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Suppression of secondary electron emission (SEE) from metal surfaces is crucial for enhancing the performance of particle accelerators, spacecraft, and vacuum electronic devices. Earlier research has demonstrated that either etching the metal surface to create undulating structures or coating it with materials having low secondary electron yield (SEY) can markedly decrease SEE. However, the effectiveness of growing vertical graphene (VG) on laser-etched metal surfaces in suppressing SEE remains uncertain. This study examined the collective impact of these methods by applying nanoscale arrays of VG coating using plasma-enhanced chemical vapor deposition on Ni substrates, along with the formation of micrometer-sized microcavity array through laser etching. Comparative tests conducted revealed that the SEY of the samples subjected to VG coating on a microcavity array was lower compared to samples with either only a microcavity array or VG coating alone. Additionally, the crystallinity of VG grown on substrates of varying shapes exhibited variations. This study presents a new method for investigating the suppression of SEE on metal surfaces, contributing to the existing body of knowledge in this field. [ABSTRACT FROM AUTHOR]

The radiofrequency-induced intestine fusion has been widely studied as an alternative for traditional suture in surgery, but fusion quality cannot be evaluated directly. Impedance measurement can evaluate fusion quality, but the relation between impedance and the fusion quality needs optimization for best results. The present study reports the optimum resistance of small intestine fusion. As the feedback signal, resistance was considered the indicator of the fusion completion for the device design of intestine fusion and an in-depth study of microstructure change. A self-design pulse source was used for the small intestine fusion with adjustable voltage, duty ratio, frequency and output time. A frequency of 440 kHz was set, whereas voltage, output time and compression pressure (CP) of the small intestine were independent variables. Different conditions of voltage, CP and time were investigated for achieving the highest burst pressure (BP) measured with a pressure gauge and a peristaltic pump. Each parameter of the equivalent circuit model was calculated by an experimental waveform. Hematoxylin–eosin staining of fusion samples was used for assessing the quality of fusion. The real-time current was measured and recorded during the fusion for the calculation of capacitance and resistance. The highest BP of 38.9 mmHg was achieved with a CP of 900 kPa, a voltage of 50 V and a time of 5 s. Finally, an optimum extracellular resistance range of 61.0–86.2 Ω was found as the optimum resistance for the end of fusion, thus indicating automatic fusion with the best fusion quality.

Background: Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent. Purpose: To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images. Material and Methods: PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity. Results: Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies. Conclusion: The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules. [ABSTRACT FROM AUTHOR]