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Peste des petits ruminants virus (PPRV; family Paramyxoviridae, genus Morbillivirus) causes a highly contagious disease with a high death rate in wild and domestic small ruminants. Four PPRV lineages (L1-L4) [...]

This work deals with minimizing fluctuations of propulsion force and improving the motion quality in a linear switched reluctance motor. In order to minimize the jerks in the moving part of the motor, a new profile is used to generate an appropriate reference speed profile. Then, a simple fuzzy logic system which is able to overcome the uncertainties problem in nonlinear systems is designed and applied to the motor. The fuzzy control system can regulate the motor performance so that it tracks the reference speed with minimum error and fluctuation. The system is designed and its efficiency is shown through simulation and experimental tests in different performance situations. The obtained results confirm that the proposed strategy outperforms other conventional methods. [ABSTRACT FROM AUTHOR]

The study used fuzzy logic to extract information and analyze nuclear medicine imaging. Data were extracted for accurate and rapid diagnosis of left ventricular cavity volume (LVV), which is very effective in rapid and accurate diagnosis of left ventricular disease. The proposed method has three steps: First, a wavelet-based analysis was performed on existing medical imaging to reduce the noise in them and improve image resolution. The brighter image of this step was used as the second step input. Image segmentation was performed using a neural network algorithm. At this stage, image classification was performed using the Gaussian function. Finally, in the third step, the area corresponding to the left ventricle was selected, and fuzzy logic was used for this selection. [ABSTRACT FROM AUTHOR]