Simulation and Experimental Study of Staple Line Reinforcement Surgery

The aim of this study was to evaluate the effectiveness of BM (basement membrane) and SIS (small intestine submucosa) composite extracellular matrix staple line reinforcement in surgical procedures through finite element modelling simulations and leak-proof performance experiments. The mechanical analyses of soft tissues with and without staple line reinforcement were performed by establishing finite element models of three tissues, namely, stomach, intestine and lungs, under the use scenarios of different anastomosis staple models;and the leak-proof performance of the staple line reinforcement was evaluated by simulating leak-proof experiments of gastric incision margins, intestinal sections, and lung incision margins in vitro. The results showed that the equivalent average stresses of the staple line reinforcement were increased by 20 kPa-68 kPa in gastric and intestinal tissues, and 8 kPa-22 kPa in lung tissues. and that the BM and SIS composite extracellular matrix staple line reinforcement could strengthen the anastomotic structure, and at the same time disperse the high stresses of the anastomosed tissues, which could effectively reduce the postoperative complications such as anastomotic bleeding and anastomotic leakage, and provide a safer and more effective optimized design for surgical mechanical anastomosis. It can effectively reduce postoperative complications such as anastomotic bleeding and anastomotic leakage, and provide a safer and more effective optimized design for surgical mechanical anastomosis.

Hierarchical Task Planning for Power Line Flow Regulation

The complexity and uncertainty in power systems cause great challenges to controlling power grids.As a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power grids.However,DRL has some inherent drawbacks in terms of data efficiency and explainability.This paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow regulation.First,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units.In addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP.Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.