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Endophytes may gradually accumulate in the new geographic range of a non-native plant, just as pathogens do. To test this hypothesis, the dynamics of colonization and diversity of foliar fungal endophytes of non-native Ageratina adenophora were investigated. Previous reports showed that the time since the initial introduction (1930s) of A. adenophora into China varied among populations. Endophytes were sampled in three provinces of Southwest China in 21 sites that varied from 20 to 70 years since the introduction of A. adenophora from its native Central America. Endophyte isolation frequencies varied from 1.87% to 60.23% overall in a total of 4,032 leaf fragments. Based on ITS sequence variations, 463 fungal endophytes were distinguished as 112 operational taxonomic units (OTUs) belonging to the Sordariomycetes (77 OTUs, 373 isolates), Dothideomycetes (18 OTUs, 38 isolates), and Agaricomycetes (17 OTUs, 52 strains) classes. Colletotrichum (28.51%), Nemania (14.90%), Phomopsis (13.17%), and Xylaria (4.97%) were the most abundant genera. Both endophyte diversity and overall isolation frequency increased with time since introduction. The genetic differentiation of the fungus Colletotrichum gloeosporioides indicated that the dispersal of endophytes was likely affected by a combination of geographic factors and the invasion history of the host A. adenophora.

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

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In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

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Data chunking algorithms divide data into several small data chunks in a certain way, thus transforming the operation of data into the one of multiple small data chunks. Data chunking algorithms have been widely used in duplicate data detection, parallel computing and other fields, but it is seldom used in data incremental synchronization. Aiming at the characteristics of incremental data synchronization, this paper proposes a novel data chunking algorithm. By dividing two data that need synchronization into small data chunks, comparing the contents of these small data chunks, different ones are the incremental data that need to be found. The new algorithm determines to set a cut-point based on the number of 1 contained in the binary format of all bytes in an interval. Thus it improves the resistance against the byte shifting problem at the expense of the chunk size stability, which makes it more suitable for the incremental data synchronization. Comparing this algorithm with several known classical or state of art algorithms, experiments show that the incremental data found by this algorithm can be reduced by 32%∼57% compared to the others with same changes between two data. The experimental results based on real-world datasets show that PCI improves the calculation speed of classic Rsync algorithm up to 70%, however, with a drawback of increasing the Transmission compression rate up to 11.8%. INDEX TERMS Data synchronization, chunking algorithm, data backup, increment.