A large amount of thermal energy is generated from burning hazardous chemical wastes, and the temperature of the flue gases in hazardous waste incinerators reaches up to (1200 °C). The flue gases are cooled to (40°C) and are treated before emission. This thermal energy can be utilized to produce electrical power by designing a system suitable for dangerous flue gases in the future depending on the results of much research about using a proto-type small steam power plant that uses safe fuel to study and develop the electricity generation process with water tube boiler which is manufactured experimentally with theoretical development for some of its parts which are inefficient in experimental work. The studied system gen

The radial wave functions of the Bear–Hodgson potential have been used to study the ground state features such as the proton, neutron and matter densities and the as- sociated rms radii of two neutrons halo 6He, 11Li, 14Be and 17B nuclei. These halo nuclei are treated as a three-body system composed of core and outer two-neutron (Core + n + n). The radial wave functions of the Bear–Hodgson potential are used to describe the core and halo density distributions. The interaction of core-neutron takes the Bear–Hodgson potential form. The outer two neutrons of 6He and 11Li interact by the realistic interaction REWIL whereas those of 14Be and 17B interact by the realistic interaction of HASP. The obtained results show that this model succee

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

A statistical optical potential has been used to analyze and
evaluate the neutron interaction with heavy nuclei 197Au at the
neutron energy range (1-20 MeV). Empirical formulae of the optical
potentials parameters are predicted by using ABAREX Code with
minimize accuracy compared with experimental bench work data.
The total elastic, absorption, shape elastic and total compound crosssections are calculated for different target nuclei and different
incident neutron energies to predict the appropriate optical
parameters that suit the present interaction. Also the dispersion
relation linking between real and imaginary potential is analyzed
with more accuracy. The results indicate the behavior of the
dispersion c

Background: Cholera has been recognized as a killer disease since earliest time. The disease is caused by infection of the small intestine by Vibrio cholerae O1 and O1391 which is characterized by severe dehydrating diarrheal condition and is one disease in modern times that is epidemic, endemic and pandemic in nature. Objective: This study was carried out to detect and isolate V. cholerae from patients suffered from watery diarrhea, which may cause severe complications such as dehydration, shock followed by death. Materials and methods: stool specimens were collected from 308 patients with watery diarrhea. These samples were tested with many criteria such as TCBS agar, gram stain, biochemical tests and VITEK-2 system to improve the isolati

In this paper, a FPGA model of intelligent traffic light system with power saving was built. The intelligent traffic light system consists of sensors placed on the side's ends of the intersection to sense the presence or absence of vehicles. This system reduces the waiting time when the traffic light is red, through the transition from traffic light state to the other state, when the first state spends a lot of time, because there are no more vehicles. The proposed system is built using VHDL, simulated using Xilinx ISE 9.2i package, and implemented using Spartan-3A XC3S700A FPGA kit. Implementation and Simulation behavioral model results show that the proposed intelligent traffic light system model satisfies the specified operational req

The objective of the investigation was to analyze the structure and administration of the political system in Iraq (post-ISIS). After 2003, the Iraqi political system suffered the fundamental problem of its failure to achieve the political and social inclusion that characterizes democratic systems, to guarantee the establishment of a "state for all", while respecting differences. Political representation has moved from the system of sectarian ethnic components, under the title of consensual democracy, to the representation of leaders and the realization of their interests and the interests of their parties at the expense of the groups that claim to represent them, which complicates the problem. In this sense, the new political syste

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi