Among fungi, species of the genus Paecilomyces are considered as promising biological control agents with high potential to reduce root-knot nematode (RKN) and other nematode populations. In this research we investigated a soil hyphomycetes, Paecilomyces marquandii and its pathogenicity on eggs of RKN in vitro. In greenhouse test, root weight, plant length, fresh weight and dry weight of plants, gall index and proportion of infected eggs were determined. P. marquandii, effectively promoted growth of plants inoculated with M. incognita by suppressing its pathogenesis as root galling and reducing egg mass production. At 25 °C, a great number of nematode eggs were parasitized by P. marquandii, inhibiting juvenile development.
Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a novel approach for simulating hysteresis with various features that combines the extreme learning machine (ELM) and least−squares support vector machine (LS−SVM). First, the hysteresis is converted into a single−valued mapping by deteriorating stop operators, a combination of stop and play hysteresis operators. Then, the converted mapping is learned by a LS−SVM model. This approach facilitates the training steps and provides more accurate results in contrast to the previous experimental studies. The proposed model is evaluated for several hystereses with various properties. These properties include rate−independent or rate−dependent, congruent or non-congruent, and symmetric or asymmetric problems. The results indicate the efficiency of the newly developed technique in terms of accuracy, computational cost, and convergence rate.
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