Engineering – Page 5 – UROP Spring Symposium 2021

Engineering

Building A Simulated World

Due to the high demand for motor vehicles, one of the key concerns for the automotive industry is to make vehicles accessible and safe. Both real test vehicles and driving simulators are used to assess the quality and performance of vehicles. However, many driving simulators are too expensive to purchase, too complex to use, take too long to run the software, and sometimes lack the desired functional characteristics. The goal of the research is to build a virtual world and an easy-to-use virtual driving simulator platform through the creative use of free software like CARLA and RoadRunner. This driving simulator will be suitable to support research on driver distraction, driver workload, and driver interfaces for partially automated vehicles. This will also inspire qualified people to use the simulation and work on safety on roads. Keywords: simulator, accident, driver distraction, 3D- map, RoadRunner.

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Chimeric Antigen Receptor T Cell Design through Systems Biology

Chimeric antigen receptor (CAR) T cells are an emerging therapy for drug-resistant cancers. So far, they have only been effective on hematological malignancies such as leukemia, but have had little success on solid tumors such as triple negative breast cancer (TNBC), due to the immunosuppressive nature of solid tumors that prevents the CAR T cells from operating effectively. In this study, we used publicly available single cell RNA sequencing data to study how fibroblasts in diseased and healthy patients affect and communicate with the CAR T cells. We used Seurat to cluster the different cells in the tumor microenvironment in healthy and diseased tissue and ran a survival analysis for each cluster. Our results show us that the cluster containing macrophages, which is defined by the FOS, DNAJB1, ZFP36, JUNB, and KLF4 genes, has a significant effect on the survival of patients with TNBC. We hope to confirm these computational results through in vivo experimentation of mice infected with TNBC. These data will shed light on how to better engineer CAR T cells to make them more effective in targeting solid tumors.

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Bioinformatics and Biochemical studies on cancer proteins

Protein sequence data is abundant, but ancestry information of those proteins and experimental analysis of the structure of those protein sequences are less available, and costly to produce. Thus, machine learning algorithms are being developed to predict protein structure. For structure prediction, available data is collected and parsed for the specific properties one’s algorithm requires, such as torsion angles for tertiary structure prediction. After an algorithm is trained against this data, the algorithm can be tested in its correctness of predicting protein structure. As a new algorithm is developed, an increase in accuracy and precision is expected.

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Machine Learning Methods for Robust ECG Beat Detection

This research was conducted to determine what types of machine learning algorithms were best to determine whether an individual is experiencing heart arrhythmias—such as atrial fibrillation, a type of arrhythmia that can lead to a number of fatal conditions such as blood clots, stroke, and heart failure. Using Python and the sklearn library, a number of machine learning models were tested for accuracy of ECG peak detection, which included Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Trees, Gaussian Naive Bayes, and Support Vector Machines. The accuracies varied based on the proportion of noise within the ECG file, so an introspective algorithm was developed to choose the optimal peak detection algorithm based on the estimated noise level measured in an ECG. This can be used in the future as a convenient way to accurately determine the presence of heart arrhythmias using wearable devices such as a smart watch.

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The Effect of Disaster-induced Displacement on Social Behaviour: The Case of Hurricane Harvey

In recent decades, whether climate change is real has become more and more of a partisan issue among everyday people. Over 97% of the scientific community, however, agrees that the climate has been changing due to human activity. In order to see if and how people’s views change when they actually experience a natural disaster that is likely due to climate change, we conduct an analysis of over 100,000 tweets before and after Hurricane Harvey.The analysis consists of labelling each tweet as part of one of six categories (climate change, Hurricane Harvey, sports, politics, policing, and other). Then, if the tweet relates to climate, the disposition of the tweet’s author towards climate change was also noted. Currently, in order to make the analysis of data more efficient, several machine learning classifiers are being built in the programming languages python and R. Once these classifier models are created, we will benchmark them and then use the best performing model to label new tweets at a much faster pace than a team of human coders. With the fully labeled corpus of climate change tweets, the study will be able to shed light on behavioral and political changes before and after climate change-induced events. Keywords: climate change, hurricane, Harvey, twitter, politics

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Using C++ to create a predictive model of twitter data to analyze social and political behavior.

As much as the media covers natural disasters extensively, little is known about the underlying effects aside from the “who was hurt or killed and what was destroyed” coverage of the disaster. Natural disasters often cause geographic displacement of individuals from their homes and communities to new ones. These events result in more than just the loss of physical property, as community and neighborhood attributes encompass many different aspects of an individual’s social relationships and the cultural institutions. Moving to a new community changes these attributes and will affect an individual’s political attitudes and behaviors. The ongoing post-Harvey migration provides a unique opportunity to examine the effect of social contact and political context in a case where these encounters could not have otherwise been anticipated by the individuals affected. This research study seeks to examine the political consequences of Hurricane Harvey by studying post-Harvey migration patterns as an unexpected or exogenous shock via data analysis techniques such as Natural Language Processing using C++ programming. This analysis will allow us to study variations in the decision of displaced individuals to move to different areas and how this affects their associated experiences once they move, and how people’s political attitudes and behaviors change in response to rapid demographic shifts in their communities in the future. This research is therefore able to address “How does local context affect political opinions and behaviors?” Using hand-coded twitter data, we were able to create various predictive models coded in R, Python, and C++. My specific research focuses on building a C++ predictive model. The C++ uses a score based system on NLP and even caught mistakes made during manual coding. We were able to determine that natural disasters such as Hurricane Harvey do influence social and political behaviors of the people affected by using an analysis of social media such as twitter. We also determined that the C++ predictive model had a very accurate prediction on sample twitter data.

