Category: CRG Stories

Taking Out the Trash Takes on New Meaning for Geoenvironmental Engineering Team

A growing concern among jurisdictions across the U.S. is the increasing amount of municipal solid waste entering treatment plants — generally comprised of and categorized as food, green, dry, and recycling. One simple option to reduce the stress on waste treatment facilities is to pre-sort the materials, which also reduces the amount of waste going to landfills. Although inexpensive and effective, this route critically depends on residents’ participation — an element that can be difficult to assure in order to achieve a successful outcome.

George Mason’s Kuo Tian and Ran Ji wanted to tackle this challenge. Tian specializes in geoenvironmental engineering and Ji’s expertise is in system engineering. Their proposal to 4-VA@Mason focused on developing a decision support framework integrating the residential waste sorting process and real-time collection demand information.

The project team aimed to incorporate a range of interdisciplinary knowledge in civil engineering, data analytics, and operations research. Their first objective was to build a database including publicly-available socio-demographic and economic information from the Environmental Protection Agency and the U.S. Census Bureau, which would provide logistical support to put the system into action. Secondly, they were interested in creating a learning-and-simulation framework to accurately predict waste generation and community participation rates in waste sorting and recycling programs. To do so, it would be necessary to consider the range of elements effecting the entire waste transfer system.

As 50–80% of total waste management expenditures are based on collection and transportation, Ji and Tian proposed the development of an optimization model to incorporate a myriad of important statistics. The model would include: staff and shift scheduling, vehicle routing and weight, truck capacity assignment, fleet size, service time windows considering traffic patterns, facility operation hours, and school/restaurant collection time requirements. The model also examined emissions released by the transportation sector. Finally under Tian’s microscope was the composition and weight variabilities associated with population density, waste generation rates, and local regulations; combined with family incomes, habits and customs, and seasonal changes. It was a tall order, but the team saw that the seed funding provided by 4-VA could provide the means to collect and capture this important information.

After receiving the grant approval, their next move was to set the theory into a real-world application. Tian and Ji connected with 4-VA colleague Weijun Xie, then in the Industrial & Systems Engineering Department at Virginia Tech. Tian selected graduate students Seyed Omid Hashemiamiri and Hanrui Zhao and undgrads Thu Le, and Kyle R Lowther to round out the research team.

Next, they worked with the Prince William County (Va.) Solid Waste Division to build the data-driven models to validate the results of the proposed decision support framework with practical data. Taking more than one year, the work was methodical and painstaking, but garnered important findings.

“Our research has achieved significant results in enhancing municipal solid waste management using a multidisciplinary approach,” says Tian. “This body of work represents a pivotal step toward smarter, more efficient, and sustainable waste management practices.”

The model has led to a publication in the Sustainability journal titled “An Integrated Location–Scheduling–Routing Framework for a Smart Municipal Solid Waste System” https://www.mdpi.com/2071-1050/15/10/7774. Upon the proposed model and approach, Hashemiamiri has further developed it into multi-layper multi-objective optimization framework for waste management, leading to a joiurnal manuscript “Multi-Objective Optimization for Sustainable Municipal Solid Waste Management Using Genetic Algorithms” (currently under review). This research also constituted a vital component for Hashemiamiri’s PhD dissertation.

Tian concludes, “Further and more complex development of this model is now underway, with the aim of submitting another paper to Waste Management, a top tier journal. Based on the proposed modeling and solution framework, the team has developed and submitted one proposal to USDA and now is working on another NSF proposal.”

George Mason and Virginia Tech 4-VA Team Studies Wastewater Treatment Systems

One of the most pressing issues in human and ecological health is the abundance of poly and perfluoroalkyl substances (PFAS) and phthalate esters (PAEs) in our ecosystem — two classes of synthetic chemicals known as ‘forever chemicals.’ In addition to their destructive nature affecting wildlife, soil, and agriculture, they are also responsible for causing human health problems such as liver damage, thyroid disease, obesity, fertility issues, and cancer.

