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This paper aims to comprehensively clarify the research status of thermal transport of supercritical aviation kerosene, with particular interests in the effect of cracking on heat transfer.

A brief review of current research on supercritical aviation kerosene is presented in views of the surrogate model of hydrocarbon fuels, chemical cracking mechanism of hydrocarbon fuels, thermo-physical properties of hydrocarbon fuels, turbulence models, flow characteristics and thermal performances, which indicates that more efforts need to be directed into these topics. Therefore, supercritical thermal transport of n-decane is then computationally investigated in the condition of thermal pyrolysis, while the ASPEN HYSYS gives the properties of n-decane and pyrolysis products. In addition, the one-step chemical cracking mechanism and SST k-ω turbulence model are applied with relatively high precision.

The existing surrogate models of aviation kerosene are limited to a specific scope of application and their thermo-physical properties deviate from the experimental data. The turbulence models used to implement numerical simulation should be studied to further improve the prediction accuracy. The thermal-induced acceleration is driven by the drastic density change, which is caused by the production of small molecules. The wall temperature of the combustion chamber can be effectively reduced by this behavior, i.e. the phenomenon of heat transfer deterioration can be attenuated or suppressed by thermal pyrolysis.

The issues in numerical studies of supercritical aviation kerosene are clearly revealed, and the conjugation mechanism between thermal pyrolysis and convective heat transfer is initially presented.

This study aims to present a comprehensive knowledge mapping and an in-depth analysis of plastic and sustainability research to understand better global trends and directions in this field that emerged between 1995 and 2022.

This study presents a visual analysis of 1933 research articles listed in the Web of Science (WoS) databases between the years 1995 and 2022 related to plastic and sustainability. The knowledge mapping based on CiteSpace and VOSviewer presents the current research status, which contains the analysis of the collaboration network, co-citation network, references with citation bursts and keyword analysis.

The results reveal that China and the USA are the most prominent countries in exploring the notion of sustainability and plastic. The Chinese Academy of Science is the most prominent institution. Chai Qiang, Friedrich Daniel, Sahajwalla Veena and Ok Yong Sik are the most prolific authors in this field. Furthermore, circular economy, bioplastic, sustainable development, polyester and bioplastics are the highly discussed issues in recent years. Not surprisingly, COVID-19 is the latest topic of discussion started in 2021 due to its negative impact on plastic pollution and the challenges it posed to sustainability.

This study is among the pioneers to shed light on the current research status of plastic and sustainability using the bibliometric method and the newest data. This study also suggests that collaborations between scholars and institutions require to be enhanced for better management of plastic pollution and to contribute to sustainable development.

This paper aim at providing decision support for operational adjustment. In order to effectively reduce the external interference impact on train operation.

According to the reality that there are two kinds of high-speed trains running in China, considering the reality that there are two speed level train running in Chinese high-speed railway, this paper proposed the high-speed railway operation adjustment model based on priority, and the objective function is to minimize interference impact on train operation.

Adjustment strategy based on priority than based on sequence can effectively reduce the interference influence on train operation, which makes the train resume the planned operation back on schedule more quickly.

The solution of large-scale cases is too slow, and the practical application is limited.

The model was verified by a case, and the case results proved that adjustment strategy based on priority than based on sequence can effectively reduce the interference influence on train operation, which makes the train resume the planned operation back on schedule more quickly.

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

This paper aims to gain insight in how the involvement of facilities management (FM) and clinical employees are practiced in new Norwegian hospital projects and to study the benefits and lessons learned from the involvement.

This study is conducted by cross-sectional case studies of eight hospital projects by using a literature review, interviews and document studies of FM and clinical employees and project leaders (PLs) among Sykehusbygg.

