The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulat

In this paper, we proposed a new class of weighted Rayleigh distribution based on two parameters, scale and shape parameters which are introduced in Rayleigh distribution. The main properties of this class are investigated and derived.

  A total  of 320  samples  of vaginal  swabs  was  obtained  from  women complaining of vaginal infection and attending two hospitals in Al-Sader city, Baghdad, namely Ibn AlBaladi Hospital for Pediatrics and Gynecology and Fatimat Al-Zahraa Hospital for Obstetrics in Al-Habibia district during the period from Desember 1997 till July 1998. Results of direct smear and culture showed  that Trichomonas vaginalis infection occurred in 19.1%, in addition to some microorganisms. The most common infection was by Candida spp. (24.7%), followed  by  Gardnerella  vaginalis (13.8%)  and  Staph. aureus  (10.9%). Infection with Escherichia coli, Klebsiella spp. and Prote

The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms

The study included studying some of the optimum environmental conditions(temperature ,light intensity ) on the production of several green algae Scendesmus quadricauda and Chlorella vulgaris in a selected culture and municipal wastewater . The study also included the recording of growth rate ,doubling time and removal of phosphate and nitrate , maximum rate was recorded to the growth with minimum in doubling time and maximum removal rate of nitrogen-nitrate and phosor- phosphate in each selected culture and municipal wastewater in each species of green algae at 25 C? and a light intensity 380 µ E / m2 / s.

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati