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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.

Seepage of the dam is an important safety problem, which may cause internal erosion of the structure. In the field of seepage monitoring in civil engineering, the distributed optical fiber sensing technology based on the temperature tracing method has been paid more attention due to its unique advantages of high sensitivity, good stability and high resolution. The purpose of this paper is to make a review of the existing related research, so as to facilitate the later scholars to understand and further study more systematically.

In this paper, three kinds of commonly used distributed fiber temperature measurement technologies are introduced. Based on the working principle, monitoring system, theoretical analysis, experimental research and engineering application of the fiber seepage monitoring technology, the present situation of dam seepage monitoring based on distributed fiber is reviewed in detail and their advantages and disadvantages are compared.

The thermal monitoring technology of seepage measurement depends on the accuracy of optical fiber temperature measurement (including the accuracy of the system and the rationality of the discrimination method), the correct installation of optical fiber and the quantitative analysis of temperature data. The accuracy of the current monitoring system can basically meet the existing measurement requirements, but the correct installation of optical fiber and the calibration of temperature data need to be further studied for different discrimination methods, and this field has great research value.

At present, there are many applications and research studies of optical fiber sensing in the field of structural health monitoring, and there are also reviews of related aspects. However, there is little or no review only in the field of seepage monitoring. This paper summarizes the research and application of optical fiber sensing in the field of seepage monitoring. The possibility of the gradient method to find its new prospect with the development of monitoring systems and the improvement of temperature resolution is discussed. The idea of extending the seepage monitoring method based on distributed optical fiber thermal monitoring technology to other monitoring fields is also given in the paper.

Construction projects have become increasingly long, complex and costly with waste and inefficiencies and often fail to achieve the desired results. Integrated project delivery (IPD) is believed to change these problems. A reasonable and fair profit distribution mechanism is a critical factor for ensuring the success of the IPD projects. This study aims to investigate the strategies of all participants in the profit distribution of an IPD project with respect to the factor of the effort level.

This study describes the influence of owners and participants on profit distribution due to their respective efforts in the IPD project alliance. The influence of effort level on profit distribution is discussed based on the Holmstrom-Milgrom model of asymmetric information game theory and principal-agent theory, combined with incentive compatibility (IC) constraints and individual rationality (IR) constraints.

The results show that the optimal level of effort by each participant optimizes the profit distribution of an IPD project. At the same time, in the revenue incentive contract, the effort level of the participants is positively correlated with the profit distribution, proportional to their contribution coefficient and inversely proportional to the square of the cost of their creative activities in terms of effort. Each party of an IPD project can adopt a series of measures to improve their own effort level and choose the optimal level of effort based on the profit distribution, while satisfying their own utility maximization.

This study introduces the Holmstrom-Milgrom model in the principal-agent theory to explore the influence of the effort level on profit distribution in IPD projects. The quantitative model can contribute to establish a fair and efficient profit distribution scheme for the IPD projects.

Virtual reality (VR), as a new type of media technology, significantly improves the audience experience with product presentation in the marketing communication field. The apartment rental market, particularly in China, has no exception in adopting VR technology in its communication strategy. VR usage has been boosted since the outbreak of COVID-19 and has become a widespread application in the global apartment rental market. Although extant studies have analyzed how real estate companies use VR technology to enhance customer experience, few studies have been made to explore the power of VR in apartment rental advertising, particularly in targeting the youth in China market. To fill this research gap, this study aims to figure out how young consumers perceive VR advertising and characteristics of VR used in apartment rental advertisements, and how VR advertising affects young consumers’ intentions to rent an apartment.

A cross-sectional survey was conducted in 2021 with 301 Chinese university students aged 18 to 23. All respondents were invited offline and guided to watch one selected rental advertisement with VR technology featuring an apartment of about 50 square meters and then complete a questionnaire.

VR’s media richness in the apartment rental advertising increases its sense of presence perceived by the survey respondents. Both VR’s media richness and sense of presence positively influence respondents’ attitudes toward the advertised apartment. If respondents evaluate the advertised apartment positively, they are more willing to rent the advertised apartment.

The sample size is not large enough to represent all Generation Z consumers in China. The use of the nonprobability sampling method also limits the generalizability of the study results.

To counter the challenges created by COVID-19, apartment rental service providers and apartment owners/landlords are suggested to enhance the application of VR technology to the apartment rental advertisements to grow young consumers’ interest in the advertised apartments and even their renting intention.

To the best of the authors’ knowledge, this is the first quantitative study to assess young consumers’ responses to VR apartment rental advertising in China.

Template matching is one of the most suitable choices for full six degrees of freedom pose estimation in many practical industrial applications. However, the increasing number of templates while dealing with a wide range of viewpoint changes results in a long runtime, which may not meet the real-time requirements. This paper aims to improve matching efficiency while maintaining sample resolution and matching accuracy.

A multi-pyramid-based hierarchical template matching strategy is proposed. Three pyramids are established at the sphere subdivision, radius and in-plane rotation levels during the offline template render stage. Then, a hierarchical template matching is performed from the highest to the lowest level in each pyramid, narrowing the global search space and expanding the local search space. The initial search parameters at the top level can be determined by the preprocessing of the YOLOv3 object detection network to further improve real-time performance.

Experimental results show that this matching strategy takes only 100 ms under 100k templates without loss of accuracy, promising for real industrial applications. The authors further validated the approach by applying it to a real robot grasping task.

The matching framework in this paper improves the template matching efficiency by two orders of magnitude and is validated using a common template definition and viewpoint sampling methods. In addition, it can be easily adapted to other template definitions and viewpoint sampling methods.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

The purpose of this perspective article is to identify the developmental trajectory of human resource management (HRM) research in the Mainland China as well as the major research gaps to be filled in the future. In particular, the paper focuses on the current challenges as well as the emerging research trends by reviewing the literature in HRM research in the Mainland China.

The paper takes a geocentric perspective of HRM theory development to analyze the status quo as well as the emerging trends of the future HRM research in the Mainland China.

HRM research in the Mainland China exhibited an obvious tendency of adopting an etic approach at the early stage of research, but displaying an emerging trend toward an emic approach at a later stage. However, the current HRM research in the Mainland China, including both etic and the emic approaches, falls seriously short of meeting the high-quality standards of the international academic community.

Through analyzing the status quo of HRM research in the Mainland China, the paper identified an emerging trend toward an integration of both etic and emic approaches in which the two approaches constitute a yin-yang duality as a unity-in-opposites toward a geocentric HRM research framework with a holistic, dynamic and duality etic-emic balance.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.