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This study aims to examine the direct effects of Green Human Resource Management (GHRM) practices on employee green behavior (EGB) in the university setting in Bangladesh and to find the indirect effects of how GHRM promotes EGB through sequentially mediating employee environmental knowledge management (EEKM) (environmental knowledge and knowledge sharing) and green self-efficacy (GSE).

For the empirical study, the researcher uses partial least squares structural equation modeling to test the proposed conceptual model built on existing literature for greening workplaces in the university in Bangladesh. The study has collected data from 288 Bangladeshi university employees using convenient sampling.

The findings that GHRM practices positively and significantly promote EGB, which captures the employee's tendencies to exercise green behavior in daily routine activities such as turning off lights, air conditioning, computers and equipment after working hours, printing on both sides, recycling (reducing, repair, reuse), disseminating good green ideas, concepts, digital skills and knowledge to peers and champion green initiatives at work. Moreover, the findings also revealed the sequential mediation of EEKM (environmental knowledge and knowledge sharing) and GSE of employees between the link GHRM and EGB. At last, the findings suggested that HR managers can implement the GHRM practices to promote green behaviors among the academic and non-academic staff of the university.

This study contributes to the field by extending knowledge of Social Cognition Theory and Social Learning Theory for greening workplaces in Bangladesh, particularly universities. Specifically, this empirical study is unique to the best of our knowledge and highlights the role of EEKM and GSE as mediation between GHRM and EGB association.

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.

With the growing deployment of variable renewable energy sources, such as wind and PV and the increasing interconnection of the power grid, multi-regional energy system models (ESMs) are increasingly challenged by the growth of model complexity. Therefore, the need for developing ESMs, which are realistic but also solvable with acceptable computational resources without losing output accuracy, arises. The purpose of this study is to propose a statistical approach to investigate asynchronous extreme events for different regions and then assess their ability to keep the output accuracy at the level of the full-resolution case.

To extract the extreme events from the residual demands, the paper focuses on analyzing the tail of the residual demand distributions by using statistical approaches. The extreme events then are implemented in an ESM to assess the effect of them in protecting the accuracy of the output compared with the full-resolution output.

The results show that extreme-high and fluctuation events are the most important events to be included in data input to maintain the flexibility output of the model when reducing the resolution. By including these events into the reduced data input, the output's accuracy reaches the level of 99.1% compared to full resolution case, while reducing the execution time by 20 times.

Moreover, including extreme-fluctuation along with extreme-high in the reduced data input helps the ESM to avoid misleading investment in conventional and low-efficient generators.