Globally, buildings use about 40% of energy. Many elements, such as the physical properties of the structure, the efficiency of the cooling and heating systems, the activity of the occupants, and the building’s sustainability, affect the energy consumption of a building. It is really difficult to predict how much energy a building will need. To improve the building’s sustainability and create sustainable energy sources to reduce carbon dioxide emissions from fossil fuel combustion, estimating the building's energy use is necessary. This paper explains the energy consumed in the lecture building of the Al-Khwarizmi College of Engineering, University of Baghdad (UOB), Baghdad, Iraq. The weather data and the building construction information were collected for a specific period and put into a specific data set. That data was used to find the value of energy consumption in the building using artificial intelligence and data analysis. A Python library called Scikit-learn is used to implement machine learning algorithms. In particular, the Multi-layer Perceptron regressor (MLPRegressor) algorithm was used to predict the consumption. The importance of this work lies in predicting the amount of energy consumed. The outcomes of this work can be used to predict the energy consumed by any building before it is built. The used methodology shows the ability to predict energy performance in educational buildings using previous results and train the model on them, and prediction accuracy depends on the amount of data available for the training in artificial intelligence (AI) steps to give the highest accuracy. The prediction was checked using root-mean-square error (RMSE) and coefficient of determination (R²) and we arrived at 0.16 and 0.97 for RMSE and R², respectively.
The increase in population resulted in an increase in the consumption of water. The present work investigates the performance of a recycling solar- powered greywater treatment system for the purposes of irrigation, used to reduce the amount of waste grey water and reduce electricity consumption and reduce the costs of constructing large scale water treatment plants. The system consumes about 3814W per hour and provides water treatment about 1.4 m3 per day. The proposed system is designed to residential, office and governmental buildings application. Tests are conducted in an office building at the Ministry of Science and Technology site in Baghdad. Laboratorial water samples testing analyses are co
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Moisture-induced damage is a serious problem that severely impairs asphaltic pavement and affects road serviceability. This study examined numerous variables in asphalt concrete mixtures to assess their impact on moisture damage resistance. Mix design parameters such as the asphalt content (AC) and aggregate passing sieve No. 4 (PNo. 4) were considered as variables during this study. Additionally, hydrated lime (HL) was utilized as a partial substitute for limestone dust (LS) filler at 1.5% by weight of the aggregate in asphalt concrete mixtures for the surface layer. This study also investigated the potential enhancement of traditional asphalt binders and mixtures by adding nano-additives, specifically nano-silica oxide (NS) and na
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The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations. A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for cell
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A rapid high performance liquid chromatography method for the determination of sphinganine (Sa) and sphingosine (So) in urine samples by employing a silica-based monolithic column is described. The samples were first extracted using ethyl acetate and derivatized using ortho-phthaldialdehyde in the presence of 2-mercaptoethanol. C20 sphinganine was used as internal standard. Under the optimized conditions, separation was achieved using a mixture of methanol:water (93:7, v/v), column temperature at 30°C, flow rate of 1 mL min−1, and an injection volume of 10 μL. Good linearity was obtained for Sa and So over the concentration range 20–500 ng mL−1(correlation coefficients ≥0.9978). The detection limits were 0.45 ng mL−1 for Sa and
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The current study aims to assess the effectiveness of the cognitive-behavioral programs in reducing stuttering and social anxiety among high-school students. The researchers used the experimental design. The sample consists of (20) male students who reported the highest score on the stuttering severity scale and social anxiety scale. The sample was divided into experimental and control groups (each group consists of 10 participants). The researcher used the type and severity of stuttering scale developed by Onslow et al (2003), translated by Mahmoud Ismail and the social anxiety scale was prepared by the authors. The results showed that there are statistically significant differences in pre-post and follow-up tests amongst the experiment
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