Furst

While scientists worldwide are racing to learn more about how to combat PFAS and PAEs in a variety of settings, Kirin Emlet Furst, Assistant Professor in George Mason’s Civil, Environmental and Infrastructure Engineering Department, has been focused on PFAS AND PAEs in water treatment and wastewater reuse.

Furst reasoned that wastewater treatment facilities are a major avenue through which PFAS and PAEs can contaminate drinking water and air, as many of these compounds are insufficiently removed by common treatment processes. Furst specifically wanted to explore the air-water interface. PFAS have a high surface activity which results in their attraction to the air-water interface. And while PAEs have a lower surface activity, they might be attracted to other materials that accumulate at the air-water interface. Research in full-scale treatment systems was needed to understand these interactions.

The 4-VA@George Mason Advisory Board recognized the importance of this research and awarded Furst’s 4-VA proposal, “The role of the air-water interface in breakthrough of PFAS and phthalate esters during wastewater treatment.”

Joining Furst in the research was 4-VA partner Zhiwu (Drew) Wang, a specialist in wastewater treatment and biological processes at Virginia Tech. Furst also tapped Mason graduate student Meghana Kuppa who was already developing analytical methods to measure PFAS and PAEs. Ethan Gasper, an undergraduate in the Department of Chemistry and Biochemistry, assisted Kuppa with much of the bench work on the project.

Kuppa collected water samples and scum, which is the material that accumulates at the air-water interface, from process unit tanks at a wastewater treatment plant to measure the target contaminants and water quality parameters known to impact partitioning behavior. Their goal? Quantify the role of the air-water interface in enabling breakthrough of PFAS and PAEs in wastewater treatment facilities and identify potential engineering solutions.

Kuppa Selects Samples from Wastewater Treatment Facility

Although developing the complex methodologies for the project was a challenge, several important outcomes were realized. First, high levels of multiple PFAS were found in the scum from both the primary and secondary treatment processes. The team concluded that the PFAS levels in the primary scum samples, especially, were much higher than they could accurately measure due to interference from particulates and oily substances in the method. However, analysis of the secondary determined any PFAS present during secondary treatment is more likely to be found in the treated water and may also contaminate the facility air due to aeration in these tanks.

While fewer PAEs were found in the scum samples, Kuppa’s experiments show that phthalates can sorb to organic material in the scum. This sorption may contribute to the difficulty in removing phthalates during wastewater treatment.

Furst reflects on the research and the 4-VA funding noting, “The 4-VA award empowered my group to pursue this new line of research and helped to support Kuppa’s innovative thesis projects. Plus, now we have preliminary data to pursue NSF and other high-impact external funding.”

Examining the Consequences of Land Ownership in Rural Virginia

Van Sant

Assistant Professor Levi Van Sant’s work in George Mason’s School of Integrative Studies focuses on environmental (in)justice surrounding food, agriculture, and land use. Previously, Van Sant has analyzed how land ownership affects the ways that racial and class dynamics of the past are reproduced in the present, focusing on the coastal United States South. More recently, however, he was interested in the ramifications of land ownership closer to home — in “the backyard” of two 4-VA partner universities, George Mason and James Madison.

Van Sant wanted to apply an existing model which suggests that higher rates of absentee and corporate-owned timberland in rural Alabama are associated with lower quality-of-life indicators such as income and education. It has also been observed that large landowners hold disproportionate political and economic power in rural communities.

Van Sant wanted to examine land use and ownership in the Shenandoah Valley and Middlesex County in Virginia. He also saw an opportunity to provide students at both schools a chance to hone their analytical skills — as a first reading of land use and ownership records often only tell part of the story. He wanted the students to research the differences in data management between municipalities, recognize the difficulties in accessing information in rural counties, and understand how land ownership has repercussions for low-income and minoritized communities.

Van Sant applied for and received a 4-VA@Mason grant for this research and set his team to work. Jeremy Campbell, the Associate Director for Strategic Engagement at George Mason’s Institute for a Sustainable Earth; and Case Watkins, Assistant Professor in the Department of Justice Studies at James Madison agreed to volunteer their time to help coordinate the project.

