climate challenges, and many climate adaptation plans anticipate that stakeholders will modify their actions while working towards enhancing resilience. However, behavioral change in climate adaptation has received limited attention. Further, there has been a recent increase in interest in the use of serious games (games developed for a societal goal) for citizen engagement. Yet, more work is needed to understand how serious games can support sustainable behavior change and effective decision-making for climate resilience. We propose to tackle climate change problems, specifically the sea level rise problem and behavior change, using serious game design. Using a co-design process, the project explores how serious game design can better support behavior change and sustainability through engagement with stakeholders. Specifically, we investigate how to incentivize behavior change through the interplay between two levels: (a) the policy level and (b) the individual and urban household levels. To understand behavioral change for climate resilience, we will focus on the problem of sea level rise in two parts of the world: Alexandria, Egypt, and the San Francisco Bay Area, California, as an emerging challenge for climate change in both regions. By interacting with stakeholders in these areas, we aim to co-design a game concept that allows us to incentivize behavior change as well as experiment with policy changes for behavior change.

Team:

  • Lead PI: Magy Seif El-Nasr, Engineering/Computational Media
  • Brent Haddad, Social Sciences/Environmental Studies

 

 

Empowering Grid Resilience: Harnessing Campus Microgrids and Battery Storage for Reinforcing Community Power

The project aims to enhance community power resilience against increasing energy demands and climate change impacts by integrating microgrids and battery storage. We will assess power outage costs and impacts on the university campus as a microgrid prototype, focusing on critical loads and vulnerabilities. We propose solutions like upgrading power plant, utilizing PV plus storage, and enhancing monitoring. In addition, the researchers will advance non-intrusive load monitoring (NILM) techniques to enable predictive maintenance, anomaly detection, load shifting, and efficiency improvements. Finally, the team strives for developing deep learning models to design optimized nanocatalysts for electrochemical energy conversion and storage, enhancing performance. The project consists of three interactive research thrusts. Thrust 1 focuses on quantifying outage impacts, identifying critical loads, and proposing microgrid upgrades for resilience. Thrust 2 involves integrating NILM for data-driven load monitoring and grid management. Thrust 3 centers on using deep learning to design advanced nanomaterials for batteries and fuel cells. In summary, the proposal aims to leverage microgrids, battery storage, AI-based load monitoring, and optimized nanomaterials to create resilient, sustainable energy infrastructure capable of withstanding grid disturbances. It has significant potential to impact marginalized communities, critical facilities, and transition toward renewable energy.

Team:

  • Lead PI: Yu Zhang, Engineering/ECE
  • Shaowei Chen, PBSci/Chemistry and Biochemistry
  • Pat Mantey, Engineering/ECE

 

 

Leveraging Robotic Sensors for Ecological Assessment

In-depth vegetation assessments form a critical data source for tracking global impacts to ecosystems across a wide variety of environments. By improving the operational tempo and accuracy of vegetation assessments, we can obtain data essential to understanding the future of our ecosystems in a changing climate. This proposal seeks to develop analytical pipelines to leverage sensor information commonly used in field robotics to the domain of vegetation assessment in three ways: to obtain tree measurements used to measure forest health, to identify and quantify forest floor debris, and to estimate decomposition status of coarse woody debris. These measurements form a critical snapshot of ecosystem health and allow ecologists to monitor ecosystem change while evaluating the effectiveness of different management actions. The techniques this proposal seeks to develop will be compared to manual, labor-intensive state-of-the-art techniques utilized by field personnel. Future applications include wildfire woodland-urban interface characterization, risk assessment, and recovery monitoring. The improved data collection techniques proposed here have the potential to enable rapid assessment of wide-scale areas to enable better land management actions in the face of a changing climate.

