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As the world grapples with the pervasive effects of the coronavirus pandemic, a notable disconnect has emerged in the public's understanding of scientific and medical research. Particularly, the travel industry has become unquestionably vulnerable amid the COVID-19 outbreak; this pandemic has interrupted the industry's operations with devastating economic consequences. This paper aims to highlight the importance of deconstructing barriers between medical science and public awareness related to COVID-19, taking tourism as a case in point. It also discusses the role of interdisciplinary research in facilitating the tourism and hospitality industry's recovery and alleviating tourists' uncertainties in the wake of COVID-19.

This paper offers a synthesis of news coverage from several media outlets, framed within the literature on knowledge transformation across disciplines. This framing focuses on the medical sciences (e.g. public health) and social sciences (e.g. tourism management) to identify gaps between medical scientific knowledge and public awareness in the context of COVID-19. The authors' experience in public health and tourism management further demonstrates a missing link between academic research and the information made available in public health and everyday settings. A potential research agenda is proposed accordingly.

This paper summarizes how salient issues related to knowledge transfer can become intensified during a global pandemic, such as medical research not being communicated in plain language, which leads some citizens to feel apathetic about findings. Reporting on the prevalence and anticipated consequences of disease outbreaks can hence be difficult, especially early in the development of diseases such as COVID-19.

By assuming a cross-disciplinary perspective on medical/health and social science research, this paper encourages academic and practical collaboration to bring medical research to the masses. This paper also outlines several research directions to promote public health, safety and sustainability through tourism.

This paper highlights that it is essential for medical knowledge to be disseminated in a manner that promotes public understanding. The tourism and hospitality industry can benefit from an essential understanding of medical findings, particularly during this pandemic. Without a firm grasp on COVID-19's origins and treatment, the tourism and hospitality industry will likely struggle to recover from this catastrophe.

Taking COVID-19 as a case in point, this study advocates leveraging the strengths of disparate domains to bring medical findings to a wider audience and showcase cutting-edge developments for the greater good. This study also emphasizes the importance of engaging the general public in reputable scientific research findings to increase public awareness in a professional and accurate manner.

This paper presents a unique and critical discussion of the gap between medical science knowledge and public awareness, as well as its implications for tourism and hospitality recovery after COVID-19, with a focus on applying medical scientific knowledge to post-pandemic industry recovery.

當全球正與冠狀病毒流行病所帶來的廣泛影響抗衡之際，公眾對科學和醫學研究的理解卻出現了一個令人關注的脫節現象。在2019冠狀病毒病爆發期間，旅遊業無疑格外受到影響。這流行病中斷了旅遊業的運作，給業界帶來毀滅性的經濟影響。本文旨在以旅遊業為一個適時的例子、強調我們必須剖析與2019冠狀病毒病相關的醫學與公共意識之間的障礙。本文亦討論跨學科研究對促進2019冠狀病毒病疫情後旅遊及酒店業的復甦、及減低旅客的不確定性所扮演的角色。

本文提供一個來自數個媒體管道的新聞報導的綜合論述，而這論述是在與跨學科知識轉化相關的文獻領域內陳述而成的。論述的重點放在醫學科學(例如公共衛生) 和社會科學(例如觀光管理) 上，目的是確定在2019冠狀病毒病的背景下、醫學知識與公共意識之間的差距。作者們在公共衛生及觀光管理方面的經歷進一步顯示學術研究、與公共衛生和日常環境上的公開資訊之間缺乏銜接，本文就此提出一個研究議程。

本文總結與知識轉化相關的顯著問題如何會在全球流行病爆發期間變得更嚴重。譬如、醫學研究的資訊會因沒有以通俗語言來傳遞而令有些國民對其研究結果漠不關心。因此，關於疾病的傳播和爆發所預期的影響的信息是難於廣傳的，特別是在像2019冠狀病毒病等疾病形成的初期。

本文就醫學知識與公共意識之間的差距作出了一個獨特、有批判性的討論，亦論述這差距對2019冠狀病毒病疫情過後旅遊及酒店業復甦的意義。討論的焦點放在如何應用醫學知識在疫情後幫助產業復甦上。

作者們採用涵蓋醫學/衛生和社會科學研究的跨學科角度，鼓勵學者與從事實務人員相互合作、以便把醫學研究帶給廣泛的民眾。本文亦概述多個研究方向，透過旅遊業來推動公共衛生、公眾安全及可持續性。

本文強調醫學知識必須透過能提高公共意識的方式來傳遞。旅遊及酒店業會因我們對醫學研究結果擁有必要的認識而受惠，尤其是在這流行病蔓延的期間。若我們對2019冠狀病毒病的病源和療法沒有確切的了解，則旅遊及酒店業將難從這災難中恢復過來。

本研究利用2019冠狀病毒病這個適時的例子，來提倡借助各個不同領域的優點，為了謀求公眾的利益、把醫學的研究結果帶給更多民眾、及傳達醫療最新發展的信息。本研究亦強調以專業、精準的方法，引起公眾對可信賴的科學研究結果產生興趣，從而提高公共意識至為重要。

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

This paper aims to study the effect of the establishment of bankruptcy courts on bond issuance market. This paper helps to predict that the introduction of bankruptcy courts in China can mitigate price distortions caused by the implicit government guarantees and promote the development of the high-risk bond market.

