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Echeveria ‘Perle von Nürnberg’ is a popular ornamental potted plant. In 2017–2018, stem rot lesions caused by Fusarium spp. were repeatedly observed on Echeveria ‘Perle von Nürnberg’ in Zhangzhou, Fujian Province, China. In this study, samples were collected to isolate the pathogen. After morphological observations, molecular identification, and pathogenicity measurements, the pathogen was confirmed as Fusarium oxysporum. Green fluorescent protein was used to label F. oxysporum to observe the pathogenic process in the host plant. Two to three days after inoculation, the pathogen conidia adhered to the surface of the host stem and germinated into hyphae. The hyphae invaded the host cortex through wounds on the plant or the stem-leaf junction. The host stem produced greyish-brown circular or irregular lesions. Five days after inoculation, the expanded hyphae invaded the vascular cylinder and proliferated. The lesions on the host stem became larger and the neighboring leaves gradually fell off. Seven days after inoculation, the thalli continued to proliferate in the host, the hyphae continued to expand, and the host pith was damaged. The lesions on the stem continued to expand; the stems and branches became withered and lodged, and numerous leaves fell off. Given the rapid infection of F. oxysporum in Echeveria ‘Perle von Nürnberg’ plants, necessary prevention and control measures should be employed prior to infection. In summary, this study demonstrated that the pathogen responsible for stem rot disease in Echeveria ‘Perle von Nürnberg’ is F. oxysporum and further revealed the invasion modes and pathways of F. oxysporum in this plant, providing an empirical basis for the diagnosis and treatment of this stem rot pathogen.

In this study, the finite element method (FEM) for phased array technology in ultrasonic time of flight diffraction (TOFD) for the defect detection of two-dimensional (2-D) geometric materials was researched. The phased array technology generated the FEM model for the TOFD signal. We have established the finite element model by the FEM software ANSYS based on the ultrasonic mechanism about the defects and the phased array transducer. A plane strain elements have simulated the reflected signal of the defect. We can compare the error ratio between simulation and experiment by using the theoretical calculation value as the benchmark, and find the feasibility of the FEM detection.

Zhangzhou in Fujian province is the primary habitat for the succulent plant Echeveria ‘Perle von Nurnberg’ (Crassulaceae), which has seriously suffered from anthracnose disease in recent years. Plants with typical disease symptoms were collected from three planting bases in Zhangzhou, and pathogens were isolated for further identification. The pathogen was identified as Colletotrichum destructivum by morphological observation, rDNA-ITS, Actin, and TUB2 gene sequence analysis, and pathogenicity identification. This is the first ever report of anthracnose caused by C. destructivum on Echeveria ‘Perle von Nurnberg.’

Centered on parallel factor analysis(PARAFAC) and support vector machine(SVM), a method of centrifugal pump(CP) fault diagnosis is proposed. A non-stationary multi-fault mode single-source signal feature extraction method is proposed, based on the theory of parallel factor analysis, i.e. spatial information is introduced in time-frequency analysis, and after parallel factor analysis, the spatial signal, the spectral signal (mode2) and the time-domain signal (mode3) are generated. The latter two can obviously describe the natural or fault state of the mechanical equipment by comparing the experimental analysis, and this function is used to evaluate the mapping relationship between the centrifugal pumps in different states and their respective mode2 and mode3. A total of six components in mode2 and mode3 are used as feature vectors, which are input to the SVM classifier, optimized by the improved particle swarm algorithm for fault classification, by constructing an experimental platform to collect the nonlinear multi-fault mode feature signals of centrifugal pumps. Compared with the wavelet packet energy features, the proposed method is more convenient for feature extraction, the classification accuracy of the proposed classifier is significantly improved, and the complexity of the proposed classifier is not significantly increased.