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Supplemental material, sj-pdf-1-tar-10.1177_1753466621998246 for PINK1/Parkin-mediated mitophagy in mechanical ventilation-induced diaphragmatic dysfunction by Hui Yong, Yun Zhou, Wanlin Ye, Tianmei Li, Gangming Wu, Jingyuan Chen, Li Liu and Jicheng Wei in Therapeutic Advances in Respiratory Disease

Supplemental material, sj-pdf-3-tar-10.1177_1753466621998246 for PINK1/Parkin-mediated mitophagy in mechanical ventilation-induced diaphragmatic dysfunction by Hui Yong, Yun Zhou, Wanlin Ye, Tianmei Li, Gangming Wu, Jingyuan Chen, Li Liu and Jicheng Wei in Therapeutic Advances in Respiratory Disease

Supplemental material, sj-pdf-2-tar-10.1177_1753466621998246 for PINK1/Parkin-mediated mitophagy in mechanical ventilation-induced diaphragmatic dysfunction by Hui Yong, Yun Zhou, Wanlin Ye, Tianmei Li, Gangming Wu, Jingyuan Chen, Li Liu and Jicheng Wei in Therapeutic Advances in Respiratory Disease

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

Tacking of testability growth test (TGT) is an assurance of the orderly development of TGT. This paper, a testability growth tracking model based on Expected Bayesian method which under entropy loss function is studied in view of the lack of existing testability growth model and the subjective tracking of testability index. Firstly, the multi-layer prior distribution of the fault detection rate (FDR) is obtained by dusing the priori constraint as a priori information. Secondly, under the entropy loss function, the Expected Bayesian method is used as the solution of the multi-layer Bayesian. Finally, the TGT is tracked according to the growth test data of a stable tracking platform. The results show that the method has strong objectivity and the tracking accuracy is in the order of 10−2 magnitude.

Testability growth is a process that aims to improve the testability level of the equipment via identifying and removing the testability design defects (TDDs). The establishment of the existing testability growth model (TGM) needs to consider a variety of factors, it's difficult to describe it accurately. To solve this problem, a TGM based on evidential reasoning (ER) method with nonlinear optimization is studied in this paper. According to the growth test data that can achieve the testability growth tracking and predicting. To estimate the parameters of the TGM accurately by using the mean square error (MSE). Finally, growth test data of a stable tracking platform is used to verify the validity of the model. The results show that the tracking accuracy is in the order of 0.0013 magnitude.

Inspection due to its relatively simple implementation is increasingly used in industry to identify the system state, to reveal system fault, and to make maintenance decisions. Researchers and engineers have almost always investigated optimal inspection policies for standby systems under an assumption that the servicing systems fail following exponential distributions. In many cases, however, the exponential distribution assumption can restrict the applicability of inspection policies due to its memory-less nature. In this paper, we consider the optimal inspection problem of a stochastically failing warm-standby system that will be needed at a random time when a failure of the main servicing system occurs. If the warm-standby system is not operational at that time, the system incurs a large penalty, which we want to avoid through inspections. To address this issue, we investigate an optimal inspection policy where the failure of the servicing system follows Weibull distribution. A model to schedule limited optimal inspection times is presented so that the total cost consisting of the expected penalty and inspection costs is minimized. The structural properties of the optimal policy are discussed in detail, and a solution algorithm is presented to find the optimal inspection times, which can guarantee the existence and uniqueness of the optimal policy. Thus, the commonly used inspection policies limited to the exponential cases turn out to be our special cases. Numerical simulations are provided to demonstrate the proposed approach.

This paper studies on mine the image semantic information through Non-negative Matrix Factorization, which is a powerful computing tools in many applications. Non-negative matrix factorization is a low-rank matrix approximation method for finding two low-rank nonnegative matrices and the product of which can provide a good approximation to the original non-negative matrix. Firstly, a multimodal training matrix is constructed according to the ground truth annotations of the training images dataset. Secondly, the matrix constructed in the first step is decomposed by non-negative matrix factorization. Afterwards, the untagged images with only visual features are represented as the matrix, and then semantic terms can be extracted from the matrix which represents the similarity between test and training images. Experimental results demonstrate the effectiveness of the proposed method.