This paper exploits the staggered introduction of bankruptcy courts across cities to implement a differences-in-differences strategy on bond issuance data. Using bonds issued in China between 2018 and 2020, the impact of bankruptcy courts on the bond issuance market can be analyzed.

This paper reveals that bond issuance credit spreads increase and is more sensitive to firm size, profitability and downside risk of issuance entity after the introduction of bankruptcy courts. It also reveals a substantive increase in bond issuance quantity and a decrease in issuer credit ratings following the establishment of bankruptcy courts. In addition, the increase of credit spreads is more prominent for publicly traded bonds, those whose issuers located in provinces with lower judicial confidence, bonds issued by SOEs and bonds with stronger government guarantees. Finally, the role of bankruptcy courts is more pronounced in regions with higher marketization.

This paper relates to previous studies that investigate the impact of laws and institutions on external financing. It helps provide new evidence to this literature on how improvements of efficiency and quality in bankruptcy enforcements relate to the marketization of bond issuance. The results provide further evidence on legal institutions and bond financing.

Research dealing with earnings management in the public-sector context is expanding. This paper aims to review the existing literature to understand how research is developing and points out gaps deserving further investigation.

This study uses the structured literature methodology to investigate the state-of-the-art and future directions of the literature on earnings management in the public sector. In total, 78 articles were explored.

The critical analysis of the literature shows that different but related streams of literature are emerging, focused on both a macro- and a micro-level perspective (mainly local governments and state-owned enterprises).

This study is the first that offers a comprehensive review of the literature on the emerging topic of earnings management in the public-sector context. The structured literature review enables the identification of future directions for the literature in this field.

This paper claims to identify the behavioral and cultural features that push to use, or not, cryptocurrencies for electronic commerce. Indeed, despite the use of cryptocurrencies for electronic commerce spreading worldwide at a fast and growing pace, there are supporters and detractors among their users. The analysis of what distinguish these two groups of users is fundamental for understanding their different intention to use cryptocurrencies for electronic commerce.

A survey has been administered to 2,532 cryptocurrencies’ users across the USA and China, collecting data on their behavioral predispositions and cultural features. Results were then analyzed through structured equation modeling.

Results showed that while attitude, subjective norms, perceived behavioral control and herding behavior have a positive impact on the intention to use cryptocurrencies for electronic commerce, financial literacy has no influence. Cultural dimensions amplified or reduced the discovered relationships and caused different effects: positive for the USA and negative for China when considering illegal attitude and perceived risk.

Theory of planned behavior, financial behavior and cultural factors can, all together, represent a useful framework for envisioning the behavior of users in adopting cryptocurrencies for electronic commerce purposes through a test of all its elements. To the best of the authors’ knowledge, this is the first study considering behavior and cultural variables on the intention to use cryptocurrencies for electronic commerce as well as being the largest carried out, in terms of sample, on the cryptocurrency topic.

This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Poverty alleviation has been a major theme of China's modernization process since the founding of New China. This paper points out that China's poverty alleviation process presents three stylized facts: “Miraculous” achievements of poverty alleviation have been made on a global scale; the poverty alleviation achievements mainly occurred in the high growth stage after reform and opening up; the poverty alleviation process is accompanied by the structural transformation of the urban–rural dual economy.

Therefore, a logically consistent analytical framework should form among the structural transformation of the dual economy, economic growth and the achievements in poverty alleviation. In logical deduction, the structural transformation of the dual economy affects rural poverty alleviation through the effects of labor reallocation, agricultural productivity improvement, demographic change and fiscal resource allocation.

The first two refer to economic growth, and the latter two are alleviation policies. The combination of economic growth and poverty alleviation policies is the main cause for poverty alleviation performance. China's empirical evidence can support the four effects by which the structural transformation of the dual economy affects poverty alleviation.

China's socialist system and its economic system transformation after reform and opening up provide an institutional basis for the effects to come into play. After 2020, China's poverty alleviation strategies will enter the “second-half” phase, namely, the phase of solving the problems of relative poverty in urban and rural areas by adopting conventional methods and establishing long-term mechanisms. This requires the facilitation of the reconnection between poverty alleviation strategies and the structural transformation of the dual economy in terms of development ideas and policy directions.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.