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Tourism via virtual reality (VR) technology has become an interesting option for consumers to “travel.” The best approaches to optimizing the VR tourism environment, improving the interactive experience of tourists and encouraging tourists to adopt VR are not yet fully understood. This study explores the willingness of tourists to adopt VR tourism from the dual aspects, richness and dynamics, of virtual social cues.

To examine the effects of richness (multiple vs. few cues) and dynamic (changeable vs. static cues) on consumers' willingness to adopt VR tourism, three virtual tourism scenes were designed and presented by head-mounted displays. The data were collected for participants in the VR laboratory and tested by ANOVA and partial least squares–structural equation modeling.

Virtual social cues can generate mental imagery through interactivity, vividness and parasocial interactions, thus increasing the consumer's likelihood of adopting VR tourism. It was also found that imagination moderates mental imagery and adoption intention. When the consumer's imagination is stronger, their mental imagery stimulates a stronger willingness to adopt VR tourism.

The authors innovatively utilize concepts of parasocial interaction and mental imagery and discuss the various influences and mediation mechanisms of social cue characteristics on consumers' adoption of VR tourism. The conclusions may provide new insights for VR tourism managers and tourism scholars.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

As an effective tool to promote rational resource allocation and facilitate the development of green management practices such as enterprise digital innovation, the green credit policy has recently gained extensive attention. The purpose of this paper is to analyze the relationship between green credit policies and the digital innovation of enterprises, and to further explore the mechanism of action between them and their boundary conditions.

Based on micro-level data on Chinese firms from 2007 to 2019, this paper constructs a difference-in-differences (DID) model to investigate the impact and intrinsic mechanisms of green credit policies on firms' digital innovation and its boundary conditions, with the help of a quasi-natural experiment, i.e. the Green Credit Guidelines.

Green credit policies inhibit digital innovation and fail to compensate for innovation. The analysis of the mechanism shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms, and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation. Further analysis reveals that the negative impact of green credit policies on digital innovation is more pronounced in state-owned enterprises, enterprises without financially experienced executives, and in the eastern regions of China.

This study provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, providing also a basis to further improve green credit policies.

In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

This paper aims to study the effect of ultraviolet (UV) light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environment.

The effect of UV light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environments were investigated by the corrosion weight gain experiment, in situ electrochemical noise, scanning electron microscope and X-ray diffraction.

UV light accelerated the corrosion process of BC550 weathering steel in the simulated marine atmospheric environment during the first 168 h. The maximum influence factor of UV light was 0.32, and it was only 0.08 after 168 h of corrosion process.

As the extension of corrosion time, the thickness and density of the corrosion product layer increased, which weakened the acceleration effect of UV light.