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Pine wilt disease (PWD), Bursaphelenchus xylophilus, is an extremely threatening invasion forest disease throughout the world, especially in Asia. B. xylophilus is spread in Asia by vector beetles of Monochamus alternatus, which has long no effective control method. Understanding of landscape effects on the dispersal and outbreaks of forest pests is crucial to establishing effective ecological control strategies. Here, we analyzed the samples of M. alternatus collected at landscapes in order to estimate the effects of landscape types on the genetic structure and dispersal of M. alternatus. The landscapes included the geographical scales, forest types and land uses. The individuals of M. alternatus were genotyped by using whole-genome resequencing. Population genetic structures were clearly differentiated at the intermediate scale, suggesting the intermediate scale is an effective barrier against natural dispersal of M. alternatus. We used the least-coat distances, least-cost transect analysis, and distance-based redundancy analysis to estimate the effects of forest types and land uses within the fine scales. The results showed that the gene flow and genetic diversity were positively correlated with host and mixed forests, whereas negatively with non-host forests. Among land-use landscapes, the roads had the positive effect on gene flow and genetic diversity but farmland and urban uses had negative effects. This highlights that human-mediated transport via roads was likely to be the main factor leading to the long-distance invasion of M. alternatus, whereas non-host landscapes could suppress the spread of this species. These findings may be useful to control the PWD dispersed by M. alternatus.

In order to improve the performance of hard anodic oxidation film of the 6061 aluminum alloy the significantly influential factors were screened by orthogonal test at room temperature. The results showed that the significant factors which contribute to increase the anode oxidation film hardness were as follows: malic acid, lactic acid, current density, sulfosalicylic acid, triethanolamine, sulfuric acid and oxidation time. And with the decreasing of malic acid, lactic acid, current density, sulfosalicylic acid, sulfuric acid and oxidation time, with the increasing of triethanolamine, the hardness of oxide film increased. We can rank these significant factors which are in favor of increasing anode oxidation film corrosion resistance by its effects as follows: current density, oxidation time, sulfosalicylic acid, sulfuric acid, aluminum sulphate and concentration of triethanolamine. Also as the current density, oxidation time and triethanolamine concentration increased, as the sulfosalicylic acid, sulfuric acid, aluminum sulfate concentration decreased, the corrosion resistance of oxide film was improved.

Color attention algorithm can improve the accuracy of object recognition significantly. In the algorithm, there are 3 parameters related to bin number of color features and shape features and the weight between color and shape, need to be set. Whether the parameters are set correctly or not poses a great impact on the accuracy of object recognition. However, searching suitable values in the parameters space related to color attention algorithm is a NP-hard problem and the parameters of the algorithm are set manually in recent research through which the right values are not guaranteed to be found. Recently, bio-inspired computing gains much attention for its advantages in complex optimization problems and differential evolution outperforms most other bio-inspired computing algorithms in divergence and stability in optimization problems. When taking the accuracy of object recognition as a fitness function, increasing the accuracy of object recognition is then an optimization problem to find the largest accuracy in parameter spaces. Therefore, we design the structure of the agent of differential evolution and take the classification accuracy with support vector machine algorithm as a fitness function. Then we use differential evolution to search the parameters space and find some suitable parameters for color attention algorithm successfully. Our experimental evaluation demonstrates that the accuracy of object recognition increases greatly with the right parameters.

Ontology for insect recognition is a powerful tool to express semantics of insects and to share the knowledge of insects. In this paper, the framework of ontology based insect recognition is proposed. The framework includes training layer, ontology layer and recognition layer as well as methods for communicating between different layers. The training layer includes insect image samples database, feature extraction, insect feature knowledge database, insect type knowledge database; the ontology layer includes insect image visual feature ontology, insect image media feature ontology, classification module, insect morphology and taxonomy ontology.

The goal of adding content-based 3d model retrieval to semantic web is to develop a rich set of 3d model semantic representation to enable both humans and machines to generate and understand model descriptions and processing which can be used to enable fast efficient retrieval from model collections. The purpose of Ontology for Content-based 3d Model retrieval is intended to describe model information regardless of storage, feature extraction, creation. In our research, the ontology includes the information on media features, low level visual descriptors and their relationship and non media features of 3d model. It is represented in RDF Schema and implemented in protege.

In order to share and reuse the insect knowledge the infrastructure of the semantic grid is needed and ontology provides the basis of management of semantics for the semantic grid. Therefore the ontology for insect morphology and taxonomy (IMTO) is developed. IMTO includes insect order sub-ontology and family sub-ontology. Protege 3.1 is used for ontology development of concepts and relationships that represent insect morphology and taxonomy