In light of the ongoing disruption that Coronavirus poses to our lives, which includes the cancellation of what would have been our 13th annual MST Research Symposium, we are making available a playlist of selected video presentations of a few senior, sophomore, and freshmen projects (juniors don’t complete formal projects as they prepare for their internships …). We are also including in this description a link to a Drive folder that has ALL of our project study done this year. Great work, Magnetos! Please take a moment to peruse the bounty of their work: 2020 MST Digital Research Symposium

In light of the ongoing disruption that Coronavirus poses to our lives, which includes the cancellation of our annual MST Research Symposium, we are publishing a series of posts on MST students and their research that have been peer selected for excellence.

We asked each group selected to give us info on their abstract, the biggest takeaway from their project study, and something they are excited about for the upcoming year, as well as a few photos.  This post covers work done by MST sophomores Pauline Do, Natalie Ge, and Nels Martin.

ABSTRACT: Currently, societies everywhere are refilling fractures in cement for sidewalks, buildings, and other structures by hand, which can be a dangerous and costly job. During cement production, CO₂ is released when calcium carbonate is thermally decomposed, which contributes to the greenhouse effect. Trichoderma reesei (T. reesei) is a fungus with the ability to secrete calcium carbonate (CaCO₃) as a product of its metabolic processes. T. reesei can be incorporated into cement to heal structural damage by lying dormant until its incubating cement is damaged. As the fungus is exposed to moisture and oxygen from the external environment, it will germinate and produce CaCO₃ to repair the fracture and then form spores again once the crack has been sealed. The purpose of this experiment is to gather more information on the efficiency of this procedure by incorporating different amounts of T. reesei into cement. This experiment found that cement infused with 4 mL of a T. reesei nutrient broth solution was able to begin healing itself within the time allotted for our experiment.  Read about their findings here: The Healing Capabilities of Trichoderma Reesei in Concrete.

When asked about their biggest takeaways from their project study this year, they said,  “Our biggest takeaway from this year’s project study was that experiments or projects may experience unexpected setbacks, and although things may not go entirely as planned or events out of your hands may fall though, it is important to persevere and continue to the best of your abilities. For example, the fungus we needed to conduct our experiment was delayed from arriving for several months, so we definitely had to make some adaptations to our plan to accommodate this change.”

As for next year, they said, “We are excited to start our internships. We are hoping to gain more hands-on experience in fields that we are interested in. The process of looking for internships may be difficult at first but all of us are very excited to see how real labs work. We are excited to learn from researchers who have much more experience and knowledge than we do and try to apply our knowledge in our future careers and further education.”

In light of the ongoing disruption that Coronavirus poses to our lives, which includes the cancellation of our annual MST Research Symposium, we are publishing a series of posts on MST students and their research that have been peer selected for excellence.

We asked each group selected to give us info on their abstract, the biggest takeaway from their project study, and something they are excited about for the upcoming year, as well as a few photos.  This post covers the work done by MST sophomores Ashton Doane, Chris Spencer, and Sunny Wang.

Abstract: Sophomores Ashton Doane, Chris Spencer, and Sunny Wang conducted research on the applicability of neural networks to music transcription. Music transcription is the task of taking some audio and creating sheet music, a notation of how to play it on an instrument. The overall goal of the project was, “to achieve great accuracy in transcribing polyphonic music through machine learning, hopefully having a higher maximum potential for transcription than humans.” Using some key insights into the inner workings of musical structure, they created multiple models to attempt to increase accuracy. Their results can be found here: Automating Music Transcription with Artificial Intelligence.

When asked about their biggest takeaways from their project study this year, they said it “…  was that a lot of preparation work must be done to effectively finish a task, yet there still needs to be room for adjustment when things go off course. Making sure that you can make a plan and stick to it without cheating or taking days off makes sure that you can finish a project.”

As for next year, they said, “Internships are approaching, which is very exciting, as they give an opportunity to work in a real scientific environment. The real excitement here is in getting new ideas and seeing if a certain job is a good fit for people. The opportunity to both try out a job and meet new people is a great advancement in life.”