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Lycium barbarum, a homology of medicine and food, contains many active ingredients including polysaccharides, polyphenol, betaine, and carotenoids, which has health benefits and economic value. The bioactive components in Lycium barbarum exhibit the effects of antioxidation, immune regulation, hypoglycemic effects, and vision improvement. Recently, the development of nutrition and health products of Lycium barbarum has been paid more and more attention with the increase in health awareness. A variety of nutrients and bioactive components in wolfberry can be retained or increased using modern fermentation technology. Through fermentation, the products have better flavor and health function, which better meet the needs of market diversification. The main products related to wolfberry fermentation include wolfberry fruit wine, wolfberry fruit vinegar, and lactic acid fermented beverage. In this review, the mainly bioactive components of Lycium barbarum and its deep-processing products of fermentation were summarized and compared. It will provide reference for the research and development of fermented and healthy products of Lycium barbarum.

In order to provide certain references for further deepening the development of processing industry of agricultural products, this paper analyzed and elaborated the basic principles, construction priorities and safeguard measures of the development of deep processing industry of agricultural products in Nanchong City of Sichuan Province. Besides, it made a scientific planning for accelerating the deep processing of agricultural products in Nanchong City in 2018-2020, to ensure the full implementation of fine and deep processing of agricultural products.

To optimize agriculture structure, it is important to develop middle and lower factors of the industrial chain and to improve its technologies and management. The current efficiency is analyzed by qualitative ways and DEA. It is argued of the relationship between economy and deep processing of primary products. Then selection and forecast of technology is applied based on self-adaptive filter forecasting.

An approach based on the gas-phase processing of secondary wastes in an HNO 3 (vapor)–air or NO x –H 2 O (vapor)–air atmosphere with further Mn and Cr leaching with water is proposed for Mn and Cr extraction from the secondary wastes of metallurgical plants (fly ash from the ore-dressing plant of the Norilsk Combine and rock refuses from the Punda Gourda Nickel Production Combine of the Cuban State-Owned Commercial Caribbean Nickel Company). It is shown that the preliminary treatment of secondary wastes in a nitriding atmosphere at a temperature of 400–425 K for 5 h makes it possible to leach Mn from the volume of secondary wastes. For the fly ash from the ore-dressing plant of the Norilsk Combine and the rock refuses from the Punda Gourda Nickel Production Combine of the Cuban State-Owned Commercial Caribbean Nickel Company, the degree of Mn extraction with water is less than ~33 and ~65% of their total content in the wastes, respectively. The degree of Cr extraction is less ~2% in all cases.

Samples of amorphous silicon dioxide (SiO 2 ) are obtained by acidic decomposition of mineral raw materials (nepheline). The physicochemical and structural surface properties of the samples are studied using chemical and granulometric analyses and the BET and BJH methods. It is established that the obtained SiO 2 samples are almost identical to pyrogenic amorphous silicon dioxide obtained from reactive raw materials (pharmaceutical remedy Polysorb MP) in impurity content and have higher specific external surface area (by a factor of 1.1–1.9) and specific pore volume (by factor of about 1.4) compared to Polysorb MP. An economic assessment of their production cost is performed, which shows that the cost of silicon dioxide obtained by the developed technologies is more than two times lower than the cost of commercial pyrogenic silicon dioxide that is present in the market. Based on the obtained results, we discuss the prospects of using silicon dioxide obtained on the basis of acidic treatment of mineral raw materials as an enterosorbent in therapeutic practice.

The mismatch between supply and demand for fresh products and those that can potentially lead to the risk of spoilage has posed huge losses for industrial companies. To reduce the risk of spoilage of fresh products, some firms have attempted to adopt a deep-processing service to alleviate the imbalance. Therefore, we developed a framework to control the spoilage of the product by taking into account the deep-processing service. First, a differential equation for an inventory model of fresh product and deep-processed product that depended on the selling price and the deteriorating rate was developed. Based on this, a profit model for fresh product and the deep-processed product was developed, and the condition of whether the deep-processing service was required was shown by optimization theory. Furthermore, the existence and its uniqueness of such proportion of deep processing and the selling price of the fresh product were proved. Research results showed the deep-processing service acted as a buffer against the mismatch between the supply and demand for the fresh product. Industrial companies should make lower profits but a quicker turnover by setting a lower selling price when both the deteriorating rate and initial freshness level are high, and vice versa.

With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in the field of natural language processing and data mining in recent ten years. The paper starts with the topic of \"Sentiment Analysis using a CNN-BiLSTM deep model based on attention mechanism classification\". First, it conducts an in-depth investigation on the current research status and commonly used algorithms at home and abroad, and briefly introduces and analyzes the current mainstream sentiment analysis methods. As a direction of machine learning, deep learning has become a hot research topic in emotion classification in the field of natural language processing. This paper uses deep learning models to study the sentiment classification problem of short and long text sentiment classification tasks. The main research contents are as follows. Firstly, Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. The feature dimension is too high, and the feature information of the pool layer is lost, which leads to the loss of the details of the emotion vocabulary. To solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined in Quora dataset. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long- term dependencies between words hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. Secondly, we propose an attention based CNN-BiLSTM hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism in IMDB movie reviews dataset. In the experiment, under the control of single variable of Data volume and Epoch, the proposed hybrid model was compared with the results of various indicators including recall, precision, F1 score and accuracy of CNN, LSTM and CNN-LSTM in long text. When the data size was 13 k, the proposed model had the highest accuracy at 0.908, and the F1 score also showed the highest performance at 0.883. When the epoch value for obtaining the optimal accuracy of each model was 10 for CNN, 14 for LSTM, 5 for MLP and 15 epochs for CNN-LSTM, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.906, and accuracy of the proposed model was the highest at 0.929. Finally, the experimental results show that the bidirectional long- and short-term memory convolutional neural network (BiLSTM-CNN) model based on attention mechanism can effectively improve the performance of sentiment classification of data sets when processing long-text sentiment classification tasks. Keywords: sentiment analysis, CNN, BiLSTM, attention mechanism, text classification