Different Immersion Temperature\u27s Impact Upon Blood Pressure of Individuals With Varied Sex and Age

Warm water immersion is known to have an effect on human cardiovascular function. This study examines how age (young = 18-30 years and older = 31-65 years) and sex influence changes in blood pressure due to submersion in different water temperatures. Fifty-eight individuals sat immersed to the neck in three different water temperature tanks. Blood pressure measurements (systolic/diastolic blood pressure [SBP/DBP], pulse pressure [PP] and heart rate [HR]) were collected every 6 minutes throughout the duration of the test. We observed significant between-group, within-group, and interaction effects for SBP, DBP, and HR. For PP, significant between-group, within-in group and interaction effects for SBP, DBP, and HR. For PP, significant between-group and within-group effects were found. Additional post hoc analyses found that from baseline to cool immersion, older females (OF) had less change in SBP values compared to younger males (YM) and younger females (YF) and less change in DBP values compared to YM. From warm immersion to recovery, older males (OM) had less change in heart rate compared to YM, and in both the warm and cool immersions, YF lower pulse pressure than YM. Understanding changes to BP during resting water immersion across different ages and both sexes could have clinical applications relevant to both physicians and those responsible for rehabilitation of cardiovascularly-compromised patients

Generative AI for learning: Investigating the potential of synthetic learning videos

Recent advances in generative artificial intelligence (AI) have captured worldwide attention. Tools such as Dalle-2 and ChatGPT suggest that tasks previously thought to be beyond the capabilities of AI may now augment the productivity of creative media in various new ways, including through the generation of synthetic video. This research paper explores the utility of using AI-generated synthetic video to create viable educational content for online educational settings. To date, there is limited research investigating the real-world educational value of AI-generated synthetic media. To address this gap, we examined the impact of using AI-generated synthetic video in an online learning platform on both learners content acquisition and learning experience. We took a mixed-method approach, randomly assigning adult learners (n=83) into one of two micro-learning conditions, collecting pre- and post-learning assessments, and surveying participants on their learning experience. The control condition included a traditionally produced instructor video, while the experimental condition included a synthetic video with a realistic AI-generated character. The results show that learners in both conditions demonstrated significant improvement from pre- to post-learning (p<.001), with no significant differences in gains between the two conditions (p=.80). In addition, no differences were observed in how learners perceived the traditional and synthetic videos. These findings suggest that AI-generated synthetic learning videos have the potential to be a viable substitute for videos produced via traditional methods in online educational settings, making high quality educational content more accessible across the globe.Comment: 12 pages, 1 table, 3 figures. International conference of Artificial Intelligence in Education (AIED 2023

Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale

As Large Language Models (LLMs) and other forms of Generative AI permeate various aspects of our lives, their application for learning and education has provided opportunities and challenges. This paper presents an investigation into the use of LLMs in asynchronous course creation, particularly within the context of adult learning, training and upskilling. We developed a course prototype leveraging an LLM, implementing a robust human-in-the-loop process to ensure the accuracy and clarity of the generated content. Our research questions focus on the feasibility of LLMs to produce high-quality adult learning content with reduced human involvement. Initial findings indicate that taking this approach can indeed facilitate faster content creation without compromising on accuracy or clarity, marking a promising advancement in the field of Generative AI for education. Despite some limitations, the study underscores the potential of LLMs to transform the landscape of learning and education, necessitating further research and nuanced discussions about their strategic and ethical use in learning design.Comment: 1 figur

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

Disentangling the stigma of HIV/AIDS from the stigmas of drugs use, commercial sex and commercial blood donation – a factorial survey of medical students in China

BackgroundHIV/AIDS related stigma interferes with the provision of appropriate care and support for people living with HIV/AIDS. Currently, programs to address the stigma approach it as if it occurs in isolation, separate from the co-stigmas related to the various modes of disease transmission including injection drug use (IDU) and commercial sex (CS). In order to develop better programs to address HIV/AIDS related stigma, the inter-relationship (or \u27layering\u27) between HIV/AIDS stigma and the co-stigmas needs to be better understood. This paper describes an experimental study for disentangling the layering of HIV/AIDS related stigmas.MethodsThe study used a factorial survey design. 352 medical students from Guangzhou were presented with four random vignettes each describing a hypothetical male. The vignettes were identical except for the presence of a disease diagnosis (AIDS, leukaemia, or no disease) and a co-characteristic (IDU, CS, commercial blood donation (CBD), blood transfusion or no co-characteristic). After reading each vignette, participants completed a measure of social distance that assessed the level of stigmatising attitudes.ResultsBivariate and multivariable analyses revealed statistically significant levels of stigma associated with AIDS, IDU, CS and CBD. The layering of stigma was explored using a recently developed technique. Strong interactions between the stigmas of AIDS and the co-characteristics were also found. AIDS was significantly less stigmatising than IDU or CS. Critically, the stigma of AIDS in combination with either the stigmas of IDU or CS was significantly less than the stigma of IDU alone or CS alone.ConclusionThe findings pose several surprising challenges to conventional beliefs about HIV/AIDS related stigma and stigma interventions that have focused exclusively on the disease stigma. Contrary to the belief that having a co-stigma would add to the intensity of stigma attached to people with HIV/AIDS, the findings indicate the presence of an illness might have a moderating effect on the stigma of certain co-characteristics like IDU. The strong interdependence between the stigmas of HIV/AIDS and the co-stigmas of IDU and CS suggest that reducing the co-stigmas should be an integral part of HIV/AIDS stigma intervention within this context.<br /

Widening Access to Applied Machine Learning With TinyML

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies