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Because of the very high power emission in a smaller containment, improper maintenance can result in failure of the data center operation in a shorter period. Hence the response time of the cooler to the temperature rise of the servers is very critical. Any delay in response will constantly lead to increased temperature and hence the server's failure.
In this paper, the significance of this delay time is understood by performing CFD simulation on different variants of High Density Modules using ANSYS Fluent. It was found out that the delay was becoming longer as the size of the data center increases. But the overload temperature, ie. the temperature rise beyond the set-point became lower with the increase in data center size. The results were common for both the single-row and the double-row model. The causes of the increased delay are accounted and explained in detail manner in this paper.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
American Sign Language (ASL) is used for Deaf and Hard of Hearing (DHH) individuals to communicate and learn in a classroom setting. In ASL, fingerspelling and gestures are two primary components used for communication. Fingerspelling is commonly used for words that do not have a specifically designated sign or gesture. In technical contexts, such as Computer Science curriculum, there are many technical terms that fall under this category. Most of its jargon does not have standardized ASL gestures; therefore, students, educators, and interpreters alike have been reliant on fingerspelling, which poses challenges for all parties. This study investigates the efficacy of both fingerspelling and gestures with fifteen technical terms that do have standardized gestures. The terms’ fingerspelling and gesture are assessed based on preference, ease of use, ease of learning, and time by research subjects who were selected as DHH individuals familiar with ASL.
The data is collected in a series of video recordings by research subjects as well as a post-participation questionnaire. Each research subject has produced thirty total videos, two videos to fingerspell and gesture each technical term. Afterwards, they completed a post-participation questionnaire in which they indicated their preference and how easy it was to learn and use both fingerspelling and gestures. Additionally, the videos have been analyzed to determine the time difference between fingerspelling and gestures. Analysis reveals that gestures are favored over fingerspelling as they are generally preferred, considered easier to learn and use, and faster. These results underscore the significance for standardized gestures in the Computer Science curriculum for accessible learning that enhances communication and promotes inclusion.