Smart Home: Household Appliance usage Recommender and Monitoring System (HARMS)

The demand for energy sources such as electricity is increasing as the population is increasing, which results in high billing costs and more energy consumption. More factors are resulting from these issues. For example, the decreased awareness from residents about how to save energy, especially kids and elderly people who forget about turning off home appliances and lights when they are not needed to be on. HARMS provide a smart solution through the concept of machine learning (ML) and recommendations, it will monitor power consumption, show recommendations and control home appliances based on the resident's behaviors, when they are willing to turn on the room light or any other home appliance and when to turn them off in order to enhance energy saving. HARMS will also track the inhabitant's usual and unusual behavior to take an action. We must note that due to this exceptional situation (Covid-19 Pandemic), HARMS may be done either using actual hardware, simulation, or both. The hardware parts will consist of microcomputer, motion, light, and current transformer sensors. The software parts will consist of a control system that collects data from sensors and monitors the power consumption, a database to store the collected data, appropriate algorithms for the recommender system, and an android application to interact with the residents. Regarding the simulation will consist of a web-based application to represent the home environment and the appliances, including the control and the recommender systems. This project will experiment at the College of Computer Sciences and Information Technology (CCSIT) at King Faisal University (KFU). © 2022 IEEE.

Self-isolation during the COVID-19 pandemic is associated with increased risk of burnout among physician trainees: A cross sectional study

The COVID-19 pandemic emerged in late 2019. Previous research has shown a significant prevalence of burnout among physician trainees, with concern that the pandemic will increase burnout. We aimed to assess this risk among trainees at a large academic hospital. We performed a cross-sectional study during the pandemic using a survey that included the Maslach Burnout Inventory. The response rate was 94.7%. Among trainees, 58.5% changed their living arrangements to protect family. Psychological wellbeing was negatively affected in 81.7% and clinical performance in 64.3%;13.8% were at high risk of burnout. Emotional exhaustion (EE) scores were high in 50% and depersonalization (DP) scores in 28.8%;a sense of personal accomplishment was low in 41.9%. Increased risk of burnout was associated with male gender and increased exposure to suspected COVID-19 cases. Risk of high EE correlated with an increased number of children and risk of high DP with male gender. High EE and DP score correlated with increased exposure to suspected and confirmed COVID-19 patients. Trainees who self-isolated to protect family were more likely to experience high DP and burnout. Trainees in surgical specialties were more likely to feel their clinical performance was negatively affected. The results suggest that a significant percentage of trainees are at high risk of burnout during the pandemic especially those that attempted self-isolation. Training programs should incorporate methods to maintain well-being and coping, including adequate time off between shifts. Future research should evaluate other aspects of trainee well-being in relation to self-isolation and/or changed living arrangements.