Similarities in Evasive Behavior of Wolf Spiders (Araneae: Lycosidae), American Toads (Anura: Bufonidae) and Ground Beetles (Coleopterea: Carabidae)

(excerpt) While collecting newly metalnorphosed American toads, Bufo anlericanus Holbrook, we have observed that they exhibited evasive behavior similar to that of adults of the wolf spiders, Pardosa saxatilis (Hentz), Pirata insularis Emerton, Pirata arerzicola Emerton, Pirata piratica (Oliver), and adults of the ground beetle, Elaplrrus ruscarius Say. When pursued or disturbed, the spiders, beetles and toads ran across the pound rapidly for short distances (ca. 1-50 cm). They then stopped abruptly and remained motionless. If they were further pursued, this escape sequence was repeated in the same or another direction. Toads and spiders occasionally moved to shallow water to avoid capture. Spiders ran across the water surface whereas the toads swam partially submerged. N\u27e observed this resemblance in evasive behavior on numerous occasions at ponds on the south edge of Carbondale, Illinois (spiders and toads), 1 krn west of Grinnell, Iowa (spiders and toads), and 1.5 km west of Bloomington, Illinois (spiders, toads and beetles). (Specimens were collected for identification from the latter site.

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)