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.
Erratum for Organic acid concentration thresholds for ageing of carbonate minerals: Implications for CO2 trapping/storage.
Objective: To suggest a weighted measure to diagnose the reasons for the low student success ratios in mathematics concerning the third grade of intermediate schools in light of components educational system represented by: [Students, Teachers, Curriculum, and Environmental reasons (others reasons)] assuming differentiated and interrelated components, Also the effectiveness forming of these components according to the gender variable. Methods: Data collection tools were prepared by constructing two questionnaires for each of (Students and Teachers), which included a number of items that involved some domains for studied components of educational system, which demonstrated a high level of validity and reliability in the pilot study, in addi
... Show MoreThe aim of this work is study the partical distribution function g(r12,r1) for Carbon ion cases (C+2,C+3,C+4) in the position space using Hartree-Fock's Wave function, and the partitioning technique for each shell which is represented by Carbon Ions [C+2 (1s22s2)], [C+3 (1s22s)] and [C+4 (1s2)]. A comparision has been made among the three Carbon ions for each shell. A computer programs (MATHCAD ver. 2001i) has been used texcute the results.
Objective: To determine the ability of uVDBP to discern SRNS from steroid-sensitive nephrotic syndrome (SSNS) in Iraqi children. Materials and Methods: This cross-sectional study enrolled children with SRNS (n=31) and SSNS (n=32) from the pediatric nephrology clinic of Babylon Hospital for Maternity and Pediatrics over three months. Patients' characteristics in terms of demographics, clinical data, and urinary investigations were collected. Quantitative analysis of uVDBP levels was undertaken via a commercially available ELISA kit. Results: The median uVDBP values were significantly higher (p-value<0.001) in the SRNS group (median=10.26, IQR=5.91 μg/mL) than in the SSNS group (median=0.953, IQR=4.12 μg/mL). A negative correlati
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Escherichia coli (E. coli) is a frequent gram-negative bacterium that causes nosocomial infections, affecting more than 100 million patients annually worldwide. Bacterial lipopolysaccharide (LPS) from E. coli binds to toll-like receptor 4 (TLR4) and its co-receptor’s cluster of differentiation protein 14 (CD14) and myeloid differentiation factor 2 (MD2), collectively known as the LPS receptor complex. LPCAT2 participates in lipid-raft assembly by phospholipid remodelling. Previous research has proven that LPCAT2 co-localises in lipid rafts with TLR4 and regulates macrophage inflammatory response. However, no published evidence exists of the influence of LPCAT2 on the gene expression of the LPS receptor complex induced by smooth or rough b
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The emergence of COVID-19 has resulted in an unprecedented escalation in different aspects of human activities, including medical education. Students and educators across academic institutions have confronted various challenges in following the guidelines of protection against the disease on one hand and accomplishing learning curricula on the other hand. In this short view, we presented our experience in implementing e-learning to the undergraduate nursing students during the present COVID-19 pandemic emphasizing the learning content, barriers, and feedback of students and educators. We hope that this view will trigger the preparedness of nursing faculties in Iraq to deal with this new modality of learning and improve it should t
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Al comentar un texto literario no se llega solamente mediante el estudio de su Historia. Ese estudio sería vano, se convertiría en una simple memorización de datos, creo yo.
Comentar un texto supone comprobar las características generales de un movimiento, estilo de un autor... en otras palabras comprender con profundidad el texto literario en s
MJ Abbas, AK Hussein, Journal of Physical Education, 2019
A new, simple, sensitive and fast developed method was used for the determination of methyldopa in pure and pharmaceutical formulations by using continuous flow injection analysis. This method is based on formation a burgundy color complex between methyldopa andammonium ceric (IV) nitrate in aqueous medium using long distance chasing photometer NAG-ADF-300-2. The linear range for calibration graph was 0.05-8.3 mmol/L for cell A and 0.1-8.5 mmol/L for cell B, and LOD 952.8000 ng /200 µL for cell A and 3.3348 µg /200 µL for cell B respectively with correlation coefficient (r) 0.9994 for cell A and 0.9991 for cell B, RSD % was lower than 1 % for n=8. The results were compared with classical method UV-Spectrophotometric at λ max=280 n
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