Using C++ to create a predictive model of twitter data to analyze social and political behavior. Read More »

Powertrain Strategies for the 21st Century: The Tesla Effect

For the Tesla research project, I, along with my mentor Bruce Belzowski and research assistant Daniel Nemmert, helped formulate and evaluate the results of a survey that asked automotive industry experts including executives, managers, from auto manufacturers, suppliers, as well as academics, consultants, government officials, and NGOs for their opinions on Tesla’s unique technology, distribution methods, and company strategies. The survey also inquires about Tesla’s future global expansion and market share. Tesla’s recent rise and success within the exclusive electric automotive industry coupled with their unorthodox approach and advanced technologies make them a potential game-changer within the industry. Additionally, the world’s seemingly inevitable shift to electric vehicles, a sector in which Tesla is a global leader, makes them all the more relevant. This critical transition period to electric vehicles poses a problem for automotive companies tasked with undergoing a major operational and technological shift into the new market. Thus, this survey, which will be distributed in February and analyzed in March, will be used to further evaluate which of Tesla’s unique company characteristics are their strengths and weaknesses and to establish predictions regarding the future of the electric vehicle market including major players and imminent advances. The survey results will give insight that will inform competing automotive companies about other methods that, based on the methods’ successes, can assist and guide them in how they conduct their business in the future. Overall, this research will help automotive companies prepare for and adapt to consumer needs and preferences more effectively, creating more efficient businesses and better customer experiences.

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Nanoparticles and bacteria – chasing data!

There is a vast amount of research currently available regarding the discovery of new nanoparticles and their effectiveness in terms of killing bacteria. However, much of the literature is highly unorganized and fails to address the question of whether the nanoparticles’ ability to kill bacteria would render it a useful antibiotic. To address this issue, this study has gathered 8000 papers regarding such nanoparticle research to date. Each paper is judged twice, once by a machine learning algorithm and once by a human researcher, to determine whether the data provided in terms of the nanoparticle-microbe interactions is sufficient. If the paper is approved, it is included in the nanoparticle-microbe database, where data regarding such nanoparticle-microbe interactions is extracted. As this research is ongoing, the database has yet to be completed. However, the completed database will likely serve as a basis for future researchers and standardize the amount and types of data that should be collected. Additionally, further research should be conducted upon the nanoparticles included in the database to determine their potential for antibiotic or antimicrobial use.

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Literature Review of Human Factors Armored Vehicle Research Conducted by the United States Army

The topic of this research is driver user interfaces for autonomous Army vehicles and the associated workload, usability, and other related factors. The Army has done experiments on autonomous and tele-operational vehicles and their interfaces for the last 20 years, without any overarching studies to display the results across all of the experiments. The question this project is answering is: what are the important trends and general direction of this Army work on autonomous and tele-operational vehicles over the last 20 years? The purpose of the project is to go through 2 decades of Army literature and experiment reports and write literature reviews to summarize them. After finishing the literature reviews the important information will be condensed into tables to create a method to compare and contrast the findings of each report in a way that has not been done yet. This research is being done by reading Army Reports and writing literature reviews to summarize the important information. These reviews will be analyzed by creating summary tables encompassing all of the findings from the reports that have been read so that similarities, differences, and trends can be identified. The information is still being condensed into summary tables and are not yet ready to identify conclusions and trends. However, when conclusions are found, they will likely identify trends in soldier preferences, where the army should go next, and what systems are more and less successful, and how that can influence future decisions.

Literature Review of Human Factors Armored Vehicle Research Conducted by the United States Army Read More »

Literature Review of Human Factors Armored Vehicle Research Conducted by the United States Army

The topic of this research is driver user interfaces for autonomous Army vehicles and the associated workload, usability, and other related factors. The Army has done experiments on autonomous and tele-operational vehicles and their interfaces for the last 20 years, without any overarching studies to display the results across all of the experiments. The question this project is answering is: what are the important trends and general direction of this Army work on autonomous and tele-operational vehicles over the last 20 years? The purpose of the project is to go through 2 decades of Army literature and experiment reports and write literature reviews to summarize them. After finishing the literature reviews the important information will be condensed into tables to create a method to compare and contrast the findings of each report in a way that has not been done yet. This research is being done by reading Army Reports and writing literature reviews to summarize the important information. These reviews will be analyzed by creating summary tables encompassing all of the findings from the reports that have been read so that similarities, differences, and trends can be identified. The information is still being condensed into summary tables and are not yet ready to identify conclusions and trends. However, when conclusions are found, they will likely identify trends in soldier preferences, where the army should go next, and what systems are more and less successful, and how that can influence future decisions.

Literature Review of Human Factors Armored Vehicle Research Conducted by the United States Army Read More »

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