Illinois Mathematics and Science Academy
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Family Reading Night 2025
No full texthttps://digitalcommons.imsa.edu/frn_images_2025/1027/thumbnail.jp
Hispanic heritage month 2025
No full textSee, I believe there are windows of hope in the present darkness of our current reality. As members of the IMSA community, we have a responsibility to find and open these windows of hope or build them if we must. -Convocation, 2021
Read-In Order
Opening Video Dr. Torres\u27s Retirement Video
Land Acknowledgement Rodrigo Sanchez
Intro to Hispanic Heritage Month Student Hosts
Remembrance Bio Student Hosts
Tribute Performance Poetic Justice
Introduction to Keynote Student Hosts
Keynote Remarks Ana Lizza Arroyo
Honor Dr. Torres\u27s Memory IMSA Community Members Past and Present
Community Readings IMSA Communityhttps://digitalcommons.imsa.edu/dei_readins/1028/thumbnail.jp
Getting Students to Do Authentic Academic Research
Get PDFMost classroom experiences bear little resemblance to actual research, and aspiring scientists and other researchers have no exposure to authentic academic work, in many cases, until graduate school. Giving high school students genuine research experience is possible and helpful for their understanding. This session will share lessons learned from IMSA\u27s research programs and discuss ways, both big and small, that teachers and their schools can get students involved in real research
Identification of effects of Matricaria chamomilla essential oil against bacteria
No full textModern medicine has been seen to cause a development of resistance after time of usage, this ineffectiveness causes stronger doses and the need for a change in antibiotics. Due to this problem, the development of more antibiotics has become crucial despite its difficult process to create and execute. Natural products have been noted to have much less resistance development over time from their usage despite dating back to ancient times. This project aims to test the antibacterial properties that are contained in the natural product of Matricaria Recutita through the creation of essential oil from ground-dried chamomile and the comparison to the store-bought counterpart compared to the current antibiotic treatment to identify the effectiveness in bacteria and its comparison to modern antibacterial medicine. Then testing the minimum inhibitory concentration to identify to what intensity this treatment must be used. The results will allow the possibility of natural products to be incorporated into modern medicine to fight bacteria
Developing a Web Platform on Wastewater Surveillance data for Public Health
No full textAs research in the health and medicine field continues to evolve, public healthsurveillance is a way to monitor the spread of diseases within communities. Providing data to the public can reduce disease transmission and support informed decision-making.This project focuses on analyzing and visualizing wastewater data to track trends of COVID-19, influenza A & B, and RSV across approximately 80 locations in Illinois. By leveraging an open-access public database, the project ensures privacy by aggregating data at the community level.The primary objective is to develop an interactive website that is updated periodically to provide the general public and public health officials with wastewater surveillance data. This platform will feature statistical modeling and data visualization techniques to illustrate trends in pathogen levels over time and identify regional similarities and disparities.
The long-term goal of this work is to develop a cost-efficient, privacy-preserving tool that increases community awareness and supports data-driven public health interventions.
Future improvements will focus on enhancing multi-location mapping, optimizing data processing, and ensuring user-friendly access to critical information an analysis
Using Neural Networks to solve the Burgers Equation
No full textPartial Differential Equations (PDEs) are differential equations that have multiple variables and one or more of their partial derivatives. However, this property makes writing explicit solutions for PDEs often impossible, and solutions such as numerical solvers can be expensive. One such example is the Burgers Equation, which models the behavior of viscous fluids, and has real world applications such as modeling turbulence. Neural Networks (NNs) are powerful machine learning models that simulate biological neural networks in animals with artificial neurons. NNs are capable of learning relationships from existing data, potentially providing fast and inexpensive solutions to PDEs.
This project seeks to create a NN to solve the Burgers Equation for any spatial location and time given an initial condition. Using a PDEBench dataset with solution fields to the Burgers Equation, we created tensors for the training and evaluation of our model. Using the PyTorch library, a neural network with linear transformations and ReLU activation layers was created. After training the model, the true values were plotted against the predicted values, where a perfect linear fit meant the model was 100% accurate in finding solutions. Overall, the model was effective, showing a relatively linear relationship between the true and predicted values
The Effects of Orai1 Channel Deletion in the Microglial Morphology of Mice
No full textMicroglia, the brain’s resident macrophages, shift between surveillance and activation in response to neuroinflammation. Previous studies have shown that Orai1, a key calcium channel, plays a critical role in regulating microglia function. Microglia morphology is directly related to function, typically more ramified in surveillant state and more amoeboid when activated. We hypothesized that the deletion of the Orai1 channel in microglia could alter the morphology of the cells. To address this, our study employed Orai1fl/flCx3CR1-Cre/ERT2 knockout mice (Orai1-KO) for microglia depletion or Orai1fl/fl (control) to investigate how Orai1 influences microglial morphology. Using fluorescent immunohistochemistry targeting Iba1 (a marker for microglia), we visualized microglial morphology in the prefrontal cortex, and performed a FIJI- based skeleton analysis. Regarding the ramification of the cells we observed a decrease in the endpoints per cell in the Orai1-KO, with no changes in process length. We also observed an increase in soma circularity with no changes in soma area. These results suggest that the Orai1- KO microglia are slightly less complex than the control group, which could indicate structural changes possibly due to lack of calcium. Further studies are needed to confirm this. Together, these findings provide insight on Orai1’s role in microglia morphology changes
Towards Understanding Large Language Models for Multilingual Semantic Encoding
No full textNatural Language Processing (NLP) has witnessed significant advancements with the emergence of large language models (LLM) capable of understanding and generating human-like text. However, there remains a critical need to explore and understand their efficiency and effectiveness, especially in processing languages beyond English. This study aims to evaluate the efficiency of various large language models in capturing semantic meaning across English, German, and Spanish sentences. Principal Component Analysis (PCA) is utilized to identify important weights for understanding semantics. Through further experimentation with various sentence structures, we aim to identify factors contributing to the effectiveness of certain models. By pinpointing the strengths and weaknesses of different models, we aim to advance NLP research for multilingual applications
Studying the Integration Between Error Correction and Quantum Machine Learning at Google Quantum Al
No full textAs we continue advancing to the transition from classical to quantum computing, we face a significant challenge: quantum error. Quantum errors come from quantum gate imperfections and decoherence. Many quantum machine learning algorithms require multiple executions to attain a proper estimation due to the variability introduced by error. The Variational Quantum Eigensolver optimizes ansatz parameters to approximate the ground state of a Hamiltonian but error misclassifies qubit readouts, distorting Pauli operator estimates. This can cause the optimizer to follow an incorrect descent path, leading to unstable convergence. This year, I\u27ve focused on learning quantum error correction and quantum machine learning and am now connecting them by testing the effects of different error correction codes on quantum machine learning, specifically the Variational Quantum Eigensolver, using a Quokka quantum simulator