Wing/body kinematics measurement and force and moment analyses of the takeoff flight of fruitflies

In the paper, we present a detailed analysis of the takeoff mechanics of fruitflies which perform voluntary takeoff flights. Wing and body kinematics of the insects during takeoff were measured using Based on the measured data, high-speed video techniques. inertia force acting on the insect was computed and aerodynamic force and moment of the wings were calculated by the method of computational fluid dynamics. Subtracting the aerodynamic force and the weight from the inertia force gave the leg force. The following has been shown. In its voluntary takeoff, a fruitfly jumps during the first wingbeat and becomes airborne at the end of the first wingbeat. When it is in the air, the fly has a relatively large ＂initial＂ pitch-up rotational velocity （more than 5 000~/s） resulting from the jumping, but in about 5 wingbeats, the pitch-up rotation is stopped and the fly goes into a quasi-hovering flight. The fly mainly uses the force of jumping legs to lift itself into the air （the force from the flapping wings during the jumping is only about 5%-10% of the leg force）. The main role played by the flapping wings in the takeoff is to produce a pitch-down moment to nullify the large ＂initial＂ pitch-up rotational velocity （otherwise, the fly would have kept pitching-up and quickly fallen down）.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.