This research is concerned to investigate the behavior of reinforced concrete (RC) deep beams strengthened with carbon fiber reinforced polymer (CFRP) strips. The experimental part of this research is carried out by testing seven RC deep beams having the same dimensions and steel reinforcement which have been divided into two groups according to the strengthening schemes. Group one was consisted of three deep beams strengthened with vertical U-wrapped CFRP strips. While, Group two was consisted of three deep beams strengthened with inclined CFRP strips oriented by 45^o with the longitudinal axis of the beam. The remaining beam is kept unstrengthening as a reference beam. For each group, the variable considered

Gas sensors are essential for detecting noxious gases that have a detrimental effect on people's health and welfare. Carbon quantum dots (CQDs) are the fundamental component of gas detectors. CQDs and graphene (Gr) were prepared using the electrochemical method. The gas sensitivity of these materials was evaluated at different temperatures (150, 200, 250 °C) to assess their effectiveness. Subsequently, experiments were conducted at different temperatures to ascertain that the combination of CQDs and Gr, with various percentages of Gr and CQDs, exhibited superior gas sensitization properties compared to CQDs alone. This was evaluated based on criteria such as sensitivity, recovery time, and reaction time. Interestingly, the combination was

Long-term organic amendments are a key strategy to build soil organic carbon (SOC) stocks in semiarid agroecosystems, where low biomass inputs and calcareous parent material constrain carbon accumulation. This 14-year field experiment in central Iraq (2000–2014) evaluated how a gradient of organic matter (OM) additions (0, 1, 2.5, 5, 10, and 20%) affects SOC dynamics, nutrient availability, and soil organic matter composition in clay-dominated, semiarid soils. Surface and subsurface samples (0–30, 30–60, and 60–90 cm) were analysed for SOC, nutrients, and mid-infrared Fourier transform infrared (FTIR) spectra, which were then integrated with Partial Least Squares (PLS) regression and RothC simulations. Moderate OM inputs (5

Recent studies have tended to look at Mottagorat texts for seeking more of the information provided by the book for the reader, has been known by several terms Mottagorat texts, including thresholds, including the margins of the text and the parallel texts, and came this difference, according to researchers who ate the subject of research and investigation. Thus, the researchers assert that all these texts must be subjected to provide information even if propaganda of the text. Hence arose the importance of these texts in Informatics scientific material being wrapped body of the text as well as being propaganda material to evoke the recipient to read, and then to the importance of this topic, the search came on five chapters, the first c

After the democratic transformation in 2003 in Iraq, young people waited a lot to take their real opportunity to participate in politics, but most of the laws regulating political and electoral work have been unfair to young people, despite developments in laws and legislation, but opportunities did not allow young people to participate in politics, which was reflected It negatively affected their participation in voting and nomination, and even their assumption of public office and positions, which is why they complained about the ruling class and the political system through protests, or when they refused to vote, and this is reflected in the recent parliamentary elections and low participation rates

The nanostructured Manganese dioxide/Carbon fiber (CF) composite electrode was prepared galvanostatically using a facile method of anodic electrodeposition by varying the reaction time and MnSO4 concentration of the electrochemical solution. The effects of these parameters on the structures and properties of the prepared electrode were evaluated. For determining the crystal characteristics, morphologies, and topographies of the deposited MnO2 films onto the surfaces of carbon fibers, the X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and atomic force microscopy (AFM) techniques were used, respectively. It found that the carbon fibers were coated with γ-MnO2 with a density that increased with increasing the de

The electrochemical behavior of carbon steel in water sweetening station in Libya has been studied in the range of ( 293–333 ^oC) using weight loss technique. Measurements were carried out over a range of Reynolds number (5000 – 25000).An apparatus was designed for studying the corrosion process in the turbulent regime, which is of industrial significance. It was found that The corrosion rate of carbon steel in water sweetening station is under diffusion control and increases with increasing Reynolds number. On the other hand the variation of corrosion rate with temperature in the range of (293–333 ^oC) was found to follow Arrhenius equation and the activation energy approximately the same except at low Reynolds

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

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