This research paper explores the influence of first-order chemical reactions on the sustainable properties of electrically conducting magnetohydrodynamic (MHD) fluids in a vertical channel with the unique characteristics of Jeffrey fluid flow. The mathematical model of MHD flow with Jeffrey fluid and chemical reaction incorporates the impacts of viscous dissipation, Joule heating, and a non-Newtonian fluid model with viscoelastic properties in the flow regions. The governing equations of the flow field were solved using the finite difference method, and the impacts of flow parameters on the flow characteristics were discussed numerically using a graphical representation. It’s revealed that increasing the Jeffrey parameter results in a decline in the velocity field profiles. Also, species concentration field profiles decline with higher values of the destruction chemical reaction parameter. The findings of this study have significant implications for various engineering applications, including energy generation, aerospace engineering, and material processing. Additionally, the inclusion of Jeffrey’s fluid flow introduces a viscoelastic component, enhancing the complexity of the fluid dynamics.

Air pollution in Jakarta has become a severe concern in the last four months. IQAir, in August 2023, revealed that the level of air pollution had reached 161 points on the Air Pollution Standard Index (APSI). The negative impact on society has placed air pollution as a concern for environmental safety and survival in danger. This condition will encourage the development of a national policy agenda to integrate environmental welfare through various energy efficiency channels. This research analyzes the relationship between air pollutant elements that can reduce air quality. The analysis includes pollutant intensity measured by APSI per unit of pollutant as a measure of efficiency. The aim is to observe energy use, which causes an increase in pollutant levels. This research utilizes dynamic system modeling to produce relationships between parameters to produce factors that cause pollution. The parameters used are motorized vehicles, waste burning in landfills, industry, and power plants. The results of historical behavioral tests and statistical suitability tests show that the behavior is suitable for the short and long term. The simulation results show that the pollution level will worsen by the end of 2027, a hazardous condition for society. The optimistic scenario simulation model proposes immediate counter-measures to reduce pollution to 45.01, the ideal condition. To accelerate improvements in air quality, the Government can plan policies to reduce the use of coal by power plants and industry, as well as the use of electric motorized vehicles, resulting in an ideal reduction in pollution by 2024. In conclusion, pollution can be reduced effectively if the Government firmly implements policies to maintain that air quality remains stable below 50 points.

This article explores the implications of directive change management, characterized by top-down leadership and minimal employee involvement, on organizational dynamics, employee morale, and job security. This approach's psychological and operational impacts are underscored, emphasizing the imperative of addressing employee perceptions and fostering trust. Strategies for rebuilding trust and enhancing morale post-directive change management are presented, including transparent communication, participative decision-making, and recognition of employee contributions. The significance of enhancing job security through clear policies, open dialogue, and robust mental health and well-being support systems is highlighted. Practices that encourage job dedication are introduced, emphasizing goal alignment, meaningful work design, and a culture of innovation and continuous improvement. Long-term strategies for cultivating a healthy workplace, such as establishing feedback mechanisms, investing in leadership development, and maintaining organizational adaptability, are also discussed. This brief article is an introductory resource for business leaders, managers, and change practitioners seeking to be better equipped with the necessary tools and strategies to navigate the post-implementation effects of directive change management. It is anticipated that this information can assist leaders and organizations in navigating the challenges of directive change management, promoting resilience, employee well-being, and sustainable organizational success.

This study addresses the impact of the tourism sector on poverty, poverty depth, and poverty severity in Indonesia, focusing on the micro-level dynamics in the province. Despite numerous tourism destinations, their strategic contribution to regional progress remains underexplored. The motivation stems from the need to comprehend the nuanced relationship between tourism and poverty at both the national and local levels, with specific attention to the untapped potential at the province level in Indonesia. We hypothesize that a higher tourism sector GRDP will be inversely correlated with poverty levels, and the inclusion of a Covid-19 variable will reveal a structural impact on poverty dynamics. Employing a Panel Regression Model, secondary data from the Central Statistics Agency (BPS) spanning 2011–2020 is utilized. A panel data regression equation model, including CEM, FEM, and REM, is employed to analyze the intricate relationship between tourism and poverty. The findings demonstrate a negative correlation between higher tourism sector GRDP and the number of poor people. The Covid-19 variable, considered a structural break, reveals a significant association between increased cases and elevated poverty and severity across Indonesian provinces. This study contributes a micro-level analysis of tourism’s role, emphasizing its impact at the provincial level. The findings underscore the need for strategic initiatives to harness the untapped potential of tourism in alleviating poverty and promoting regional progress.

The main purpose of this paper was to examine the impact of generative artificial intelligence (AI) on employee well-being and work dynamics. Using qualitative methodology, three semi-structured interviews were conducted to investigate the implications of generative AI on employee outcomes such as efficiency, job satisfaction, ethical considerations, and work-life balance. The findings highlighted the potential benefits and risks associated with generative AI implementation in the workplace. The study contributed to the literature by adopting a qualitative approach, allowing in-depth exploration of individual experiences with generative AI in the workplace. The study discussed the implications for employers, employees, and society.