The project centered on two partnerships with communities for whom patterns of land use and ownership are crucially important: small farmers in the Shenandoah Valley, which was overseen by Watkins, and the Indigenous Tribal Nations of the Middle Peninsula Region, which was facilitated by Campbell.

“We produced a large database of land ownership records for the two study regions in rural Virginia. From this database we created a series of maps that represent trends in land ownership across both study regions. We also compiled a set of maps to contextualize and present this work. The datasets and maps are significant resources for further analysis,” noted Van Sant. “This grant provided invaluable support for further developing our research methods; creating a solid dataset for on-going research; and, most importantly, building analysis and tools for future community engagement.”

Several students were involved with the project. George Mason graduate student Tyler Grant received funding for his work handling Geographic Information System analysis and data organization. Undergraduate students Yonna Angeles, Erin MacMonigle, and Jacquelyn Batchelor received funding as mapping assistants, as well as Tamar Gorgadze, who acted as a research assistant.

At JMU, three students also worked on the project — Gina Bigo analyzed land ownership trends, Madelynn Warren looked at county land use, and Ally Windham considered the historical overview and analysis.

George Mason and Virginia Tech Team Up to Improve Online Searches

Although algorithms can make online searches faster and easier, they can also be fraught with dangerous biases. Research has shown that image search engines can exhibit discrimination against females or people of color, and bias is also found in online searches in employment recruiting and the healthcare field.

While efforts have been made to unveil and tackle fairness and bias glitches in search and recommendation systems, two key issues have been largely overlooked: Existing research treats different types of bias in isolation, resulting in specialized methods that are difficult to generalize; and, they focus on bias in the static environment leaving the dynamic nature of the search and recommendation process unexplored.

Zhu

Ziwei Zhu, Assistant Professor in George Mason University’s College of Engineering and Computing, wanted to demystify the underlying correlation of different types of bias and develop new multi-task and graph learning algorithms to support fair and unbiased searches and recommendations. Using this information, he wanted to create and release open-source software on this subject for the research community, significantly advancing trustworthiness of AI techniques. Finally, Zhu was intent on ensuring the debias system would be sustained long term.

Zhu enlisted the help of 4-VA partner Dawei Zhou in Virginia Tech’s Department of Computer Science. Together, they responded to the 4-VA call for proposals, and, “Towards Consolidated and Dynamic Debiasing for Online Search and Recommendation” was approved for funding.

Joining the effort at George Mason was graduate student Jinhao Pan, who handled algorithm implementation and paper writing. Pan was supported by a team of Zhu’s student researchers (pictured below).The group began by developing an end-to-end adaptive local learning framework to provide recommendations to both mainstream and niche users.

Zhu sees the audience as other researchers focusing on fair and unbiased recommendations and searches, or practitioners — software developers and AI engineers — in the industry who want to improve the fairness and trust of their systems. To that end, Zhu’s group created a boosting-based framework designed to decrease a broad spectrum of biases. This framework employs a series of sub-models, each tailored for different users and item subgroups.

The results were impressive, with experiments demonstrating superior debiasing capabilities against state-of-the-art methods across four model bias types.

However, Zhu knew that their new framework for recognizing and removing biases would only be effective if implemented. To that end, the group made the algorithm implementation open source through various options — https://github.com/JP-25/end-To-end-Adaptive-Local-Leanring-TALL- and https://github.com/JP-25/CFBoost.

They also presented and published Combating Heterogeneous Model Biases in Recommendations via Boosting at the Association for Computing Machinery International Conference on Web Search and Data Mining. End-to-End Adaptive Local Learning for Alleviating Mainstream Bias in Collaborative Filtering was also presented and published at the European Conference on Information Retrieval.

In addition to the framework developed, the project increased collaboration between George Mason and Virginia Tech through coursework. The new algorithms have been integrated into materials of Mason’s undergraduate and graduate level Data Mining courses CS584 and CS484.

Zhu has used the outcome of this project as the foundation for a proposal submitted to the National Science Foundation Computer and Information Science and Engineering Core program.

