The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Pore pressure means the pressure of the fluid filling the pore space of formations. When pore pressure is higher than hydrostatic pressure, it is named abnormal pore pressure or overpressure. When abnormal pressure occurred leads to many severe problems such as well kick, blowout during the drilling, then, prediction of this pressure is crucially essential to reduce cost and to avoid drilling problems that happened during drilling when this pressure occurred. The purpose of this paper is the determination of pore pressure in all layers, including the three formations (Yamama, Suliay, and Gotnia) in a deep exploration oil well in West Qurna field specifically well no. WQ-15 in the south of Iraq. In this study, a new appro

The Early-Middle Miocene succession in Iraq is represented by the Serikagni, Euphrates and Dhiban formations, which deposited during the Early Miocene. The Jeribe and Fatha successions were deposited during Middle Miocene age. This study includes microfacies analysis, depositional environments, sequence stratigraphy and basin development of Early – middle Miocene in Hamrin and Ajeel oil fields and Mansuriyha Gas Field. The study area includes four boreholes in three oil fields located in central Iraq: Hamrin (Hr-2) and Ajeel (Aj-13, and 19) oil feilds, and Mansuriyha (Ms-2) Gas Field. Five facies associations were distinguished within the studied fields: deep marine, slop, platform-margin, open marine, restricted interior platform

In this study an illustrated key for the identification of the Iraqi species of the genus Chalcophorella Kerr. is given with information about general distribution for each species. Information is also given about period of collection, plants on which the specimens were collected. The real host plant if they are available and known are also given referring to the previous works.

The research problem lies in identifying the challenges facing spreading the culture of peace in Iraq, and the most important factors of its instability from the point of view of leaders and intellectual elites.The research derives its importance from the fact that the peace imposed by justice leads to a stable Iraq and a prosperous future.

The study aims to identify the most prominent challenges and obstacles created by wars and faced the process of spreading the culture of peace and instability in Iraq, and to shed light on the reasons leading to the employment of the capabilities of community leaders in spreading the culture of peace.The objective of the current research is to identify the challenges of spreading the culture o

Background: Mycoplasma pneumoniae (M. pneumoniae) is an important respiratory bacterial pathogen, especially among children. It causes acute upper and lower respiratory infections.Objective: This study was aimed to measure anti- M. pneumoniae antibodies among hospitalized children who were admitted to hospital diagnosed with acute respiratory tract infections.Method: Automated ELISA technique was performed to detect anti- M. pneumoniae antibodies (IgM and IgG antibodies) in serum from 108 children less than 5 years old. The children were admitted to the Pediatric Teaching Hospital in Suleimani city/Kurdistan Region/Iraq because of acute respiratory tract infections. A questionnaire was designed to collect demographic and clinical data fr