Team:

  • Lead PI: Steve McGuire, Engineering/ECE
  • Greg Gilbert, Social Sciences/Environmental Studies

 

2022-23

Climate Resilience

Generating green hydrogen by electrolysis of seawater: upending the conventions

The project aims at transforming our unique technology originally developed for coral reef restoration (CRR) [1] into an innovative technology capable of manufacturing hydrogen. Conventional electrolysis of seawater (EOS) for CRR precipitates calcium carbonate (CaCO3) of which coral reefs are made. Conventional EOS uses electrical current densities (Jc) smaller than 1 A/cm2 (Jc<1 A/cm2) to improve material properties of CaCO3. The use of Jc<1 A/cm2 also curbs the generation of byproducts harmful to marine ecosystems. In our recent study that challenged the norm (i.e., the use of Jc<1 A/cm2), we discovered that the use of Jc larger than 10 A/cm2 (Jc>10 A/cm2) dramatically reduced the generation of byproducts harmful to marine ecosystems [2], suggesting that, although the use of Jc>10 A/cm2 is unsuitable for CRR because CaCO3 degrade as Jc increases, the use of Jc>10 A/cm2 should be explored with the goal of manufacturing hydrogen because the production rate of hydrogen should increase as Jc increases while the generation of byproducts harmful to marine ecosystems is reduced. The proposed project calls for addressing critical engineering issues found in conventional EOS to realize commercial implementation of EOS for the production of hydrogen economically and environmentally acceptable. With the support from CITRIS, we will be able to advance our preliminary study [2] by upgrading the existing EOS system to confirm its scalability, by further understanding underlying electrochemical characteristics, and by employing energy management via information systems to optimize hydrogen production within 12 months.

Team:

  • Lead PI: Nobuhiko Kobayashi, Electrical and Computer Engineering
  • Donald Potts, PBSci – Ecology & Evolutionary Biology

 

Autonomy for Small Electric Tractor Farming

The project’s aim is to work on a technology that will allow the fully autonomous farming of plants with minimal supervisory intervention by humans. The project is focused on autonomous farming by small electrically powered tractors (EPTs) since their use can dramatically reduce the environmental impact of farming and goes hand in hand with supporting organic agriculture (OA). OA systems are known for the maintenance of higher levels of soil carbon, increased water retention and lower emission of greenhouse gases. Therefore, the use of EPTs that can be powered by solar or wind energy sources is a pathway to address the impact of farming to climate change. The scope of the project is to work on stepping stones of the technology and its adoption in farming. The project is not only about vision perception for farming and vision-based navigation along plant lines, but on incorporation of underrepresented farmers and farm communities in the technology development. The plan is to use the results of the project as preliminary results for follow-up funding applications which will be strengthened by publications resulting from the project.

Team:

  • Lead PI: Dejan Milutinovic, Electrical and Computer Engineering
  • Stacy Philpot, Social Sciences – Environmental Studies/Center for Agroecology

 

Net-Zero Water System for Residential Home

We propose to design and build a water capture, remediation, and sensor system to demonstrate a fully net-zero water residence for a family of 4 that is resilient against climate change. This system will be installed into a sustainable affordable off-grid house that is being built for UCSC’s entry into the Orange County Sustainability Decathlon and will be showcased to the public in Fall, 2023. This project combines Carter’s (Physics) expertise in sustainable buildings, greenhouses and their construction and Campbell’s (Environmental Studies) knowledge of waste-water treatment and sustainable system modeling. The goal of this project is to reduce household dependence on the use of public water systems and wells and prepare households to transition to a dramatic reduction in local fresh water resources caused by the changing climate.

Team:

  • Lead PI: Sue Carter, PBSci – Physics
  • Elliott Campbell, Social Sciences – Environmental Studies

 

Ecological Monitoring and Sample Return Through an Integrated Aerial Robot-Ground Robot-Human Team

We are developing novel technologies in the pursuit of automating the identification and remediation of plant disease in the context of agriculture and wildland maintenance. By developing several novel robotic exploration technologies, we propose an end-to-end method of surveying and obtaining samples of interest for further study at farm scales. Through the use of aerial robots, ground vehicles, and expert human operators, our proposal leverages our prior work in aerial observation to design a complete prototype system for identifying areas of interest and gathering samples for offline analysis. Our proposal includes novel contributions in semantic scene understanding, multi-modal sensor fusion, constraint-aware path planning, GPS-denied navigation, and human-robot teaming. By gathering longitudinal data about the progression of plant changes, our proposal gathers source data needed to create predictive models to mitigate negative impacts proactively. Early, timely response to plant health concerns allows for narrow, tailored responses to the environmental challenges associated with climate change.

