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Redundant degree-of-freedom (DOF) manipulators offer increased flexibility and are better suited for obstacle avoidance, yet precise control of these systems remains a significant challenge. This paper addresses the issues of slow training convergence and suboptimal stability that plague current deep reinforcement learning (DRL)-based control strategies for redundant DOF manipulators. We propose a novel DRL-based intelligent control strategy, FK-DRL, which integrates the manipulator's forward kinematics (FK) model into the control framework. Initially, we conceptualize the control task as a Markov decision process (MDP) and construct the FK model for the manipulator. Subsequently, we expound on the integration principles and training procedures for amalgamating the FK model with existing DRL algorithms. Our experimental analysis, applied to 7-DOF and 4-DOF manipulators in simulated and real-world environments, evaluates the FK-DRL strategy's performance. The results indicate that compared to classical DRL algorithms, the FK-DDPG, FK-TD3, and FK-SAC algorithms improved the success rates of intelligent control tasks for the 7-DOF manipulator by 21%, 87%, and 64%, respectively, and the training convergence speeds increased by 21%, 18%, and 68%, respectively. These outcomes validate the proposed algorithm's effectiveness and advantages in redundant manipulator control using DRL and FK models. [ABSTRACT FROM AUTHOR]

Simple Summary: Coccidiosis is an intestinal parasitic disease caused by Eimeria protozoa, which endangers the health and growth of animals. However, little is known about the intestinal resident microbiota of chickens with different resistance to Eimeria tenella (E. tenella) infection and the changes in the microbiota from infection to recovery periods. The metagenomic sequencing analyses show that many potential differential microbial taxa were identified in different resistant chickens, respectively. Through the comparative analysis from infection to recovery periods, the number of microorganisms with the same trend of change between the control and resistant groups was greater than that between the control and susceptible groups. These findings provide a reference for further research into how E. tenella infection affects the intestinal microbiota and thus the growth, development, and health of chickens. To investigate the effect of Eimeria tenella (E. tenella) infection on the cecal microbiota, resistant and susceptible families were screened out based on the coccidiosis resistance evaluation indexes after E. tenella infection. Subsequently, a comparative analysis of cecal microorganisms among control, resistant, and susceptible groups as well as between different periods following the E. tenella challenge was conducted using metagenomic sequencing technology. The results showed that the abundance of opportunistic pathogens, such as Pantoea, Sporomusa, and Pasteurella in the susceptible group and Helicobacter and Sutterella in the resistant group, was significantly higher on day 27 post-inoculation (PI) (the recovery period) than on day 5 PI (the infection period). Additionally, the abundance of Alistipes, Butyricicoccus, and Eubacterium in the susceptible group and Coprococcus, Roseburia, Butyricicoccus, and Lactobacillus in the resistant group showed a significant upward trend during the infection period compared with that in the recovery period. On day 5 PI, the abundance of Faecalibacterium and Lactobacillus was decreased in both the resistant and susceptible groups when compared with that in the control group and was greater in the resistant group than in the susceptible group, while Alistipes in the susceptible group had a relatively higher abundance than that in other groups. A total of 49 biomarker taxa were identified using the linear discriminant analysis (LDA) effect size (LEfSe) method. Of these, the relative abundance of Lactobacillus aviarius, Lactobacillus salivarius, Roseburia, and Ruminococcus gauvreauii was increased in the resistant group, while Bacteroides _AGMB03916, Fusobacterium_mortiferum, Alistipes _An31A, and Alistipes _Marseille_P5061 were enriched in the susceptible group. On day 27 PI, LDA scores identified 43 biomarkers, among which the relative abundance of Elusimicrobium _An273 and Desulfovibrio _An276 was increased in the resistant group, while that of Bacteroides _43_108, Chlamydiia, Chlamydiales, and Sutterella _AM11 39 was augmented in the susceptible group. Our results indicated that E. tenella infection affects the structure of the cecal microbiota during both the challenge and recovery periods. These findings will enhance the understanding of the effects of changes in the cecal microbiota on chickens after coccidia infection and provide a reference for further research on the mechanisms underlying how the intestinal microbiota influence the growth and health of chickens. [ABSTRACT FROM AUTHOR]

Aiming at the situation that the top coal of the close distance coal seams is goaf and the remaining 30 m coal pillar, taking the 2-1 coal seam and 2-2 coal seam of Hulusu Coal Mine as the research background, the mechanical model of the upper coal goaf and the remaining coal pillar under force is established, the stress distribution state of the strata under the coal pillar in the upper coal section is analyzed, and the reasonable spacing of the stoping roadway under the close distance coal seams goaf is studied. The research shows that the optimal offset distance should be 80 m after the protective layer is fully discharged; from the influence range of the overlying coal pillar, the optimal offset distance is 40-80 m; considering the degree of influence on the upper coal roadway, the optimal offset distance should be greater than 80 m; when the offset distance is 160 m, the lower working face mining has the lowest influence on 21101 return air alley. By combining 4 different angles and calculating the intersection of the influence range, the optimal offset distance of the first working face of 22103 was finally determined to be 80 m.

Modern industries face increasing demands to improve the precision and workability of equipment, leading to stringent requirements for hydraulic servo systems in terms of flow, accuracy, and output power. High‐flow servo valves and multi‐valve synchronous control methods have seen more applications in the industry to meet these demands. However, high‐flow servo valves are expensive and have unsatisfactory performance, and synchronous control methods require servo valves to have the same hydraulic characteristics, resulting in higher cost and lower accuracy of high‐flow hydraulic servo systems. Also, very little has been conducted on controlling single actuators using multiple valves. In this study, we design a novel dual‐valve hydraulic servo system structure with a high‐flow proportional directional valve that is connected in parallel with a low‐flow servo valve and further develop the mathematical model of this dual‐valve system. Next, a harmonic control scheme was proposed, which included a flow allocation layer and trajectory tracking layer. Thereby, we achieved optimal control of two valves, and robust performance was guaranteed. Finally, we investigate the relationships between the flows of two valves, the deadband of the proportional directional valve, and trajectory tracking errors. Experimental results show that the proposed control scheme can effectively adjust the output flow of each valve dynamically according to the tracking trajectory and the hydraulic characteristics of the two valves, and the tracking performance of the servo system is significantly improved. This method is promised to enable the realization of an economical, high‐flow, high‐precision hydraulic servo system that can be generally used in various fields, such as marine engineering and construction. [ABSTRACT FROM AUTHOR]