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BackgroundSuperficial duodenal epithelial lesions are precancerous lesions of duodenal carcinoma. Upper gastrointestinal endoscopy has been widely used in the screening and treatment of this disease. This article will collect the data of patients who underwent endoscopic resection of superficial duodenal epithelial lesions in our hospital from 2010 to 2021, aiming to describe the efficacy and safety of endoscopic resection, as well as to explore the risk factors of non-en bloc resection and non-R0 resection.MethodsPatients who underwent endoscopic resection for superficial duodenal epithelial lesions in our hospital from January 2010 to September 2021 were selected. The curative effect was expressed by the en bloc resection rate and R0 resection rate. The safety was expressed by intra- or postoperative complications, such as bleeding and perforation. The potential risk factors of curative effect were analyzed by logistic regression.ResultsA total of 137 patients were included. The en bloc resection rate was 95.62% (131/137), R0 resection rate was 91.97% (126/137), the postoperative bleeding rate was 2.19% (3/137), and no postoperative perforation was found. The histology result of ectopic gastric mucosa was the risk factor of non-en bloc resection (OR: 8.86, 95% CI: 1.38-56.92); the lesion size ≥2 cm was the risk factor of non-R0 resection (OR: 12.55, 95% CI: 2.95-53.38).ConclusionEndoscopic resection is a safe and effective method for the treatment of superficial duodenal epithelial lesions. The histology result of ectopic gastric mucosa was the risk factor of non-en bloc resection and the lesion size ≥2 cm was the risk factor of non-R0 resection.

Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP) in which state representation, action representation, reward function, and optimal mixed rule policy, are described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the results validate the feasibility and effectiveness of the proposed approach.

Airfoil is a key factor that influences the hydrodynamic performance of vertical axis tidal current energy turbine. In order to explore the influences from the runner’s blade airfoil towards its hydrodynamic performance, three-dimensional unsteady flow numerical simulation on four airfoils of straight blade H type of tidal current energy turbines was carried out, under the precondition of same turbine compactness. Through investigating the influences from the four different airfoils of H blade towards the runner in terms of its dynamic torque feature and hydropower utilization coefficient, this research has analyzed the hydrodynamic performance of the tidal current energy turbine. As the research result indicates, the maximum dynamic torque value of the single-blade turbine with NACA0015 airfoil is significantly higher than that of the other three airfoils; while the maximum value of the overall runner torque is determined by the operating conditions such as different stream speed etc. As a whole, hydropower utilization coefficient of the turbine with asymmetric airfoil NACA4415 is better than that of the other three airfoils. This research has provided references for the design and airfoil selection for the new type of tidal current energy turbine.