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This paper investigates a novel control algorithm to deal with trajectory tracking control problems of robotic manipulators based on adaptive backstepping integral nonsingular terminal sliding mode control (BINTSMC). The proposed approach is developed based on an integration between the integral nonsingular terminal sliding mode control (INTSMC) and stability analysis procedure of backstepping control technique. In addition, the extreme learning machine (ELM) learning algorithm is introduced to approximate lumped uncertain component of the dynamics model. The approximated lumped uncertain terms are transmitted to rebuild the dynamics and provide a compensation feedback for control systems. Moreover, the robust term is utilized to counteract the approximation residual error, wherein the nonconstant ELM approximation error is analyzed. The superior performance of the BINTSMC is validated by comparing conventional NTSMC in the simulations.

This article addresses the problem of fast fixed-time tracking control for robotic manipulator systems subject to model uncertainties and disturbances. First, on the basis of a newly constructed fixed-time stable system, a novel faster nonsingular fixed-time sliding mode (FNFTSM) surface is developed to ensure a faster convergence rate, and the settling time of the proposed surface is independent of initial values of system states. Subsequently, an extreme learning machine (ELM) algorithm is utilized to suppress the negative influence of system uncertainties and disturbances. By incorporating fixed-time stable theory and the ELM learning technique, an adaptive fixed-time sliding mode control scheme without knowing any information of system parameters is synthesized, which can circumvent chattering phenomenon and ensure that the tracking errors converge to a small region in fixed time. Finally, the superior of the proposed control strategy is substantiated with comparison simulation results.

This paper mainly investigates the trajectory tracking control problems for manipulator systems with unknown dynamics and external disturbances. Firstly, an extreme learning machine (ELM) is adopt to compensate unknown dynamics of the manipulator. Then, an updating law is derived to ensure the convergence of ELM output wights. Besides, an indirect method is developed to avoid the potential singularity problem of the fixed-time sliding mode surface. Moreover, a robust adaptive controller is designed based on the outputs of ELM and sliding mode technique. By using the Lyapunov stability theory, the fixed-time convergence and stability of the adaptive control system can be guaranteed. Finally, simulation results is presented to show the efficiency of the proposed control structure with respect to different initial conditions.

Objective To investigate the effects of DUSP1 on the hippocampal injury of young rats with epilepsy (EP) through mediating ERK1/2 signaling pathway. Methods Young SD rats were selected and divided into Control, EP, EP + LV-GFP, EP + LV-DUSP1, EP + LV-siDUSP1, and EP + LV-siDUSP1 + U0126 groups. Morris Water Maze Test was used to detect the spatial learning and memory. Nissl staining and TUNEL staining were conducted and the inflammatory factors and oxidative stress-related indicators were also measured. Western blotting was utilized to detect the expression of DUSP1 and ERK1/2 pathway. EP cell model was constructed in vitro to verify the in vivo results. Results Compared with Control group, young rats in EP group had decreased spatial learning and memory abilities and increased apoptotic rate and decreased number of Nissl positive cells. Besides, the up-regulated levels in inflammatory factors (IL-1β, IL-6), MDA content, and p-ERK1/2/ERK1/2 protein expression, as well as the down-regulated levels in DUSP1 protein expression and SOD content were also observed in EP rats. The EP rats treated with LV-DUSP1 showed obvious improvements regarding the above indicators, while those treated with LV-siDUSP1 had aggravated injury. But the effect of LV-siDUSP1 can be reversed by the treatment with ERK1/2 pathway inhibitor U0126. Further in vitro investigation verified the in vivo results. Conclusion DUSP1 may ameliorate the oxidative stress and inflammatory injury, as well as improve spatial learning and memory abilities via inhibiting ERK1/2 pathway, eventually playing protective roles in hippocampal injury of young rats with EP.

Objectives: The objectives were to identify drugs related with anemia in children and evaluate the novelty of these correlations. Methods: The authors established a two-step method for detecting the relationship between drugs and anemia using electronic medical records (EMRs), which were obtained from 247,136 patients in Beijing Children's Hospital between 2007 and 2017. The authors extracted potential drugs by mining cases for hemoglobin abnormalities from the EMR and then performed a retrospective cohort study to correlate them with anemia by calculating the matched odds ratios and 95% confidence interval using unconditional logistic regression analysis. Results: In total, nine positive drug-anemia associations were identified. Among them, the correlations of drugs fluconazole (OR 3.95; 95%CI: 2.65-5.87) and cefathiamidine (OR 3.49; 95%CI: 2.94-4.15) with anemia were considered new signals in both children and adults. Three associations of drugs, vancomycin, cefoperazone-sulbactam and ibuprofen, with anemia were considered new signals in children. Conclusion: The authors detected nine signals of drug-induced anemia, including two new signals in children and adults and three new signals in children. This study could serve as a model for using EMR and automatic mining to monitor adverse drug reaction signals in the pediatric population.