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The purpose of this paper is to establish an orbital launch window for manned Moon‐to‐Earth trajectories to support China's manned lunar landing mission requirements of high‐latitude landing and anytime return, i.e. the capability of safely returning the crew exploration vehicle at any time from any lunar parking orbit. The launch window is a certain time interval during which the transearth injection may occur and result in a safe lunar return to the specified landing site on the surface of the Earth.

Using the patched conic technique, an analytical design method for determining the transearth trajectories is developed with a finite sphere of influence model. An orbital launch window has been established to study the mission sensitivities to transearth trip time and energy requirements. The results presented here are limited to a single impulsive maneuver.

The difference between the results of the analytical model and high‐fidelity model is compared. This difference is relatively small and can be easily eliminated by a simple differential correction procedure. The launch window duration varies with launch date, from less than one hour to greater than 20 h, and the launch window occurs every day in the sidereal month.

The solution can be used to serve as an initial estimate for future optimization procedures.

The orbital launch window can be used to provide the basis for the preparation of an orbital launch timetable compatible with lunar missions and re‐entry conditions requirements.

Previous studies were mainly concentrated on the launch windows for the departure from the Earth. This paper investigates and establishes the orbital launch window for Moon‐to‐Earth trajectories.

This paper addresses the issue of internal spatial environmental pollution in non-blasting tunnel construction by proposing a comprehensive evaluation model. The model aims to provide a scientific basis for environmental pollution prevention in non-blasting tunnel construction, thereby facilitating green tunnel construction and sustainable development management.

The study firstly refines and constructs the evaluation index system from the perspective of pollution sources. A novel weight calculation method is introduced by integrating the analytic hierarchy process (AHP) with the ordered weighted averaging (OWA) operator, and a comprehensive evaluation model for internal environmental pollution in non-blasting tunnels is established by incorporating the grey clustering evaluation method. Finally, an empirical study is conducted using the Erbaoshan Tunnel as a case study to verify the feasibility and effectiveness of the model.

The study develops an evaluation system for internal environmental pollution in non-blasting tunnels and applies it to the Erbaoshan Tunnel. The results classify the pollution level as “general pollution,” confirming the rationality and applicability of the evaluation system and model while also identifying the primary pollution factors.

This study first developed a comprehensive evaluation system for environmental pollution in non-blasting tunnel construction from the pollution source perspective, making the system more comprehensive. Additionally, it innovatively combined AHP–OWA and gray clustering methods to scientifically assess pollution levels, providing valuable scientific guidance for the evaluation and management of non-blasting tunnels and similar underground projects.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.