Purpose: This qualitative research aimed to create a developmentally and gender-appropriate game-based intervention to promote Human Papillomavirus (HPV) vaccination in adolescents. <br/>Background: Ranking as the most common sexually transmitted infection, about 80 million Americans are currently infected by HPV, and it continues to increase with an estimated 14 million new cases yearly. Certain types of HPV have been significantly associated with cervical, vaginal, and vulvar cancers in women; penile cancers in men; and oropharyngeal and anal cancers in both men and women. Despite HPV vaccination being one of the most effective methods in preventing HPV-associated cancers, vaccination rates remain suboptimal in adolescents. Game-based intervention, a novel medium that is popular with adolescents, has been shown to be effective in promoting health behaviors. <br/>Methods: Sample/Sampling. We used purposeful sampling to recruit eight adolescent-parent dyads (N = 16) which represented both sexes (4 boys, 4 girls) and different racial/ethnic groups (White, Black, Latino, Asian American) in the United States. The inclusion criteria for the dyads were: (1) a child aged 11-14 years and his/her parent, and (2) ability to speak, read, write, and understand English. Procedure. After eligible families consented to their participation, semi-structured interviews (each 60-90 minutes long) were conducted with each adolescent-parent dyad in a quiet and private room. Each dyad received $50 to acknowledge their time and effort. Measure. The interview questions consisted of two parts: (a) those related to game design, functioning, and feasibility of implementation; (b) those related to theoretical constructs of the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). Data analysis. The interviews were audio-recorded with permission and manually transcribed into textual data. Two researchers confirmed the verbatim transcription. We use pre-developed codes to identify each participant’s responses and organize data and develop themes based on the HBM and TPB constructs. After the analysis was completed, three researchers in the team reviewed the results and discussed the discrepancies until a consensus is reached.<br/>Results: The findings suggested that the most common motivating factors for adolescents’ HPV vaccination were its effectiveness, benefits, convenience, affordable cost, reminders via text, and recommendation by a health care provider. Regarding the content included in the HPV game, participants suggested including information about who and when should receive the vaccine, what is HPV and the vaccination, what are the consequences if infected, the side effects of the vaccine, and where to receive the vaccine. The preferred game design elements were: 15 minutes long, stories about fighting or action, option to choose characters/avatars, motivating factors (i.e., rewards such as allowing users to advance levels and receive coins when correctly answering questions), use of a portable electronic device (e.g., tablet) to deliver the education. Participants were open to multiplayer function which assists in a facilitated conversation about HPV and the HPV vaccine. Overall, the participants concluded enthusiasm for an interactive yet engaging game-based intervention to learn about the HPV vaccine with the goal to increase HPV vaccination in adolescents. <br/>Implications: Tailored educational games have the potential to decrease the stigma of HPV and HPV vaccination, increasing communication between the adolescent, parent, and healthcare provider, as well as increase the overall HPV vaccination rate.

As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.