Concludes Zhu, “This 4-VA grant helped me set up some computational resources so that I can conduct further research and supported travel to academic conferences to disseminate our research and learn from others. We believe this provided the groundwork for some very important first steps in this field.”

Bringing Context and Focus to the Music of Early Black Virginians

In higher education, “You don’t know what you don’t know” is often the axiom that spurs research to shine a light on a subject not thoroughly illuminated. For Emily Green, music historian and faculty member in George Mason University’s College of Visual and Performing Arts, this maxim served as a motivator to launch a 4-VA@Mason research project titled Music of Early Black Virginians. Green, whose previous research focused on music publishing and marketing in eighteenth- and nineteenth-century western Europe and America, became especially interested in the backstory of this area of music history while teaching Nineteenth-Century African American Music, a graduate course at Mason

Green recognized that much of this important music history needed to be cataloged and made publicly available. “It was a long-standing wish of mine to create a resource for educators to help them understand the variety of genres in Black-American music,” she explained. “K-12 music educators do not always have access to library databases or peer-reviewed journals. I wanted to create an open-access landing point to help teachers navigate the rich resources they can use to learn and quickly enhance their knowledge—and build lesson plans for students of a variety of ages.”To that end, Green sought the involvement of scholars in the field at several 4-VA partner schools to help her put the project into motion: Mary Caton Lingold at VCU and Bonnie Gordon at UVA. Both readily volunteered their time to bring this multi-level and multi-faceted research to fruition. Michael Nickens (a.k.a. Doc Nix — most recognized as the leader of George Mason University’s “Green Machine,”) also eagerly joined the team. Additionally, Maria Ryan, at Florida State University, came on to collaborate on the project.

4-VA funding provided UVA graduate students Laura Carrington OBrion and Sergio Manuel Silva and George Mason undergrads Crystal D. Williams and Jaelin Mitchell the time and space to build a website of nineteenth-century sources that reference Black-American music making: https://masonlibraries.gmu.edu/blackmusic/s/music-black-va/page/home.

From there, the team then published a wide-ranging website Early Black Music in Performance https://earlyblackmusic.4va.gmu.edu/.

A zoom conference helped introduce and promote the site: Presenters included 4-VA cohort, Kayondra Reid, music educator at Oak Street Elementary School in Falls Church, VA, and Tyler Diaz, Hunter College, NY. Artist Shodekeh Talifero also gave a presentation for the Dewberry School of Music entitled “Breaths along the Potomac.”

As an added bonus resulting from the effort, Green, Lingold, and Ryan have been contracted to develop a related anthology for Oxford University Press, Sources in Early Black Atlantic Music, which is expected to be published in 2026.

“Most music educators are not taught much about the variety of Black American music in bachelor’s or master’s degrees in our field. Between the database and the web resource, and soon the anthology, our hope is that educators can learn more about reliable print and online resources to use in the future,” says Green.

George Mason and Virginia Tech Collaboration Creates Connections in Science Policy

Today, Virginia is one step closer to bridging the gap between science and policy — a critical relationship necessary to navigate a range of socio-technical issues including climate change, biodiversity loss, pandemics, and poverty. Creating this connection was the result of a yearlong research project to identify U.S. and Virginia science policy programs which engage STEM-H scientists and engineers in science policy with the end result of helping both elected and non-elected officials access technical information to assist in decision making.

This work was completed thanks to a coalition of faculty and students supported though a 4-VA@Mason grant. The team was led by KL Akerlof in George Mason’s Department of Environmental Science & Policy and Todd Schenk, Chair and Associate Professor in the Urban Affairs and Planning Program of the School of Public and International Affairs at Virginia Tech and Director of the Science, Technology & Engineering in Policy Program.

To advance best practices for professional mentorship of early career researchers and to build capacity for training researchers to engage in public policy, the team developed a database of U.S. science policy programs and conducted a case study of those in Virginia through surveys and interviews with their leaders. Akerlof and Schenk were assisted in the study by Adriana Bankston, an expert in science policy, and Mason students Kelsey Mitchell, Kate Saylor, and Aniyah Syl, and Kenneth Dewberry at Virginia Tech.

