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The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

With the increase in population, the energy needs of countries are also increasing. These countries have difficulties in meeting these increasing needs. Countries that cannot meet this need have to import energy from abroad. This situation adversely affects the current account balance of countries. Nuclear energy investments allow countries to obtain their own energy, although there are some criticisms. In this framework, while some countries in the world increase their nuclear energy investments, some countries do not have any nuclear power plants (NPP). There are 32 such countries where nuclear energy projects are running till date. Therefore, it is very important to determine the socio-economic variables of countries that have nuclear energy investments. In this context, a detailed literature analysis will be made first to determine socio-economic criteria. Then, the importance weights of these factors will be calculated using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The profiles of the countries that make nuclear energy investments demonstrate that education level is the most essential socio-economic factor for the improvement of nuclear energy investments. Also, income inequality is another important variable in this regard. However, consumption behaviour and saving behaviour have the lowest weights.

The purpose of this paper is to add expanded perlite (EP) immobilized microorganisms that replace part of the standard sand in mortar to improve the self-healing ability of mortar cracks and reduce the water absorption of mortar after healing.

Bacillus pseudofirmus spores were immobilized with EP particles as self-healing agents. The effects of adding self-healing agents on the compressive strength of mortar specimens were observed. The ability of mortar specimens to heal cracks was evaluated using crack microscopic observation and water absorption experiments. The filler at the cracks was microscopically analyzed by scanning electron microscope and X-ray diffraction experiments.

First, the internal curing effect of EP promotes the hydration of cement in mortar, which generates more amount and denser crystal structure of Ca(OH)2 at mortar cracks and improves the self-healing ability of mortar. Second, the self-healing ability of mortar improves with the increase of self-healing agent admixture. Adding a self-healing agent of high admixture makes the planar undulation of calcite crystal accumulation at mortar cracks more significant. Finally, the initial crack widths that can be completely healed by adding EP and self-healing agents to the mortar are 200 µm and 600 µm, respectively.

The innovation points of this study are as follows. (1) The mechanism of the internal curing effect of EP particles on the self-healing ability of mortar cracks was revealed by crack microscopic observation tests and microscopic experiments. (2) The effect of different self-healing agent amounts on the self-healing ability of mortar cracks has been studied. (3) The effects of EP particles and self-healing agents on healing different initial widths were elucidated by crack microscopic observation tests.

Digital influencers, besides being a personal branding phenomenon, constitute, nowadays, important partners for brands in the digital environment. Despite the benefits for both parties, the brand and the endorser, there is a risk when the individuals or the brand take part in some scandal or questionable activity, which causes a loss of credibility and reputation. The contagious effect of personal or corporate brand crisis with the partners is well reported in the literature. However, there are no studies about such occurrence in the digital environment, considering that the phenomenon of digital influence has some particularities differing from traditional celebrity endorsement. This chapter focuses on the research of the consequences of a change in the credibility of influencers or brands, after the occurrence of a negative situation, and the way in which this change affects the two parties involved, the brand and the influencer. We conducted a netnographic study of the social media pages of three digital influencers and the social media pages of a recognized fast fashion brand recognized in the digital world. We analyzed 1500 social media comments from their audiences and found that influencers suffer from a loss in sympathy and credibility by scandals while crisis communication strategies in corporate brands can prevent such damages. Strategic implications for brand managers and digital influencers managing their endorsements are addressed in the discussion.

In confronting the challenge of climate change and progressing towards dual carbon goals, China is actively implementing low-carbon city pilot policy. This paper aims to focus on the potential impact of this policy on enterprise green governance, aiming to promote the reduction and balance of carbon emissions.

Based on the panel data of China's large-scale industrial enterprises from 2007 to 2013, this paper uses the Difference-in-differences (DID) method to study the impact and path mechanism of the implementation of low-carbon city pilot policy on enterprise green governance. Heterogeneity analysis is used to compare the effects of low-carbon city pilot policy in different regions, different enterprises and different industries.

The low-carbon pilot can indeed effectively enhance corporate green governance, a conclusion that still holds after a series of robustness tests. The low-carbon city pilot policy mainly enhances enterprise green governance through two paths: an industrial structure upgrade and enterprise energy consumption, and it improves green governance by reducing enterprise energy consumption through industrial structure upgrade. The impact of low-carbon city pilot policy on enterprise green governance shows significant differences across different regions, different enterprises and different industries.

