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Summary: Energy is the cornerstone of the existence and development of modern society. The energy consumption continues to increase, and the ensuing environmental pollution and increasing lack of traditional energy sources have also become the world's most concerned issues. The emergence of new energy has eased the current unfavorable situation. Among them, distributed photovoltaic (PV) power generation has been favored because of its abundant resources and no geographical restrictions. However, due to the scattered construction of solar PV power stations, it is not easy to manage. Therefore, research and development of PV remote monitoring systems for unified monitoring and management of PV power stations. In addition, the problem of solar cell output efficiency is also an important reason that restricts the utilization of solar energy. Improving the output efficiency of the battery based on the existing solar cell conversion efficiency is also a focus of current research. Based on the above background, the research content of this article is the network communication monitoring system for distributed PV power generation systems. This article first conducts the overall design of the PV remote monitoring system, constructs the system's distributed design structure, and divides the underlying hardware part of the system into control modules and transmission. According to the application requirements, the STM32 series single‐chip microcomputer is selected as the controller of the system control module and the transmission module. The hardware circuit design and software process implementation of the system are studied to realize the data collection, MPPT control, and remote data transmission of the photovoltaic system. Features: Finally, through experimental simulation, it was verified that the light intensity increased from 600 to 800 W/m2, and the output current of the inverter increased accordingly. The output current of the distributed PV power generation system was directly proportional to the change of the light intensity. The designed system is feasible. [ABSTRACT FROM AUTHOR]

• Based on the derivation method, a new derived grey multi-parameter prediction model (DMGM (1, n)) is proposed. • The mathematical analysis is used to discuss the main reason why DMGM (1, n) is superior to GMC (1, n). • DMGM (1, n) outperforms the other grey multivariable models by comparing the results of these models in three cases. In this study, in order to reduce the morbidity and improve the structural stability of the existing grey multivariable convolution forecasting model, a new derived multivariable grey model based on the derivation method, abbreviated as DMGM (1, n), is presented. Firstly, the time response formula of DMGM (1, n) is deduced by derivation method, which can avoid solving the inverse matrix so as to reduce the morbidity of the model. Secondly, the parameter identification of the model is given based on the least-squares method. Then, it is proved theoretically that DMGM (1, n) is superior to GMC (1, n) because the solution of the former overcomes the shortcoming of the latter that the original model does not take full advantage of all the information from the raw data for modeling. Finally, three real cases with different variables were performed. The fitting and prediction results indicate that DMGM (1, n) is better than GMC (1, n) and the other multivariate grey prediction models in these cases, which also demonstrates that this novel model outperforms the other grey models in this paper. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effect of preserving the muscle attachments when performing standard artificial temporomandibular joint replacement (TJR).Patients and Methods: The clinical and radiological imaging data of patients who underwent standard artificial TJR with and without preservation of lateral pterygoid muscle (LPM) and masseter muscle attachments from January 2017 to December 2019 were collected. The maximum interincisal opening (MIO), lateral excursions and protrusion distances, visual analogue scale (VAS) scores of pain, diet, and quality of life (QoL) were recorded before the operation, and 1, 3, 6, and 12 months after the operation. The volumes of LPM and masseter muscles were measured and analyzed by computed tomography (CT) scans.Results: Twenty-seven patients with 36 joints were included in the study. Among them, 11 joints had muscle attachment preserved, and 25 had no muscle attachment preserved. After surgery, the MIO, lateral excursions, and scores of diet, pain, and QoL in the preserved muscle attachment group were significantly better than those in the unpreserved group (P < .05). The measurement volumes of LPM and masseter muscles in the preserved group were significantly larger than that in the unpreserved group (P < .05). Changes of lateral excursion from the TJR side with and without LPM preservation were statistically correlated with the LPM volume (P < .05).Conclusion: Preserving muscle attachment for the standard artificial TJR is beneficial to the recovery of postoperative mandibular function. [ABSTRACT FROM AUTHOR]

Multi-surrogate assisted evolutionary algorithms, utilizing inexpensive global and local surrogate models to assist evolutionary search, have demonstrated a remarkable ability to solve expensive optimization problems (EOPs). The selected training samples of the local surrogate model used in the current algorithms may not be updated continuously, which could bring about a waste of computing resources. This paper proposes a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy (MS-MTGAwA), in which the local surrogate model is updated adaptively based on information from established local models to enhance the capability in exploiting the optimal solution. The adaptive training sample selection strategy (ATS) applies optimal points found in historical local models as training samples. In addition, MS-MTGAwA inherits the optimization framework of a multi-tasking genetic algorithm. The radial basis function model is chosen as the modeling basis of the global and local surrogate models. A set of common benchmark functions whose dimensions vary from 10 to 100 and the tension/compression spring design problem are adopted to validate the performance of the proposed algorithm. Based on the average best fitness value over thirty independent runs on the benchmark functions, MS-MTGAwA ranks first in Friedman's test compared to five state-of-the-art algorithms including S-JADE, GORS-SSLPSO, SAHO, SAMSO, and MS-MTO. The proposed ATS can be expected to be applied in any algorithms with local surrogate to make full use of historical information of the local model to improve the prediction accuracy of the next local model. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Use the predictive best of historical local surrogate models to update the local model. • Design an adaptive training sample selection strategy for the local surrogate model. • Develop a framework that combined multi-surrogate and multi-population-based multitasking. • Propose a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy. • MSMTGAwA shows strong competitiveness in comparison with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]