The mediator role of well-being in the effect of COVID-19 anxiety on occupational commitment: research in the aviation sector

Optimizing learning comfort: ergonomic influences on children's transition to online education

The COVID-19 pandemic necessitated the implementation of lockdown measures, resulting in children attending classes from their homes through online platforms. This study aimed to examine the impact of studying at home on children by investigating various aspects related to their study environment. Data were gathered through two questionnaires administered in Jordan: One targeted children aged 8-15 year through interviews, while the other targeted parents with at least one school-aged child through an online survey. The interview-based survey (N = 152) identified significant factors such as the study location, excessive use of computer peripherals, and physical discomforts like back, hand, and ear pain. Similarly, the parent survey (N = 1,152) revealed various significant factors including parents' monthly income, educational level, occupation, living area, sources of COVID-19 information, fear of the virus, TV time, eating and reading habits, smartphone usage, difficulty in concentrating while studying, engagement with social media, internet usage, elbow position, and eye protection.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

COVID-19 pandemic and air transportation: Summary of Recent Research, Policy Consideration and Future Research Directions

The COVID-19 pandemic can be considered an unparalleled disruption to the aviation industry in the last century. Starting with an at-that-time inconceivable reduction in the number of flights from March 2020 to May 2020, the aviation industry has been trying to navigate through and out of the crisis. This process is accompanied with a significant number of scientific studies, reporting on the direct and indirect impact of the COVID-19 pandemic on aviation and vice versa. This paper reviews the impacts in context of the recent literature. We have collected nearly 200 well-published papers on the subject in the years 2021/2022 and dissected them into a framework of eight categories, built around: airlines, airports, passengers, workforce, markets, contagion, sustainability, and economics. We highlight the essence of findings in the literature and derive a set of future research directions and policy considerations which we deem important on the way towards pandemic-resilient aviation.