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For the sake of conducting distribution network reliability prediction in an accurate and efficient manner, a model for distribution network reliability prediction (IAO-LSSVM) based on an improved Aquila Optimizer (IAO) optimized mixed-kernel Least Squares Support Vector Machine (LSSVM) is thus proposed in this paper. First, the influencing factors that greatly affect the distribution network reliability are screened out through grey relational analysis. Afterwards, the radial basis kernel function and polynomial kernel function are combined and a mixed kernel LSSVM model is constructed, which has better generalization ability. However, for the AO algorithm, it is easy to fall into local extremum. In such case, the AO algorithm is innovatively improved after both the improved tent chaotic initialization strategy and adaptive t-distribution strategy are introduced. Next, the parameters of the mixed-kernel LSSVM model are optimized and the IAO-LSSVM distribution network reliability prediction model is established through using the improved AO algorithm. In the end, the prediction results and errors of the IAO-LSSVM prediction model and other models are compared in the actual distribution network applications. It is revealed that the IAO-LSSVM prediction model proposed in this paper features higher accuracy and better stability. [ABSTRACT FROM AUTHOR]

In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN model is realized by training the Restricted Boltzmann Machine (RBM) layer by layer, then the BP algorithm is used for reverse fine-tuning to complete the training process of the entire network. finally, the reliability analysis of distribution network is performed by the trained DBN. Compared with the BP neural network method and the traditional Monte Carlo simulation method, it is verified that the proposed model of distribution network reliability analysis has high accuracy.

Abstract: Core–shell SiO2-coated iron nanoparticles were synthesized using a one-step Stöber method in the presence and absence of PEG400. The 2,4-dichlorophenol degradation and antioxidation abilities of the nanoparticles were investigated. The effects of isopropanol/H2O ratio, NaOH dosage, tetraethyl orthosilicate (TEOS) dosage, and reaction time used in the synthesis were investigated. The SiO2-coated iron nanoparticles were characterized using transmission electron microscopy, scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The results showed that N2 protection during filtration and drying were unnecessary, and the iron particle preparation was simple. The nanoparticles were stable in the presence of PEG400. The effects of isopropanol/H2O ratio and TEOS dosage on 2,4-dichlorophenol degradation and antioxidation abilities were significant, although the effects of NaOH dosage and reaction time were small. The isopropanol/H2O ratio changed the nanoparticle nucleation process, whereas NaOH dosage, TEOS dosage, and reaction time changed the mesoporous coating structure. A comparison of the removal rates of 2,4-dichlorophenol using pure iron nanoparticles and using SiO2-coated iron nanoparticles showed an improvement of about 30% using SiO2-coated iron nanoparticles, suggesting that SiO2-coated iron nanoparticles improve the antioxidation abilities and reducing capacity of iron nanoparticles; such nanoparticles could have wide applications in chlorophenol degradation. [Copyright &y& Elsevier]

As to the funerals of husband and wife when one husband had more than one wife, there were five forms of funerals which were the husband being buried together with all his wives, the husband being buried separately, the husband being buried together with his first wife, the husband being buried with his first wife in the same coffin while his second wife's coffins being nearly, the husband being buried with his second wife. The most popular form was that the husband was buried together with all his wives, then was that the husband was buried with his first wife. These two forms should be considered as the normal funeral forms. [ABSTRACT FROM AUTHOR]

To understand the effect of pour point depressants (PPD) on the wax growth is important for designing PPD additives for use with different oils with high efficiency and good economics. In our current study, molecular mechanics, molecular dynamics, and quantum mechanics calculations were performed to investigate the prohibited mechanism of ethylene/vinyl acetate (EVA) additives on the paraffin deposition in oils. On the wax surface, a single C18 molecule and clusters were preferably deposited on the wax surface (010) in a parallel conformation, which resulted in the formation of large blocks of wax crystal. MD simulation indicated that the linear conformation of EVA was more favorable to be adsorbed onto the carbon backbone of the wax surface (010) with the polar fragments of vinyl acetate staying upside of the surface. Furthermore, four EVA molecules can efficiently optimize the inhibition effect for the deposition of the solute C18 molecules over 10x8 size wax surface (010). According to the simulation results, a simplified rational model was established to estimate the minimum dosage of EVA-type PPD for fuels with different paraffin contents. In a certain degree, this simplified model has provided an effective route to correlate microstructures and the properties of polymer-involving systems, which will shed light on the application of theoretical studies in industries.