Abstract:
IPv6 has many advantages such as the massive amount of addresses, high security, and high robustness, which are beneficial for wireless sensor networks (WSNs). However, it is almost impossible to use IPv6 directly in WSN due to its huge energy consumption. This paper proposes a double adaptively clustering hierarchy (DACH) algorithm which enables using IPv6 in WSN in an efficient and reliable way. Firstly, we present a clustering method to adaptively divide the whole sensor network into clusters according to its energy consumption in the last round. Then we propose an adaptive cluster head selection algorithm which employs a strategy to choose the most suitable cluster heads; meantime, this selection algorithm is integrated into DACH. Finally, the complete framework is built between headers and their slave nodes based on IEEE 802.15.4, and IPv6 is used to connect the headers and the base stations. Experimental and simulation results demonstrate that the DACH algorithm has lower time and energy consumption. Moreover, it is more reliable and applicable than many other IP-based WSN algorithms. 1. Introduction One of the most important techniques of this decade is wireless sensor networks (WSNs). In the last twenty years, interpersonal communication has become very popular with the booming internet technology. Similarly, with the development of？？WSNs [1], the same phenomenon will occur, and people will benefit a lot from this new information exchange technology. When WSN is as widely used as the internet, people can turn on their air conditioners at home when they are still on their way; the information of snow depth of every valley of Alps can be measured and collected by sensors and sent to people for making decisions about holiday skiing; any equipment of a city can send an alarm to the fire station automatically when the temperature is beyond the normal range, and so forth. Without access to the internet, WSN is just a usual local network with its limited power. However, when IPv6 joins, WSN becomes magic and powerful, for IPv6 has a lot of advantages, such as massive addresses, high security, and good QoS service [2]. Since TCP/IP is limited with factors like too much energy cost and low battery frequent data transmission at the sensor nodes, IPv6-based WSN is more favorite for the researchers. However, for WSN, header overhead problem in IPv6 is more serious than that in IPv4. Usually, the monitoring signal, control signal, and measured data of a sensor is no more than 10 bytes [3]. If IPv6 is introduced directly, the header overhead will consume more

Abstract:
A dual real-time quantitative polymerase
chain reaction assay (drtqPCR) was established to detect and differentiate
between Porcine circovirus-2a (PCV-2a) and Porcine circovirus-2b (PCV-2b).
Genotype-specific primer sets and probes were designed by using sequence data
published for different PCV-2 strains. Specificity and sensitivity of the
drtqPCR were examined by using PCV-2 isolates with known genotype. Among 367
tissue samples, 44.69% (164/367) were PCV-2 positive. From 164 PCV-2 positive
samples, 10.98% (18/164), 92.56% (137/164), and 3.31% (9/164) were positive for
PCV-2a, PCV-2b, and both genotypes, respectively. These results suggest that
the dif-ferential drtqPCR can be used to detect PCV-2 and to differentiate the
2 genotypes from field sam-ples. The PCV-2 infection is quite common in swine
of Shanghai area. Furthermore, the PCV-2b infective ratio is far higher than
PCV-2a, and PCV-2a/2b mixed infections are also observed but at a lower
prevalence in Shanghai area.

Abstract:
In the paper, a class of fuzzy matrix equations AX=B where A is an m × n crisp matrix and is an m × p arbitrary LR fuzzy numbers matrix, is investigated. We convert the fuzzy matrix equation into two crisp matrix equations. Then the fuzzy approximate solution of the fuzzy matrix equation is obtained by solving two crisp matrix equations. The existence condition of the strong LR fuzzy solution to the fuzzy matrix equation is also discussed. Some examples are given to illustrate the proposed method. Our results enrich the fuzzy linear systems theory.

Abstract:
AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, and k-nearest neighbor (kNN). This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost). We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

Abstract:
Recruitment prediction is a key element for management decisions in many fisheries. A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. In order to deal with the local minimum problem in training neural network with back-propagation algorithm and to enhance forecasting precision, neural network’s weights are adjusted by optimization algorithm. It is demonstrated that a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the prediction of fish stock recruitment.

Abstract:
The carbon layers on implanted steel surface have been studied by means of Auger spectra. It is shown that the thickness of the carbon layer is proportional to the dose of implanted ions. By comparison with the results of friction and wear tests, the friction coefficient is smaller than 0.20 at the first part of the friction coefficient curve. It is considered that the graphitic carbon layer on the top of steel is helpful to reducing the surface friction coefficient of steel.

Abstract:
The fuzzy symmetric solution of fuzzy matrix equation , in which is a crisp nonsingular matrix and is an fuzzy numbers matrix with nonzero spreads, is investigated. The fuzzy matrix equation is converted to a fuzzy system of linear equations according to the Kronecker product of matrices. From solving the fuzzy linear system, three types of fuzzy symmetric solutions of the fuzzy matrix equation are derived. Finally, two examples are given to illustrate the proposed method. 1. Introduction Linear systems always have important applications in many branches of science and engineering. In many applications, at least some of the parameters of the system are represented by fuzzy rather than crisp numbers. So, it is immensely important to develop a numerical procedure that would appropriately treat general fuzzy linear systems and solve them. The concept of fuzzy numbers and arithmetic operations with these numbers was first introduced and investigated by Zadeh [1], Dubois et al. [2], and Nahmias [3]. A different approach to fuzzy numbers and the structure of fuzzy number spaces was given by Puri and Ralescu [4], Goetschell et al. [5], and Wu and Ming [6, 7]. Since Friedman et al. [8, 9] proposed a general model for solving an fuzzy linear systems whose coefficients matrix is crisp and the right-hand side is an arbitrary fuzzy numbers vector by an embedding approach in 1998, many works have been done about how to deal with some fuzzy linear systems with more advanced forms such as dual fuzzy linear systems (DFLSs), general fuzzy linear systems (GFLSs), fully fuzzy linear systems (FFLSs), dual full fuzzy linear systems (DFFLSs), and general dual fuzzy linear systems (GDFLSs). These works were performed mainly by Allahviranloo et al. [10–13], Abbasbandy et al. [14–17], Wang et al. [18, 19] and Dehghan et al. [20, 21], among others. However, for a fuzzy matrix equation which always has a wide use in control theory and control engineering, few works have been done in the past decades. In 2010, Guo et al. [22–24] investigated a class of fuzzy matrix equations in which is an crisp matrix and the right-hand side matrix is an fuzzy numbers matrix by means of the block Gaussian elimination method and the undetermined coefficients method, and they studied least squares solutions of the inconsistent fuzzy matrix equation by using the generalized inverses. In 2011, Allahviranloo and Salahshour [25] obtained fuzzy symmetric approximate solutions of fuzzy linear systems by solving a crisp system of linear equations and a fuzzified interval system of linear equations.