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The objectives of this study were to evaluate knowledge on the coronavirus disease 2019 (COVID-19) transmission, attitude toward the measures of COVID-19 prevention and control, explore social responsibility behaviors and test for the association between variables and social responsibility among university students.

It was a cross-sectional study. The population was 7,754 students from a university in Yala province. The sample size was 370 students according to Krejcie and Morgan table. Google form was introduced to make the self-administered questionnaires. Content validity was evaluated by three experts, and for reliability of knowledge and attitude, the coefficient was 0.707 and 0.702. Data collection was done in the early April 2020. Descriptive statistics and χ² were used to analyze the data. Ethical approval number 095–63.

The results found that students' knowledge on COVID-19 transmission was in a moderate level, attitude toward the state measures for COVID-19 prevention and control was at a high level. Their social responsibility behaviors were at a high level. Their knowledge, attitude and payment were significant association with social responsibility behaviors among university students (p < 0.05).

University students' knowledge on disease transmission as well as positive attitude toward the measures of regulation led them to have cooperation. Social distancing and lockdown measures affected their lifestyle. However, they have responsibility behaviors to the society, especially in the pandemic situation.

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 coronavirus disease 2019 (COVID-19) pandemic has been reported to have a major impact on the mental health of an individual. Healing the mental stress, anxiety, depression and insomnia of an individual's immediate surroundings play a major role. Therefore, this study reviews how the built environment impacts the healing of an individual's state of mind.

Various works of literature on healing environments were analysed to create frameworks that can facilitate psychological healing through architectural elements. Articles were selected from various journals like SAGE, PubMed, Journal of Applied and Computational Mechanics (JACM), Routledge Taylor and Francis, Journal of Contemporary Urban Affairs (JCUA), ScienceDirect, and Emerald databases, news articles, official web pages, and magazines that have been referred.

Indicators (spatial, sensory comfort, safety, security, privacy and social comfort) are linked to sub-indicators (access, distractions and views) and design characteristics (indoor climate, interior view, outside view, privacy, communication, noise, daylighting, temperature) which help in better connection of the built environment with individual's mental health. From the above indicators, sub-indicators and design characteristics, the authors have come to a conclusion that a view to the outside with better social interaction has an in-depth effect on an individual's mental health.

This study predominantly talks about healing in hospitals but quarantining of COVID-19 patients happens in residences too. So, it is important to find the healing characteristics in residences and in which typology the recovery process is high.

This paper has been written completely by the author and the co-authors and has not been copied from any other sources.

This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results.

This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018.

It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments.

This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers.

This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars.

This study searched five databases for relevant literature published between 2009 and 2018, selecting 64 papers for this review. The selected literature was categorised according to two dimensions: a service supply chain link perspective and a behavioural factor perspective. Comparative analysis was used to identify gaps in the literature, and five future research agendas were proposed.

In terms of the perspective of service supply chain link, extant literature primarily focuses on service supply and service co-ordination management, and less on service demand and integration management. In terms of the behavioural factor’s perspective, most focus on classic behaviour factors, with less attention paid to emerging behaviour factors. This paper thus proposes five research agendas: demand-oriented management and integrated supply chain-oriented behavioural research; broadening the understanding of the scope of behavioural operations; integrating the latest backgrounds and trends of service industry into the research; greater attention to behavioural operations in service sub-industries; and multimethod combination is encouraged to be used to dig into the interesting research problems.

This study constitutes the first systematic review of service supply chain research from a behavioural perspective. By categorising the literature into two dimensions, the state of existing research is evaluated with an eye towards future research avenues.

Constant or decreasing returns and increasing returns to scale are two kinds of mechanism in economic growth. The goal of supply-side structural reform is to promote the establishment of the mechanism with increasing returns to scale. The paper aims to discuss this issue.

This paper argues that the overall economic structure of the developing economy has been divided into the sector of constant or decreasing returns to scale and the sector of increasing returns to scale due to the dual economic structure. Among them, the supply-side structural reform is mainly to reduce the sector of decreasing returns to scale and increase the sector of increasing returns to scale. Based on the hypothesis of such two-sector economic structure in the supply side of developing economies and on the industrial data, this paper empirically tests the returns to scale of China’s supply structure. The result suggests that so far the sector of constant or decreasing returns to scale dominates the supply structure of China’s economic growth, which results in the state of decreasing returns to scale in China’s overall economy.

Therefore, to realize the long-term sustained growth and transformation of the development pattern of China’s economy, the authors must carry out the supply-side structural reform, vigorously develop the modern industrial sectors characterized by modern knowledge and technology, and promote the development of an innovation-driven economy.

Besides, the authors must accelerate the transformation from traditional industrial sectors to modern industrial sectors, actively promote China’s industrial structure toward rationalization and high gradation, as well as build a modern industrial system so as to facilitate the formation of the mechanism of increasing returns to scale and accelerate the transformation of the driving force of China’s economic growth.

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.

The type 120 emergency valve is an essential braking component of railway freight trains, but corresponding diaphragms consisting of natural rubber (NR) and chloroprene rubber (CR) exhibit insufficient aging resistance and low-temperature resistance, respectively. In order to develop type 120 emergency valve rubber diaphragms with long-life and high-performance, low-temperatureresistant CR and NR were processed.

The physical properties of the low-temperature-resistant CR and NR were tested by low-temperature stretching, dynamic mechanical analysis, differential scanning calorimetry and thermogravimetric analysis. Single-valve and single-vehicle tests of type 120 emergency valves were carried out for emergency diaphragms consisting of NR and CR.

The low-temperature-resistant CR and NR exhibited excellent physical properties. The elasticity and low-temperature resistance of NR were superior to those of CR, whereas the mechanical properties of the two rubbers were similar in the temperature range of 0 °C–150 °C. The NR and CR emergency diaphragms met the requirements of the single-valve test. In the low-temperature single-vehicle test, only the low-temperature sensitivity test of the NR emergency diaphragm met the requirements.

The innovation of this study is that it provides valuable data and experience for future development of type 120 valve rubber diaphragms.