Poly(ethylene terephthalate) (PET) resin is one of the most widely used engineering plastic with high performance, but the poor impact strength limits its applications for the notch sensitivity. In this research, toughened PET alloy was prepared by blending recycled PET with polycarbonate (PC) and MDI (methylenediphenyl diisocyanate). Intrinsic viscosity and melt viscosity measurements proved increase of the molecular weights of PET via chainextending reaction. FTIR and DMA results proved that some PET-PC copolymers were produced and the compatibility of PET phase and PC phase was improved. In addition, the reaction induced by MDI also affected the crystallization behaviors of PET, as observed from DSC results, and the crytallinity of PET decreased with the increase of MDI content. For all of these effects of MDI of increasing of molecular weight, improving of compatibility, and limiting the crystallization behaviors of PET/PC alloy, the notched-impact strength was greatly improved from 17.3 to 70.5 kJ/m 2 .

This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder. Methods: We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard. Results: A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%. Conclusion: Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.

The most possible oxidative decomposition reaction path of EC–BOB−cluster.

No abstract