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Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

Construction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.

A cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.

The results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.

This study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.

This study investigates how role overload in the sharing economy leads to emotional exhaustion, which restricts value co-creation activity, and also investigates the moderating effect of perceived platform support.

Two experimental investigations and field research questionnaires were given to respondents with shared mobility industry expertise.

First, role overload detrimentally affects service providers' value co-creation behavior; second, emotional exhaustion acts as a mediator between role overload and value co-creation behavior; and finally, perceived platform support moderates the adverse effect of role overload on emotional exhaustion.

To the best of the authors' knowledge, this study is the first to explore the antecedents of value co-creation behavior from the service provider's perspective, extending the application of COR theory in a sharing economy context.

First, alternative mediators between role overload and emotional exhaustion were not identified. Second, other dimensions of role overload and their impacts were not examined. Lastly, this study did not explore broader perspectives beyond algorithms.

This study recommends that managers reduce role overload ex ante in terms of clarifying responsibilities and obligations, providing substantive resource support and rationalizing order allocation, respectively.