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Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

Francois Quesnay, known as the “Confucius of Europe”, was profoundly influenced by traditional Chinese culture to form his thoughts, which contained strong Chinese characteristics. This paper aims to examine economic thought of Francois Quesnay from the perspective of the construction of socialist political economics with Chinese characteristics.

Moreover, his thoughts also profoundly influenced subsequent economists, such as Adam Smith and Karl Marx. It can be said that Francois Quesnay was at the intersection of Chinese, Western and Marxist thought systems, so it is quite important to examine his thoughts critically and conduct source-tracing in China.

Hence, in the process of constructing and developing socialist political economy with Chinese characteristics, there is an urgent need to focus on exploring the value of excellent traditional Chinese culture at the theoretical level and combining the development and dissemination of the history of thoughts and the historical position of Chinese reality to realize the innovation and development of socialist political economy with Chinese characteristics.

Meanwhile, while absorbing nutrition from excellent traditional Chinese culture, it is necessary to establish scientific coordinates rather than use the discourse systems and paradigms of Western economics to interpret ancient Chinese economic thoughts. It is necessary to adhere to, inherit and develop Marxist political economy and absorb nutrition from Chinese excellent traditional culture to construct socialist political economy with Chinese characteristics.

The real costs of trade, the transport and other costs of doing business internationally, are very important determinants of a country's ability to participate fully in the world economy. Remoteness and poor transport and communications infrastructure isolate countries, inhibiting their participation in global production networks. This paper investigates the dependence of transport costs on geography and infrastructure It shows that infrastructure is quantitatively important in determining transport costs, and improvements in infrastructure can dramatically increase trade flows. It also finds that the low level of Northeast Asian countries' trade flows is largely due to poor infrastructure. Competition among countries in East Asia to maintain or become a logistic hub in the region is severe. This is reflected in the competition to build or expand airports and seaports in the region. Competing countries need to find ways of cooperating to achieve an efficient resource allocation in the region as a whole.

The purpose of this study is to investigate the transport phenomena of smoke flow in a semi-open vertical shaft.

The large eddy simulation (LES) method was used to model the movement of fire-induced thermal flow in a full-scale vertical shaft. With this model, different fire locations and heat release rates (HRRs) were considered simultaneously.

It was determined that the burning intensity of the fire is enhanced when the fire attaches to the sidewall, resulting in a larger continuous flame region in the compartment and higher temperatures of the spill plume in the shaft compared to a center fire. In the initial stage of the fire with a small HRR, the buoyancy-driven spill plumes incline toward the side of the shaft opposite the window. Meanwhile, the thermal plumes are also directed away from the center of the shaft by the entrained airflow, but the inclination diminishes as HRR increases. This is because a greater HRR produces higher temperatures, resulting in a stronger buoyancy to drive smoke movement evenly in the shaft. In addition, a dimensionless equation was proposed to predict the rise-time of the smoke plume front in the shaft.

The results need to be verified with experiments.

The results could be applied for design and assessment of semi-open shafts.

This study shows the transport phenomena of smoke flow in a vertical shaft with one open side.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

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 purpose of this study is to unearth the factors that influence tourists’ revisit intention. The proposed model of the study is grounded on using the theory of planned behaviour (TPB) and extending it with additional variables, i.e. satisfaction, destination image, perceived risk, service quality and perceived value.

This study adopted a cross-sectional approach to collect data. The data were collected by conducting a field survey questionnaire on 330 respondents and were analysed using partial least squares version 3.2.9.

The results show that perceived behavioural control, perceived value, destination image and satisfaction significantly affect visitors’ revisit intention. The influence of perceived value, perceived service quality and destination image on satisfaction is also confirmed. On the other hand, satisfaction is found to be a significant mediator between perceived service quality, destination image and perceived value.

The extended TPB model that includes perceived service quality, perceived value, perceived risk and satisfaction provided a model with a theoretical basis to explain tourist revisit intentions to a tourist destination.

El objetivo principal del estudio es descubrir los factores que influyen en la intención de revisita de los turistas. El modelo propuesto para el estudio se basa en el uso de la teoría del comportamiento planificado y se amplía con variables como la satisfacción, la imagen del destino, el riesgo percibido, la calidad del servicio y el valor percibido.

Este estudio adoptó un enfoque transversal para la recogida de datos. Los datos se recopilaron mediante un cuestionario de campo en el que participaron 330 encuestados. Los datos se analizaron utilizando la versión 3.2.9 de PLS.

Los resultados muestran que el control conductual percibido, el valor percibido, la imagen del destino y la satisfacción afectan significativamente a la intención de revisita. También se confirma la influencia del valor percibido, la calidad de servicio percibida y la imagen del destino sobre la satisfacción. Por otra parte, la satisfacción resulta ser un mediador significativo entre la calidad de servicio percibida, la imagen del destino y el valor percibido.

El modelo TPB ampliado que incluye la calidad de servicio percibida, el valor percibido, el riesgo percibido y la satisfacción proporcionó un modelo con una base teórica para explicar las intenciones de revisita de los turistas a un destino turístico.

本研究的目的是揭示影响游客重访意向的因素。本研究提出的模型以计划行为理论（TPB）为基础, 并以额外的变量（即满意度、目的地形象、感知风险、服务质量和感知价值）进行扩展。

本研究采用了横断面的方法来收集数据。通过对330名受访者进行实地调查问卷来收集数据, 并使用偏最小二乘法3.2.9版进行分析。

结果显示, 感知行为控制、感知价值、目的地形象和满意度对游客的再访意向有显著影响。感知价值、感知服务质量和目的地形象对满意度的影响也被证实。另一方面, 满意度被发现是感知服务质量、目的地形象和感知价值之间的一个重要中介因素。

包括感知服务质量、感知价值、感知风险和满意度在内的扩展TPB模型为解释游客对旅游目的地的再访意向提供了理论基础。

关键词: 满意度; 旅游; 计划行为理论; PLS-SEM; 目的地形象