Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

From 50 stool samples collected from children with diarrhea of both sexes who visited various hospitals in Baghdad, 26 isolates of E.coli were found to belong to the phylogenetic group E. The findings revealed that the percentage of E.coli for thephylogenetic group E is (52%) , making it the dominant group among the other phylogenetic groups. The findings demonstrated that 100% of the E.coli isolates from phylogenetic group E are resistant to penicillin, and only 15% are resistant to imipenem. Multi-drug resistance (MDR) was found to be 15%, while XDR reached 85%. The results of thephylogenetic group for the remaining species of isolates in this study were group A (2/50 and by 4%), gr

Background: Cardiomyopathy (CM) is a cardiac muscle disorder that can lead to heart failure (HF). It has several phenotypes, including dilated, hypertrophic, and restricted. Pro-inflammatory cytokines play a crucial part in the development and advancement of CM. Objective: This study aimed to measure the concentration of certain cytokines [Interleukin- 1β (IL-1β), Interleukin-6 (IL-6), and Tumor Necrosis Factor (TNF-α)] in the serum of Iraqi patients with CM. Materials and Methods: Sixty CM patients and 30 healthy individuals with age ranged from 40 to 70 years old were enrolled in this study through their presence at Iraqi Center for Heart Diseases, Ghazy Al-Hariri Hospital for Surgical Specialties in the Medical City in Baghdad. The st

With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.