In collaboration with Moog Broad Reach and Arizona State University, a<br/>team of five undergraduate students designed a hardware design solution for<br/>protecting flash memory data in a spaced-based radioactive environment. Team<br/>Aegis have been working on the research, design, and implementation of a<br/>Verilog- and Python-based error correction code using a Reed-Solomon method<br/>to identify bit changes of error code. For an additional senior design project, a<br/>Python code was implemented that runs statistical analysis to identify whether<br/>the error correction code is more effective than a triple-redundancy check as well<br/>as determining if the presence of errors can be modeled by a regression model.

This study investigated how mindset intervention in freshman engineering courses influenced students’ implicit intelligence and self-efficacy beliefs. An intervention which bolsters students’ beliefs that they possess the cognitive tools to perform well in their classes can be the deciding factor in their decision to continue in their engineering major. Treatment was administered across four sections of an introductory engineering course where two professors taught two sections. Across three survey points, one course of each professor received the intervention while the other remained neutral, but the second time point switched this condition, so all students received intervention. Robust efficacy and mindset scales quantitatively measured the strength of their beliefs in their abilities, general and engineering, and if they believed they could change their intelligence and abilities. Repeated measures ANOVA and linear regressions revealed that students who embody a growth mindset tended to have stronger and higher self-efficacy beliefs. With the introduction of intervention, the relationship between mindset and self-efficacy grew stronger and more positive over time.

This investigation evaluates the most effective time series model to forecast the stock price for companies that started trading during the COVID-19 stock market crash. My research involved the analysis of five companies in the technology industry. I was able to create three different machine-learning models for each company. Each model contained various criteria to determine the efficacy of the model. The AIC and SBC are common metrics among Autoregressive, autoregressive moving averages, and cross-correlation input models. Lower AIC and SBC values indicated better-fitted models. Additionally, I conducted a white-noise test to determine stationarity. This yielded an Auto-correlation graph determining whether the data was non-stationary or stationary. This paper is supplemented by a project plan, exploratory data analysis, methodology, data, results, and challenges section. This has relevance in understanding the overall stock market trend when impacted by a global pandemic.