The service design approach with a structured interaction between the PL’s of Sykehusbygg, and the different disciplines of clinical employees and FM specialist was rewarding and efficient. The facilitator role of Sykehusbygg is essential to manage such a broad and complex involvement process using a wide range of various techniques at the different stages of the projects (dialogue meetings, review meetings, workshops, post-it notes, 2-D drawings, mock-up and 3-D models, as well as virtual reality (VR) and Building Information Modeling technology). The clinical employees’ framework is stronger and much more structured than the involvement of FM competences through the different stages of the projects. The property management competences were involved at the early concept phase and design phase, whereas the Operation and Maintenance (O&M) competences were getting involved through the construction and commissioning phase. The value of FM involvement in all stages of the project is seen beneficial, particularly when the FM specialist become a part of the design team and located physically at the same place. The main reported benefits of early FM involvement are cost-effective technical solutions and installations, less design flaws and improved functionality, as well as a stronger ownership and mutual respect between the clinical and FM disciplines. However, not all hospital organizations see the benefits of the FM involvement of all stages, as they are driven by reducing capital cost. In one of the new projects, other ways of involving the FM competences were tested. Additionally, particularly for the O&M competences, a dialogue meeting with a clear focus of sharing experiences with different technical solution was found rewarding in terms of cost benefits.

This study does not consider the social impact of the choices made in the design phase. The findings also indicated a certain development of the FM involvement. This is not studied in two of the newest projects where they are still in the design phase and the FM role was not interviewed.

The PL role is important as a facilitator role of the involvement process.

A dialogue meeting with a group of O&M people was found rewarding and valuable for knowledge sharing. This methodology can be further developed and tested, as this group of stakeholders is not always available for giving input in the project.

The value of this study is the description of the interaction between the PLs and the hospital organization in the eight projects and lessons learned by the involvement of FM competences and clinical employees.

Drawing on attribution theory, the current paper aims to examine the effects of review content structures on online review helpfulness, focusing on three pertinent variables: review sidedness, information factuality, and emotional intensity at the beginning of a review. Moreover, the moderating roles of reviewer reputation and review sentiment are investigated.

The review sentiment of 144,982 online hotel reviews was computed at the sentence level by considering the presence of adverbs and negative terms. Then, the authors quantified the impact of variables that were pertinent to review content structures on online review helpfulness in terms of review sidedness, information factuality and emotional intensity at the beginning of a review. Zero-inflated negative binomial regression was employed to test the model.

The results reveal that review sidedness negatively affects online review helpfulness, and reviewer reputation moderates this effect. Information factuality positively affects online review helpfulness, and positive sentiment moderates this impact. A review that begins with a highly emotional statement is more likely to be perceived as less helpful.

Using attribution theory as a theoretical lens, this study contributes to the online customer review literature by investigating the impact of review content structures on online review helpfulness and by demonstrating the important moderating effects of reviewer reputation and review sentiment. The findings can help practitioners develop effective review appraisal mechanisms and guide consumers in producing helpful reviews.

The paper aims to present the comprehensive picture on green human resource management (GHRM) based upon bibliometric investigation, including different criteria, such as key authors, documents, keywords, bibliographic coupling (BC), etc.

Bibliometric technique has been used with the application of VOSviewer. Further, the Scopus database has been utilised to extract the research articles.

The present study has exposed the due relevance of GHRM in the area of human resource management (HRM) for researchers as well as practitioners. The study has revealed that number of citations in the area is high from the researchers of China. Also, Jabbour, C.J.C., has been identified as the lead contributor and author from the area with 43 publications and 1,352 citations. The journal of cleaner production has been identified as the journal with the largest number of publications from the area. Also, prominent keywords used in the area are sustainability, GHRM, environmental management, sustainable environment and HRM.

The study has offered the comprehensive publication analysis on the area which will assist the researchers in gaining review on topic and development of empirical models. Further, with the utilisation of keywords criterion they can easily explore the publications done in the area. Also, study will help the scholars and researchers to identify the region and countries along with the institutions and universities to pursue research on the GHRM topic.

The present study will help the mangers to focus on GHRM with more focus to acquire positive consequences for human resource management.

The present paper has presented various new and recent facets associated with the area of GHRM. Previously the similar analysis with bibliometric technique is rarely done with the present Scopus database.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.