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This paper searches for a deployment method with minimum sensor nodes to achieve agricultural intelligent monitoring. Because of the various features, topographies and complicated weather conditions, the selection of the location for deploying sensor nodes is difficult, especially in hilly areas. This paper puts forward a sensor node deployment algorithm for agricultural intelligent monitoring based on an optimized theory. It first develops a mathematical model for the node location selection. During the solution process, the features of the locations are of primary concern, as a location with more features is easier to choose than other locations. Next, a theoretical analysis and experimental simulation are carried out. Finally, OASNDFA is used in 3000 mu orange orchards in China. The result demonstrates that OASNDFA could perform relevant monitoring tasks with fewer monitoring nodes. [ABSTRACT FROM AUTHOR]

In this paper, the premature failure of the integral droppers in high speed railway catenary in China was investigated. Series of microscopic characterization and analysis were conducted to identify the root causes of the unexpected failure. By numerically simulating the existing crimping behavior, distribution of the contact stress in the dropper was obtained. It was found that there was stress concentration at the dropper sliced end, which became the primary reason for the permanent distortion and damage on the droppers, and consequently resulted in the untimely final fracture with bending fretting fatigue acting. Through the failure analysis and numerical simulation, a scheme of the improvements in measuring the existing crimping was brought up. Meanwhile, a plan was also put forward to change the current crimping model and reduce the depth of indentation of clamp tube. Static tensile tests and bending fretting fatigue tests were carried out to evaluate the optional plans, which would help extend the service life of integral dropper in practice. [ABSTRACT FROM AUTHOR]

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]