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Selenium (Se) is one of the essential trace elements in animal organisms with good antioxidant and immune-enhancing abilities. In this study, we investigated the effect and mechanism of Se deficiency on skeletal muscle cell differentiation. A selenium-deficient skeletal muscle model was established. The skeletal muscle tissue and blood Se content were significantly reduced in the Se deficiency group. HE staining showed that the skeletal muscle tissue had a reduced myofiber area and nuclei and an increased myofascicular membrane with Se deficiency. The TUNEL test showed massive apoptosis of skeletal muscle cells in Se deficiency. With Se deficiency, reactive oxygen species (ROS) and malondialdehyde (MDA) increased, and the activities of glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC), superoxide dismutase (SOD), and catalase (CAT) were inhibited. In in vitro experiments, microscopic observations showed that the low-Se group had reduced C2C12 cell fusion and a reduced number of differentiated myotubes. In addition, qPCR results showed that differentiation genes (Myog, Myod, Myh2, Myh3, and Myf5) were significantly reduced in the low Se group. Meanwhile, Western blot analysis showed that the levels of differentiation proteins (Myog, Myod, and Myhc) were significantly reduced in the low-Se group. This finding indicates that Se deficiency reduces the expression of skeletal muscle cell differentiation factors. All the above data suggest that Se deficiency can lead to oxidative stress in skeletal muscle, resulting in a reduction in the differentiation capacity of muscle cells.

As an intensive and efficient way of transportation, drop and pull transport has become the mainstream transportation organization of foreign developed countries and regions. Based on the freight demand vector and aging of a given initial state, this paper established a goal planning and scheduling model based on the maximization of time window revenue to get the most efficient deployment plan under the condition of high customer satisfaction. The genetic algorithm based on task urgency was used to solve this model to obtain the optimal allocation scheme of total income. Finally, the number of algorithm iterations was analyzed, which proved the effectiveness of the algorithm performance. Through the sensitivity analysis of the number of tractors, this paper gave suggestions for more efficient tractor configuration.

The demand for smart control technology is glowing according to the increase of expectation for autonomous driving. This paper presents an integrated planning and control scheme for automated lane-change behavior based on real-time nonlinear model predictive control (NMPC). In the proposed method, switched weighting parameters are used in the cost function over the prediction horizon. This enables us to reduce the number of reference points for control compared with the conventional framework. In addition, some safety constraints, such as collision avoidance and/or speed limitation are considered in a consistent manner. Although the NMPC usually requires huge computational cost, by applying the Continuation/GMRES (C/GMRES) method, it can be drastically reduced and become possible to be implemented in real time. Finally, validity of the proposed method is demonstrated by simulation study.

This paper presents a control system for autonomous driving based on MPC in which driving style can be easily modified by changing control parameters. Each of the motion controls for the longitudinal and the lateral direction are formulated as the model predictive control problem. Finally the experimental verification by using driving simulator and a real electric vehicle is performed by implementing MPC on each platform, and it is confirmed that the proposed system can produce a large variety of driving characteristics. The implemented MPC package will also be beneficial to the developers and researchers in various fields other than control engineering field.

This paper presents an autonomous lane tracking that reflects different driving characteristics using model predictive control (MPC). We consider that human driver minimizes a cost function depending on his/her skill, experience, and preference on driving. The cost function of MPC can be used to model personal driving characteristics. To identify the parameters of cost function, at first, we analyze the difference of driving characteristics between skilled and un-skilled drivers through experiments of driving behavior on a driving simulator (DS). Next, we introduce "meta-performance indices" that can evaluate human driving data of experiment and results of autonomous driving. These parameters are expected to express the driving characteristics as a group, i.e., not tuned to personal driver. Finally, the validity of the proposed system is verified.

In this paper, an adaptive controller of a plant with unknown parameters and unknown nonlinear frictional force is proposed and applied to the control of a single link robot. The measurement for control is only the motor angle. The proposed adaptive controller is composed of an adaptive pole placement controller and a neural network. The neural network is designed to compensate the nonlinear frictional force and linearize the robot plant. To improve the efficiency of learning of the neural network, an inverse model is introduced. Several considerations are made to improve the robustness of the controller. Many experiments are carried out. The experimental results show the effectiveness of developed control system.

This paper is concerned with the application of gain-scheduling H∞ technique to the vibration control of SCARA type robot manipulators. The dynamics of multi-link robot manipulators, such as the SCARA type robot manipulators, are of highly nonlinear time-varying property, and this fact makes it difficult for a single linear time-invariant controller to achieve the desired performance. The gain-scheduling H∞ technique is an extension of standard H∞ synthesis technique to the case that a plant depends affinely on a time-varying parameter vector δ(t). The vibration controller designed via this technique is automatically gain-scheduled according to the position of the links of the manipulators and makes the dynamics of the closed-loop system unchanged along the input trajectory. The effectiveness of the proposed controller is demonstrated by simulation.

Many industrial robot manipulators have a cantilever structure at the base end of which the driving motors are located. Flexibility also exists at the transmission part between the motor and the corresponding arm. In terms of control, such structure has less rigidity so that residual vibration arises at the end point of the manipulator when it is brought to a sudden stop during operation. Our aim is to design a robust controller via μ-synthesis, which eliminates the vibration, or suppresses it to an acceptable level, at the end point of the third arm of a SCARA-type robot using available information of the motor angles of the first and the second arms. The effectiveness of the derived controller is examined through simulations and experiments in comparison with a PD controller. The experimental results show that the controller can significantly suppress the residual vibration with an excellent robust performance against the uncertainty of the robot parameters, while the PD controller gives poor performance in such a case.

This paper deals with the application of gain-scheduling H/sub /spl infin// technique to the vibration control for SCARA type robot manipulators. The dynamics of a multi-link robot manipulator, such as the SCARA type robot manipulator, is of highly nonlinear time-varying property, and this fact makes it difficult for a single linear time-invariant controller to achieve the desired performance. The gain-scheduling H/sub /spl infin// technique is an extension of standard H/sub /spl infin// synthesis technique to the case where the plant depends affinely on a time-varying vector /spl delta/(t). The controller designed via this new technique is automatically gain-scheduled according to the position of the manipulator and makes the dynamics of the closed-loop system unchanged along the input trajectory. The effectiveness of the proposed controller is demonstrated by simulation.