is a Â鶹ӳ»­´«Ă˝ (Â鶹ӳ»­´«Ă˝) non-profit research facility, with a STEAM-focused approach toward prosthetics. The philosophy has led to a program devoted to beautiful and functional electromyographic bionic limbs for children which are currently being evaluated through clinical trial research.

This semester 40 students are interning at Limbitless. They all bring their own talents based on their fields of study while learning to work as a team and gaining skills outside their area of study. The research environment blends engineering, art and communication with innovative tech, including the prosthetic arms for children. Part of the process of getting children ready for Limbitless prosthetics involves preparing their muscles for the kind of work required to use the prosthetics. That’s accomplished through using a video game controller and special EMG-based video games designed at Limbitless Solutions.

In 2016, Limbitless and Â鶹ӳ»­´«Ă˝ faculty members Matt Dombrowski ’08MFA with and Peter Smith ’05MS ’12PhD with created video games to train children’s muscles in anticipation of receiving bionic arms.

The student research team presenting evaluates the effectiveness and usability of a custom EMG video game controller and the game mode used by the children between pre- and post-tests. The study focuses on the mobile video game, Limbitless Runner, developed in-house and now available in app stores.

The findings of the study will assess the influence of using focused training games with the EMG controller to teach Limbitless’ bionic kids how to use their prosthetic.

“My time at Limbitless has been filled with a variety of different learning experiences, each of which has brought me closer to my peers and pushes me to become more and more passionate about our main goal: supporting our bionic kids,” says Calvin MacDonald, one of the team members presenting at Research Week. He is a 20-year-old sophomore from Melbourne Beach studying health sciences.

Other team members are Shea McLinden (health sciences), Devon Lynn (biomedical sciences), Katherine Tran (health sciences) and Kelsey Robinson (statistics).

“This experience has sparked my interest in pursuing a career which incorporates healthcare, as well as clinical research opportunities,” says McLinden who is in her junior year.

This same team also presented their work at the Florida Undergraduate Research Conference (FURC) in February.  is one of the nation’s largest multidisciplinary research conferences and is open to all Florida undergraduate students. This was the 11^th year of the conference and the first time held at Â鶹ӳ»­´«Ă˝.

The method, published recently in the journal , uses infrared light spectroscopy and can be used in a portable, tabletop device.

“Fentanyl is a leading cause of drug overdose death in the U.S.,” says Mengyu Xu, an assistant professor in Â鶹ӳ»­´«Ă˝â€™s and the study’s lead author. “It and its derivatives have a low lethal dose and may lead to death of the user, could pose hazards for first responders and even be weaponized in an aerosol.”

Fentanyl, which is 50 to 100 times more potent than morphine according to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes made and used illegally.

Subith Vasu, an associate professor in Â鶹ӳ»­´«Ă˝â€™s , co-led the study.

He says that rapid identification methods of both known and emerging opioid fentanyl substances can aid in the safety of law enforcement and military personnel who must minimize their contact with the substances.

“This AI algorithm will be used in a detection device we are building for the Defense Advanced Research Projects Agency,” Vasu says.

For the study, the researchers used a national organic-molecules database to identify molecules that have at least one of the functional groups found in the parent compound fentanyl. From that data, they constructed machine-learning algorithms to identify those molecules based on their infrared spectral properties. Then they tested the accuracy of the algorithms. The AI method had a 92.5 percent accuracy rate for correctly identifying molecules related to fentanyl.

Xu says this is the first time a systematicalanalysis has been conducted that identifies the fentanyl-related functional groups from infrared spectral data and uses tools of machine learning and statistical analysis.

Study co-author Chun-Hung Wang is a postdoctoral scholar in Â鶹ӳ»­´«Ă˝â€™s and helped study the compounds’ spectral properties. He says identifying fentanyls is difficult as there are numerous formulations of analogues of fentanyl and carfentanil.

Artem Masunov, a co-author and an associate professor in Â鶹ӳ»­´«Ă˝â€™s NanoScience Technology Center and , investigated the functional groups that are common to the chemical structures of fentanyl and its analogues.

He says that despite differences in the analogues, they have common functional groups, which are structural similarities that enable the compounds to bind to receptors within the body and perform a similar function.

