Research on the pyrolysis characteristics and mechanisms of waste printed circuit boards at fast and slow heating rates.

The pyrolysis treatment of waste printed circuit boards (WPCBs) shows great potential for sustainable treatment and hazard reduction. In this work, based on thermogravimetry (TG), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), and density functional theory (DFT), the thermal weight loss, product distribution, and kinetics of WPCBs pyrolysis were studied by single-step and multi-step pyrolysis at fast (600 °C/min) and slow (10 °C/min) heating rates. The heating rates of TG and Py-GC/MS were the same for each group of experiments. In addition, the bond dissociation energy (BDE) of WPCBs polymer monomers was calculated by DFT method. Compared with slow pyrolysis, the final weight loss of fast pyrolysis is reduced by 0.76 wt%. The kinetic analysis indicates that the activation energies of main pyrolysis stages range from 98.29 kJ/mol to 177.59 kJ/mol. The volatile products of fast pyrolysis are mainly phenols and aromatics. With the increase of multi-step pyrolysis temperature, the order of the escaping volatiles is phenols, hydrocarbyl phenols, aromatics, and benzene (or diphenyl phenol). The pyrolysis residue of WPCBs may contains phenolics and polymers. Based on the free radical reactions, the mechanism and reaction pathways of WPCBs pyrolysis were deduced by the DFT. Moreover, a large amount of benzene is produced by pyrolysis, and its formation mechanism was elaborated.

Prediction of Pulmonary Function Parameters Based on a Combination Algorithm.

OBJECTIVE: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. METHODS: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. RESULTS: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. CONCLUSION: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.

ECG-based biometric under different psychological stress states.

BACKGROUND AND OBJECTIVE: In recent years, people have been exploring methods for biometric identification through electrocardiogram (ECG) signals. Under the same psychological pressure state, biometric identification through ECG signals is a traditional verification method. However, ECG signals are affected by changes in psychological stress, and ECG-Based biometric under different psychological stress states are still challenging. In this paper, we propose a method combining manual and automatic features for ECG-based biometric under different psychological stress states. And propose a new indicator Stress Classification Coefficient (SCC) that assesses the effect of different psychological stress on heart rate variability (HRV) features. METHODS: In our method, we obtain manual features to be a three-step process: first, HRV features obtained from the ECG signals. Second, based on HRV features, the mental state of the experimental subjects is assessed by using the Gaussian mixture model (GMM). Finally, use cluster centers to process the original HRV features to reduce the Stress Classification Coefficient (SCC). Also, the one-dimensional convolutional neural network is constructed to automatically extract the implied features of ECG signals. Finally, the manual feature and the automatic feature are combined, and the final recognition result is obtained through the support vector machine (SVM) model. The major attribute of the proposed method is that it can perform ECG biometric under different psychological stress states. The combination of manual and automatic features expands the application scenarios of ECG-based biometric. RESULTS: Based on this method, we used the Montreal stress model with calculation experiment in the laboratory to induce stress on 23 healthy students (10 women and 13 men, aged 20-37), and obtain their ECG signals under different stress conditions. Through this method to recognize the above data, an average recognition rate of more than 95% can be achieved, the average F1 score is 0.97. CONCLUSIONS: The proposed method in this article is a promising approach to deal with the effects of different psychological stresses on ECG-Based biometric. It provides the possibility of ECG-Based biometric under different psychological stress.