Abstract-In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

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A spontaneous late-ripening mutant of ‘Jincheng’ (C. sinensis L. Osbeck) sweet orange exhibited a delay of fruit pigmentation and harvesting. In this work, we studied the processes of orange fruit ripening through the comparative analysis between the Jincheng mutant and its wild type. This study revealed that the fruit quality began to differ on 166th days after anthesis. At this stage, fruits were subjected to transcriptome analysis by RNA sequencing. 13,412 differentially expressed unigenes (DEGs) were found. Of these unigenes, 75.8% were down-regulated in the wild type, suggesting that the transcription level of wild type was lower than that of the mutant during this stage. These DEGs were mainly clustered into five pathways: metabolic pathways, plant-pathogen interaction, spliceosome, biosynthesis of plant hormones and biosynthesis of phenylpropanoids. Therefore, the expression profiles of the genes that are involved in abscisic acid, sucrose, and jasmonic acid metabolism and signal transduction pathways were analyzed during the six fruit ripening stages. The results revealed the regulation mechanism of sweet orange fruit ripening metabolism in the following four aspects: First, the more mature orange fruits were, the lower the transcription levels were. Second, the expression level of PME boosted with the maturity of the citrus fruit. Therefore, the expression level of PME might represent the degree of the orange fruit ripeness. Third, the interaction of PP2C, PYR/PYL, and SnRK2 was peculiar to the orange fruit ripening process. Fourth, abscisic acid, sucrose, and jasmonic acid all took part in orange fruit ripening process and might interact with each other. These findings provide an insight into the intricate process of sweet orange fruit ripening.

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.