This study focuses on synthesizing Niobium pentoxide (Nb2O5) thin films on silicon wafers and quartz substrates using DC reactive magnetron sputtering for NO2 gas sensors. The films undergo annealing in ambient air at 800 °C for 1 hr. Various characterization techniques, including X-ray diffraction (XRD), atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDS), Hall effect measurements, and sensitivity measurements, are employed to evaluate the structural, morphological, electrical, and sensing properties of the Nb2O5 thin films. XRD analysis confirms the polycrystalline nature and hexagonal crystal structure of Nb2O5. The optical band gap values of the Nb2O5 thin films demonstrate a decrease from 4.74 to 3.73 eV

This work deals with preparation of zeolite 5A from Dewekhala kaolin clay in Al-Anbar region for drying and desulphurization of liquefied petroleum gas. The preparation of zeolite 5A includes treating kaolin clay with dilute hydrochloric acid 1N, treating metakaolin with NaOH solution to prepare 4A zeolite, ion exchange, and formation. For preparation of zeolite 4A, metakaolin treated at different temperatures (40, 60, 80, 90, and 100 °C) with different concentrations of sodium hydroxide solution (1, 2, 3, and 4 N) for 2 hours. The zeolite samples give the best relative crystallinity of zeolite prepared at 80 °C with NaOH concentration 3N (199%), and at 90 and 100°C with NaOH concentration solution 2N (184% and 189%, respectively). Ze

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

The production companies in the Iraqi industry environment facing many of the problems related to the management of inventory and control In particular in determining the quantities inventory that should be hold it. Because these companies  adoption on personal experience and some simple mathematical methods which lead to the identification of inappropriate quantities of inventory.

       This research aims to identify the economic quantity of production and purchase for the Pepsi can 330ml and essential components in Baghdad soft drinks Company in an environment dominated by cases of non ensure and High fluctuating as a result of fluctuating demand volumes and costs ass

            Diabetes mellitus caused by insulin resistance is prompted by obesity. Neuropeptide Nesfatin-1 was identified in several organs, including the central nervous system and pancreatic islet cells. Nesfatin-1 peptide appears to be involved in hypothalamic circuits that energy homeostasis and control food intake. Adiponectin is a plasma collagen-like protein produced by adipocytes that have been linked to the development of insulin resistance (IR), diabetes mellitus type 2 (DMT2), and cardiovascular disease (CVD). Resistin was first identified as an adipose tissue–specific hormone that was linked to obesity and diabetes.  The aim of this study was to estimate the relationship between human serum nesfatin-1, adiponect

Coronavirus 2019 (COVID-19) pandemic led to a massive global socio-economic tragedy that has impacted the ecosystem. This paper aims to contextualize urban and rural environmental situations during the COVID-19 pandemic in the Middle East and North Africa (MENA) Region.

The Iranian nuclear program was one of the most prominent issues in the international arena, in light of Iran's insistence on using it in various ways, and according to international changes, it came under pressure from the American side to impose packages of sanctions, which continued to our time, as it went to Russia and China to gain support in developing its nuclear activities. And, by virtue of the common interests that are linked between them, Iran has received great support from the last two countries in all fields to reach the final agreement in 2015, regarding asserting the peace of these activities, in addition to lifting all restrictions represented in the previous sanctions according to the Security Council resolutions, Aldo

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe