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Photochemical oxidation removal of NO and SO2 from simulated flue gas of coal-fired power plants by wet scrubbing using UV/H2O2 advanced oxidation process was studied in a semicontinuous and small-scale ultraviolet (UV)-bubble column reactor with different conditions, including UV, H2O2 concentration, solution initial pH value, solution temperature, and liquid layer height. The results show that under all conditions studied, the SO2 is removed completely. The UV has an obvious impact on NO removal efficiency. With the increase of H2O2 concentration or liquid layer height, the NO removal efficiency greatly increases at first, and then the growth rate of NO removal efficiency gradually become smaller. The NO removal process is enhanced by the solution initial pH value, but it is inhibited by the solution temperature. The gaseous and liquid reaction products are determined using ion chromatography and gas analyzer, respectively. The removal path of NO and SO2 are also preliminarily discussed.

Background and study aims: Qualified esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). Our previous report showed that artificial intelligence system could monitor blind spots during EGD. Here, we updated the system to a new one (named ENDOANGEL), verified its effectiveness on improving endoscopy quality and pre-tested its performance on detecting EGC in a multi-center randomized controlled trial. Patients and methods: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD examination in 5 hospitals were randomly assigned to ENDOANGEL-assisted (EA) group or normal control (NC) group. The primary outcome was the number of blind spots. The second outcome includes performance of ENDOANGEL on predicting EGC in clinical setting. Results: 1,050 patients were recruited and randomized. 498 and 504 patients in EA and NC groups were respectively analyzed. Compared with NC, the number of blind spots was less (5.382±4.315 vs. 9.821±4.978, p<0.001) and the inspection time was prolonged (5.400±3.821 min vs. 4.379±3.907 min, p<0.001) in EA group. In the 498 patients from EA group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all 3 EGC (1 mucosal carcinoma and 2 high-grade neoplasia) and 2 advanced gastric cancer, with a per-lesion accuracy of 84.69%, sensitivity of 100% and specificity of 84.29% for detecting GC. Conclusions: The results of the multi-center study confirmed that ENDOANGEL is an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

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