Following the yearlong collaboration, the results—along with program blogs and a listserv—has been developed and is featured on a new website created to support this emerging network of science policy programs: https://scipolprograms.org/.

The results of their research showed that the majority (57%) of U.S. science policy programs are state based. These programs include student organizations, government placements and fellowships, and academic certificates, degrees, and other training. However, it was recognized that there is only a limited ability to implement evidence-based approaches within these programs.

The team compared programmatic outcomes from Virginia’s science policy programs with those described in academic literature. Academic and professional scholarship in science communication, and public affairs suggests that curricula for engaging scientists and engineers should broadly cover communication and policy processes.

In addition, they found that training programs would benefit from evaluation models and measures, although the lack of consistent theoretical foundations and constructs across this highly multidisciplinary scholarship reduces their utility. It was concluded that a common framework, which includes shared terms and relationships, is needed to promote the transdisciplinary growth of the field.

With the results of their analysis in hand, the team held a state webinar on science policy programs featuring 11 of the 13 Virginia program leaders. The project was also highlighted at a Virginia Sea Grant Symposium and was the topic of an American Geophysical Union e-lightning talk.

Bankston and Akerlof presenting research findings

While the results of the study proved important, the project also provided rich opportunities to the students participating in the effort. Undergraduate student Syl and doctoral candidate Mitchell helped lead the webinar and participated as co-authors on the publications. Further, Syl co-presented at the Virginia Sea Grant Symposium. Plus, master’s student Saylor successfully defended her thesis on the Commonwealth of Virginia Engineering and Science (COVES) Policy Fellowship. She subsequently presented her results to the President of the Virginia Academy of Science, Engineering, and Medicine, Dr. James Aylor.

The first of two research articles based on the project has just been accepted with minor revisions by the journal Evidence & Policy. A second article has also been submitted for publication. They are also in conversation with the leadership of the Virginia Academy of Science, Engineering, and Medicine about the development of a new weeklong science policy program for undergraduate students from across the Commonwealth’s public universities.

Akerlof reflects on receiving the 4-VA@Mason award, “This funding supported our cross-institutional collaboration and ability to conduct baseline research and networking that have been fundamental to understanding how the landscape of science policy training programs is evolving across the United States.”

Following a Slow Start, 4-VA@Mason Research on Species Resilience Produces Landmark Results

Associate Professor David Luther, who has spent the last 14 years in George Mason’s Biology Department studying ecology, evolution, and conservation, recognizes the importance of playing the long game in research and education. Great outcomes don’t happen overnight. But even Luther couldn’t have imagined the hurdles and roadblocks ahead of him following 4-VA@Mason’s approval of his 2019 Collaborative Research Grant proposal “Species richness resilience to habitat fragmentation and restoration in tropical rainforests.”

Luther’s vision was to document and measure differences, using audio and video devices, in the animal community composition and the rate of recovery of animals in secondary forest and forest fragments – areas where contiguous forested areas are broken into smaller forest patches, separated by barriers such as roads, agriculture, or utility corridors. His plan was to install recording equipment at 50 sites as part of the Biological Dynamics of Forest Fragments Project (BDFFP) in the Amazon rainforest of Brazil. Luther paved the way for this project by connecting with the Brazilian National Institute of Amazonian Research (INPA), an Amazon research institution based in Manaus.

The proposed budget was entirely devoted to purchasing the wide array of materials necessary for the effort — cameras, acoustic recorders, batteries, and other supplies — along with the international travel needed to bring the project to fruition. Luther then assembled a team of faculty and student research volunteers at 4-VA partner schools and on the George Mason campus.

Just underway in 2019, all efforts came to a complete halt in March 2020 as Covid-19 struck worldwide. Luther faced a myriad of challenges: the inability to travel to Brazil and enter the field site; students selected for the research had to pivot to new endeavors which would allow them to graduate while studying remotely; and partner schools needed to move on to other projects during what would be the two-year waiting period. What’s more, one of the key members of the planning team, George Mason’s Tom Lovejoy, passed away in December of 2021. Lovejoy was recognized as one the world’s leading conservation biologists and often referred to as the “godfather of biodiversity.” In his passing, Luther lost a critical member of the team and a mentor.