Anthony Terracciano, study co-author and a research engineer in Â鶹ӳ»­´«Ă˝â€™s Department of Mechanical and Aerospace Engineering, worked with Wang to examine the infrared spectra properties. He says profiling and analysis of infrared spectra is rapid, highly accurate, and can be done with a tabletop device.

The current research used infrared spectral data from compounds in gas form, but the researchers are working on a similar study to use machine-learning to detect fentanyl and its derivatives in powder form. The product of the technology is expected to be mature for practical on-site rapid identification by 2021.

Xu received her doctorate in statistics from the University of Chicago and joined Â鶹ӳ»­´«Ă˝â€™s Department of Statistics and Data Science, which is part of Â鶹ӳ»­´«Ă˝â€™s , in 2016.

Before joining Â鶹ӳ»­´«Ă˝â€™s Department of Mechanical and Aerospace Engineering, part of Â鶹ӳ»­´«Ă˝â€™s College of Engineering and Computer Science, in 2012, Vasu was a postdoctoral researcher at Sandia National Laboratory. He earned his doctorate from Stanford University in 2010. He is a member of the at Â鶹ӳ»­´«Ă˝, is an associate fellow of the  American Institute of Aeronautics and Astronautics and a member of the International Energy Agency’s Task Team on Energy. Vasu is a recipient of DARPA’s Director’s Fellowship, DARPA Young Faculty Award, a young investigator grant from the Defense Threat Reduction Agency, an American Chemical Society’s Doctoral New Investigator award, American Society of Mechanical Engineers’ Dilip Ballal Early Career Award, and the Society of Automotive Engineers SAE Ralph R. Teetor Educational Award. He has received many of the highest honors at Â鶹ӳ»­´«Ă˝ including the Â鶹ӳ»­´«Ă˝ Luminary and Reach for the Stars awards.

The researchers from the Departments of Statistics and Data Science and Computer Science caution, however, that their projections — built using the latest artificial intelligence and deep-learning models — don’t account for variables like the NBA relocating to Orlando, schools reopening in August or tourists visiting Orange County.

“The current predictions are based on the data to date, and the future may change,” says Shunpu Zhang, professor and chair of the Department of Statistics and Data Science, who worked on the project along with Associate Professor of Computer Science Liqiang Wang and graduate student Dongdong Wang.

The trio developed the projections by feeding data from Johns Hopkins University and The New York Times into 10 different compartmental models informed by 10 deep neural networks. Each deep neural network was trained with about 50,000 simulations from classic epidemic mechanistic models, including SIR and SEIR, both widely accepted by epidemiologists. The resulting models include the variables to help policy makers see the best-case and worst-case scenarios.

Based on the observation data available through July 22, those scenarios include:

• The daily increase of COVID-19 positive cases will begin to slow around early August for the U.S. and the end of July for Orange County.
• The maximum infection rate is expected to be approximately 12 million for the U.S., 1.6 million for Florida and 70,000 for Orange County.
• The number of ICU beds occupied by COVID-19 patients will peak in early August for the U.S. and Florida, end of July in Orange County.
• There is a strong correlation between Florida and the U.S., indicating the state plays a prominent role in informing national policy. Similarly, Orange County tracks Florida’s numbers.
• The researchers also explored a hypothetical situation that extended Florida’s shutdown, instead of opening up into Phase 2 in early June. Charts show a dramatic rise in positive cases almost immediately after reopening began and the numbers tell the same story.
• The projected ultimate total infection rate tripled in the U.S. from 4 million to 12 million; expanded 16 times in Florida, from 100,000 to 1.6 million; and jumped 35 times in Orange County, from 2,000 to 70,000.
• More detailed and latest results can be found at .

Charting COVID-19’s rise and fall took an innovative modeling approach. The novelty of the virus and its corresponding limited data would typically call for a physics-based analysis, Zhang says. The challenge of this approach is its dependence on accurate calibration.

“However, this computational difficulty can be easily resolved by deep learning,” Wang says.

Following guidance from Zhang and Wang, Dongdong Wang developed an approach by blending compartmental model and deep learning to more efficiently and accurately fit observed data and generate more reliable infection trajectory.

“Our method is flexible, and could be generalized into a variety of combinations,” said Zhang.

Â鶹ӳ»­´«Ă˝ launched an Artificial Intelligence and Big Data Initiative in June to position the university as a preeminent leader in the data science industry. The 23-member panel’s recommendations will form a roadmap toward that goal.