Effects of different carbon sources and C/N values on nonvolatile taste components of Pleurotus eryngii

Pleurotus eryngii, the second largest industrial cultivation mushroom in China, is usually cultivated on substrates mainly consisting of sawdust and corncob. In this study, experiments were performed to determine the effects of different carbon sources and C/N values on nonvolatile taste components of P. eryngii. The effects of different carbon sources on nonvolatile taste components levels revealed that sawdust was beneficial to high levels of crude protein, amino acids, 5′‐nucleotides and equivalent umami concentration, while corncob was beneficial to high contents of carbohydrate, polysaccharides and trehalose. At the similar C/N values, relatively higher sawdust content was beneficial to umami amino acid production, while relatively higher corncob content was beneficial to high contents of carbohydrate, polysaccharides and mannitol. Higher C/N value was beneficial to high levels of crude protein, amino acids, 5′‐nucleotides and equivalent umami concentration, while lower C/N value was beneficial to high contents of carbohydrate, polysaccharides and trehalose. These results provided information for P. eryngii fruit body industrial cultivation to obtain specific nonvolatile taste components with high levels.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

基于承包商预期收益不变的不平衡报价模型

在招标实践中,通常采用最低价中标原则,使得投标人不得不压低其投标价格。基于承包商预期收益不变的不平衡报价模型,是在“工程量清单报价”的条件下,考虑到施工中工程量的变化及施工顺序的影响,体现了资金的时间价值,在保持承包商预期收益不变的前提下,通过调整各分项工程的单价,来降低工程报价,以增加中标机会而建立的。所建立的模型简单,易于操作,并配以具体的工程实例,通过计算机求得其最优解,可供承包商投标报价时参考。     

A comparison of transformation methods for evaluating two‐dimensional weakly singular integrals

Accurate numerical evaluation of integrals arising in the boundary element method is fundamental to achieving useful results via this solution technique. In this paper, a number of techniques are considered to evaluate the weakly singular integrals which arise in the solution of Laplace's equation in three dimensions and Poisson's equation in two dimensions. Both are two‐dimensional weakly singular integrals and are evaluated using (in a product fashion) methods which have recently been used for evaluating one‐dimensional weakly singular integrals arising in the boundary element method. The methods used are based on various polynomial transformations of conventional Gaussian quadrature points where the transformation polynomial has zero Jacobian at the singular point. Methods which split the region of integration into sub‐regions are considered as well as non‐splitting methods. In particular, the newly introduced and highly accurate generalized composite subtraction of singularity and non‐linear transformation approach (GSSNT) is applied to various two‐dimensional weakly singular integrals. A study of the different methods reveals complex relationships between transformation orders, position of the singular point, integration kernel and basis function. It is concluded that the GSSNT method gives the best overall results for the two‐dimensional weakly singular integrals studied. Copyright © 2002 John Wiley & Sons, Ltd.     

人工神经网络预测煤的发热量

应用人工神经网络处理部分广东地区煤质分析数据,建立网络模型,预测出煤的发热量,经实测值与预测值比较,在一定的煤种范围内预测值与实测值比较接近。     

A note on the Gauss-Jacobi quadrature formulae for singular integral equations of the second kind

A fast and efficient numerical method based on the Gauss-Jacobi quadrature is described that is suitable for solving Fredholm singular integral equations of the second kind that are frequently encountered in fracture and contact mechanics. Here we concentrate on the case when the unknown function is singular at both ends of the interval. Quadrature formulae involve fixed nodal points and provide exact results for polynomials of degree 2n − 1, where n is the number of nodes. Finally, an application of the method to a plane problem involving complete contact is presented.     

火工品压药机装配误差的研究

针对军用雷管生产线中压药机结构,分别用极值法和概率法对压药机冲子和雷管的配合精度,进行了装配尺寸链的描述和计算,并对所得的结果进行了比较。     

邻—(4—乙基苯甲酰基)苯甲酸的研究

本文阐述了通过Friedel-Crafts反应合成邻-(4-乙基苯甲酰基)苯甲酸的方法.研究了影响产物质量的因素,提出了适宜的工艺条件.