Automated detection of clinical depression based on convolution neural network model.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.

The array and interfacial activity of sodium dodecyl benzene sulfonate and sodium oleate at the oil/water interface.

There is a close correlation between the interfacial activity and the adsorption of the surfactant at the interface, but the detailed molecular standard information was scarce. The interfacial activity of two traditional anionic surfactants sodium dodecyl benzene sulfonate (SDBS) and sodium oleate (OAS) were studied by experimental and computer simulation methods. With the spinning drop method and the suspension drop method, the interfacial tension of oil/aqueous surfactant systems was measured, and the influence of surfactant concentration and salinity on the interfacial tension was investigated. The dissipative particle dynamics (DPD) method was used to simulate the adsorption of SDBS and OAS at the oil/water interface. It was shown that it is beneficial to decrease interfacial tension if the hydrophobic chains of the surfactant and the oil have similar structure. The accession of inorganic salts causes surfactant molecules to form more compact and ordered arrangements and helps to decrease the interfacial tension. There is an osculation relation between interfacial density and interfacial activity. The interfacial density calculated by molecular simulation is an effective parameter to exhibit the interfacial activity.