Acacia angustissima (prairie acacia).

A. angustissima is a thornless shrub up to 4 m in height. This species is originally from Central America and tolerates a wide range of soils and climates, from high altitude pine-oak forests to extremely dry habitats in the lowlands of Mexico. A. angustissima is a deep-rooted nitrogen fixing pioneer, and is valuable in reducing soil erosion. It is a useful browse species, and may also be planted on acid soils. The species flowers prolifically and is a valuable honeybee plant. A. angustissima has shown great promise in recent alley cropping experiments in lowland Papua New Guinea (Brook, 1992), and may outperform more familiar species of Calliandra and Leucaena in biomass production. It performed best in a multipurpose tree evaluation in a range of sites in the same region from 20-1650 m in altitude (Brook et al., 1992). This species also showed promise in a range of trial sites in Zimbabwe, Ghana to Hawaii. Trials so far have been based on seed collected on an opportunistic basis, and further research is required on genetic improvement of the species throughout its range for international provenance trials. In some regions it may become an aggressive colonizer.

Research on the early warning method of transformer fire fault in ocean booster station based on fault tree model

As the heart of the sea booster station, the transformer has an extremely important position. When the transformer has various faults, it is very easy to cause a fire accident, so the safe operation of the transformer is very important. First, use the fault tree analysis method to construct a transformer fire fault tree evaluation model for different types of fire fault events during the operation of the transformer, then through the calculation of the fire failure probability, the number of measuring points of the composite fire detector is obtained and the measuring points are reasonably arranged to detect the operation status of the transformer in real time. Therefore, it is of great significance to ensure the safe and reliable operation of offshore booster station by improving the accuracy of transformer fire warning.

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.