They Did It!
The U.S. Army Educational Outreach Program (AEOP) is pleased to announce the 2019-20 National Winners of the 18th Annual eCYBERMISSION Competition. The winning teams were announced last Friday at the Virtual National Judging & Educational Event (NJ&EE) Awards Ceremony.
If you're new to eCYBERMISSION, we are a web-based science, technology, engineering and mathematics (STEM) program—sponsored by the U.S. Army and administered by the National Science Teaching Association (NSTA)—is designed to cultivate student interest in STEM by encouraging students in grades six through nine to develop solutions to real-world problems in their local communities.
Last Friday at our NJ&EE National Awards Ceremony, one team in each of the four grades was named a winner.
Congratulations to all these teams and their hard work over the past year! We hope to see you all again next year for #ecyber21!
-Mission Control
If you're new to eCYBERMISSION, we are a web-based science, technology, engineering and mathematics (STEM) program—sponsored by the U.S. Army and administered by the National Science Teaching Association (NSTA)—is designed to cultivate student interest in STEM by encouraging students in grades six through nine to develop solutions to real-world problems in their local communities.
Last Friday at our NJ&EE National Awards Ceremony, one team in each of the four grades was named a winner.
Sixth Grade:
Carbon Keepers
Team Members: Felipe de Farias, Briley Siemens and Eliza Cole-Smith
Team Advisor: Laura Wilbanks
The Carbon Keepers discovered an urgent action against Climate Change to prepare for a better world by 2030. During their research, they discovered that soil carbon sequestration is a process in which CO2 is removed from the atmosphere and stored in soil as organic matter. Working with local and federal community partners and university mentors, different soil treatments were measured for carbon sequestration in response to drought, salinity, acidity, wildfire and fertilizer. Further research led to the use of soil treatments such as manure, mycorrhizae fungi and compost to hold carbon. Grass was planted in soils from five regions, adding four concentrations of the treatments, for over 200 samples and five repetitions. After four weeks, the plants and roots were analyzed. Carbon organic matter in the soil greatly increased with the use of mycorrhizae as an additive.
Seventh Grade:
Code Red
Team Members: Alexa Tindall, Josiah Morales and Ethan Djajadi
Team Advisor: Milene de Farias
Code Red is determined to find ways to lower the risk of firefighters receiving a positive cancer diagnosis. Several tests took place in order to find a proper solution. Results show dangerous elements on the gear of firefighters, in water exposed to smoke, and in rinse water collected between wash cycles of bunker gear. Gross decontamination on site with water-only was found to have some effect on reducing exposure, but washing gear multiple times instead of once greatly reduced the level of contaminants.
Eighth Grade:
Aquatech
Team Members: Saranya Gadwala, Remi Ladia, Anish Paspuleti, and Advaith Gajulapally
Team Advisor: Hima Lanka
Aquatech plans to reduce water usage by designing a device that alerts the user to leave the shower after a certain amount of water is used. They developed a device that was environmentally friendly, cost-effective, hydro-electricity powered with cutting edge IoT technology using Arduino IDE. The device measures the volume of water generated using magnets and sensors that calculate the volume of water used through formulas involving the number of rotations from a paddle wheel inside a shower head. Not only does this device allow users to set a recommended threshold to receive an alert from a buzzer, but it also stores and displays information in an accompanying application, where data is constantly updated.
Ninth Grade:
Psychologigals
Team Members: Sruthi Kotlo, Divya Lidder and Anjana Ramachandran
Team Advisor: Ramu Ramachandran
Psychologigals aimed to identify a solution to combat depression in our society by creating a cost-effective app. They explored the correlation between vocal accoustic features and depression and found that phonic patterns can be indicative of a disorder like depression. Their solution includes both a python-based machine learning program and a statistical model which analyzes extracted acoustic data from voice samples and attempts to classify each voice as depressed or non-depressed.
Congratulations to all these teams and their hard work over the past year! We hope to see you all again next year for #ecyber21!
-Mission Control
Colleen Minan
AEOP Communications & Marketing Specialist
cminan@nsta.org
AEOP Communications & Marketing Specialist
cminan@nsta.org
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