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This paper investigates the current psychological state of Chinese tourism practitioners and their career resilience during the ongoing COVID-19 pandemic. It empirically examines the effects of COVID-19 on Chinese tourism practitioners' professional attitudes and their career belief in the future. The study is intended to guide enterprises and governments to design effective strategies/policies to deal with the effect of this unfavorable environment.

The sample consists of 442 tourism practitioners in 313 tourism enterprises in China. The data were collected via a targeted online survey based on a well-structured questionnaire. The data were analyzed using statistical procedures including multilevel regression analysis.

The study results show that Chinese tourism practitioners have strong career resilience in the face of current turbulent time. After testing, the model shows that career beliefs and social support have a significant positive impact on the professional attitudes of tourism practitioners, and that career resilience has a partial mediating effect on their career beliefs, social support and professional attitude.

This study enriches the existing literature on career belief, social support and career resilience. It provides a new interpretation on how career belief and social support impact career resilience and thus shape tourism practitioners' professional attitudes during pandemics.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

University–industry (UI) collaboration is essential for knowledge and technology exchange between higher education institutions and industries, enabling enterprises to accelerate innovation. However, few studies have investigated the collaborative innovation mechanism through which UI collaboration can enhance the accumulation of firms' intellectual capital (IC) and how this, in turn, affects their innovation-driven development.

Drawing from the knowledge management and collaborative innovation theory, this research proposes a theoretical framework of the inter-organization relationship between enterprises and universities to investigate the influence mechanism of UI collaboration, including academic engagement and commercialization, on corporate performance as well as the mediating role of IC by employing survey that covers 177 UI collaborations.

Empirical results show that human capital and relational capital fully mediate the relationship between academic engagement UI collaboration and corporate economic performance, while human capital partially mediates the relationship between commercialization UI collaboration and corporate economic performance. Additionally, structural capital and relational capital partially mediate the relationship between academic engagement and corporate innovation performance, while structural capital fully mediates the relationship between commercialization and corporate innovation performance.

This study empirically investigates how academic engagement and commercialization impact corporate performance (i.e. innovation dimension or economic dimension). It uncovers this relationship's underlying mechanism by documenting the IC's mediating impact.

The purpose of this paper is to apply the concept of equivalent initial flaw size (EIFS) to the anisotropic nickel-based single crystal (SX) material, and to predict the fatigue life on this basis. The crack propagation law of SX material at different temperatures and the weak correlation of EIFS values verification under different loading conditions are also investigated.

A three-parameter time to crack initial (TTCI) method with multiple reference crack lengths under different loading conditions is established, which include the TTCI backstepping method and EIFS fitting method. Subsequently, the optimized EIFS distribution is obtained based on the random crack propagation rate and maximum likelihood estimation of median fatigue life. Then, an effective driving force based on anisotropic and mixed crack propagation mode is proposed to describe the crack propagation rate in the small crack stage. Finally, the fatigue life of three different temperature ESE(T) standard specimens is predicted based on the EIFS values under different survival rates.

The optimized EIFS distribution based on EIFS fitting - maximum likelihood estimation (MLE) method has the highest accuracy in predicting the total fatigue life, with the range of EIFS values being about [0.0028, 0.0875] (mm), and the mean value of EIFS being 0.0506 mm. The error between the predicted fatigue life based on the crack propagation rate and EIFS distribution for survival rates ranges from 5% to 95% and the experimental life is within two times dispersion band.

This paper systematically proposes a new anisotropic material EIFS prediction method, establishing a framework for predicting the fatigue life of SX material at different temperatures using fracture mechanics to avoid inaccurate anisotropic constitutive models and fatigue damage accumulation theory.

Deglobalization and the coronavirus disease 2019 (COVID-19) pandemic have severely hindered multinational enterprise (MNE) investment. At the same time, digital technology is seriously challenging it with traditional production factor flows. Few studies have realized that the impact of digitalization is not limited to either transaction costs or the location-boundness of firm-specific advantages (FSAs), but extends to profound changes in the fundamental essence of MNEs. There is still limited understanding of this body of knowledge as a whole, including how its subtopics are interrelated. This study took the production factor change perspective to review MNE theory in the digital era. Therefore, this study aims to identify any upcoming and undeveloped themes in order to provide a platform suited to direct future research.

