Carbendazim sensitivity in populations of Colletotrichum gloeosporioides complex infecting strawberry and yams in Hubei Province of China

The ascomycete fungus Colletotrichum gloeosporioides is a devastating plant pathogen with a wide host range and worldwide distribution. Carbendazim has been widely used to control anthracnose caused by the C. gloeosporioides complex in China for more than 30 years and resistance to carbendazim has been reported in China. A total of 125 Colletotrichum isolates of strawberry and yam were collected from different geographical regions in Hubei Province, China. Approximately 52.8％ of Colletotrichum spp. isolates showed resistance to carbendazim. The isolates tested in this study belong to four species, and the frequencies of resistant isolates differed across Colletotrichum species. Resistant isolates were found in C. siamense and C. fructicola. In contrast, all isolates of C. gloeosporioides and C. aenigma were sensitive to carbendazim. Highly carbendazim-resistant isolates harbored the E198A mutation in the β-tubulin 2 （TUB2） gene, whereas moderately carbendazim-resistant isolates harbored the F200Y mutation in the TUB2 gene. Carbendazim-sensitive Colletotrichum isolates in this study were not genetically similar enough to form a separate cluster from resistant isolates. The result of this study emphasizes the importance of knowing which Colletotrichum sp. is present, when strategies for disease control are made.

Simulation of Rock Complex Resistivity Using an Inversion Method

The complex resistivity of coal and related rocks contains abundant physical property information,which can be indirectly used to study the lithology and microstructure of these materials.These aspects are closely related to the fluids inside the considered coal rocks,such as gas,water and coalbed methane.In the present analysis,considering different lithological structures,and using the Cole-Cole model,a forward simulation method is used to study different physical parameters such as the zero-frequency resistivity,the polarizability,the relaxation time,and the frequency correlation coefficient.Moreover,using a least square technique,a complex resistivity“inversion”algorithm is written.The comparison of the initial model parameters and those obtained after inversion is used to verify the stability and accuracy of such approach.The method is finally applied to primary-structure coal considered as the experimental sample for complex resistivity measurements.

Prediction of high frequency resistance in polymer electrolyte membrane fuel cells using long short term memory based model

High-frequency resistance(HFR)is a critical quantity strongly related to a fuel cell system’s performance.It is beneficial to estimate the fuel cell system’s HFR from the measurable operating conditions without resorting to costly HFR measurement devices.In this study,we propose a data-driven approach for a real-time prediction of HFR.Specifically,we use a long short-term memory(LSTM)based machine learning model that takes into account both the current and past states of the fuel cell,as characterized through a set of sensors.These sensor signals form the input to the LSTM.The data is experimentally collected from a vehicle lab that operates a 100 kW automotive fuel cell stack running on an automotive-scale test station.Our current results indicate that our prediction model achieves high accuracy HFR predictions and outperforms other frequently used regression models.We also study the effect of the extracted features generated by our LSTM model.Our study finds that only very few dimensions of the extracted feature are influential in HFR prediction.The study highlights the potential to monitor HFR condition accurately and timely on a car.