However, Luther stayed the course, revamped his team, re-wrote the schedule, and maintained his commitment to get the project moving forward as soon as possible. Finally, in June 2022, he received the green light to move ahead. Between June and October 2022, 136 cameras and 81 acoustic devices were installed across 50 sites at BDFFP.

Today, to Luther’s great delight, the results have proved far more successful than he could have ever anticipated. Tens of thousands of animal images from camera traps and audio recordings have already been collected.

To analyze the data, Luther built a team of 15 George Mason undergraduate researchers, artificial intelligence experts, and a non-profit organization (Arbimon) that specializes in analyzing acoustic recordings from the tropics to help identify animals.

In the fall of 2022, Luther mentored student researchers to help with the endeavor. Aline Medeiros, a PhD student in Environmental Science and Policy (ESP), helped manage the undergraduate researchers working on the audio files. Volunteer students on this project were Alexis Lembke, Amanda Jones, Adriana Em, Madison Cheung, Morgan Ellingsworth, and Grace Carriero. Medeiros will also use the captured data as the basis of her PhD research.

Another set of students helped identify animals in the camera images and entered that information into a large database. Hibo Hassan, Jordan Seidmeyer, Katie Russell, Carolian Sanabria, Adrian Em, Alix Upchurch, Piper Robinson, Tristan Silva-Montoya, and Estefany Umana spent hours creating this treasure trove of records. Emilia Roberts, a MS student in ESP, managed these undergraduate researchers.

Explains Luther, “For the acoustic recordings, we built templates for 250 bird species, and trained AI models to automatically detect and classify songs for each. We have already detected 201 of the 250 species. The model performed very well in our evaluations, achieving an average precision across all classes of 0.94. Thanks to our model, new recordings can be passed through it to automatically detect species calls, facilitating long-term monitoring and efficient analyses moving forward. We are now working with local experts in Manaus, Brazil to apply the same platform for frogs at our study sites in the Amazon rainforest.”

The biodiversity data of birds and mammals is being used to assess how each species responds to variations in forest structure and recovery from forest fragmentation. Luther brought on Konrad Wessels from George Mason’s Geography & Geoinformation Science Department to assist with satellite information from the Global Ecosystem Dynamics Investigation instrument (GEDI). GEDI uses high resolution lasers to provide detail in three-dimensional forest structure. The GEDI results will build predictive models looking at how the three-dimensional forest structure can forecast mammal and bird diversity and individual species occurrence in tropical rainforests. In an important finding, the team has determined that the diversity of three-dimensional forest structure heights and density of foliage is the biggest predictor of mammal and bird diversity.

The project continues to gain traction. The team has created a website featuring the results of the acoustic portion of the research, https://bio.rfcx.org/bdffp-acoustics, which has been very well received.

In addition, some of the acoustic training models were used by teams competing for the X-Prize, a competition designed to encourage technological developments supporting “radical breakthroughs for the benefit of humanity.”

Luther also applied for and received a $200,000 National Science Foundation grant which built off of the 4-VA funded study and is being used in part to continue both the camera and acoustic research. Luther and Wessels recently submitted a grant to NASA to expand on the research findings and apply them to the entirety of the Amazon basin.

Concludes Luther, “Through 4-VA@Mason, this project is up, running, and delivering fantastic information that will help scientists worldwide better design monitoring schemes for biodiversity in remote tropical forests, as well as those interested the relationship between habitat structure and degradation and species resilience to disturbance. The grant helped us get to the first step, and we are confident this project will continue to expand in the future with our excellent Brazilian collaborators, current NSF funding, and other future external funding.”

George Mason Team Identifies Technology to Enhance Artificial Photosynthesis

When a 4-VA Collaborative Research Grant results in the production of a novel concept for technology solutions to support energy and climate issues, while also sharing resources and data between higher education institutions in Virginia and providing faculty and student research opportunities, it is another win for the program.

