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The raffinate in the extraction process contains plenty of acid and metallic ions, the dispose of which causes the resource loss and environmental pollution. The present study proposed an innovative way to solve this problem by using raffinate as the leaching agent in the copper leaching process. The effects of significant leaching parameters were investigated and optimized using the central composite design (CCD) of response surface methodology (RSM). A maximum leaching efficiency of 96.74 % can be obtained under the 53.22 Kg/t of initial acidity, 2.54 h of leaching time and 5.95 of liquid/solid ratio. In addition, the comparative features of samples before and after leaching process were analyzed by using the X-ray diffraction (XRD), sieving test, scanning electron microscope (SEM) and adsorption-desorption isotherms. We found that the majority of copper components have been removed from copper ores, causing the reduction of size distributions. Specifically, the content of

The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.

As an organic acid with the characters of low corrosivity and extensive source, 5-sulfosalicylic acid (5-SSA) was firstly utilized as a potential leaching reagent for the recovery of zinc from metallurgical slag. Effects of stirring speed, leaching temperature, 5-SSA concentrations and size fraction on the leaching zinc leaching rate were investigated. A zinc leaching efficiency of 94.2% was achieved under the appropriate operating conditions (450 rpm of stirring speed, 50 °C of leaching temperature, 0.3 mol/L of 5-SSA concentration and d90=65 µm of size fraction), indicating that 5-SSA was an excellent leaching reagent of zinc oxide. SEM-EDS and specific surface aperture analyzer further reveal the well-developed micropores and cracks from zinc metallurgical slag, which could be assigned to the removal of zinc oxide encapsulated in the sample. In addition, the leaching kinetics of zinc metallurgical slag in the 5-SSA was studied. It was found that the surface chemical reaction model satisfactorily predicted the zinc leaching rate. A reaction kinetic equation was finally established for the zinc leaching rate. [ABSTRACT FROM AUTHOR]

PURPOSE: Aggressiveness of adolescents is a major cause for the violence events in campus. The research results, however, were inconsistent regarding the effectiveness of physical activity reducing aggressiveness among this population. The current study was to examine the relationship about reducing adolescent aggressiveness resulted in exercise and sports. METHODS: We used a systematic meta-analysis approach to synthesize data from the published studies from 1969 to 2019. Two-language electronic databases were searched with ScienceDirect, PubMed, SpringerLink, Wiley, and EBSCOhost in English, and CNKI net, WIP net, and Wanfang Data in Chinese. The key search words included adolescent, physical activity, exercise, sports, fitness, aggressiveness, violence, and delinquency. The Review Manager 5.3 and meta-analysis of Stata SE were used to process and analyze the data. RESULTS: From the initial search, 761 articles were retrieved. Forty-three independent studies were identified as the qualified data to be included in the final analysis, representing a sample size of 129,853 adolescents (age range of 12-18 yrs). Of 43 articles, 20 with 34,952 adolescents were conducted in the correlation analysis between exercise and sports and aggression, 9 with 78,620 adolescents for externalizing, 10 with 1,419 adolescents for delinquency, and 4 with 202 adolescents for violence. Study quality was assessed with Jaded's with an average score 1.7. Little publication bias was observed. The results showed a medium-negative correlation between aggressiveness of adolescents and exercise and sports (Summary Fisher' Z = -0.11, p <0.0001; Q = 1,578.5, p <0.05; I-squared = 97.3%, 95% CI, -0.14 to -0.07). CONCLUSIONS: Participating exercise and sports is associated with improvement or reduction in selected aggressiveness in the sample of adolescents included. A need exists for a large, well-designed, and more inclusive meta-analysis and clinical investigations. [ABSTRACT FROM AUTHOR]