Illinois Mathematics and Science Academy
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Family Reading Night 2025
No full texthttps://digitalcommons.imsa.edu/frn_images_2025/1017/thumbnail.jp
Family Reading Night 2025
No full texthttps://digitalcommons.imsa.edu/frn_images_2025/1001/thumbnail.jp
Family Readign Night 2025
No full texthttps://digitalcommons.imsa.edu/frn_images_2025/1023/thumbnail.jp
The HBCU Experience: Why I Chose My HBCU and How It Helped Me Prepare for My Career
Get PDFPanel/Event Coordinator: Angela Richardson
Moderated by: Dr. Anita White
Panelists: Justin Alfred : Southern University and A&M College, Baton Rouge. Civil Engineering / Computer science. Lockheed Martin. Joseph Bertrand ‘20 : Senior at North Carolina Agricultural and Technical State University, Greensboro, NC. Mechanical Engineering. Darryl Burrell Jr. : Morgan State University, Baltimore, MD.Civil Engineering. City of Houston,Houston Public Works. Dr. Garry J. Kennebrew Jr. ‘04 : Morehouse College, Atlanta BS Biology Loyola University Chicago Stritch School of Medicine.Doctorate in Medicine. Jaida Lewis ‘15 : Texas Southern University Chemistry. Owner, Poised Lips by Jai Program Coordinator, CVS Health. Ja’Nae McGee : Southern University and A&M College, Baton Rouge. Mechanical Engineering.DOW Chemical Company. Zoe Mitchell ‘19 : Xavier University,New Orleans.Computer Science.Software Engineer for Block Inc. Joseph Pollard : Tuskegee University, Tuskegee, Alabama. BS Physics/BS Mechanical Engineering. Product Security & Penetration. Tester at Emerson Electric. Tony Richardson II : Southern University and A&M College, Baton Rouge Mechanical Engineering. The Boeing Company. Jayda Yancy ‘19 : North Carolina Agricultural & Technical State University, Greensboro, NC Chemistry. Process Chemist at Eli Lilly and Company.https://digitalcommons.imsa.edu/dei_panels/1004/thumbnail.jp
Understanding Entanglement for Quantum States
No full textEntanglement is a fundamental concept in quantum mechanics that describes when two particles are intrinsically correlated and that one of the particles cannot be described without the others. The phenomenon of entanglement is not well understood by physicists. However, we may be able to better understand its behaviors by quantifying entanglement through entanglement measures. Entanglement measures aim to quantify the degree of entanglement, which are particularly useful when considering mixed states. In our study, we studied two entanglement measures in particular: entanglement entropy, which measures the number of Bell pairs to either create or can be extracted from the given state, and geometric measure of entanglement, which calculates the minimum distance from a given state to the set of separable states. Using the Python library QuTiP, we have implemented these two methods and analyzed the results of inputting different types of quantum states
Reducing the Load of the Haptic Brain Stimulator
No full textWe engineer a haptic device that renders touch and force-feedback across the user’s entire body by stimulating the brain. Our technique builds upon transcranial magnetic stimulation (TMS), a neuroscience technology that non-invasively stimulates the brain using an electromagnetic coil. Medical-grade TMS coils are typically large, handheld devices, which makes integrating them into an actuated system challenging. Our technical contribution is to provide a novel robotic platform that precisely actuates the coil towards the target brain areas while ensuring user comfort and wearability. We explore methods to alleviate the load on the user, utilizing a supporting backpack or a hanging counterweight. By employing a lighter TMS coil, reducing the degrees of freedom of the actuator, using primarily laser-cut acrylic and FDM printed parts, and separating the electronics from the headset with Bowden cables, we have constructed a lighter and more comfortable device
Testing the Type 2 Diabetes Risk Prediction Efficacy of a Synthetically Trained Machine Learning Model
No full textSeveral machine learning models trained on electronic health records (EHR) data have been able to predict risk for Type 2 Diabetes accurately, but the efficacy in risk prediction for models trained on synthetic genotype data remains to be tested extensively. Using data gathered from Genome-Wide
Association Studies (GWAS) analyses, we identified several genes correlated with Type 2 Diabetes, each with hundreds of single nucleotide polymorphisms (SNPs). We are currently generating synthetic genotype data based on the GWAS summary statistic results and using it to train a supervised machine learning model. We will compare the accuracy of the risk prediction generated by our synthetically trained model with PrimeT2D, a model trained on EHR data. Synthetically trained models have more accessible data and can thus assist or even replace existing models that predict the risk for Type 2 Diabetes in patients if consistently found to be more accurate