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A key challenge in the utilization of waste polyester fibers (PET fibers) is the development of fiber-reinforced composites with high filler content and the improvement of fiber length retention. Herein, the effects of a two-rotor continuous mixer and a twin-screw extruder on the structure and properties of waste polyester fiber composites were evaluated. The results revealed that the mechanical properties of the composites were improved significantly with increasing fiber content, especially when processed using the twin-rotor continuous mixer. This mixer facilitated the formation of a robust fiber network structure, leading to substantial enhancements in tensile strength, flexural strength, and heat resistance. Specifically, compared to those processed by the twin-screw extruder, with 60 wt% fibers content, the tensile and flexural strengths of specimens processed by the twin-rotor continuous mixer increase by 21% and 13%, respectively. The average fiber length in specimens processed by the twin-rotor continuous mixer was 32% longer than that in specimens processed by the twin-screw extruder, attributable to the lower shear frequency and the higher tensile ratio of the former. This blending technique emerges as an effective strategy, contributing significantly to promoting the development and practical application of waste textile fiber-reinforced polymer composites.

Robot-assisted surgical systems have been widely applied for minimally invasive needle biopsies thanks to their excellent accuracy and superior stability compared to manual surgical operations, which lead to possible fatigue and misoperation due to long procedures. Current needle biopsy robots are normally customed designed for specific application scenarios, and only position-level kinematics are derived, preventing advanced speed control or singularity analysis. As a step forward, this paper aims to design a universal needle biopsy robot platform which features 6 DoF 3-RRRS (Revolute–Revolute–Revolute–Spherical) parallel structure. The analytical solutions to its nonlinear kinematic problems, including forward kinematics, inverse kinematics, and differential kinematics are derived, allowing fast and accurate feedback control calculations. A multibody simulation platform and a first-generation prototype are established next to provide comprehensive verifications for the derived robotic model. Finally, simulated puncture experiments are carried out to illustrate the effectiveness of the proposed method.

The accumulation of exogenous silver nanoparticles (AgNPs) will terminally bring about liver injury, including cell death, where DNA methylation tends to be a crucial epigenetic modulator. The change in the cell autophagy level verified to be closely associated with hepatocyte death has been followed with wide interest. But the molecular toxicological mechanisms of AgNPs in relation to DNA methylation, autophagy, and cell death remain inconclusive. To address the issue above, in LO2 cells treated with increasing concentrations of AgNPs (0, 5, 10, and 20 μg/mL), a cell cytotoxicity assay was performed to analyze the level of cell death, which also helped to choose an optimal concentration for next experiments. An immunofluorescence assay was used to determine the autophagic flux as well as TFEB translocation, with qRT-PCR and western blot being used to analyze the expression level of autophagy-related genes and proteins. According to our findings, in the determination of cell viability, 20 μg/mL (AgNPs) was adopted as the best working concentration. LO2 cell death, autophagy, and TFEB nuclear translocation were induced by AgNPs, which could be inhibited by lysosome inhibitor chloroquine (CQ) or siRNA specific for TFEB. Moreover, AgNP exposure led to DNA hypermethylation, with DNMT1 taking part mainly, which could be obviously prevented by 5-Aza-2′-deoxycytidine (5-AzaC) or trichostatin A (TSA) treatment or DNMT1 knockout in LO2 cells. Our studies suggest that through TFEB-dependent cell autophagy, increased DNMT1 may facilitate cell death induced by AgNPs.

A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07°. Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively.