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In this article, a robust inverter fault diagnosis algorithm is proposed under microgrid environment considering unbalanced state and overcurrent component interference. First, the detected signals are transformed into different data triangles by the proposed slidable triangularization processing which can conveniently and effectively extract the multiscale trend features and data jitter components. Further, the signed features are obtained by the proposed jitter signing processing method, which can reduce the influence of amplitude, and facilitate the logical operation and mixed operation. Then, a quantitative trend fault degree is calculated by the signed information to reflect the fault degree and location information, which has the characteristics of quantitative change. It can reduce the fluctuation and quantity of fault data features. Finally, echo state network is used to implement the intelligent classification. Because the design of hidden layer and the network training speed is simple and fast, which makes the design, implementation, and debugging of fault diagnosis more convenient. Compared with the existing fault diagnosis algorithms, it is robust to unbalanced state and overcurrent component interference of microgrid. These features make it more suitable for inverter fault diagnosis of microgrid environment. The effectiveness of fault diagnosis algorithm is verified by the experiments and comparisons.

Background: Aldosterone is an important hormone in the renin-angiotensin-aldosterone system (RAAS), and playing a pivotal role in the development of hypertension, heart failure, and other cardiovascular diseases. Material and method: In this study, the role of the aldosterone in vascular calcification was underwent in rat model compared with other drugs. Vascular calcification, calcium concentration, activity of alkaline phosphatase (ALP), aldosterone, Urotensin II, mineralocorticoid receptor (MR) and Osteopontin (OPN) were detected or confirmed by the von Kossa staining, colorimetric assays, immunohistochemistry and radioimmunoassay, separately. Result: Results revealed that the aldosterone was significantly increased compared calcification + aldosterone group with calcification group, whereas it was notably decreased in calcification + Spironolactone group in the aortic wall. Compared with control group and aldosterone group, calcium content in vascular tissues was increased in calcification group and calcification + aldosterone group. As the immunoreactivity of the MR, OPN, Urotensin II, IL-6, monocyte chemoattractant protein-1, and deposition of collagen in calcification group and aldosterone group, they all were increased slightly, but were significantly increased in calcification + aldosterone group. Conclusion: It is implied that aldosterone may be involved in the development of vascular calcification, however, the mechanism needs to be further studied.

For the inverter fault diagnosis of permanent magnet synchronous machine drives, it is difficult to simply and conveniently use the small low-frequency data without needing additional hardware and affecting the accuracy of the diagnosis results. This problem often leads to unnecessary computation and wastes a lot of corresponding system memory, increases the hardware requirements and realization difficulty, and affects practicability. In order to improve this problem, to make the design, debugging, and implementation of fault diagnosis more convenient and at low cost, and further to improve the practicability of the algorithm, a novel multi-switches fault diagnosis algorithm is proposed. First, a second low frequency processing method is used to obtain the small low-frequency data simply from the feedback signals of the controller. These small low-frequency data contain the main feature information of different switch states. Second, these small low-frequency data are effectively normalized by the single extremum normalization method, which is based on the symmetry features of different state data. These processed data can keep the asymmetry feature of the fault state of an inverter very well. Third, the main fault components and features are extracted from the distortion part and envelope change of the processed small low-frequency data. Fourth, echo state network is used by combining it with the extracted features to implement the intelligent classification. Different from the traditional neural network, the design of structure of hidden layer network is simplified, and the network training speed is very fast. Therefore, the debugging of this network is very convenient. Compared with the existing fault diagnosis algorithms, the proposed algorithm is convenient and at low cost to realize the multi-switches fault diagnosis. The effectiveness of fault diagnosis algorithm is verified by the experiment.

This paper presents a new open-circuit fault diagnosis algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault diagnosis algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault diagnosis algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault diagnosis algorithm is verified through the detailed simulation and experiment results.

In this article, through the preparation of artificial contaminated soil in laboratory and simulated acid rain pollution to zinc magnesium phosphate cement (MPC for short) solidified soil erosion, the unconfined compressive strength characteristics of a single heavy metal (Zn) pollution under the above conditions was studied. In addition, water glass was added to improve the acid resistance of magnesium phosphate cement solidified soil. The effects of the mixing ratio of curing agent (MgO/KH2PO4, M/P for short), initial pH value by simulating acid rain, and the water glass content on the physical and mechanical properties of solidified zinc contaminated soil were analyzed. Studies have shown that when M/P=4, solidified soil contaminated by zinc has better solidification strength and solidification effect than M/P=6; adding water glass can increase the unconfined compressive strength of solidification of zinc contaminated soil by cement. 4% water glass content has a better effect to improve the strength of solidified soil; When the cement with M/P=4 has better engineering characteristics and acid resistance than that with M/P=6. Therefore, water glass can improve the engineering properties of solidified soil contaminated by zinc under sour environment effectively.

The bearing capacity of the pile foundation determines the safety and stability of the upper structure. However, during the thermal cycling of the energy pile, the stress of the pile will be changed due to temperature changes., which will affect the bearing capacity of the energy pile foundation. In order to analyze the mechanical properties of the energy pile under multi-field coupling, the finite element software COMSOL Multiphysics is used to establish the thermal-mechanical coupling analysis model of the energy pile. We apply thermal load on the pile body and vertical load on the top of the pile. The results show that: Under the multi-field coupling of temperature and mechanical load, the energy pile body expands when heated, and the displacement of the pile top decreases; this will cause uneven settlement between the energy pile foundation laying the heat exchange tube and other pile foundations, which will lead to the tilt and damage of the superstructure; the stress of the pile during the heating of the energy pile will increase.

This paper is concerned with the exponential stability and stabilization of memristive neural networks (MNNs) with delays. First, we present some generalized double-integral inequalities, which include some existing inequalities as their special cases. Second, combining with quadratic convex combination method, these double-integral inequalities are employed to formulate a delay-dependent stability condition for MNNs with delays. Third, a state-dependent switching control law is obtained for MNNs with delays based on the proposed stability conditions. The desired feedback gain matrices are accomplished by solving a set of linear matrix inequalities. Finally, the feasibility and effectiveness of the proposed results are tested by two numerical examples.

This paper investigates the problem of global exponential synchronization for memristor-based neural networks with delay. Based on the nonsmooth analysis and differential inclusion theory, a new analytic technique is employed to design a discontinuous state feedback controller, which ensures the memristor-based drive system exponential synchronize with the response system. The succinct synchronization conditions is closely relate to the switching jumps. The estimated rate of the exponential synchronization can be obtained by solving a sample algebra equation. Simulation results are given to show the effectiveness and benefits of the proposed methods.