C2B Orders Decision-making in Multiple Supply Chains Under Cloud Manufacturing

Considering the background of cloud manufacturing and cluster supply chain, we build the basic model to assign the orders priority within each capacity. Then, considering the inter-chain horizontal cooperation, the extended model is proposed to parallel allocation of cross-chain orders as the orders exceeding one single- chain’s capacity. Lagrange algorithm is implemented, and the simulation analysis shown that the opportunity cost of rejected orders factor and cross-chain orders manufacturing cost factor have significant impacts on orders’ allocation decision, and there is a critical point in the combinations of those two factors. Through combinations, the cluster supply chain can make the acceptance decisions policy and production schedules of priority orders and cross- chain orders, so that customers’ satisfaction and the cluster supply chain’s total profits achieve the best situations

Sulphur and cerium co-doped mesoporous titanium dioxide photocatalysts and their photocatalytic activity in the degradation of methylene orange

A series of sulphur and cerium co-doped mesoporous titanium dioxide photocatalysts have been prepared by a template method using thiourea, ammonium ceric nitrate and tetrabutyl titanate as precursors and Pluronic P123 as a template. The morphology, crystal structure, surface structure and optical absorption properties of the prepared samples are characterized by scanning electron microscopy, X-ray diffraction, N2 adsorption-desorption measurements and UV-vis absorption spectra. The microcrystal of the co-doped photocatalyst comprises the anatase­ phase. Compared with mesoporous titanium dioxide, the co-doped samples extends the photoabsorption edge into the visible light region. The photocatalytic activities of the obtained photocatalysts under UV and visible light have been estimated by measuring the degradation rate of methylene orange in aqueous solution. Results show that the co-doped mesoporous titanium dioxide exhibits higher photocatalytic activity than mesoporous titanium dioxide under light irradiation. The synergistic effect of sulphur and cerium co-doping plays an important role in improving the photocatalytic activity.

Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm

To improve the robotic assembly effects in unstructured environments, a reinforcement learning (RL) algorithm is introduced to realize a variable admittance control. In this article, the mechanisms of a peg-in-hole assembly task and admittance model are first analyzed to guide the control strategy and experimental parameters design. Then, the admittance parameter identification process is defined as the Markov decision process (MDP) problem and solved with the RL algorithm. Furthermore, a fuzzy reward system is established to evaluate the action–state value to solve the complex reward establishment problem, where the fuzzy reward includes a process reward and a failure punishment. Finally, four sets of experiments are carried out, including assembly experiments based on the position control, fuzzy control, and RL algorithm. The necessity of compliance control is demonstrated in the first experiment. The advantages of the proposed algorithms are validated by comparing them with different experimental results. Moreover, the generalization ability of the RL algorithm is tested in the last two experiments. The results indicate that the proposed RL algorithm effectively improves the robotic compliance assembly ability

Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm

To improve the robotic assembly effects in unstructured environments, a reinforcement learning (RL) algorithm is introduced to realize a variable admittance control. In this article, the mechanisms of a peg-in-hole assembly task and admittance model are first analyzed to guide the control strategy and experimental parameters design. Then, the admittance parameter identification process is defined as the Markov decision process (MDP) problem and solved with the RL algorithm. Furthermore, a fuzzy reward system is established to evaluate the action–state value to solve the complex reward establishment problem, where the fuzzy reward includes a process reward and a failure punishment. Finally, four sets of experiments are carried out, including assembly experiments based on the position control, fuzzy control, and RL algorithm. The necessity of compliance control is demonstrated in the first experiment. The advantages of the proposed algorithms are validated by comparing them with different experimental results. Moreover, the generalization ability of the RL algorithm is tested in the last two experiments. The results indicate that the proposed RL algorithm effectively improves the robotic compliance assembly ability

Intra- and inter-protein couplings of backbone motions underlie protein thiol-disulfide exchange cascade

Abstract The thioredoxin (Trx)-coupled arsenate reductase (ArsC) is a family of enzymes that catalyzes the reduction of arsenate to arsenite in the arsenic detoxification pathway. The catalytic cycle involves a series of relayed intramolecular and intermolecular thiol-disulfide exchange reactions. Structures at different reaction stages have been determined, suggesting significant conformational fluctuations along the reaction pathway. Herein, we use two state-of-the-art NMR methods, the chemical exchange saturation transfer (CEST) and the CPMG-based relaxation dispersion (CPMG RD) experiments, to probe the conformational dynamics of B. subtilis ArsC in all reaction stages, namely the enzymatic active reduced state, the intra-molecular C10–C82 disulfide-bonded intermediate state, the inactive oxidized state, and the inter-molecular disulfide-bonded protein complex with Trx. Our results reveal highly rugged energy landscapes in the active reduced state, and suggest global collective motions in both the C10–C82 disulfide-bonded intermediate and the mixed-disulfide Trx-ArsC complex

Recycling of Cotton Fibers Separated from the Waste Blend Fabric

Cotton fabric has a steadily increasing proportion of the municipal and industrial waste going into landfill. The waste textiles are generally blended fibers; chemical separation is an effective way. The paper describes the major recycling technologies of cotton fibers separated from the waste blend fabric (WBFs). Up to now, the recycling method of cotton fibers from WBFs is to break the cotton fibers’ physical or chemical structure, including dissolution and degraded

Sulphur and cerium co-doped mesoporous titanium dioxide photocatalysts and their photocatalytic activity in the degradation of methylene orange

780-785A series of sulphur and cerium co-doped mesoporous titanium dioxidephotocatalysts have been prepared by a template method using thiourea, ammonium ceric nitrate and tetrabutyltitanate as precursors and Pluronic P123 as a template. The morphology, crystal structure, surface structure and optical absorption properties of the prepared samples are characterizedby scanning electron microscopy, X-ray diffraction, N2 adsorption-desorption measurements and UV-vis absorption spectra. The microcrystal of the co-doped photocatalyst comprises the anatase­ phase. Compared with mesoporous titanium dioxide, the co-doped samples extends the photoabsorption edge into the visible light region. The photocatalytic activities of the obtained photocatalysts under UV and visible light have been estimated by measuring the degradation rate of methylene orange in aqueous solution. Results show that the co-doped mesoporous titanium dioxide exhibits higher photocatalytic activity than mesoporous titanium dioxide under light irradiation. The synergistic effect of sulphurand cerium co-doping plays an important role in improving the photocatalytic activity