This study aims to expand tourism value co-creation to include resident-tourist social interactions. Specifically, we aim to empirically verify the effects of residents' life satisfaction and their ...perceived benefits and costs of tourism development on their value co-creation with tourists. An online questionnaire survey was conducted with a sample of 380 urban residents in China. Partial Least Square structural equation modelling was adopted to analyze the data. The results show that residents' perceived economic and social-cultural benefits of tourism development have positive effects on both value co-creation and life satisfaction, while perceived costs have negative effects. Life satisfaction influences value co-creation. This study contributes to the literature by conceptualizing tourism value co-creation between tourists and residents and empirically investigating residents' participation in value co-creation in the tourism context. Moreover, we introduced broaden-and-build theory to analyze a tourism phenomenon for the first time. This study also provides important managerial implications.
•Value co-creation in tourism includes resident-tourist social interactions.•Positive perceptions of tourism development encourage residents' value co-creation with tourists.•Perceived costs of tourism development are negatively related to residents' value co-creation.•Life satisfaction positively influences residents' value co-creation with tourists.
The COVID-19 pandemic has caused serious economic and social consequences. Recent research shows that the pandemic has not only caused a physical health crisis but also caused many psychological and ...mental crises. Based on the contemporary cognitive-behavioral models, this article offers a conceptual analysis of how the pandemic affects individual mental health and coping behaviors from the perspective of individual economic status, individual context, and social context. The analysis shows that (1) the pandemic has led to increased economic uncertainty, increased unemployment and underemployment pressure, increased income uncertainty, and different degrees of employment pressure and economic difficulties; (2) these difficulties have stimulated different levels of mental health problems, ranging from perceived insecurity (environmental, food safety, etc.), worry, fear, to stress, anxiety, depression, etc., and the mental health deterioration varies across different groups, with the symptoms of psychological distress are more obvious among disadvantageous groups; and (3) mental health problems have caused behavior changes, and various stress behaviors such as protective behaviors and resistive behaviors. Future research directions are suggested.
Glass fiber-reinforced polymer (GFRP) reinforcements are taken as an alternative solution for the deterioration of civil infrastructures. GFRP bars have received increasing attention due to low cost ...compared to carbon fiber-reinforced polymer (CFRP) bars. Bond characteristic of GFRP bars in concrete is the most critical parameter for implementation of the material to the corrosion-free concrete structures. Unlike steel reinforcement, GFRP materials behave anisotropic, non-homogeneous and linear elastic properties, which may result in different force transfer mechanism between reinforcement and concrete. With the purpose of covering the most valuable contributions regarding bond mechanism in the past work, a comprehensive review focusing on the failure mode and bond strength is carried out in this paper. A database consisted of 682 pullout-test specimens was created to observe the factors affecting bond behavior. Basic relationship between bond strength/slip and factors was analyzed accordingly. In addition, the development of bond degradation under environmental conditions, such as freezing-thawing cycling, wet-dry cycling, alkaline solutions and high temperature was presented thereafter. These environmental influences need to be further investigated.
The dependence of the strength of the electron-phonon coupling and the electron heat capacity on the electron temperature is investigated for eight representative metals, Al, Cu, Ag, Au, Ni, Pt, W, ...and Ti, for the conditions of strong electron-phonon nonequilibrium. These conditions are characteristic of metal targets subjected to energetic ion bombardment or short-pulse laser irradiation. Computational analysis based on first-principles electronic structure calculations of the electron density of states predicts large deviations (up to an order of magnitude) from the commonly used approximations of linear temperature dependence of the electron heat capacity and a constant electron-phonon coupling. These thermophysical properties are found to be very sensitive to details of the electronic structure of the material. The strength of the electron-phonon coupling can either increase (Al, Au, Ag, Cu, and W), decrease (Ni and Pt), or exhibit nonmonotonic changes (Ti) with increasing electron temperature. The electron heat capacity can exhibit either positive (Au, Ag, Cu, and W) or negative (Ni and Pt) deviations from the linear temperature dependence. The large variations of the thermophysical properties, revealed in this work for the range of electron temperatures typically realized in femtosecond laser material processing applications, have important implications for quantitative computational analysis of ultrafast processes associated with laser interaction with metals.
The COVID-19 pandemic and its related public health restrictions are having an increasingly serious impact on mental health, and measures need to be taken to curb this trend. The positive ...relationship between physical exercise and mental health has been well-established, but during the COVID-19 pandemic, with various restrictions, the space and facilities for physical exercise are limited. This article explores the relationship between physical exercise and mental health during the COVID-19 pandemic based on the latest research findings published in 2019-2021. We offer a novel model that consists of three central arguments. First, physical exercises during COVID-19, especially supervised exercises, are conducive to enhancing happiness and improving mental health. Second, physical exercise reduces people's anxiety, sadness and depression during the COVID-19 pandemic. Third, the maintenance and improvement of mental health are related to the intensity and frequency of physical exercise. Intensive and frequent physical exercise are conducive to maintaining mental health. Finally, this article proposes important directions for future research.
This study presents the bond durability of glass fiber-reinforced polymer (GFRP) bars to fiber-reinforced concrete (FRC) exposed to saline solutions. Total 105 pullout specimens reinforced with steel ...and polyvinyl alcohol (PVA) fibers were prepared and immersed in the saline solutions at 50 and 70°C under 30, 45, and 60days, respectively. Their durability was quantified in terms of failure mode, adhesion stress as well as the bond strength. Test results revealed that the steel FRC samples exhibited the better bond durability than that of PVA FRC ones, when the same fiber volume fraction was used. Besides, the experimental data also aided to calibrate the analytical models, mBPE and CMR models, to better define the GFRP bond to FRC by considering the environmental effects. Also, a detailed procedure using Arrhenius law and time shift factor (TSF) methods was developed to predict the long-term bond degradation under different environmental temperatures and relative humidity. Degradation predictions over 75years showed the FRC with 1.0% steel fiber volume fraction will have 96.76%, 92.88%, and 74.47% bond strength retention under dry, moist, and saturated environments in cold regions, respectively; while these values will decrease to 95.83%, 90.85%, and 67.19% in warm regions, respectively.
•Two psychological factors are integrated into TAM to predict continuance intention.•Both personal innovativeness and subjective knowledge are significant predictors.•Personal innovativeness also has ...an indirect effect on continuance intention.
This study integrates the individual psychology constructs (personal innovativeness and subjective knowledge) with the Technology Acceptance Model to develop and test a model of airline passengers’ continuance intention towards online flight check-in services. Predictions were tested with data from a sample of airline passengers in China who have experienced the online check-in service. The findings of this study demonstrate that airline passengers’ innovativeness and subjective knowledge have a direct effect on continuance intention, and an indirect effect through partial mediation of perceived ease of use and perceived usefulness. Theoretical and managerial implications are discussed.
The fast development of machine learning and artificial intelligence has led to a great improvement of the smart tourism recommendation system, however many problems associated with the choice of ...transport modes in city tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized day itineraries with consideration of transport mode choice. With improved particle swarm optimization and differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was carried out in Chengdu, China to examine the performance of our approach. The results show that compared with extant methods, our approach achieves better performance. In addition, our approach can create more sensible, multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app providers could integrate our proposed model into their existing smart service systems, as part of their e-business or digital strategy for enhancing tourist experience.
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal ...process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.
Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the ...level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.
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