This paper examines the impact of low-carbon city pilot policy on enterprise green governance. However, due to availability of data, there are still some limitations to be further tackled. The parallel trend test in this paper shows that the pilot policy has a significant positive effect on the green governance of enterprises. However, due to serious lack of data in some years, the authors only selected the enterprise data of a shorter period as our experimental data, which leads the results to still have certain deficiencies. For the verification of the impact mechanism, the conclusions obtained in this paper are relatively limited. Although all the mechanism tests are passed, the reliability of the results still needs to be further tested through future data samples. In addition, as the pilot policy of low-carbon cities is still in progress, the policy can be tracked and analysed in the future as more data are disclosed, and further research can be carried out through dimensional expansion.

Low-carbon city pilot policy plays an important role in inducing the green governance of enterprises. Therefore, policy makers can continue to strengthen the construction of low-carbon city pilots by refining pilot experience, building typical cases, actively promoting pilot policy experience, expanding pilot scope and enhancing the implementation efficiency of pilot policy nationwide, which will contribute to the optimization and upgrading of the regional industrial structure at the urban level and will provide experience and reference for the synergistic implementation plan of pollution reduction and carbon reduction.

The impact of the low-carbon city pilot policy on enterprise green governance not only exists in two separate paths of urban industrial upgrading and enterprise energy consumption but also exists in a chain transmission path from macro to micro. The authors find that the effect value of each influence path is different, and there is an obvious leading influence path for the role of enterprise green governance. Therefore, in the process of implementing a low-carbon city pilot policy, policies should be designed specifically for different mechanisms. Moreover, complementing and coordinating several paths should be advocated to give full play to the green governance effect of enterprises brought by different paths and to further expand the scope of industries and enterprises where policies play a role.

To the best of the authors’ knowledge, for the first time, this paper connects macro mechanisms with micro mechanisms, discovering a macro-to-micro transmission mechanism in the process of low-carbon city pilot policy affecting enterprise green governance. That is, the low-carbon city pilot policy can facilitate industrial structure upgrading, resulting in reduced enterprise energy consumption, ultimately enhancing enterprise green governance.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

This paper aims to investigate the effect of gamified e-service quality (GE-SQ) on customer value co-creation, relationship quality and purchase intention in e-retailing.

A questionnaire was used to collect data from a sample of 549 e-retailing customers who participated in a gamified online service process. A structural equation modeling approach was adopted to analyze the model.

The empirical evidence confirmed that GE-SQ encouraged customer value co-creation behavior and relationship quality. Customer value co-creation and relationship quality led to higher purchase intention. Also, value co-creation and relationship quality partially mediated the relationship between GE-SQ and purchase intention.

The findings increase our knowledge of GE-SQ and its behavioral consequences. Moreover, the study proposes and validates a theoretical framework based on GE-SQ, value co-creation and customer relationship quality. This study provides insight into using gamification as a practical tool in the e-retailing industry.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

Online food delivery has been prevalent in recent years worldwide, especially during the COVID-19 pandemic, and people's consumption behaviors have changed significantly. This study aims to investigate the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic and focuses on the dominant factors influencing their decision to use online food delivery platforms.

Semi-structured interviews including 14 young adults aged 18–25 living in Hong Kong were conducted to collect data about their perspectives on online food delivery platforms in five areas. This research adopted the stimulus-organism-response model (S-O-R model) to analyze how the factors influence young adult users' loyalty and satisfaction with online food delivery platforms.

Thematic analyses revealed that young adults were attracted to online food delivery platforms for their numerous benefits. They had a high frequency of usage and significant spending. Usability, usefulness, satisfaction and loyalty influenced young adults' behaviors on online food delivery platforms. Participants were overall satisfied with their experiences, but platforms still had room for improvement.

Few prior studies investigated the factors affecting the consumer experience and behavioral intention of online food delivery for young adults in Asia. This study contributes to understanding young adults' experiences and problems with online food delivery platforms. It provides practical insights for system engineers and designers to improve the current services and for the governments to enhance the existing regulatory loopholes.

This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.