This was achieved following 4-VA’s approval of a proposal by George Mason’s Yun Yu, an Assistant Professor in Chemistry and Biochemistry Department, for a grant entitled Nanoscale Visualization of Electrocatalytic Carbon Dioxide Reduction Activity at Cu Nanocatalysts. Yu’s goal was to investigate options in catalytic electrode materials to improve and enhance electrocatalysis, a process essential for harnessing sustainable energy sources for artificial photosynthesis. While nanostructures are currently recognized as the most successful catalyst for many chemical reactions, there is more to understand about tailoring their crystalline planes to improve activity and selectivity.

Yu wanted to gain deeper insights into various nanocatalysts used in carbon removal technologies. The conventional approach to conducting this study often involves measuring the entire catalyst, composed of numerous small particles with varying sizes and shapes. However, critical information, such as the impact of heterogeneities on performance, is often lost in such ensemble measurements. Yu saw the potential for leveraging the the nanoscale scanning electrochemical microscopy on the George Mason campus to obtain detailed surface reactivity maps of nanocatalysts. However, to do so, Yu needed to acquire shape-controlled nanostructures, including copper nanowires, copper nanocubes, and nickel–iron layered nanosheets. He did so through a partnership with Sen Zhang, Associate Professor of Chemistry at UVA.

Yu’s team, graduate student Dan Tran and undergraduate students Solyip Kim, Melissa Nguyen, and Mackenzie Dickinson played a key role in the project, receiving funding and real-world research experience. Together, they identified furfural reduction, an important reaction for sustainable biofuel generation. They noted a distinct contrast in activity between copper and graphite support. “These preliminary experiments have demonstrated the viability of our scanning electrochemical technique in spatially resolving catalytic activity across nanoscopic structures,” explains Yu. They further expanded the application to the study of nickel–iron catalysts. “Our data suggested that adding trace amount of cerium oxide to the catalysts significantly enhances water oxidation activity. We would not have these insights without this powerful electroanalytical technique.” says Yu.

The initial results have provided Yu with a springboard to develop external grant proposals to systematically study the role of cerium oxide and quantify the effects of its loading on the apparent catalytic activity of the developed catalysts. “This 4-VA opportunity allowed us to create a partnership with UVA, create a team to implement further investigation via George Mason’s nanoscale scanning electrochemical microscopy, and now apply for further funding to move this project forward,” concludes Yu.

Pictured in Featured Image: Graduate student Dan Tran operating the scanning electrochemical microscope.

Researchers Develop Computational Models to Support Successful Organization of Local Events

As illustrated in Robert Putnam’s renowned book “Bowling Alone: The Collapse and Revival of American Community,” Americans have become increasingly isolated over the decades, often spending leisure time alone without social gatherings. During the COVID-19 pandemic, this issue of isolation was exacerbated, calling further attention to the public health crisis of loneliness and isolation in the United States.

To help encourage in-person gatherings, Event-Based Social Networks (EBSNs), such as Meetup.com and Facebook Events, have become an increasingly vital tool for facilitating these occasions based on shared interests — ranging from farmers’ markets to game nights. To maximize the effectiveness of EBSNs, a group of Mason faculty members with interests in community engagement, machine learning, and geographical data analysis wanted to take a closer look at how these arranged local gatherings fluctuated depending on community and group characteristics. They were able to undertake this analysis following the approval of their 4-VA@Mason Collaborative Research Grant proposal entitled “AI for AI: Toward Community-level Human-AI Collaborations in Local Meetups.”

Led by Myeong Lee, Mason’s Assistant Professor of Information Science and the Director of the Community Informatics Lab, the researchers also included former College of Science faculty members Olga Gkountouna, who assisted with machine learning model development, and Ron Mahabir who provided insight on geographical data analysis. Amr Hilal of Virginia Tech helped with data analytics from a machine learning perspective.

