Jianrong Liu

Anaerobic biodegradation is the most affordable method for the degradation of N,N-dimethylformamide. However, the degradation efficiency depends on the concentration. To elucidate the responses of microbial community to N,N-dimethylformamide load, microbial diversity, composition and functional changes at different concentrations of 100, 2000, and 3500 mg/L were analyzed. Results showed that as the N,N-dimethylformamide influent concentration increased from 100 to 2000 mg/L, the removal rate stabilized at 90%, whereas it decreased to ~75% at concentrations over 2000 mg/L. Microbial community diversity increased, and specialists were enriched at 3500 mg/L. Patescibacteria (42.88% and 42.90%), Bacillota (18.52% and 18.54%), and Pseudomonadota (7.13% and 7.09%) were the dominant phyla at 100 mg/L and 2000 mg/L, respectively, and Patescibacteria (16.88%) and Pseudomonadota (15.34%) were the dominant phyla at 3500 mg/L. Methylotrophic methanogeneic (Methanolobus and Methanomassiliicoccus) and syntrophic electron-donating bacteria (Clostridium and Trichococcus) were significantly enriched. DMF-degrading genes (fdh, rfA/nrfH, and ATPase) and methylotrophic methanogenesis genes (mcr, mta, and mtm) were significantly upregulated. Therefore, the degradation of N,N-dimethylformamide was characterized by a parallel carbon flux distribution, “methylamine-driven methanogenesis + further oxidation/integration of single-carbon intermediates”, and the nitrogen flux tended to enter a reductive nitrogen cycle characterized by retention and reuse.

This paper presents a novel Proportional Damping Injection (P + d) control scheme that incorporates a damping regulation strategy and an adaptive method for networked telerobotic systems. Unlike the traditional P + d controller with a fixed damping coefficient, the proposed approach includes a dynamic damping adjuster, designed based on position error, to enhance the position tracking speed and improve the system robustness. To address uncertainties in dynamic models and external forces, Radial Basis Function (RBF) neural networks and an adaptive strategy are employed for dynamic estimation. The closed-loop stability of the teleoperation system was rigorously established using the Lyapunov–Krasovskii method, and the relationship between the controller gains and communication delay boundaries was explicitly derived. Finally, simulations and experimental results validated the system’s stability and effectiveness, demonstrating the advantages of the proposed controller.