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The purpose of this study is to investigate existing and emerging technology-driven stressors using the transactional model of stress and coping (TMSC).

In-depth semi-structured interviews with 36 professionals were performed to obtain qualitative data to explore emerging techno-stressors. The findings were validated a year into the pandemic with human resource (HR) professionals.

The authors identify a previously unreported techno-stressor, Techno-Isolation (TIS), which arises from a heavy dependence on information communication technologies for professional social interactions. Additionally, several considerations of interaction characteristics are identified that, based on the platform used, affect the experience of TIS, further expanding the TMSC with the addition of medium-interaction compatibility. The authors present a testable model and discuss implications.

This study identifies three new information communication technology (ICT)-based antecedents leading to a new techno-stressor, as well as the importance of medium-interaction compatibility in the experiences of stressors as strains. The authors discuss how these elements fit with and extend the existing stress literature.

The COVID-19 pandemic has had a significant impact on medical education and training, with many medical schools and training programs having to adapt to remote or online learning, social distancing measures and other challenges. This paper aimed to examine the disruption for clinical training, as it has reduced the opportunities for students and trainees to gain hands-on experience and interact with patients in person.

The ethnographic qualitative research design was chosen as the research methodology. Using Gibbs' reflective cycle, the researcher explored the psychiatry clerks' (final-year medical students) reflections on the disruption of their clinical training during the COVID-19 pandemic.

The findings demonstrated that the students had a significant psychological impact on their coping capacities as the crisis progressed from shock and depression to resilience. The students being the key stakeholders provided a concrete foundation for the development of a framework for improving practices during uncertain times.

Students' reflections provided valuable insight into the pandemic’s impact on their psychosocial lives with uncertainty and incapacity to cope up with changing stressful dynamics. The results will assist in planning how to best support medical students' well-being during interruptions of their educational process brought about by similar future crises.

This study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle.

This methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations.

The production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research.

Originality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.