Oxalate oxidase (OxO, EC 1.2.3.4.) was purified to homogeneity from wheat (Triticum aestivum) seedlings by sequential thermal treatment, ultrafiltration, Sephadex G-100 gel filtration and affinity chromatography with concanavalin A. The enzyme was purified 66.11-fold with a recovery of 21.97%. It showed a subunit molecular mass of 32.6 kDa on SDS-PAGE and a native molecular mass of 170 kDa on Sephadex G-150 filtration, suggesting that it is a pentamer. The wheat OxO had a maximum activity at pH 3.5. Its K m for oxalate was 0.21 mM. Chemical modification revealed that cysteine, lysine and carboxylate residues were essential for OxO activity, whereas arginine, serine, threonine and tryptophane residues were not essential.

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In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

When senors transmit their data to the sink via multi-hop communication, the sensors closer to the sink are burdened with heavy relay traffic and tend to die early. On the contrary, if all sensors transmit datas to the sink via single-hop communication, the sensors further from the sink will die much more quickly than those closer to the sink. In this paper, we first develop an analytical model to derive the optimal cluster radius. Then we propose a mixed communication method on grid-based where the sensors can transmit data to the sink in either single-hop or multi-hop. Finally, we conduct extensive experiments and show that our method outperforms LEACH and HEED in terms of network lifetime by balancing energy consumption.

The intelligent manufacturing systems are in networked framework via a variety of networking communication systems integrating the heterogeneous collections of manufacturing worker, material, devices and real-time information. This paper presents a intelligent manufacturing system that is implemented by Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN). The system monitors and controls with the clear objective of maximizing the Quality of Service (QoS) provided by the manufacturing resources and to analyze and make decision. This study describes the design and implementation of the system developed as well as performance testing and evaluation results, in terms of system transmission delay and energy consumption.