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Labeled Seed-Dressing Fungicides For Wheat Seeds
Get PDFChart of fuingicides by brand name and active ingredient
Integration of Behavioral Modeling Into Engineering Design Decisions and Adoptability Predictions of Health Preserving Technologies
Get PDFThe purpose of this research is to understand the decision making process behind end use of agroindustrial technology designed to improve health and safety. The first objective of this dissertation in Chapter II was to detail the profile of grain bin temperature probe adopters as contrasted with nonadopters in order to prevent grain bin entrapment deaths and injury. The extended technology acceptance model (TAM2) from Venkatesh and Davis (2000) was used to create a predictive model based on surveyed Texas farmers. Prior exposure to hazards and network relationships (relationships with extension offices, peers, and private companies) did not influence end use of the probes or any other technologies. The strongest predictors of behavior were access to the technology (financial), access to information about the technology, as well as compatibility with and the demonstrated quality of the technology.
The second objective in Chapter III extended the model from Chapter II to understand the behavior among Central American agroexporters surrounding agricultural technologies and practices (ATPs), including novel non-thermal plasma technologies. Educational materials were assessed on their ability to motivate behavioral change. Similarly to Chapter II, behavioral predictors included access to the technology, access to information, and compatibility. This group's behavior was also predicted by image (status) and mandated use. The educational materials developed were effective in increasing general and specific knowledge while catalyzing behavioral change toward adoption.
The third objective in Chapter IV was to design a mobile atmospheric cold plasma (ACP) treatment system using 3D printed materials based on adoption concerns found in Chapters II and III. ACP treatment is a postharvest method for increasing food safety through pest, toxin, and microbial inactivation. PLA functioned well as dielectric barriers evidenced by methylene blue degradation and observed stable plasma formation. This updated design meets technical requirements for microbial inactivation as demonstrated by both methylene blue discoloration tests (99% discoloration at 70kV for 3 minutes). Incorporation of the PCI to the design phases addressed supra-technical considerations which would have been otherwise excluded. Chapter IV demonstrates the benefits of designing with behavioral models in mind
Use and Accuracy of the Diphenylamine Field Kit for Determining the Presence of Toxic Nitrate Levels in Forage Samples
Get PDFIntegrating AI Large Language Models into PubMed Searching for a Medical Student Grand Rounds Course
Get PDFFiles include 1) presentation slides, 2) text of the Discussion prompt used in the Learning Management System and 3) text of an announcement to the students summarizing their experiences with the LLM search exercise.Background: Librarians teach PubMed searching via lecture and graded exercise in a Medical Student Grand Rounds (MSGR) course at Texas A&M University School of Medicine. In this semester-long course, students work with mentors to discover and present about translational basic sciences research on a clinical topic at Grand Rounds Day. ChatGPT and other Large Language Models (LLMs) are intriguing options to streamline the search process but require discernment for proper use. The teaching team set out to incorporate guided exposure to LLM opportunities and challenges into the existing search training and exercise process.
Description: Students in MSGR must use PubMed to search the biomedical literature to inform a semester-end presentation about translational research with potential to impact clinical care. Librarians have taught PubMed search skills in the course for several years, including the use of MeSH terms and subheadings, filters, and keywords. These skills are reinforced with a graded exercise taking students through the steps to gradually refine a search. Recognizing that students would likely experiment with ChatGPT to save time on their searches, librarians worked with course directors to revise the lecture and exercise to emphasize the process of searching, to highlight where artificial intelligence (AI) is already used in PubMed, and to suggest both uses and caveats of current LLMs to generate usable PubMed searches. The manual searches in the exercise were condensed to accommodate the additional generative AI search instructions. Students were then asked to use the LLM of their choice to generate search terms for the same topic, critique the LLM response, and share their experience with peers in a course discussion board. Grading and feedback centered on good-faith efforts to perform the search process and to evaluate thoughtfully the LLM tool.
Conclusion: The recent emergence of publicly available tools for generative AI based on LLM presents new challenges for instruction, particularly when students are required to submit ���original��� work in assignments. The PubMed searching instruction and exercise have traditionally garnered strong positive feedback in prior student course evaluations. This paper will present student feedback on the inclusion of generative AI tools in the exercise and will describe differences between students��� work with and without the use of generative AI for their PubMed assignment. Librarians and course leadership will review the Spring 2024 course evaluations as well as the next developments in generative AI to continually improve the relevance of the course content. We expect the approach to evolve regularly
