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Based on the theory of planned behavior, this study aims to examine antecedents of older workers' intentions to engage in postretirement work (PRW) and actual planning for PRW.

A cross-sectional survey design was used. About 469 nurses (≥45 years old) from Midwestern United States completed an online survey containing various self-reports on attitude, perceived control, subjective norm, intentions to engage in PRW and actual planning for PRW.

Results found that attitude and subjective norm (but not perceived control) were positively related to older employees' intentions to engage in PRW. Perceived control was positively related to actual planning for PRW. PRW intentions mediated the effects of attitude and subjective norm on older workers' actual planning for PRW. Finally, perceived control enhanced the positive effect of PRW intentions on actual planning for PRW.

This study contributes to the research by testing the application of the theory of planned behavior to the literature on PRW, further increasing our knowledge of the roles of individuals' attitudinal and cognitive factors in predicting older employees' PRW intentions and actual planning for PRW.

The authors said their exploratory research question was “does perceived control interact with PRW intentions in influencing actual planning for PRW?”

Participants were nurses from the Midwestern US, and all were 45 years and older. A total of 469 qualified and complete questionnaires were received with an average age of 54.6.

The results showed that attitude and subjective norm were significantly related to PRW planning, but not perceived control. But perceived control correlated with PRW planning. Meanwhile, PRW intentions were positively related to PRW planning. The data also suggested that variables of TPB significantly predict older employees’ PRW intentions above and beyond age, health status and retirement income satisfaction. Finally, CIs indicated that the indirect effects of attitude and subjective norm on actual planning for PRW via PRW intentions were significant.

Previous studies have shown that people who plan for PRW are more likely to actually do it. However, there have been very few studies on the antecedents of older workers’ actual planning.

The COVID-19 pandemic has led to changes in academic routines. These changes have also contributed to an increase in the number of papers submitted to journals, citations and, ultimately, to changes in metrics. This study aims to address a gap between theory and practice, analysing the changes in the impact factor (IF) of a sample of 30 environment/sustainability-related journals, in the face of the COVID-19 pandemic.

The study used an expert-driven literature analysis and an assessment of a selected sample of 30 environment/sustainability-related journals’ increased trends.

The unprecedented trend observed when analysing the results obtained in the IF of environment/sustainability-related journals contribute to the body of knowledge on this topic, allowing us to understand how specifically the COVID-19 pandemic has influenced scientific publishing, despite the restrictions imposed by lockdowns and access to research knowledge and facilities.

Based on a sample of 30 environment/sustainability-related journals, this study can highlight lessons learned from the COVID-19 pandemic, suggesting specific measures which may be addressed to contribute to upkeep levels of publishing in the environment/sustainability field covered in this study.

This study will contribute to setting the stage for additional research on the influences of the COVID-19 pandemic on scientific publishing’s impact in environment/sustainability areas of knowledge. The implications of this research will allow us to set the basis for more extensive research in other areas of knowledge.

To the best of the authors’ knowledge, this study is unique, as it addresses the implications of the COVID-19 pandemic in 30 considered exemplary environment/sustainability-related journals, the main research area of all the authors involved in this publication.

Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos.

The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence.

The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy.

The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work.

The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.