Team:

  • Lead PI: Steve McGuire, Electrical and Computer Engineering
  • Greg Gilbert, Social Sciences – Environmental Studies

 

2021-22

Drone Technology

Measuring the Fate of Snowmelt

Water melted from the Sierra Nevada snowpack is critical for California’s economy and ecosystems, making up a critical portion of the state’s water supply. Despite this importance, we do not know how climate-related changes to the snowpack will be affected by the physical soil structure and feedback on plant root development. Many critical watersheds face increasing water and forest management issues due in large part to recent population growth, increasing water demands, wildfire risk, fluctuating year-to-year snowpack levels, and increased drought severity.

To help address this challenge, PI Michael Loik from Environmental Studies and co-PI Margaret Zimmer from Earth & Planetary Sciences will use drone flights to capture data on evapotranspiration (ET, water loss from leaves and soils). Using that data, they will create spatial “heat maps” showing daily and seasonal ET from different shrub species and open soil in relation to prior snow depth. These data can be used for projection of water availability in communities that rely on remote watersheds, for the prediction of and response to wildfires, and for the protection of endangered species.

Team:

  • Lead PI: Michael Loik, Social Sciences – Environmental Studies
  • Margaret Zimmer, PBSci – Earth & Planetary Sciences

 

Online Plant Disease Detection via Hyperspectral UAV Imaging

Plant diseases in agriculture cause loss of crop yield, reduced food quality, supply chain disruption, and excessive preemptive use of toxic pesticides. Diseases in wildlands disrupt ecological processes, increase intensity of wildfires, and lead to quarantines with widespread economic impacts. Vast fields and remote, inaccessible forest areas make traditional methods of detection, which require physical inspection, impractical. Capturing aerial data, while faster, can still present long processing delays before actionable results are available, which prohibits real-time and online investigation of sites of interest.

PI Steve McGuire from Electrical and Computer Engineering and co-PI Greg Gilbert from Environmental Studies will use drone-based multispectral sensors combined with a machine learning back end to create a rapid-assessment tool for detecting disease outbreaks, allowing operators to investigate these areas of interest directly and take action.

Team:

  • Lead PI: Steve McGuire, Engineering – Electrical and Computer Engineering
  • Greg Gilbert, Social Sciences – Environmental Studies

 

EUREKA: A Decision Support Tool for Wildfire Risk Assessment Using a Drone-Assisted, Scalable and Efficient UAV-Assisted IoT Monitoring Network

Monitoring the conditions in vast, inaccessible forest areas can be next to impossible, but is critical in predicting and mitigating wildfires. PI Katia Obraczka from Computer Science and Engineering and co-PIs Kai Zhu from Environmental Studies and Ricardo Sanfelice from Electrical and Computer Engineering are developing a low-cost network of both ground- and UAV-based sensors to continuously collect fine-grained environmental data to improve the accuracy of wildfire risk assessment models. The system aims to assist in predicting near-future extreme weather events, and to provide a decision support tool to help mitigate the threat wildfires pose to communities.

“We are very grateful to CITRIS UCSC for the support,” said Professor Obraczka. “It will allow us to jump-start the development of EUREKA, which will help mitigate wildfire risk by providing environmental information at adequate spatio-temporal timescales and thus improve the accuracy of wildfire risk assessment models.”

