Kaasgormsen8985

The lingual-based mucoperiosteal flap, a novel flap, was unclear about the effects on the prognosis of surgery for impacted mandibular third molars. This study aimed to compare the lingual- and buccal-based mucoperiosteal flaps with respect to postoperative responses and complications.

A systematic review with a meta-analysis was designed and the PubMed, Cochrane Library, EMBASE, and Web of Science databases and Google Scholar from January 1, 2000 to April 30, 2020 were searched for randomized clinical trials. The predictor variable was buccal- or lingual-based flap in the surgery, and the outcome variables were pain, swelling, trismus, operative time, and wound dehiscence. Other study variables were sex and retention depth of impacted teeth. RevMan 5.3 software was used for data analysis. Mean differences or standardized mean differences and risk ratios were computed to assess associations between 2 variables, where statistical significance was set at P<.05.

Seven publications met the inclusion critlingual-based flap was associated with better primary wound closure in third molar removal. The comma flap, as a subtype, was preferable for relieving postoperative pain, swelling, and trismus over the buccal-based flap.Stochastic resonance (SR) is an effective tool to enhance weak signal by utilizing noise to reach a certain synergistic effect, which has been widely studied in the field of weak signal detection. Currently, using SR to enhance the weak fault feature of wind turbine faces two challenges First, it is difficult for SR to select the optimal system parameters, while the traditional adaptive method based on SNR needs to predict the precise frequency of the target signal. Second, the wind turbine load changes frequently, making the vibration and noise large. As a result, the traditional SR cannot effectively highlight the target fault feature by inducing a stable resonance phenomenon at the target frequency. To improve the ability of SR to enhance the weak fault feature of wind turbine under strong noise, this paper proposes an adaptive fractional SR method based on weighted correctional signal-to-noise ratio (WCSNR). Firstly, the proposed method considers the adiabatic approximation applicable condition in the SR system and combines characteristics of the expected output signal to construct the WCSNR evaluation index to quantify the system output response, so that the system can adaptively obtain optimal parameters without predicting the accurate frequency of the target signal. Then, the fractional-order theory is applied to the SR system to overcome the shortcoming that the integer-order SR cannot induce stable resonance phenomenon at the target frequency when enhancing the fault feature of wind turbine, and use WCSNR to search for the optimal fractional order to further enhance the weak fault characteristics. Simulation and engineering actual data analysis results verify the effectiveness and superiority of the proposed method in the fault feature enhancement of wind turbine. The analysis results show that compared with the traditional SR method, the method proposed in this paper can more effectively reduce the interference of background noise and accurately enhance the weak fault feature.In this paper, the robust filtering problem for uncertain complex networks with time-varying state delay and stochastic nonlinear coupling based on H∞ performance criterion is studied. The random connections of coupling nodes are represented by utilizing independent random variables and the multiple fading measurements phenomenon is characterized by introducing diagonal matrices with independent stochastic elements. Moreover, the probabilistic time-varying delays in the measurement outputs are described by white sequences with the Bernoulli distributions. Furthermore, All system's matrices are supposed to have uncertainty and a quadratic bound is assumed for nonlinear part of the network. This bound can be obtained by solving a sum of squares (SOS) optimization problem. By applying the Lyapunov theory, we design a robust filter for each node of the network so that the filtering error system is asymptomatically stable and the H∞ performances are met. Then, the parameters of the filters are achieved by solving a linear matrix inequality (LMI) feasibility problem. Finally, the applicability and performance of the proposed H∞ filtering approach are demonstrated via a practical example.Run-to-failure experiment is efficient and effective to investigate bearing deterioration process. Periodic transient waveform carries rich information of health conditions of bearings but the transient waveform matching is a challenging problem for evaluating bearing fatigue life because the shapes and parameters of the waveform vary with the evolution of the bearing degradation. A wavelet function such as a Morlet wavelet is able to extract essential features from the transient waveform but limited to a single transient component. The multi-wavelet may provide a solution to fit a variety of primary components in the transient waveform, so as to track the degradation trend of the bearing; however, very limited work has been done to address this issue. To bridge the research gap in the transient waveform matching, a novel ascension multi-wavelet method is proposed in this paper for diagnosing the undergoing degradation state and predicting the remaining useful life (RUL) of the bearings. Firstly, the transient waveform was matched using the combination of multiple wavelets. Then, the entropy of the multiple-wavelet signal was calculated to quantify the periodic transients to generate a monotone trend of the bearing degradation. The degradation state of the bearing was identified using the entropy. Lastly, the ensemble learning method was employed to establish an RUL predictor. Both simulation and experiments were carried out to evaluate the proposed method. BMS1inhibitor The analysis results demonstrate satisfactory diagnostics and prognostics performance of the proposed method. The RUL prediction accuracy of the multi-wavelet matching is better than that of the single-wavelet matching.