While it is known that EBSN users’ participation in Meetup events are influenced by group organizers’ promotions and event frequency, the effects of ecological factors, such as the number of similar groups surrounding a Meetup group, had not been previously studied. The goals of the project were to quantitatively examine how EBSN groups’ ecological features shape the performances of Meetup groups within that organizational ecology. They also wanted to create baseline benchmarks for how state-of-the art AI technologies can predict Meetup groups’ success.

To do so, the team conducted two studies of Meetup data for 500 cities in the US, extracting factors pertaining to “Meetup niches,” which considers similar groups surrounding a Meetup location.

The results revealed intriguing patterns, one of which was that if a Meetup group’s description resembles other groups in their geographical area, it tends to attract more participants. In a second finding, the team implemented three advanced machine learning models to predict the success of local Meetup groups, finding that the performances of these prediction models vary across different categories and cities, with some outperforming the state-of-the-art models.

“Overall, our research during the 4-VA project period will provide a basis for understanding human-AI collaboration at the community level by revealing how various factors shape and predict the success of local groups,” says Lee.

Lee credits the success of their findings to a strong team of student researchers, including graduate students Julia Hsin-Ping Hsu who worked on developing deep learning models and ecological features and Ishana Shinde who assisted in calculating community-level features. Undergraduates Victoria Gonzales focused on descriptive statistics of variables; Joel Adeniji managed visualization; and Nnamdi Ojibe handled data cleaning and geographical data aggregation.

The group is now disseminating their findings in the field – one study was published at the International Conference on Communities and Technologies (C&T), and the other is under submission to a premier journal. Lee is planning to write an external NSF grant using the preliminary results from the research, proposing the curation of Meetup-based social gathering data with the promising community-level ecological factors.

“The 4-VA@Mason grant significantly helped me and my team jump-start the project and develop the research studies,” says Lee. “What’s more, it allowed the team to connect with researchers outside of Mason to discuss additional meaningful community-based topics, thus broadening our future possibilities.”

4-VA Team Applies Novel Technology to Functional Magnetic Resonance Imaging

Incorporating control-theoretic methods into neuroscientific research was the interest that brought together Xuan Wang, Assistant Professor in Mason’s Electrical and Computer Engineering, and Mainak Patel, Assistant Professor of Mathematics at William and Mary. Supported by a 4-VA grant, the two wanted to look closer at adapting cutting edge technology in functional magnetic resonance imaging (fMRI) to create a new approach to facilitate the prediction and regulation of the firing rate dynamics of brain neurons. The real-world application of this research is to facilitate brain disease treatment, such as epilepsy, and brain-computer interface.

“As a result of this project, we have developed two network models, a firing rate dynamics model describing the microscale neuronal activities of the brain; and another to measure the small changes in blood flow that occur with brain activity,” explains Wang. “We have also created an effective data-driven algorithm that can reconstruct and predict the rate and fMRI dynamics of the brain.”

Wang and Patel received human brain fMRI data from United States Naval Academy through Assistant Professor Duy Duong-Tran and support from Li Shen, Professor of Informatics in Biostatistics and Epidemiology at the University of Pennsylvania.

Results of the research have been publicly shared via two abstracts at the Organization for Human Brain Mapping conference. Follow-up work submitted to the 2024 Medical Image Computing and Computer-Assisted Intervention Conference is currently under review. Another paper on the project was submitted to the Institute of Electrical and Electronics Engineers Transaction on Automatic Control and is currently being considered for publication.

Graduate student Muhammad Umair (left), who gathered and processed fMRI and firing rate data for the research, won first place at the College of Engineering and Computing Innovation Week at Mason with a poster titled ‘Subject and Task Fingerprint using Dynamic Reconstruction from fMRI Time-series Data’.

Based on the results of the 4-VA project, Shen, Duong-Tran, and Wang are currently preparing a National Science Foundation grant proposal for more extensive research.

“Thanks to the seed funding from 4-VA, my collaborators were able to jump-start our research. We successfully validated preliminary hypotheses and will now leverage our findings further. Currently, we are in the process of applying for larger grants to sustain and expand our efforts on this topic,” adds Wang.