Team:

  • Lead PI: Katia Obraczka, Engineering – Computer Science and Engineering
  • Kai Zhu, Social Sciences – Environmental Studies
  • Ricardo Sanfelice, Engineering – Electrical and Computer Engineering

 

 

2020-21

Pandemic/Disaster Response

Development of Open Source Robotic Platforms for Ultrasensitive, High-Throughput and Low-Cost Testing of SARS-CoV-2 Viral Load

COVID-19 testing in the U.S. has run into a number of bottlenecks, from lack of nasal swabs to not having enough chemicals to run the tests. With the reopening of economy, a new one is rapidly emerging: a shortage of high-throughput automated machines that can perform hundreds of tests at once. From hospitals to the Pentagon, shortages of high-throughput testing machines are now widely reported. Automated testing machines are sophisticated and difficult to manufacture; ramping up their production is not straightforward. Furthermore, these machines are too expensive for most laboratories. The advent of the affordable open-source liquid handling robots has opened the door to low-cost automated solutions. However, sophisticated high-throughput machines cannot be readily replaced with these low-cost open source alternatives. Molecular assays, require rigorous extraction and intricate enzymatic processes that are far too complicated to perform using low-cost open source robotics. Here, we aim to address this problem by approaching it from both directions. We propose to merge our proprietary Surrogate Generation-Assisted (SURGE) Assay technology, enabling ultrasensitive viral antigen detection using simple immunoassay kinetics, with low-cost open source robotics to demonstrate a proof-of-concept high-throughput low-cost platform. Our interdisciplinary team has more than 10 years of diagnostic assay development for viral infections. Most recently, we successfully manufactured recombinant SARS-CoV-2 antigens and monoclonal antibodies against them. We have all the necessary resources to complete the proof-of-concept part of this project through CITRIS funding. We developed connections with National Emerging Infectious Diseases Laboratories and industrial partners to further develop our platform.

Team:

  • Lead PI: Ahmet Yanik, Engineering – Electrical and Computer Engineering
  • Rebecca DuBois, Engineering – Biomolecular Engineering

Related Follow-on Funding:

 

Interactive Virtual Platform for Intergenerational Wellbeing of Essential Worker Communities in a Medical Desert: Promoting Health Equity During and After Pandemic

This cross divisional and community collaborative project focuses on the intersections of economic and social engagements by interlinking two areas: enhancing social interactions for elderly members within essential worker communities and creating new career opportunities for minority youths. The COVID 19 pandemic exacerbates pre-existing health inequalities especially for seniors who face pre-existing chronic health concerns. The shelter in place has deepened social isolation for many seniors who already faced difficulties in accessing food and health care as well as maintaining social ties. Our research hypothesizes that equitable health technologies may transform access as well as facilitate intergenerational connections during and after the pandemic. We plan to collaborate with Santa Cruz County non-profit organizations to (1) determine guidelines for telemedicine technologies to assist seniors in essential worker communities, (2) identify the most pressing technological barriers of use, risk, and needs for privacy and transparency, and (3) build a virtual platform with emerging technologies to facilitate wellbeing and preventative care needs for current and future disasters.

We propose to develop a senior-friendly multiplatform online telepresence system that facilitates intergenerational communication within essential worker communities of Pajaro Valley (currently a medical desert), with functions such as crowdsourced online and remote physical health assistance, tutorials for seniors to take full advantage of online services, and social interactions to alleviate social isolation. The cultural knowledge on local health practices to be gathered in this process will also enable youths to gain insights on narrative medicine, a growing field in the health care professions.

Team:

  • Lead PI: Sri Kurniawan, Engineering – Computational Media
  • Nancy Chen, Humanities – Anthropology

 

Towards High-Quality Digitally-Supported Experiential Education via Micro-Role Hierarchies

While educational technologies have helped to scale content-based learning in ways that enable personalized, just-in-time learning, they have struggled to do the same with experiential, project-based courses. With the shift towards remote learning during COVID-19, this difference has been particularly stark, with many experiential courses canceled or delivered with a significant degradation in quality. One reason for this is that a significant aspect of experiential learning relies on close mentorship and critique that often needs to be tailored to each project team. A second reason is that typical projects already often stretch the limits of the standard 4-person student team, requiring both intense dedication and highly effective collaboration. But both mentorship and collaboration become significantly harder in a remote setting, exacerbating an already fragile situation for both mentors and students.

