The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients.

Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA).

This study aimed to optimize machining parameters to obtain better surface roughness and remnant depth ratio values under dry turning of NiTi-shape memory alloy (SMA). During the turning experiments, various machining parameters were used, including three different cutting speeds (105, 144, and 200 m/min), three different feed rates (0.05, 0.

Safety restraint use linked to hospital use and charges in Arkansas.

Crash data from 2001-2005 was linked to hospital discharge data to determine the impact of safety restraint use on crashed-related hospital charges and use for 4013 hospitalizations. Safety restraint use, year of hospitalization and age group affected the hospital charges and length of stay after a crash. Mean hospital charges were 44% greater for unrestrained patients ($44,736 versus $30,990); mean length of stay was 23% longer for the unrestrained (9.