In industries, analysis and control of pressure and flow rate control is very difficult, this leads to factory closings and heavy maintenance through PLC and SCADA implementation. Automatic control plays an important role in the continuous operation of the system. In this project, the actual pump performance is tested in terms of pressure, efficiency, and flow rate of the pump for the operating system using a pipeline by crossing pressure in the pipeline with a length of 2 m by connecting pressure sensing equipment. The global control unit SIMATIC S7-1214 as the main decision-making unit that uses this data to make the required decisions. Thus the operation and stopping of the pump as it asks the details related to all other correct information to SCADA to monitor and control the parameters in the pipeline system of the oil pumping station. Then it performs continuous supervision of oil pump station pipeline in order to allow solving any problem and thereby regulating the control system in the structure consists of three layers. These include the first layer of field devices second remote terminal units, and third domain controllers. The signals are sent from the devices via the transmitters to the dedicated PLC boards in the second layer. The central level of SCADA contains a high-speed computer to supervise or operate the station remotely in order to display information through the LABVIEW screens that was showed the final results of pressure and flow rate by operating the system with a voltage rated by Plus With Modulation at a value of 2.73 V for both light and heavy crude oil. It was showed that the pressure value is 0.22bar and the value of the flow rate is 0.25 L/M for heavy oil. While the light is 0.23bar for pressure and the flow rate is 0.652L/M. In addition, to conducting several experiments that show that an increase in the value of the voltage obtained using heavy oil by a value of 3.77 V the pressure value becomes 0.35 bar the flow rate is at 4.78L/M, while light oil has shown the results are that the rated voltage value is 3.77 V, the pressure value is 0.33bar, and the flow rate value through the tube becomes 5.17 L/M. Thus, the greatest value of the voltage 3.77 V, The result when the flow rate through the pipeline increases rapidly, and the pressure decreases, the pipeline passes through the pipeline faster

Current study generated complexes of the novel heterocyclic ligand 3-(1-methyl-2-((1E,2E)-3-phenylallylidene) hydrazinyl)-5-phenyl-4H-1,2,4-triazole with Cr(lll), Co(ll), and Cu(II). Using techniques including 1H-NMR, mass spectrometry, FT-IR, and elemental analysis, scientists could determine key characteristics of the novel ligand. As spectra, Fourier transforms infrared (FTIR), magnetic susceptibility, atomic absorption, and conductance measurements were used to describe ligand complexes in contrast. Bacteria were used in an assay to test the antibacterial activity of each novel compound (Staphylococcus and Escherichia coli). Quantum chemical simulations performed using the DFT approach at the B3LYP/6-311++G level complemented the experimental data. Using Hyperchem 8.02 and the PM3 approach, this study hasdetermined the ligand's electrostatic potential and its complexes' geometries. The electrostatic potential that reveals useful data about the location's complexity.

Speech recognition is one type of technology, which make a computer to recognize the voice of words that an individual speaks through a microphone and convert it into the written text. In this paper, the proposed system for helping illiterate and blind peoples to open applications with them voices. The proposed system includes two parts; the first part is the training part while the second part is used for the testing. The system contains seven-steps; the first step is the recording of voices and the second step is voice pre-processing. The third step is the feature extraction using MFCC (Mel Frequency Cepstrum Coefficient) method that involves seven steps. The fourth step is for classification voices, there are 1400 voices samples used in training by using a Naïve Bayesian method as a classifier. The fifth step is the matching step using the Correlation Coefficient, there are 200 voices samples in testing. The sixth step was to convert voice into text and the seventh step for execution one of 20 commands. The accuracy of results from using the Naïve Bayesian algorithm in the training phase gives (100 %) while the accuracy of results in the testing phase using Correlation Coefficient gives (98%).