Effects of Modulation Methods on the Drying Characteristics and Nutritional Quality of Medicago sativa Hay

Two-year-old Medicago sativa at budding initial stage was taken as research materials.Five methods were used to make green hay,including flatting stems ＋ spraying 2.5% K2CO3,flatting stems,sun curing,drying in shade and drying under 105 ℃ condition（CK）.Besides,effects of different green hay making methods on dry characteristics and nutritional quality of M.sativa green hay were studied,and a comprehensive evaluation of M.sativa green hays was conducted.Results showed that,except CK,the drying rates in other making methods were all fast at first,and then slow down.Both of drying under 105 ℃ condition and flatting stems ＋ spraying K2CO3 could speed up drying rate and reduce nutritional losses of green hay.Sun curing could also speed up drying rate,but it could not maintain the quality of green hay.The results of Grey Relational Analysis on five green hay making methods indicated that CK had the best comprehensive performance,followed by green hays made by flatting stems ＋ spraying K2CO3.Therefore,flatting stems ＋ spraying K2CO3 was a quick and easy method to make green hay,and it was worth to be recommended in practical production.

Expression of DLK1 protein and its correlation with renal cell carcinoma pathological characteristics

Objective To identify the expression of DLK1 protein in different types of renal cell carcinomas and its correlations with pathological characteristics and metastasis. Methods Immunohistochemistry analysis was performed to evaluate the expression of DLK1 protein in

Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as diabetes.Current dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several limitations.These limitations are due to the existence of a special user group and an imbalance of non-simple attributes.Making use of traditional dietary recommendation algorithm researches,we combine the Adaboost classifier with probabilistic matrix factorization.We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics.From this,we can make use of those features with strong influence while discarding those with little influence.After incorporating these changes into our approach,we evaluated our algorithm’s performance.Our results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a result.The algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of foods.Considering many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.

Research on Feature Extraction Method of Social Network Text

The development of various applications based on social network text is in full swing.Studying text features and classifications is of great value to extract important information.This paper mainly introduces the common feature selection algorithms and feature representation methods,and introduces the basic principles,advantages and disadvantages of SVM and KNN,and the evaluation indexes of classification algorithms.In the aspect of mutual information feature selection function,it describes its processing flow,shortcomings and optimization improvements.In view of its weakness in not balancing the positive and negative correlation characteristics,a balance weight attribute factor and feature difference factor are introduced to make up for its deficiency.The experimental stage mainly describes the specific process:the word segmentation processing,to disuse words,using various feature selection algorithms,including optimized mutual information,and weighted with TF-IDF.Under the two classification algorithms of SVM and KNN,we compare the merits and demerits of all the feature selection algorithms according to the evaluation index.Experiments show that the optimized mutual information feature selection has good performance and is better than KNN under the SVM classification algorithm.This experiment proves its validity.

Selection of characteristic spectral bands for the analysis by the NIR correlation coefficient method

We analyzed the infrared 0R）-near infrared （NIR） 2D correlation spectra of drugs perturbed by temperature. By identification of functional groups by IR spectrum and by the correlation analysis of IR-NIR spectrum, we identified the characteristic spectral bands that were closely related to the structure of a drug substance of interest. These characteristic spectral bands were relatively less interfered by other ingredients for analysis by the NIR correlation coefficient method. With these characteristic spectral bands, the accuracy of screening illegally added Sildenafil citrate, Tadalafil and Metforrnin hydrochloride in Chinese patent drugs and healthcare products reached about 90%, which met the requirements of rapid screening.

ERRATUM TO “CHARACTERISTICS OF PRESSURE DROP AND CORRELATION OF FRICTION FACTORS FOR SINGLE-PHASE FLOW IN ROLLING HORIZONTAL PIPE”

This is the erratum to the article [zhang Jin-hong, Yan Chang-qi, Gao Pu-zhen, Journal of Hydrodynamics, 2009, 21(5)]. The Fig.5 is corrected.