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This study aims to conduct a bibliometric analysis of sustainable tourism research published in Scopus-indexed journals covering the period from 1997 to 2021. Articles published during these 25 years were subjected to science mapping and performance analysis to propose potential areas for future research.

A bibliometric analysis using performance analysis and science mapping was conducted on 1,754 research papers retrieved from the Scopus database using the keyword “sustainable tourism.” Biblioshiny and VOSviewer are commonly used bibliometric tools. Science mapping techniques use coauthorship, keyword co-occurrence and co-citation analyses.

This study revealed the sustainable tourism publications’ spatial and temporal patterns, indicating a yearly growth rate of 19.9% during a 25-year period. The study identified Stefan Gossling as the most influential author, the “Journal of Sustainable Tourism” as the leading journal and Australia as the most productive country in sustainable tourism literature. The study used co-citation analysis to identify five thematic clusters, namely, reconceptualization and criticism, the role of residents, eco-labeling and the role of stakeholders, community-based tourism and the shift toward establishing sustainability indicators and effective governance and policymaking. The coauthorship analysis identifies the most influential author in collaborative efforts, and the most common pattern of collaboration is between researchers from different institutions in the same country, such as China and the Philippines, followed by collaborations between authors from other countries. The keyword co-occurrence analysis uncovered keywords that aligned with theme clusters generated from the co-citation analysis.

This study comprehensively uncovers five thematic clusters that have never been extracted so far in the literature. Also, it attempts to fill the gaps related to sustainable tourism by suggesting directions for future research.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but also changes along time. A thermal conductivity prediction model for aerogel considering heat treatment effect is carried out and applied to solve the heat conduction problem of a TPS. The aim of this study is to provide theoretical and numerical references for further development of aerogels applying to TPSs.

A thermal conductivity prediction model for aerogel is established considering treatment effect. The heat conduction problem of a TPS is derived and solved by combining the differential quadrature method and the Runge–Kutta method. The prediction results of aerogel thermal conductivities are verified by comparing with those in literature, while the calculated temperature field of TPS is verified by comparing with that by ABAQUS.

Numerical results show that when applying the current prediction model, the calculated high temperature area in the aerogel layer is narrowed due to the decrease of the thermal conductivity during heat treatment process.

This study will be beneficial to carry out the precise design of TPS for long endurance HFVs.