Scanning electron microscopy (SEM) results revealed that an increase in Dex-AE content led to an initial decrease in pore size of the Dex-AE/PEGDA hydrogels, but a further increase in Dex-AE content resulted in a slightly increase of pore size. The swelling data indicated that the swelling ratio depended on the precursor feed ratio. GS was incorporated into the hydrogels through 2 different methods, i.e., immersed and crosslinked. The crosslinked GS-Dex-AE/PEGDA hydorgels exhibited stronger antimicrobial activities against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. Finally, the viscoelastic properties of crosslinked GS-Dex-AE/PEGDA hydorgels were investigated.
Marx bank pulse circuit based on avalanche transistors can generate high amplitude, high-repetition-rate and low-jitter ultra-wideband pulses. The rising edge of the pulse is determined mainly by the performance of avalanche transistors. The amplitude of the pulse can be adjusted by changing the number of stage and the value of charging capacitors. In this paper, A 25 stages Marx bank pulse circuit is designed and tested, In order to reduce the jitter of the output pulses, an optimized driver circuit is designed and the charging resistors and capacitors are tuned to achieve a higher amplitude. Finally, 2.7 kV peak voltage of the output pulse is obtained, the FWHM is 1 ns, the maximum repetition rate is 10 kHz.
ObjectiveTo build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).MethodsWe systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.ResultsThe AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).ConclusionThe prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.
Milling carbon fiber reinforced polymer (CFRP) composites faces significant challenges, such as rapid tool wear leading to machining damage, even for a polycrystalline diamond (PCD) cutter. However, the effects of the milling conditions on the tool wear of PCD cutters remain unclear. This study investigated the effects of the cutting speed and fiber orientation on the tool wear of a PCD cutter and the corresponding machining quality in milling CFRP. These effects were analyzed by considering fiber and cutter interactions, and milling experiments with a PCD cutter were conducted for validation based on the analyses of cutting-edge profiles, cutting forces, burr area, and machined surface of CFRP. The worn cutting edge profiles were found to be elliptical in shape for cutting CFRP in different fiber orientations owing to the more severe wear on the flank face and cutting edge. Tool wear decreased with increasing cutting speed under a consistent feed per tooth, which further led to a reduction in the cutting force, burr area, and surface roughness. In addition, the maximum and minimum variations of worn cutting edge profiles were found in the cutting of 45° and 135° CFRP, respectively, suggesting that frequently changing the tool working position at 45° could suppress burr damage and increase tool life.
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