This paper presents a summary and a review of 151 articles published between 2007 and 2020. Such review was conducted to systematically explain the connotations and influential mechanisms of digital empowerment on MNE theory. This was achieved by using the CiteSpace citation visualization tool to build a keyword co-occurrence network.

The research findings pertain to how digitalization expands, breaks through, and even reshapes traditional MNE theory from four distinctive angles: the influential factors of internationalization, the process of internationalization, competitive advantage, and location choice. The findings are followed by the presentation of future research directions.

This paper presents an examination of MNE theory in the digital era from the perspective of production factor change. In doing so, it identifies significant theoretical innovation opportunities for future scholarly research priorities.

This study aims to realize the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified Color Index Pigment Red 57:1 (C.I. Pigment Red 57:1, PR 57:1).

In this paper, the inorganic materials (sepiolite and SiO₂·nH₂O) were used in both PR 57:1 production wastewater treatment and its core-modification. The inorganic material firstly adsorbed 3-hydroxy-2-naphthoic acid (bon acid) in the pigment wastewater to reduce chemical oxygen demand. Then, the inorganic material adsorbed with bon acid was reused to prepare core-modified PR 57:1.

In the pigment wastewater adsorption experiment, it was found that under pH = 3, the adsorption percentage of bon acid by inorganic material can reached up to 46.00%. The pigment characterization results showed that the core-modified PR 57:1 had a core-shell structure. Under UV light irradiation for 1 h, the core-modified PR 57:1 prepared with sepiolite and SiO₂·nH₂O showed total color difference ΔE value of 1.43 and 2.05, respectively, which was lower than that of unmodified PR 57:1 (ΔE = 2.89). In addition, the transmittance of pigment water suspension test results showed that the core-modified PR 57:1 showed better water dispersibility.

This paper attempts to develop a synergistic strategy based on the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified PR 57:1.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The purpose of this paper is to investigate the effect of mullite on the mechanical properties and friction of carbon fiber (CF)-reinforced friction material.

CF-reinforced friction materials with varying content of mullite were fabricated by hot press molding, and then the tribological properties were tested on the MRH-3-type tribometer under ambient conditions with the ring-on-block configuration.

The experimental results indicated that the addition of mullite increased the density and compressive strength of friction material. However, the flexural strength of friction material decreased by 16% with the addition of 15 Wt.% mullite. The friction coefficient was proportional to the mullite content. Friction material with 12.5 Wt.% mullite showed the highest friction stability under different loads, whereas friction material with 10 Wt.% mullite exhibited the highest friction stability under different sliding speeds.

By boosting the resistance to deformation under load and increasing the specific heat capacity, mullite contributed significantly to the friction stability of the friction material.

The study explores and builds theories in Customer Perceived Values (CPVs) that drive counterfeit buying intention, using a Grounded Theory Approach (GTA) in an emerging market, India.

Counterfeit studies have either resorted to a survey approach or modelling approach in investigating various aspects and dimensions. This study, among a few, attempted a GTA in building theory on CPVs. Based on the observations and recorded responses that emerged through several Focus Group Discussions (FGDs); conducted in two metropolitan cities (India), newer insights into this illicit phenomenon of “Counterfeiting” were derived.

Adding to the counterfeit literature, the study presents a comprehensive view of the CPVs. Findings reveal economic, socio-normative, pleasure-based, euphemistic, acquisition-centrality, self-regulating, situational and sustainable consumption values that influence counterfeit attitudes and in turn impact counterfeit buying intentions. Although Economic Values (ECV) have been the primary motivation for counterfeit purchase, complex and newer values that emerged through this research study bears significance.

As a single point of reference, this study will provide impetus to scholars and academicians in expanding the counterfeit research domain. While aiding policymakers and marketers in further understanding this illicit practice, it will also guide brand managers in strategizing their offerings and reaching out to the masses with strong brand aesthetic values.

Based on a systematic literature review using the 4 Ws framework, this study is one of the few attempts that has adopted a GTA to explore and develop theories on CPVs in counterfeit research.