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This paper aims to examine the impact of the COVID-19 infodemic on the public’s online information behaviour, offering insights critical for shaping effective informational responses in future public health emergencies.

This paper uses a structured online survey with 27 targeted questions using a five-point Likert scale to measure eight variables. Data analysis is conducted through structural equation modelling on 307 valid responses to rigorously test the research hypotheses.

This paper indicates that information quality significantly impacts the public’s capacity to select, share and use online information. Additionally, the comprehensibility of information plays a crucial role in shaping the public’s behaviours in terms of online information exchange and usage. The credibility of information sources emerges as a key determinant influencing the public’s online information selection, exchange and utilization behaviour. Moreover, social influence exerts a substantial effect on the public’s online information selection, acquisition, exchange and utilization behaviour. These findings highlight the presence of universality and sociality, mediation and guidance, as well as the purposefulness and selectivity performed by the public’s online information behaviour during an infodemic.

This paper introduces a novel research model for assessing the influence and identifies the patterns of the public’s online information behaviour during the COVID-19 infodemic. The findings have significant implications for developing strategies to tackle information dissemination challenges in future major public health emergencies.

Based on imprinting theory and episodic future thinking, this paper aims to study how CEOs’ attributes and experiences inform innovation in tourism and hospitality businesses. It also explores ways to quantify innovation in this sector.

The authors quantitatively analysed innovation in tourism and hospitality using extensive data from companies’ annual reports. They further adopted multivariate regression to test how CEOs’ experience affects enterprise innovation.

Results demonstrate that CEOs’ academic education and rich work experience can promote corporate innovation. The authors also identified a mediating role of the tone of narrative disclosure in annual reports between CEOs’ academic education and corporate innovation. The imprinting effects of career experience and educational experience appear both independent and interactive.

CEOs are more inclined to engage in corporate innovation when influenced by the combined imprinting effects of strategic management training and work experience. Additionally, leaders should consider how communication styles indirectly influence innovation activities.

This paper introduces an integrated perspective that blends imprinting theory and episodic future thinking to bridge knowledge gaps regarding the interaction of CEOs’ past experiences. This work enhances understanding of how CEOs’ imprinted experiences, together with their capacity for envisioning future scenarios, can drive corporate innovation.

The transient loads on the spherical hybrid sliding bearings (SHSBs) rotor system during the process of accelerating to stable speed are related to time, which exhibits a complex transient response of the rotor dynamics. The current study of the shaft center trajectory of the SHSBs rotor system is based on the assumption that the rotational speed is constant, which cannot truly reflect the trajectory of the rotor during operation. The purpose of this paper truly reflects the trajectory of the rotor and further investigates the stability of the rotor system during acceleration of SHSBs.

The model for accelerated rotor dynamics of SHSBs is established. The model is efficiently solved based on the fourth-order Runge–Kutta method and then to obtain the shaft center trajectory of the rotor during acceleration.

Results show that the bearing should choose larger angular acceleration in the acceleration process from startup to the working speed; rotor system is more stable. With the target rotational speed increasing, the changes in the shaft trajectory of the acceleration process are becoming more complex, resulting in more time required for the bearing stability. When considering the stability of the rotor system during acceleration, the rotor equations of motion provide a feasible solution for the simulation of bearing rotor system.

The study can simulate the running stability of the shaft system from startup to the working speed in this process, which provides theoretical guidance for the stability of the rotor system of the SHSBs in the acceleration process.

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.