The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

The aim of the research is to examine the multiple intelligence test item selection based on Howard Gardner's MI model using the Generalized Partial Estimation Form, generalized intelligence. The researcher adopted the scale of multiple intelligences by Kardner, it consists of (102) items with eight sub-scales. The sample consisted of (550) students from Baghdad universities, Technology University, al-Mustansiriyah university, and Iraqi University for the academic year (2019/2020). It was verified assumptions theory response to a single (one-dimensional, local autonomy, the curve of individual characteristics, speed factor and application), and analysis of the data according to specimen partial appreciation of the generalized, and limits

The study aimed to detect the VrPIP2;7 gene using PCR approach, as well as to know the effect of the treatment with four increased melatonin concentrations of 50, 100, 150 and 200 ppm in addition to control treatment were 0 ppm on the gene expression of plasma membrane intrinsic proteins (PIP) genes in Vigna radiata L. plant exhibition for five periods of drought which is irrigation every 24 hours, 48 hours, 5 days, 10 days and every 15 days. The electrophoresis of agarose gel at a concentration of 2% showed one band when detecting the VrPIP2;7 gene with a sizeable 732 bp and using the 100 bp volume index. This gene was selected for sequencing study based on its importance as well as on the results of its gene expression. The sequencing of

The current study aimed to detect the effect of gentamicin stress on the expression of hla (encodes hemolysin) and nuc (encodes nuclease) genes of Staphylococcus aureus. Fifty-eight isolates identified as S. aureus were isolated locally from different clinical specimens. Disk diffusion method was used to detect the resistance to S. aureus. The minimum inhibitory concentration (MIC) of gentamicin was estimated by broth microdilution method. hla and nuc genes were determined by polymerase chain reaction technique. The biofilm was evaluated using the microtiter plate method in the presence and absence of gentamicin at sub-MIC. The results showed that 18 (31%) and 40 (69%) S. aureus isolates were sensitive and resistant to gentamicin, respectiv

     Pseudomonas aeruginosa produces an extracellular biofilm matrix that consists of nucleic acids, exopolysaccharides, lipid vesicles, and proteins. Alginate, Psl and Pel are three exopolysaccharides that constitute the main components in biofilm matrix, with many biological functions attributed to them, especially concerning the protection of the bacterial cell from antimicrobial agents and immune responses. A total of 25 gentamicin-resistant P. aeruginosa selected isolates were enrolled in this study. Biofilm development was observed in 96% of the isolates. In addition, the present results clarified the presence of pelA and pslA in all the studied isolates. The expression of these genes was very low. Even though all biof

Abstract 

This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per