Metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the explorationexploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems; thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems; thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://github.com/JiahongXu123/OSPO-algorithm.git.

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated.

Purpose The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot. Design/methodology/approach The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable. Findings The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy. Originality/value This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.

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Background: MicroRNAs (miRNAs) may participate in the process of vascular calcification. However, the role of microRNA-17-5p in vascular calcification has not been clarified. In this study, we showed the effects of microRNA-17-5p on vascular calcification. Materials and Methods: Vascular smooth muscle cells (VSMCs) were transfected with miR-17-5p mimics, an miR-17-5p inhibitor or a negative control (NC) using Lipofectamine 2000. Then the cells were induced by an osteogenic medium. Alkaline phosphatase (ALP) activity and mineralization were determined. Osteocalcin (OC), bone morphogenetic protein 2(BMP-2), Col1agren Ia (Colla), Runx2 and ankylosis protein homolog (ANKH) gene expressions were determined by reverse transcription-polymerase chain reaction. Vascular calcification was developed using a renal failure model. Results: The ALP activity was increased when miR-17-5p mimics were transfected, whereas the miR-17-5p inhibitor reduced ALP activity (p < 0.05). The number and average area of mineral node in miR-17-5p mimics group were larger than those in corresponding control and NC groups (p < 0.05). The number and average area of the mineral nodes in the miR-17-5p inhibitor group were smaller than those in corresponding control and NC groups (p < 0.05). Bmp2, OC, Col1a and Runx2 were higher in the miR-17-5p mimics group compared to those in the control and NC groups. ANKH expression was decreased in VSMCs with the miR-17-5p mimics and increased in VSMCs with miR-17-5p inhibitor. ANKH siRNA intervention also promoted mineralization. The miR-17-5p expression was upregulated and ANKH was down-regulated in the aortic arteries with calcification. Conclusion: Our data showed that miR-17-5p may promote vascular calcification by inhibiting ANKH expression.