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Sulcus vocalis is an indentation parallel to the edge of vocal fold, which may extend into the cover and ligament layer of the vocal fold or deeper. The effects of sulcus vocalis depth d on phonation and the vocal cord vibrations are investigated in this study. The three-dimensional laryngeal models were established for healthy vocal folds (0 mm) and different types of sulcus vocalis with the typical depth of 1 mm, 2 mm, and 3 mm. These models with fluid-structure interaction (FSI) are computed numerically by sequential coupling method, which includes an immersed boundary method (IBM) for modelling the glottal airflow, a finite-element method (FEM) for modelling vocal fold tissue. The results show that a deeper sulcus vocalis in the cover layer decreases the vibrating frequency of vocal folds and expands the prephonatory glottal half-width which increases the phonation threshold pressure. The larger sulcus vocalis depth makes vocal folds difficult to vibrate and phonate. The effects of sulcus vocalis depth suggest that the feature such as phonation threshold pressure could assist in the detection of healthy vocal folds and different types of sulcus vocalis.

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. Glottal flow waveform directly reflects the state of glottal excitation. Extracting acoustic features from glottal source signals may contribute to the detection of pathological voice. To improve the performance of voice pathology detection, this article investigates the contribution of the glottal flow waveform for pathological voice detection by evaluating the classification result using features extracted from raw speech utterances and corresponding glottal flow waveforms. The individual feature sets used are extracted from raw or glottal voice utterances with identical parameter settings, which are openSMILE acoustic features, audio features computed by Moving Picture Experts Group-7 standard and classical glottal source features. In addition, a feature selection method in terms of the wrapper approach is used to combine the single features ranked by using the Fisher discrimination ratio. Voice pathology detection experiments were carried out using Random Forest. The best accuracies of 88.52% for the Saarbrücken Voice database and 100.00% for the Massachusetts Eye and Ear Infirmary database are achieved using the combined feature set extracted from the glottal source signal, with improvement of 0.44-3.13% in the accuracies obtained by using raw speech utterances. Compared to state-of-the-art methods, the proposed method achieves the highest accuracy for the Massachusetts Eye and Ear Infirmary database and an increase of 2.75-17.16% in detection accuracy compared to other conventional pipeline systems for the Saarbrücken Voice databse. The experimental results demonstrate that using glottal flow waveform as source signal can improve the performance of pathological voice detection.

In this study, sulfonated carbon was obtained by sulfonating pine carbon with fuming sulfuric acid. Characterized by elemental analyzer, fourier transform infrared spectroscopy and X-ray photoelectron spectrometry, it showed that –SO3H was successfully bonded onto the pine carbon’ skeleton in the sulfonation reaction. The effects of pine carbon and sulfonated carbon on the catalytic carbonization and smoke suppression of sawdust boards, and its flame-retardant mechanism were investigated by cone calorimetry, thermogravimetric analysis, scanning electron microscopy, and pyrolysis–gas chromatography–mass spectrometry. And the results demonstrated that sawdust boards containing sulfonated carbon showed superior performance, determined by comparing the peak heat release rate, total heat release, smoke release rate, and total smoke product. Furthermore, with the addition of sulfonated carbon, a carbon layer was formed with a strong oxygen-containing hybrid structure and carbohydrate structure, the pyrolysis of sawdust boards was inhibited, and its char yield was increased.

The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.

In order to study the tribological properties of V-shaped texture under oil lubrication conditions, the loading force and speed are selected as the influencing factors, each factor selected six levels. Experimental study on friction and wear of V-shaped texture with ring arrangement is finished by MMG-10 Multifunctional Friction and Wear Testing Machine, and the data of the experimental results are analyzed by using Matlab. The results show that there is a near linear relationship between the friction coefficient and the loading force and velocity. The relationship between the friction coefficient and the loading force and velocity can be expressed by a functional equation. The loading force has a greater influence on the friction coefficient than the speed. The SEM images of the post-test specimens show that the main weared zone of the V-shaped texture is the tip part. Create a single V-shaped texture model with Solidworks and use CFD to divide the mesh into the Fluent solution. According to the pressure distribution cloud diagram and the turbulence intensity cloud diagram, the stability of the oil film is improved due to the enhanced fluidity of the oil film and the small change in the oil pressure. The tip portion is also the region with a large turbulence intensity value. The improvement of the stability of the oil film is the key to reduce the friction coefficient of the V-shaped texture when the loading force increases.

