U.S. Millennials and The Hook-Up Culture: An Online Descriptive Survey of Hook-Up Attitudes, Beliefs, and Experiences

The purpose of this online survey study was to collect and analyze the attitudes and experiences of 18-24-year-old U.S. millennials. Previous studies have focused exclusively upon hook-up attitudes and experiences among college students using mostly paper-and-pencil surveys or interviews, this study described hook-up experiences and attitudes using a sample of U.S. millennials who were not enrolled full-time in college. Using a Qualtrics panel, 106 respondents (54 males and 52 females), 66 with prior hooking-up experience and 40 with no prior experience, completed an online demographic survey and The Millennial Hook-Up Attitudes and Beliefs Survey. The results revealed more than half of the respondents reported being a part of the hook-up culture and having some prior hooking-up experiences. A majority reported that at least 1-25 peers shared hook-up stories with them. Respondents without prior experience reported significantly more positive attitudes about hooking-up compared to the respondents with prior hooking-up experience. The respondents preferred the use of social media apps (i.e., Tinder) to arrange hook ups. The positives to hooking-up included the lack of commitment and the social excitement. The negatives were the risk of infection, pregnancy, and negative social repercussions. As this population, and those that follow, grows, recommendations include conducting more informed studies to provide insight into industries and various professional settings that are working and growing alongside the aging millennial population and its successors

Development of an empirically based dynamic biomechanical strength model

The focus here is on the development of a dynamic strength model for humans. Our model is based on empirical data. The shoulder, elbow, and wrist joints are characterized in terms of maximum isolated torque, position, and velocity in all rotational planes. This information is reduced by a least squares regression technique into a table of single variable second degree polynomial equations determining the torque as a function of position and velocity. The isolated joint torque equations are then used to compute forces resulting from a composite motion, which in this case is a ratchet wrench push and pull operation. What is presented here is a comparison of the computed or predicted results of the model with the actual measured values for the composite motion

Vision-Based American Sign Language Classification Approach via Deep Learning

Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.Comment: 4 pages, Accepted in the The Florida AI Research Society (FLAIRS-35) 202