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To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network.

First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model.

The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10^–3°/h and the angular velocity random walk value is 2.132 × 10^–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10^–2°/h/vHz of the original output signal of the gyro.

This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

This paper explores the relationship between state-led urbanization and primary industry development using the difference-in-differences (DiD) method.

The study uses the DiD method.

Exploiting county-city mergers during 2010–2018, the key strategy to expand the city outward and promote urbanization on the urban fringe by local government, the authors find that county-city mergers led to the growth of primary industry decline by 4.23%. The result can be explained by the loss of essential production factors, including land and labor used for farming. In addition, the negative effect is more pronounced for counties with more substantial manufacturing. The results indicate that urbanization in China relocates land and labor; however, it does not improve the efficiency of agricultural output.

This paper contributes to the understanding of urbanization and rural development from the perspective of the primary industry by showing production factor redistribution. Second, this study complements the literature on local government mergers.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.