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CAIA/CCI Mini-Grants for Experiential Learning in Data Analytics and Cyberbiosecurity for Agriculture Awarded

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Three CAIA-Commonwealth CyberInitiative (CCI) Mini-grants for Experiential Learning were awarded to help advance the educational efforts in cyberbiosecurity and data analytics with relevance to the agriculture and food system. Acknowledgement and appreciation is extended to Commonwealth CyberInitiative Southwest Virginia node for contributing support. The products from these projects will be published as open educational resources (OER) through VTWorks. You will be able to access, use and modify the OER materials to support your teaching or outreach activities on these topics, thus helping you incorporate introductory and advanced learning opportunities on these subjects in your courses. The graduate student Principal Investigators will present their project progress, as posters, at the CAIA Big Event session on Friday afternoon, February 18. See the CAIA schedule and plan to attend this event. 



Awarded projects include:


Cyberbiosecurity Introductory Course Material Creation for Food Science.

PI: Rebekah Miller, Food Science and Technology (FST) (Masters student). Mentors: Dr. Yun Yin (FST), Dr. Susan Duncan (FST/VAES), Dr. Andrew Ray (Dept Information Technology, Radford University). Target audience: university students [undergraduate; graduate].

Rebekah plans to create lecture material that will focus on introducing cyberbiosecurity as a discipline, technology and automation in the food industry, data collection and storage, and risk assessment of cyber concerns. Case studies will be integrated into the material to demonstrate the applications of the themes. Guided discussions will engage how cyber risk can be identified and mitigated as well as what a cyberattack could mean to the data, company, and supply chain.

Using Machine Learning Approach to Predict Sorption of Organic Contaminants to Soils. PI: Muhit Islam Emon, Computer Science (CS) (Ph.D. student). Mentors: Dr. Liqing Zhang (CS), Dr. Kang Xia (School of Plant and Environmental Science), Dr. Rick Clark, Virginia Western Community College. Target audience: Community college students.

Muhit will develop learning materials on the application and evaluation of different machine learning algorithms in environmental sciences, focusing on the sorption of various organic contaminants into soils. Students will gain hands-on experience on various machine learning models by applying and testing them on sorption data.

Course Module in Precision Livestock Farming (PLF): ‘How PLF contributes to animal welfare and productivity’. PI: Barbara Roqueto dos Reis, Animal and Poultry Sciences (APSC) (PhD student). Mentors: Dr. Robin White (APSC), TBN CCI mentor. Target audience: University students [undergraduate].

Barbara anticipates advancing the training of the future workforce by cross-training between animal science and precision livestock applications. She will develop a course module, initially targeted to Animal Nutrition and Feeding (ALS 3204), to enable students to describe the use of precision technologies in livestock science, identify current technologies in agricultural use, and deploy sensors for data collection from animals.