This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

The study aims at  evaluating  the penalty of  semi- intentional killing felony in the Egyptian and Algerian criminal law following the Islamic Law (Shari'a). The  study used the descriptive, evalutive and analytical  methodology  to  reach the topic in question. To meet the theoretical significance of the study, much data has been collected to give a comprehensive picture about the topic under examination. As for the practical significance of the study, it helps the juridical power to reconsider and phrase the legal materials of the semi-intentional killing penalty based on the Islamic law. The study has come to the conclusions that the Islamic Law (Shari'a) imposes a compensation (blood-money) to be g

Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.

The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Nutrient enrichment of Sawa lake water was made using different nitrogen and phosphorus concentrations during autumn and spring at three stations. Different concentrations of nitrogen, phosphorus and N: P ratios were used to test variations in phytoplankton population dynamics. Nitrogen at a concentration of 25 µmole.l-1 and N: P ratio of 10:1 gave highest phytoplankton cell number at all stations and seasons. A total of 64 algal taxa dominated by Bacillariophyceae followed by Cyanophyceae and Chlorophyceae were identified. The values of Shannon index of diversity were more than one in the studied stations.

Background: Uncontrolled hyperphosphatemia is the main difficulty facing staff treating patients with end-stage renal disease on hemodialysis. Sevelamer and calcium-containing phosphate binders have been associated with cost burden and tissue calcification, respectively. Therefore, the current trial was targeted to investigate the efficacy of a new phosphate binder, ferric citrate, in a sample of Iraqi patients with end-stage renal disease on hemodialysis. Keywords: Ferric citrate, Hemodialysis Phosphate binder