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The purpose of this paper is to provide a historical review of China’s anti-corruption efforts, from the ancient period of Chinese slavery societies to the late 1970s before China launched its profound economic reform, under the current status of the harsh crusade against corruption that the Chinese new leadership initiated.

This paper is mainly based on a great deal of historical literature and empirical findings, with relevant comparative analysis on policies and regulations between various periods of China.

The phenomenon of corruption has existed in Chinese history for thousands of years, throughout Chinese slavery societies, feudal societies, republic period and the People’s Republic of China (PRC). Anti-corruption laws formed an important part of ancient Chinese legal system, and each dynasty has made continuous and commendable progress on fighting such misconduct. Innumerable initiatives have also been taken by the ruling party Chinese Communist Party (CCP) since the founding of the PRC. The PRC government created various specially designed government organizations and a series of updated regulations for preventing economic crimes. They have realized that periodic movements against corruption would no longer be helpful, and the paramount issue nowadays is indeed how bold the leaders are in striking out those unhealthy tendencies.

This paper fills in the blanks in the Western world with a comprehensive description of, and comments on, the historical efforts on China’s corruption and economic crime prevention. It also, in various ways, provides meaningful information that links to China’s current furious war against corruption.

This study aims to improve the corrosion resistance of TA2-welded joints by superhydrophobic surface modification using micro-arc oxidation technology and low surface energy substance modification.

The microstructure and chemical state of the superhydrophobic film layer were analyzed using scanning electron microscopy, energy dispersive X-ray spectroscopy, three-dimensional morphology, X-ray diffraction, X-ray photoelectron spectroscopy and Fourier transform infrared absorption spectroscopy. The influence of the superhydrophobic film layer on the corrosion resistance of TA2-welded joints was investigated using classical electrochemical testing methods.

The characterization results showed that the super hydrophobic TiO₂ ceramic membrane was successfully constructed on the surface of the TA2-welded joint, and the construction of the super hydrophobic film greatly improved the corrosion resistance of the TA2-welded joint.

The superhydrophobic TiO₂ ceramic membrane has excellent corrosion resistance. The micro nanostructure in the superhydrophobic film can intercept air to form an air layer to prevent the corrosion medium from contacting the surface, thus, improving the corrosion resistance of the sample.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

The purpose of this study is to develop a piezoresistive absolute micro-pressure sensor for altimetry. For this application, both high sensitivity and high overload resistance are required. To develop a piezoresistive absolute micro-pressure sensor for altimetry, both high sensitivity and high-overload resistance are required. The structure design and optimization are critical for achieving the purpose. Besides, the study of dynamic performances is important for providing a solution to improve the accuracy under vibration environments.

An improved structure is studied through incorporating sensitive beams into the twin-island-diaphragm structure. Equations about surface stress and deflection of the sensor are established by multivariate fittings based on the ANSYS simulation results. Structure dimensions are determined by MATLAB optimization. The silicon bulk micromachining technology is utilized to fabricate the sensor prototype. The performances under both static and dynamic conditions are tested.

Compared with flat diaphragm and twin-island-diaphragm structures, the sensor features a relatively high sensitivity with the capacity of suffering atmosphere due to the introduction of sensitive beams and the optimization method used.

An improved sensor prototype is raised and optimized for achieving the high sensitivity and the capacity of suffering atmosphere simultaneously. A general optimization method is proposed based on the multivariate fitting results. To simplify the calculation, a method to linearize the nonlinear fitting and optimization problems is presented. Moreover, a differential readout scheme attempting to decrease the dynamic interference is designed.