Optimization analysis of non-contact ultrasonic degradation system based on synchronous resonance of multiple modes

To solve the defects of low degradation efficiency and high cross-contamination risk in the conventional ultrasonic processor, a non-contact ultrasonic degradation system was developed. Through finite-element modal analysis, the natural frequencies of the first ten modes of the diaphragm with dome-shaped structure were found to have a ladder-like distribution, which provides a possibility to utilize the synchronous resonance of multiple modes. The effects of curvature radius, thickness and preload force on the synchronous resonance of multiple modes are studied using the control variate method, and then the structural parameters and the preload force are optimized. Based on these results, the diaphragm was manufactured, the experimental platform of the non-contact ultrasonic degradation was established, and its degradation efficiency was evaluated. The experimental results proved that the non-contact ultrasonic degradation system was able to effectively degrade Bacillus atrophaeus

A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters.

Early diagnosis and prevention play a crucial role in the treatment of patients with ARDS. The definition of ARDS requires an arterial blood gas to define the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2 ratio). However, many patients with ARDS do not have a blood gas measured, which may result in under-diagnosis of the condition. Using data from MIMIC-III Database, we propose an algorithm based on patient non-invasive physiological parameters to estimate P/F levels to aid in the diagnosis of ARDS disease. The machine learning algorithm was combined with the filter feature selection method to study the correlation of various noninvasive parameters from patients to identify the ARDS disease. Cross-validation techniques are used to verify the performance of algorithms for different feature subsets. XGBoost using the optimal feature subset had the best performance of ARDS identification with the sensitivity of 84.03%, the specificity of 87.75% and the AUC of 0.9128. For the four machine learning algorithms, reducing a certain number of features, AUC can still above 0.8. Compared to Rice Linear Model, this method has the advantages of high reliability and continually monitoring the development of patients with ARDS