Effect of Sacubitril-Valsartan on Restoration and Maintenance of Sinus Rhythm in Patients With Persistent Atrial Fibrillation.

Background: Existing studies have shown that sacubitril-valsartan ameliorated atrial remodeling in atrial fibrillation (AF) and favored maintenance of sinus rhythm in patients with AF and heart failure. However, the effect of sacubitril-valsartan in patients with persistent AF is yet unknown. We aimed to evaluate the effect of sacubitril-valsartan on restoration and maintenance of sinus rhythm in patients with persistent AF who underwent electrical cardioversion (ECV).

Tracking moving target for 6 degree-of-freedom robot manipulator with adaptive visual servoing based on deep reinforcement learning PID controller.

In this paper, an image-based visual servoing (IBVS) controller with a 6 degree-of-freedom robotic manipulator that tracks moving objects is investigated using the proposed Deep Q-Networks and proportional-integral-derivative (DQN-PID) controller. First, the classical IBVS controller and the problem of feature loss and large steady-state error for tracking moving targets are introduced. Then, a DQN-PID based IBVS method is proposed to solve the problem of feature loss and large steady-state error and improve the servo precision, as the existing methods are hard to use for solve the problems.

A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map.

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map.

Robot grasping method optimization using improved deep deterministic policy gradient algorithm of deep reinforcement learning.

Robot grasping has become a very hot research field so that the requirements for robot operation are getting higher and higher. In previous research studies, the use of traditional target detection algorithms for grasping is often very inefficient, and this article is dedicated to improving the deep reinforcement learning algorithm to improve the grasping efficiency and solve the problem of robots dealing with the impact of unknown disturbances on grasping. Using the characteristic that deep reinforcement learning actively explores the unknown environment, a Gaussian parameter Deep Deterministic Policy Gradient (Gaussian-DDPG) algorithm based on the Importance-Weighted Autoencoder (IWAE) is proposed to realize the robot's autonomous learning of the grasping task.

A Cocktail-Inspired Male Birth Control Strategy with Physical/Chemical Dual Contraceptive Effects and Remote Self-Cleared Properties.

Inspired by cocktails, we designed a medium term (2-20 weeks) male contraceptive strategy. Through a sequential injection process of four reagents (calcium alginate hydrogel, PEG-Au nanoparticles (PEG-AuNps), EDTA, and PEG-AuNps), physical clogging of the vas deferens and chemical inhibition of the sperm motility were realized simultaneously. The contraceptive period could be directly preset by adjusting the injection ratio of each reagent.