Engineering – Page 2 – UROP Summer Symposium 2021

Engineering

MATLAB Programming for Data Packaging

Research is being conducted within the University of Michigan Direct Brain Interface (UM-DBI) Laboratory to develop an option for people with physical impairments to access communication devices without physical movement through brain-computer interfaces (BCIs). Currently, the majority of the BCI data from the laboratory is stored in a BCI2000 file format which is raw data and cannot be edited or modified after it is obtained. The MATLAB Programming for Data Packaging Project focuses on improving the data storing and editing process for a custom Data Packaging Graphical User Interface (GUI) within MATLAB.

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Establishing Forster Resonance Energy Transfer Protocol for Extracellular Matrix Proteins

Understanding protein interactions is important as it could serve as a basis for designing extracellular matrices (ECM) in-vitro to mimic in-vivo characteristics which would help elicit better biological responses from cells and be useful for disease modeling and drug testing. The purpose of this research is to create Forester Resonance Energy Transfer (FRET) technique for a protein called fibronectin to study its conformational changes and molecular interaction with other proteins.

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Ontological Representation and Machine Learning Prediction of Drugs for COVID-19 Treatment

Background: SARS-CoV-2 is a human coronavirus that has caused COVID-19 and is able to rapidly mutate and spread throughout the world. While the usage of COVID-19 vaccines has drastically reduced illness, new variants of the virus continue to show up and reduce vaccination efficiency. Given the continuous spreading of the disease, effective drugs for treating COVID-19 are urgently needed; however, very effective drugs for COVID-19 have not yet been approved for public use. Drug repurposing is a strategy to discover new uses for thousands of approved drugs previously used for other illnesses. It is possible to use the drug repurposing strategy to find drugs for effective COVID-19 treatment. This study aims to analyze drugs and their effects on the human body to further predict effective drugs for COVID-19 using machine learning algorithms.

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2021 Powertrain Strategies for the 21st Century

My Research Project pertains to Powertrain Strategies for the 21st Century survey project of the Automotive Futures Group. This survey of automotive industry experts including manufacturers, suppliers, government, NGOs, academia, and consultants looks to forecast the percentage of different powertrains that will be sold in 2025 and 2030 as well as asking experts about their expectations for the Biden Administrations CAFÉ regulations impacts, how will the industry use its credits to meet their goals, will manufacturers be able to meet aggressive state goals for eliminating gas fueled vehicle sales, and how will the auto industry make the transition to EVs, including questions on charging infrastructure. Our analyses will address these questions and report differences, if any, among the three groups of respondents (manufacturers, suppliers, and the group of government, NGOs, academia, and consultants). A unique characteristic of our analyses will be our weighting of responses by the confidence respondents report about their answers. Our results will be a continuation of this survey that dates back to 2007 that has tracked powertrain expert responses to the future of powertrains in the US.

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Gene Editing for Combating Disease

The APP (amyloid precursor protein) is particularly relevant in the investigation of neurodegenerative diseases, notably Alzheimer’s, as a result of rare mutations in APP protein coding gene. Quantifying cell mRNA levels is crucial to the determination of cell genomic status: mRNA is transcribed from cell DNA, and ultimately translated to cell proteins. In this project, we will administer the CRISPR/Cas9 complex in neuroblastoma cells, and confirm the alteration of APP gene expression.

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Data Mining for Modeling Drivers’ Response to a Leading Vehicle’s Merging/Demerging Maneuver

Although there have been many recent strides in automated vehicle features (e.g., lane-keeping assist or automatic emergency braking) and autonomous vehicles (e.g., Cruise), there is still a significant need for research on human drivers’ behavior and decision-making. One larger goal of this research is to increase users’ acceptance and comfort with these driving technologies by helping align the technology’s actions / warnings more closely with human behavior and expectations. This research project, in particular, applies data mining (including statistical and machine learning methods, primarily via MATLAB code) to naturalistic driving data in order to model drivers’ behavior in response to two specific scenarios: cut-ins and cut-outs of the lead vehicle on US freeways.

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Data Mining for Modeling Drivers’ Response to a Leading Vehicle’s Merging/Demerging Maneuver

Although there have been many recent strides in automated vehicle features (e.g., lane-keeping assist or automatic emergency braking) and autonomous vehicles (e.g., Cruise), there is still a significant need for research on human drivers’ behavior and decision-making. One larger goal of this research is to increase users’ acceptance and comfort with these driving technologies by helping align the technology’s actions / warnings more closely with human behavior and expectations. This research project, in particular, applies data mining (including statistical and machine learning methods, primarily via MATLAB code) to naturalistic driving data in order to model drivers’ behavior in response to two specific scenarios: cut-ins and cut-outs of the lead vehicle on US freeways.

Data Mining for Modeling Drivers’ Response to a Leading Vehicle’s Merging/Demerging Maneuver Read More »

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