Wentworthmcdonough1297

ent in the maximum achievable video bit rate, the average viewing video bit rate, and perceived delay.In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. Sunitinib For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.The COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching-learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.Research on information systems has identified a variety of factors across a range of adoption models that determine their acceptance. In this research, the unified theory of acceptance and use of technology (UTAUT), which integrates determinants across eight models, was utilised to analyse students' intentions to use and their actual usage of Moodle, an e-learning system at Hashemite University, a public university in Jordan, one of developing countries. Four principal determinants of intention and usage were explored performance expectancy, effort expectancy, social influence, and facilitating conditions. Data were collected from 370 undergraduate students and analysed using structural equation modelling techniques. The results indicated that performance expectancy and effort expectancy affected behavioural intentions to use Moodle whereas social influence did not. In addition, the results confirmed the direct impact of behavioural intentions and facilitating conditions on students' use of Moodle. UTAUT thus provides a valuable tool that enables university decision makers, faculty members, and designers to understand the factors driving e-learning system acceptance and thus facilitate the adoption of the system by students. The study will help educational institutions prepare e-learning systems, which is especially important during a state of emergency such as that caused by COVID-19.It is known that virtual reality (VR) experience may cause cyber sickness. One aspect of VR is an immersion or otherwise sense of presence, the sense of feeling oneself in a virtual world. In this paper an experiment which was conducted in order to find the link between level of immersion and cyber sickness felt by participants is presented. Eighty-nine participants aged between 19 and 36 years have been equally divided into four groups with different level of VR immersion. The low-immersive group was represented by PC with monoscopic screen, the semi-immersive group was represented by CAVE with stereoscopic projector, the fully immersive group was represented by VR head-mounted display, and the last group was the control group without any kind of immersion. The task for the participants was to navigate through the maze for a specified amount of time (10 min). The Simulator Sickness Questionnaire was used as a subjective measure tool for cyber sickness level and Grooved Pegboard Test for assessing the fine dexterity, both before and after the experiment. Regarding the time spend in VR the fully immersive environment had the biggest problems as more than half of the participants had to stop before 10 min (p  less then  0.001). Concerning the cyber sickness, the significant increase in nausea score between pre-test and post-test scores has been observed in semi-immersive group (p = 0.0018) and fully immersive group (p  less then  0.0001). The increase in oculomotor score was smaller. The significant difference was noted only in fully immersive group (p = 0.0449). In spite of great nausea factor after the VR immersion the participants did not show a decrease of fine dexterity in any group (p  less then  0.001).