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. A voice pathology detection and multi-classification method using an audio feature set extracted from glottal flow waveform is proposed in this work. In addition, a feature selection method of Fisher discrimination criterion is used to screen more valuable features and eliminate the features with low efficiency. The discrimination ability and effectiveness of the selected features were verified using three dimensional scatter plot and box plot in this study. One of the contributions of this paper is to investigate and evaluate the performance of different machine learning classifiers value for voice pathology detection and multi-classification. All experiments were carried out using the Massachusetts eye and ear infirmary database. Accuracy, sensitivity, specificity and receiver operating characteristic area are used as evaluation indexes to compare the performance of different machine learning classifiers. Each machine learning classifier had a good performance using the proposed feature set with nearly 100% accuracy in detection and higher than 90% accuracy in multi-classification. The experimental results indicate that the proposed method is valuable for voice pathology detection and multi-classification, especially in multi-classification.

In this paper, the spectrogram features of voice are studied, and the differences between normal voice and pathological voice are analyzed. 53 cases of normal voice and 133 cases of pathological voice are selected from Massachusetts Eye and Ear Infirmary(MEEI) database for experiments. As the popularity of deep learning is increasing, convolutional neural network(CNN) based on tensorflow2.1 is applied to extract features from spectrograms., and the voice data are augmented for overfitting of the trained model. The recognition accuracy of pathological voice detection is 96.51%. The experimental results show that the characteristic parameters extracted from spectrograms by CNN have distinguish ability.

This paper established a mathematical model of hydrodynamic lubrication through theoretical analysis, solved the key variables of the mathematical model through simulation and validated the mathematical model using the experimental results. The purpose is to summarize a set of surface texture theory and optimal selection equations for complex working conditions. After comparing the experimental results with the results obtained by the mathematical model, it is found that the two are basically the same in the ranking of the anti-friction properties of different textures, and there is an error of 10%-40% in the friction coefficient value.

Xiang Zhang,1,2 Dezhi Cheng,1,2 Yu Liu,1,2 Yuanbo Wu,2 Zhifeng He1,21Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 2Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of ChinaBackground: The mTOR pathway is altered in a multitude of cancers, including lung cancer; however, abnormal activation in this pathway is less common in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). Gephyrin is a highly conserved and widely expressed ancient protein in vertebrate tissues. Its role and molecular mechanism in lung cancer development are largely unknown.Method: We analyzed the expression profile of gephyrin and overall survival rates in LUAD and LUSC. The LUSC cells (H520 and SK-MES-1) were transfected with pLV-gephyrin to establish gephyrin stable overexpression cell lines. Real-time quantitative PCR and Western blot were performed to detect the mRNA and protein levels. The cell growth and cell cycle were detected by the MTT assay and flow cytometry. Finally, a xenograft tumor model was established to determine cell tumorigenesis in vivo.Results: Our results show that gephyrin was reduced in LUAD and LUSC, and its low expression in LUSC patients indicated poor prognosis. Gephyrin overexpression suppressed LUSC cell proliferation, arrested cell cycle progression, and decreased the expression of cell-cycle related proteins such as cyclin D1, cyclin-dependent kinase-2 (CDK2), and proliferation-related protein proliferating cell nuclear antigen (PCNA). Conversely, knockdown of gephyrin promoted LUSC cell growth. Moreover, gephyrin reduced mTOR pathway activation to inhibit cyclin D1 and CDK2 translation. Mechanistically, gephyrin suppressed mTOR pathway activation by promoting mTOR degradation. Furthermore, gephyrin overexpression suppressed LUSC tumorigenesis.Conclusion: Gephyrin suppressed LUSC development by reducing mTOR pathway activation, implicating gephyrin as a potential molecular target for LUSC management.Keywords: gephyrin, lung squamous cell carcinoma, mTOR pathway, degradation

In this study, [PW12O40]3− anions have been intercalated into Zn,Al-Layered Double Hydroxides(ZnAl-NO3-LDHs) to synthesize ZnAl-PW12O40-LDHs by ion-exchange reaction. The chemical composition and structure were analyzed by XRD, FT-IR, ICP, and SEM. Then, the nanocellulose based 3D lightweight foam thermal insulation materials was prepared by ZnAl-PW12O40-LDHs and CNF compound with H3BO3. The TG showed that ZnAl-PW12O40- LDHs significantly reduced the maximum decomposition rate and increased the carbon residual rate at 800 °C, which indicated that ZnAl-PW12O40-LDHs can improve the oxidation resistance of CNF composite materials at high temperature.The fire resistance of different samples were evaluated by back temperature test and alcohol lamp flame test, CNF/50%ZnAl-PW12O40-LDHs /2%H3BO3 showed best thermal stability and flame-retardant properties, which can be contributed to the decomposition products of ZnAl-PW12O40-LDHs, acting both as phase flame retardant and condensed phase flame retardant.