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The accuracy of slurry concentration is an important guarantee for the quality of mine filling, and enhancing the robustness of the control system is an effective way to improve the accuracy. Based on sliding mode control, this study proposes a novel robust control method for the slurry concentration. Combined mechanism analysis and system response, the controller design model of a slurry preparation process is set up. An improved exponential reaching law with adaptive gain is developed and applied to the design of system controller. Additionally, a proportional-integral observer is used to estimate the state and disturbance of the system, and the disturbance is compensated by the design of the control law. The results demonstrate that the proposed closed-loop system has strong robustness and only measurable output is required for the implementation of the control law. At the same time, the accuracy of slurry concentration is greatly improved. [ABSTRACT FROM AUTHOR]

The Monarch Butterfly Optimization (MBO) algorithm has been proved to be an efficient meta-heuristic to directly address continuous optimization problems. In the MBO algorithm, the migration operator cooperates with the butterfly adjusting operator to generate the entire offspring population. Since the individual iterations of the MBO algorithm are not self-learning, the cooperative intelligence mechanism is a random process. In this study, an improved MBO algorithm with a knowledge-driven learning mechanism (KDLMBO) is presented to enable the algorithm to evolve effectively with a self-learning capacity. The neighborhood information extracted from the candidate solutions is treated as the prior knowledge of the KDLMBO algorithm. The learning mechanism consists of the learning migration operator and the learning butterfly adjusting operator. Then, the self-learning collective intelligence is realized by the two cooperative operators in the iterative process of the algorithm. The experimental results demonstrate and validate the efficiency and significance of the proposed KDLMBO algorithm. [ABSTRACT FROM AUTHOR]

Various optimization problems with multiple decision variables and complex constraints, which exist widely in the real world, are difficult to be solved by traditional methods. Brain storm optimization (BSO) algorithm, an advanced swarm intelligence optimization method, has high efficiency and flexibility in solving large-scale problems independent of problem characteristics. The essence of swarm intelligence optimization algorithm is that a population iteratively searches for the optimal solution in the solution space, and the process has randomness and blindness. To enhance the searching ability of BSO and strengthen the theoretical guidance of the algorithm, a brain storm optimization algorithm with feature information knowledge and learning mechanism (FIBSO) is proposed in this paper. In the process of BSO iteration, information interaction exists between individuals of each generation. The new individual is generated from the old individual, and the dominant individual contributes to the new individual. Theoretically, using the knowledge of characteristic information between individuals guides the evolution of the population in the dominant direction. Moreover, three search strategies guided by global and local optimal individuals are presented to balance the global and local search capabilities of the algorithm. The results of FIBSO and several comparison algorithms on the CEC2017 test suite indicate that FIBSO has superior performance to the state-of-the arts algorithms. The FIBSO is introduced to the no-wait flow shop scheduling problem, and the results show that FIBSO has the significant ability to solve practical engineering problems. [ABSTRACT FROM AUTHOR]

In multiple model control, the model set is very important to the control performance. A novel multiple model control algorithm based on dynamic optimization is proposed for a nonlinear system with jumping parameters. The model set includes linear and nonlinear models. The linear models include some fixed and adaptive models which approximate the linear part of the system. Specially, the fixed models are constantly optimized using the new available data. For the nonlinear part of the system, a neural network is used to describe it. On this basis, the controller set corresponding to multiple models is designed according to the principle of asymptotic tracking. In practice, the switching between controllers is determined by a performance index. The results show that the optimization of the model set is helpful to improve the system control performance. The effectiveness of the proposed method is verified by the simulation results. [ABSTRACT FROM AUTHOR]

Production scheduling plays a pivotal role in smart factories due to the development of intelligent manufacturing. As a typical scheduling problem, the blocking flow-shop scheduling problem (BFSP) has attracted enormous attention from researchers. In this paper, an ensemble discrete water wave optimization algorithm (EDWWO) is proposed with the criterion to minimize the makespan. In the proposed algorithm, a constructive heuristic is presented to suit the needs of initial solutions quality. The constructive heuristic is based on a new dispatching rule combined with the well-known NEH heuristic. The algorithmic characteristics are explored and effective technologies, such as data-driven mechanism in the propagation phase, a block-shifting operator based on the framework of the variable neighborhood search in the breaking phase, and perturbation strategy, are employed to improve the performance of the algorithm. The effectiveness of operators and parameters in EDWWO are analyzed and calibrated based on the design of experiments. To evaluate the algorithmic performance, the well-known benchmark problem is adopted for comparison with five other state-of-the-art algorithms. Meanwhile, the statistical validity of the results is investigated by introducing the Friedman-test and Wilcoxon-test. The statistical results demonstrate the effectiveness of EDWWO for solving the BFSP. [ABSTRACT FROM AUTHOR]

Synovial mesenchymal stem cells (SMSCs) have the potential to attenuate osteoarthritis (OA)-induced injury. The role and mechanism of SMSC-derived exosomes (SMSC-Exos), pivotal paracrine factors of stem cells, in OA-associated injury remain unclear. We aimed to confirm the effect of SMSC-Exos with specific modifications on OA-induced damage and to investigate the potential molecular mechanisms. Exosomes derived from miR-155-5p–overexpressing SMSCs (SMSC-155-5p-Exos) and SMSCs (SMSC-Exos) were isolated and characterized. CCK-8, Transwell, and Western blot analyses were used to detect proliferation, migration, extracellular matrix (ECM) secretion, and apoptosis of osteoarthritic chondrocytes. The therapeutic effect of exosomes in a mouse model of OA was examined using immunohistochemical staining and OARSI scores. SPSS 17.0 and GraphPad software were used for all statistical analyses in this study. The SMSC-Exos enhanced the proliferation and migration and inhibited the apoptosis of osteoarthritic chondrocytes but had no effect on ECM secretion. The miR-155-5p–overexpressing exosomes showed common characteristics of exosomes in vitro and further promoted ECM secretion by targeting Runx2. Thus, the SMSC-155-5p-Exos promoted proliferation and migration, suppressed apoptosis and enhanced ECM secretion of osteoarthritic chondrocytes, and effectively prevented OA in a mouse model. In addition, overexpression of Runx2 partially reversed the effect of the SMSC-155-5p-Exos on osteoarthritic chondrocytes. Given the insufficient effect of the SMSC-Exos on the ECM secretion of osteoarthritic chondrocytes, we modified the SMSM-Exos and demonstrated that the SMSC-155-5p-Exos could prevent OA. Exosomes derived from modified SMSCs may be a new treatment strategy to prevent OA. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells (MSCs) hold great potential to treat tissue damage based on their multipotent property, and are also considered as suitable cell resources to create tissue-engineered grafts for tendon repair. However, the clinical application of MSCs is still limited by the lack of efficient methods to induce tenogenic differentiation. In this study, by performing the experiments in transwell system, we found that paracrine factors from tenocytes could induce MSCs to undergo the tenogenic differentiation. We further verified that tenocytes could secrete exosomes and these tenocyte-derived exosomes efficiently initiated the tenogenic differentiation of MSCs. Finally, we revealed that the TGF-β existing in tenocyte-derived exosomes mediated the process, as the inhibition of TGF-β signaling abolished the effects of tenocyte-derived exosomes on MSCs. By investigating the effects of tenocytes on MSCs, we found that tenocytes-derived exosomes can induce tenogenic differentiation of MSCs in a TGF-β dependent manner. These studies provided critical information about the multipotency of MSCs and suggested potential strategies for clinical translation. [ABSTRACT FROM AUTHOR]