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This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but also changes along time. A thermal conductivity prediction model for aerogel considering heat treatment effect is carried out and applied to solve the heat conduction problem of a TPS. The aim of this study is to provide theoretical and numerical references for further development of aerogels applying to TPSs.

A thermal conductivity prediction model for aerogel is established considering treatment effect. The heat conduction problem of a TPS is derived and solved by combining the differential quadrature method and the Runge–Kutta method. The prediction results of aerogel thermal conductivities are verified by comparing with those in literature, while the calculated temperature field of TPS is verified by comparing with that by ABAQUS.

Numerical results show that when applying the current prediction model, the calculated high temperature area in the aerogel layer is narrowed due to the decrease of the thermal conductivity during heat treatment process.

This study will be beneficial to carry out the precise design of TPS for long endurance HFVs.