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Digital Resource 4: PWAM, 1990-2022, Author Page Count, Gender, as a percentage normalized, zoom
No full textReferenced in Chapter 4 of the book "Digital Literary Redlining: African American Anthologies, Digital Humanities, and the Canon.
The Effect of Various Temperature Schedules on the Comfort and Energy Efficiency of Radiant Room Heating
Get PDFThe automation of finding a balance between energy consumption and resident comfort has been a core issue with home heating system designers. However, there has been limited research regarding intelligent control in East Asian style radiant-heating home systems, which controls temperature with a heating valve through which hot water runs beneath the floor. While there was one study that attempted to use Reinforcement Learning based off of resident input to control the heating system, our approach is to design a fixed target temperature schedule for the heating valve to adhere to that finds a balance between energy consumption and resident comfort by reducing energy usage as much as possible without an excessive cost to resident comfort. This comes with the assumption that there are typical patterns in the optimal temperature setting for each individual, and that these patterns are relatively constant. We decided to use square-wave policies, which are schedules that have the target temperature alternate between the optimal temperature and 1 ��C lower, and compared between a policy that alternates every hour, another policy that alternates every two hours, and the default policy of always setting the target temperature at the optimal temperature. We tested our square-wave policy in an experimental residential unit, and observed the amount of energy consumed and the average deviation from the optimal temperature setting. Our results show that within a typical 4-hour temperature setting zone, the energy consumption for the square-wave policies was reduced by ~30% ��� ~50% compared to the default policy, while the average temperature deviation of the square wave policies only differentiated from the default by 0.05 ��C, which is too little to have any noticeable effects. However, although we analyzed these determined square-wave policies and determined their benefits, the unique heating and cooling characteristics of a specific residential unit may decide which one to choose or whether further fine-tuning of these schedules is needed. To explore in this direction, we developed an Artificial Neural Network to learn the heating and cooling dynamics in each individual residential unit and predict future temperature fluctuations based on past conditions; the results were very promising with highly accurate predictions. This will allow us to further fine-tune the square-wave interval. We expect these approaches we presented above to enable significant energy savings while maintaining comfortable indoor temperature levels
Fueling the mind, feeding the world: Communication in agriculture - Listening effectively (COM07)
No full textFor additional information, visit the Texas A&M University Science Communications Lab at https://scicomm.tamu.edu/.This packet contains instructional materials and online modules prepared for Fueling the mind, feeding the world: Communication in agriculture - Listening effectively (COM07). It includes curriculum, PowerPoint slides, activities, handouts, grading considerations, and notes for instructors. These materials were created as part of the USDA Grant entitled "Fueling the Mind, Feeding the World: Enhancing Communication and Decision-Making Skills of Secondary Agricultural Education Students."
MODULE OVERVIEW: Listening is a crucial part of communication for two major reasons. First, we spend more time listening than writing, reading, speaking, or using other forms of communication. Second, listening is not the same as hearing. Effective listening includes attentiveness, thinking critically about the information, engaging in the conversation through head nodding, and watching nonverbal movements and cues. Without effective listening, messages might be misunderstood. Therefore, employers look for those who can listen effectively and identify keywords and concepts needed to make informed decisions.Secondary Education, Two-Year Postsecondary Education, and Agriculture in the K-12 Classroom (SPECA) Challenge Grants Program no. 2019-38414-30265 and Hatch Project No. TEX09825 from the USDA National Institute of Food and Agriculture
Machine Learning Based Monte Carlo Simulation of Protein Folding
Get PDFAccurately modeling protein folding is essential in order to determine the treatment and causes of many debilitating illnesses that arise from protein misfolding. Protein folding, however occurs on a time frame that is difficult to model using conventional means of simulation such as molecular dynamics simulations. Instead, new methods must be used to reduce the computational complexity of the problem and allow solutions to be discovered in a timely manner. Monte Carlo simulations are one viable solution that produce the same thermodynamic information as molecular dynamics simulations but in a faction of the time. To increase the accuracy of the Monte Carlo simulation, an artificial neural network, referred to as a variational autoencoder, was trained on structural snap shots extracted from molecular dynamics simulations of TRP-Cage and coarse grained HTT 17 proteins. The coarse-grained Monte Carlo simulation was found to successfully model a short simulation of the protein HTT 17. Over the simulation the protein was observed approaching the native confirmation of the protein. The full atom TRP-Cage protein was, however, not successfully encoded with the neural networks that were used in this experiment