Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

Objective: To Evaluate the Roley of Cytotoxic T-Lymphocytek antigen 4 Polymorphism and soluble immune checkpoint level (PD-1,PDL-1 and CTLA-4 ) in SARS-Cov-2 patients. Methods: Fromt October 2020 to April 2021, the currentk study was conducted in Baghdad-Iraq. Ninety patients with Confirmatory SARS-Cov-2 by PCR were inclusion in the study, and they were seeking treatment at Medical City in Baghdad's Teaching Hospital (BTH). Patients with SARS-Cov-2 were divided into two groups: those with Sever SARS-Cov-2 symptom and those with mild - moderate SARS-Cov-2 symptoms (cross sectional study. Patients with another form of autoimmune illness, malignant, diabetes, under the age of 18 and pregnant women were excluded. Results: Data rega

One hundred of dialysis patients' mean age ( 51.18±8.28) years and one hundred healthy control group , where carried out  from different hospitals of Baghdad city , during the period between November /2012 until March/2013. Blood samples were collected before dialyzing for estimation   the concentration of urea, creatinine, uric acid, random blood sugar , calcium and cholesterol by enzymatic method detected spectrophotometerically.

The  aim of this  study is   to determine concentration of urea, creatinine, uric acid, RBS , calcium and cholesterol in hemodialysis patients in Baghdad . The results showed that there were  highly significant increases (P<0.01) in the mean of creatinine ,

Periodontitis is a chronic inflammatory disease resulted from aggravated immune response to a dysbiotic subgingival microbiota of a susceptible host. Consequences of periodontitis are not only limited to the devastating effect on the oral cavity but extends to affect general health of the individual and also exerts economic burdens on the health systems worldwide. Despite these serious outcomes of periodontitis; however, they are avoidable by early diagnosis with proper preventive measures or non-invasive interventions at earlier stages of the disease. Clinically, diagnosis of periodontitis could be overlooked due to certain limitations of the conventional diagnostic methods such as periodontal charting and radiographs. Utilization of re

Abortion is categorized as the termination of conception caused by the failure or removal of the embryo from the uterus before the conclusion of pregnancy. Microorganisms and genetic factors are two of the many factors associated with abortion. Cytomegalovirus is a widespread congenital virus infection pathogen that affects a wide variety of people. The prothrombin gene is one of the essential causes that trigger blood clotting and the function of abortion women, therefore the aim of the study is to detect and associate Cytomegalovirus and prothrombin gene mutation (Gene ID: 14061 in NCBI) with abortion through genetic and immunological methods. Five ml of whole blood was collected from an intravenous puncture and divided into two tubes,