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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.

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.

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 paper aims to investigate the thermal stability and hydrophobicity of difference alkyl chain of silanes with silicon (Si) micro- and nanoparticles.

Sol-gel methods have been used to design superhydrophobic glass substrates through surface modification by using low-surface-energy Isooctyl trimethoxysilane (ITMS) and Ethyl trimethoxysilane (ETMS) solution. Hierarchical double-rough scale solid surface was built by Si micro- and nanoparticles to enhance the surface roughness. The prepared sol was applied onto glass substrate using dip-coating method and was dried at control temperature of 400°C inside the tube furnace.

The glass substrate achieved the water contact angle as high as 154 ± 2° and 150.4 ± 2° for Si/ITMS and Si/ETMS films, respectively. The Si/ITMS and Si/ETMS also were equipped with low sliding angle as low as 3° and 5°, respectively. The Si micro- and nanoparticles in the coating system have created nanopillars between them, which will suspend the water droplets. Both superhydrophobic coatings have showed good stability against high temperature up to 200°C as there are no changes in WCA shown by both coatings. Si/ITMS film sustains its superhydrophobicity after impacting with further temperature up to 400°C and turns hydrophobic state at 450°C.

Findings will be useful to develop superhydrophobic coatings with high thermal stability.

Sol method provides a suitable medium for the combination of organic-inorganic network to achieve high hydrophobicity with optimum surface roughness.

Application of different alkyl chain groups of silane resin blending with micro- and nanoparticles of Si pigments develops superhydrophobic coatings with high thermal stability.

This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction capacitors of the primary side switching and the parasitic parameters of the secondary side diodes converted to the primary side, which will cause the voltage gain decreased when the switching frequency decreased.

This paper proposes an optimization parameters design method to solve this problem, which was based on impedance model considering the parasitic parameters of switching devices and diodes.

The effectiveness of the proposed method is verified by impedance Bode plots and experimental results.

From the perspective of impedance modeling, this paper finds the reasons for the insufficient voltage regulation capability of LLC resonant converters under heavy load and finds solutions through analysis.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

The high turnover rate among interns exacerbates the shortage of human resources in the hospitality industry. This study is based on previous concerns about the impact of occupational stigma and internship on turnover intention. This study aims to explore how the career adaptability of interns influences their perception of occupational stigma and occupational identity.

By using stratified sampling, semistructured interviews were conducted with 34 respondents who had academic and vocational education backgrounds. The data analysis was performed using the thematic analysis method.

This study demonstrates that a high level of career adaptability helps to reduce interns’ perception of occupational stigma and strengthen their occupational identity. Students from academic and vocational schools display different levels of career adaptability in terms of job matching and career promotion. In addition, the long-term influence on occupational identity is more significant from professional development potential compared to job adaptation.

This study provides valuable insights into the complex relationship between occupational stigma and occupational identity from the perspective of career adaptability. Moreover, it highlights the importance of job adaption, matching, promotion and professional development in retaining talent within the hospitality industry.

This study innovatively focuses on job matching and career promotion for coping with occupational stigma. It also considers interns’ educational backgrounds, facilitating further understanding of occupational identity under the influence of stigma. A fresh perspective on talent adaptation and retention in the hospitality industry is provided.

实习生的高流动率加剧了酒店业人力资源的短缺。本研究在职业污名与实习对离职倾向影响的研究基础上, 主要探讨了实习生的职业适应如何影响其职业污名感知和职业认同。

研究采用分层抽样的方法, 对34名拥有学术和职业教育背景的受访者进行半结构访谈, 并采用主题分析法进行数据分析。

研究发现, 良好的职业适应有助于减少实习生的职业污名感知, 并增强其职业认同。学术学校和职业学校的学生在工作匹配和职业晋升方面表现出不同程度的职业适应。此外, 职业发展潜力对职业认同的影响比工作适应更加长远。

本研究从职业适应的角度对职业污名与职业认同之间的复杂关系提供了有价值的见解。此外, 它还强调了工作适应、匹配、晋升和专业发展在酒店业人才保留方面的重要性。

本研究创新地关注了职业匹配与职业晋升对应对职业污名和提升职业认同的影响。研究还考虑了实习生教育背景的差异性, 进一步深化对污名影响下的职业认同的理解, 为酒店行业的人才适应和保留提供了一个新的研究视角。

La elevada tasa de rotación entre los trabajadores en prácticas agrava la escasez de recursos humanos en el sector de la hostelería. Este estudio se basa en preocupaciones previas sobre el impacto del estigma ocupacional y las prácticas en la intención de rotación. Explora principalmente cómo influye la adaptabilidad profesional de los becarios en su percepción del estigma ocupacional y la identidad ocupacional.

Mediante un muestreo estratificado, se realizaron entrevistas semiestructuradas a 34 encuestados con formación académica y profesional. El análisis de los datos se realizó mediante el método de análisis temático.

Este estudio demuestra que un alto nivel de adaptabilidad profesional contribuye a reducir la percepción de estigma ocupacional de los estudiantes en prácticas y a reforzar su identidad ocupacional. Los estudiantes de escuelas académicas y de formación profesional muestran diferentes niveles de adaptabilidad profesional en términos de adecuación al puesto de trabajo y promoción profesional. Además, la influencia a largo plazo sobre la identidad ocupacional es más significativa del potencial de desarrollo profesional en comparación con la adaptación laboral.

El estudio aporta información valiosa sobre la compleja relación entre el estigma ocupacional y la identidad ocupacional desde la perspectiva de la adaptabilidad profesional. Además, pone de relieve la importancia de la adaptación del puesto de trabajo, el emparejamiento, la promoción y el desarrollo profesional para retener el talento dentro de la industria de la hostelería.

Este estudio se centra de forma innovadora en la adecuación al puesto de trabajo y la promoción profesional para hacer frente al estigma laboral. También tiene en cuenta los antecedentes educativos de los becarios, lo que facilita una mayor comprensión de la identidad ocupacional bajo la influencia del estigma. Se aporta una nueva perspectiva sobre la adaptación y la retención del talento en la industria de la hostelería.