This proposal explores the use of digitally-supported organizational structures for scaffolding experiential learning to enable greater scale and greater collaboration. Instead of organizing learning based on a sequence of topics, the idea is to organize learning after the workplace, with a project goal for the course decomposed into a hierarchy of small experiential roles representing smaller scopes of work. This structure makes it possible to create scaffolding that can be tailored to project needs, to coordinate robust collaboration between large teams of students, and to facilitate peer support and mentorship. In this project, we aim to build on a proof-of-concept to deliver a full course and to develop generalizable principles and patterns for creating a micro-role based curriculum.

Team:

  • David Lee, Engineering – Computational Media

 

 

2019-20

Open Call

Sustainability Analytics Model of Biorefineries for Algae (SAMBA): A Sensor-Based Approach for Visualization and Decision-Making

Algae biorefineries potentially can contribute to deep carbon reductions. But their sustainable infrastructure role is uncertain due to energy-intensive inputs that can lead to diverse sustainability outcomes. To address this critical knowledge gap, we propose to develop a smart decision-making tool based on carbon, energy, and water analytics called the Sustainability Analytics Model of Biorefineries for Algae (SAMBA). SAMBA will assimilate biorefinery sensor-based data through open-source software, with output visualizations tailored to a diverse range of processes, demonstrated through our industry partnership with Cellana, LLC.

Team:

  • Lead PI: Anne Kapuscinski, Social Sciences – Environmental Studies
  • Yihsu Chen, Engineering – Computer Science and Engineering
  • Elliot Campbell, Social Sciences – Environmental Studies.

Related Follow-on Funding:

 

Submersible, data-driven lab-on-a-chip for real-time monitoring of water quality

Critical water monitoring systems currently lack continuous monitoring, such as that used in aircraft, and water-related disease and contamination are increasingly reported every year. This project seeks to open new avenues of research in chemistry and life sciences by combining artificial intelligence and advanced sensor technology. We will combine nanotechnology, plasma-enhanced surface modification, and suppression technique with low-noise, low-power, and high-speed circuits and computation capabilities to develop a platform for real-time monitoring of the water quality.

Team:

  • Lead PI: Shiva Abbaszadeh, BSOE – ECE
  • Jin Zhang, PBSci – Chemistry & Biochemistry

 

Uses and Abuses of Data and Learning Analytics for Higher Education

Through a year-long Fellows program and a conference, we will explore issues related to the increasing use of data and predictive analytics in higher education in general, and UC/UCSC instructional settings in particular. We aim to increase awareness about and generate solutions for the ethical and practical implications associated with the use of big data, artificial intelligence, and learning analytics in instructional settings. We will host a Fellows program and conference as well as conduct a companion research project focused on understanding the role of data and learning analytics from the faculty and staff perspectives.

Team:

  • Lead PI: Abel Rodriguez, BSOE – Statistics
  • Jody Greene, Humanities – Literature
  • Rebecca London, Social Sciences – Sociology

 

Diversion and Recovery of PLA Plastic from the UC Santa Cruz Waste Stream

This project seeks to divert polylactic acid (PLA) plastic from recycling and waste streams at UC Santa Cruz. PLA is now a common food and drink container but is not recyclable and in fact becomes a contaminant when mixed with other thermoplastics. Our multidisciplinary research team will use a combination of chemistry, materials science, computer science and social science to address this growing, expensive problem. The end goal is to divert PLA from landfills, through monitoring and analysis, as well as the development of techniques to de-polymerize or prepare for extrusion to filament feedstock, which will be tested on a dedicated 3D printer. Market analysis will determine the economy of scale required to produce and sell filament below market price to users across the UC system, while providing a future revenue stream for the project.

Team:

  • Lead PI: Scott Oliver, PBSci – Chemistry & Biochemistry
  • Narges Norouzi, BSOE – Computer Science and Engineering
  • Tamara Ball, Social Sciences – Institute for Scientist & Engineer Educators