Optimization for sound insulation of a sandwich plate with a corrugation and auxetic honeycomb hybrid core

A sandwich plate with a corrugation and auxetic honeycomb hybrid core is constructed,and its sound insulation and optimization are investigated.First,the motion governing equation of the sandwich plate is established by the third-order shear deformation theory(TSDT),and then combined with the fluid-structure coupling conditions,and the sound insulation is solved.The theoretical results are validated by COMSOL simulation results,and the effects of the structural parameter on the sound insulation are analyzed.Finally,the standard genetic algorithm is adopted to optimize the sound insulation of the sandwich plate.

Vibration and sound transmission loss characteristics of porous foam functionally graded sandwich panels in thermal environment

This study investigates the vibration and acoustic properties of porous foam functionally graded(FG)plates under the influence of the temperature field.The dynamics equations of the system are established based on Hamilton's principle by using the higher-order shear deformation theory under the linear displacement-strain assumption.The displacement shape function is assumed according to the four-sided simply-supported(SSSS)boundary condition,and the characteristic equations of the system are derived by combining the motion control equations.The theoretical model of vibro-acoustic coupling is established by using the acoustic theory and fluid-structure coupling solution method under the simple harmonic acoustic wave.The system's natural frequency and sound transmission loss(STL)are obtained through programming calculations and compared with the literature and COMSOL simulation to verify the validity and reliability of the theoretical model.The effects of various factors,such as temperature,porosity coefficients,gradient index,core thickness,width-to-thickness ratio on the vibration,and STL characteristics of the system,are discussed.The results provide a theoretical basis for the application of porous foam FG plates in engineering to optimize vibration and sound transmission properties.

A scoring system for predicting the risk of breast cancer-related lymphedema

Objective:We aimed to establish a scoring system to predict the risk of breast cancer-related lymphedema.Methods:From April 2017 to December 2018,533 patients who previously underwent surgery for breast cancer were enrolled in this cross-sectional study.Univariate analysis was performed to explore and define the risk factors.A scoring system was then established on the basis of odds ratio values in the regression analysis.Results:The additive scoring system values ranged from 6 to 22.The receiver operating characteristic(ROC)curve of this scoring system showed a sensitivity and specificity of 83.3%and 57.3%,respectively,to predict the risk of lymphedema at a cut-off of 15.5 points;the area under the curve was 0.736(95%confidence interval:0.662-0.811),with x2=5.134(P=0274)for the Hosmer-Lemeshow test.Conclusions:The predictive efficiency and accuracy of the scoring system were acceptable,and the system could be used to predict and screen groups at high risk for breast cancer-related lymphedema.

Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network

Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning.To address this issue,our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention(PCGAN-EASA),which incrementally improves the quality of generated EEG features.This network can yield full-scale,fine-grained EEG features from the low-scale,coarse ones.Specially,to overcome the limitations of traditional generative models that fail to generate features tailored to individual patient characteristics,we developed an encoder with an effective approximating self-attention mechanism.This encoder not only automatically extracts relevant features across different patients but also reduces the computational resource consumption.Furthermore,the adversarial loss and reconstruction loss functions were redesigned to better align with the training characteristics of the network and the spatial correlations among electrodes.Extensive experimental results demonstrate that PCGAN-EASA provides the highest generation quality and the lowest computational resource usage compared to several existing approaches.Additionally,it significantly improves the accuracy of subsequent stroke classification tasks.

Boundary element method for calculation of elastic wave transmission in two-dimensional phononic crystals

A boundary element method(BEM) is presented to compute the transmission spectra of two-dimensional(2-D) phononic crystals of a square lattice which are finite along the x-direction and infinite along the y-direction.The cross sections of the scatterers may be circular or square.For a periodic cell,the boundary integral equations of the matrix and the scatterers are formulated.Substituting the periodic boundary conditions and the interface continuity conditions,a linear equation set is formed,from which the elastic wave transmission can be obtained.From the transmission spectra,the band gaps can be identified,which are compared with the band structures of the corresponding infinite systems.It is shown that generally the transmission spectra completely correspond to the band structures.In addition,the accuracy and the efficiency of the boundary element method are analyzed and discussed.