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The purpose of this paper is to propose a multi-feature, multi-metric and multi-loop tightly coupled LiDAR-visual-inertial odometry, M³LVI, for high-accuracy and robust state estimation and mapping.

M³LVI is built atop a factor graph and composed of two subsystems, a LiDAR-inertial system (LIS) and a visual-inertial system (VIS). LIS implements multi-feature extraction on point cloud, and then multi-metric transformation estimation is implemented to realize LiDAR odometry. LiDAR-enhanced images and IMU pre-integration have been used in VIS to realize visual odometry, providing a reliable initial guess for LIS matching module. Location recognition is performed by a dual loop module combined with Bag of Words and LiDAR-Iris to correct accumulated drift. M³LVI also functions properly when one of the subsystems failed, which greatly increases the robustness in degraded environments.

Quantitative experiments were conducted on the KITTI data set and the campus data set to evaluate the M³LVI. The experimental results show the algorithm has higher pose estimation accuracy than existing methods.

The proposed method can greatly improve the positioning and mapping accuracy of AGV, and has an important impact on AGV material distribution, which is one of the most important applications of industrial robots.

M³LVI divides the original point cloud into six types, and uses multi-metric transformation estimation to estimate the state of robot and adopts factor graph optimization model to optimize the state estimation, which improves the accuracy of pose estimation. When one subsystem fails, the other system can complete the positioning work independently, which greatly increases the robustness in degraded environments.

Under the trend of global economic integration and the new context of stagflation, frequent fluctuations in international interest rates are exerting far-reaching impacts on the world economy. In this paper, the transmission mechanism of the impact of fluctuations in international interest rates (specifically, the American interest rate) on the bankruptcy risk in China's pillar industry, the construction industry (which is also sensitive to interest rates), is examined.

Using an improved contingent claims analysis, the bankruptcy risk of enterprises is calculated in this paper. Additionally, an individual fixed-effects model is developed to investigate the mediating effects of international interest rates on the bankruptcy risk in the Chinese construction industry. The heterogeneity of subindustries in the industrial chain and the impact of China's energy consumption structure are also analysed in this paper.

The findings show that fluctuations in international interest rates, which affect the bankruptcy risk of China's construction industry, are mainly transmitted through two major pathways, namely, commodity price effects and exchange rate effects. In addition, the authors examine the important impact of China's energy consumption structure on risk transmission and assess the transmission and sharing of risks within the industrial chain.

First, in the research field, the study of international interest rate risk is extended to domestic-oriented industries. Second, in terms of the research content, this paper is focused on China-specific issues, including the significant influence of China's energy consumption structure characteristics and the risk contagion (and risk sharing) as determined by the current development of the Chinese construction industry. Third, in terms of research methods a modified contingent claim analysis approach to bankruptcy risk indicators is adopted for this study, thus overcoming the problems of data frequency, market sentiment and financial data fraud, which are issues that are ignored by most relevant studies.

Fun at workplace is considered an important initiative to build co-working communities, and this study aims to study its role in promoting the innovative behaviour of co-workers [members of co-working spaces (CWS)] and the mechanism of its influence.

Based on the theory of social exchange and resource conservation, the authors conducted a qualitative study to explore the four dimensions of workplace fun and a quantitative study to empirically analyse the relationship between community embeddedness, organisational embeddedness, workplace fun and creativity of co-workers, taking K-space as an example.

Workplace fun is positively correlated with co-workers' creativity. Community embeddedness plays a complete mediating role between workplace fun and organisational embeddedness. Community embeddedness and organisational embeddedness play a chain-mediating role between workplace fun and creativity.

This study explores the process and impact of fun on employee creativity in a shared office environment by clarifying the composition of fun in CWS workplaces and the transmission mechanism of fun through informal community embeddedness and formal organisational embeddedness, expanding the research perspective on the factors influencing employee creativity in the new office model and enriching the research findings on the impact of fun at work on job performance.

Nurses' work engagement is critical for the service quality of the hospital. Thus, investigation on the influencing factors of nurses' work engagement has become an important issue. This study addresses this issue by exploring the effect of daily family-to-work conflict (FWC) on next-day work engagement among Chinese nurses.

The theoretical model was tested using 555 experience sampling data from 61 nurses collected for 10 workdays in China.

Nurses' daily FWC is associated with their next-day ego depletion. Moreover, increased ego depletion ultimately reduces their next-day work engagement. In addition, a between-individual factor of frequency of perceived patient gratitude mitigates the effect of FWC on ego depletion and the indirect effect on work engagement via ego depletion.

This study is important to the management of health-care organizations as it carries significant implications for theory and practice toward understanding the influence of FWC among nurses. On the one hand, the authors apply the job demands-resources (JD-R) model as the overarching theoretical framework, which contributes to the authors’ understanding of how FWC impairs work engagement. On the other hand, the authors extend extant theoretical models of FWC by identifying the frequency of perceived patient gratitude as an important contextual factor that counteracts the negative effects of FWC among nurses. Moreover, organizations could encourage patients to express their gratitude to nurses by providing more channels, such as thank-you notes, to offer nurses some support for overcoming the destructive effect of FWC.

Artificial intelligence (AI) applications pose a potential threat to users' data security and privacy due to their high data-dependence nature. This paper aims to investigate an understudied issue in the literature, namely, how users perceive the threat of and decide to use a threatening AI application. In particular, it examines the influencing factors and the mechanisms that affect an individual’s behavioral intention to use facial recognition, a threatening AI.

The authors develop a research model with trust as the key mediating variable by integrating technology threat avoidance theory, the theory of planned behavior and contextual factors related to facial recognition. Then, it is tested through a sequential mixed-methods investigation, including a qualitative study (for model development) of online comments from various platforms and a quantitative study (for model validation) using field survey data.

Perceived threat (triggered by perceived susceptibility and severity) and perceived avoidability (promoted by perceived effectiveness, perceived cost and self-efficacy) have negative and positive relationships, respectively, with an individual’s attitude toward facial recognition applications; these relationships are partially mediated by trust. In addition, perceived avoidability is positively related to perceived behavioral control, which along with attitude and subjective norm is positively related to individuals' intentions to use facial recognition applications.

This paper is among the first to examine the factors that affect the acceptance of threatening AI applications and how. The research findings extend the current literature by providing rich and novel insights into the important roles of perceived threat, perceived avoidability, and trust in affecting an individual’s attitude and intention regarding using threatening AI applications.