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.
Mixed ligand metal complexes of CrIII, FeIII,II, NiII and CuII have been synthesized using 5-chlorosalicylic acid (5-CSA) as a primary ligand and L-Valine (L-Val) as secondary ligand. The metal complexes have been characterized by elemental analysis, electrical conductance, magnetic susceptibility measurements and spectral studies. The electrical conductance studies of the complexes indicate their electrolytic nature. Magnetic susceptibility measurements revealed paramagnetic nature of the all complexes. Bonding of the metal ion through –OHand –COOgroups of bidentate to the 5-chlorosalicylic acid and through –NH2 and –COOgroups of bidentate to the L-valine by FT-IR studies . The agar diffusion method has been used to study the antib
... Show MoreMixed ligand metal complexes of CrIII, FeIII,II, NiII and CuII have been synthesized using 5-chlorosalicylic acid (5-CSA) as a primary ligand and L-Valine (L-Val) as secondary ligand. The metal complexes have been characterized by elemental analysis, electrical conductance, magnetic susceptibility measurements and spectral studies. The electrical conductance studies of the complexes indicate their electrolytic nature. Magnetic susceptibility measurements revealed paramagnetic nature of the all complexes. Bonding
This study is concerned with the comparison of the results of some tests of passing and dribbling of the basketball of tow different years between teams of chosen young players in Baghdad. Calculative methods were used namely (Arithmetic mean, Value digression and T.test for incompatible specimens). After careful calculative treatments, it has been that there were abstract or no abstract differences in the find results of chestpass, highdribble and cross-over dribble. The clubs were: (Al-Khark, Air defence, Police and Al-Adamiyah) each one separate from the other for the year (2000-2001). After all that many findings were reached such as the lack of objective valuation (periodical tests) between one sport season and the other. In the light
... Show MoreAssessing water quality provides a scientific foundation for the development and management of water resources. The objective of the research is to evaluate the impact treated effluent from North Rustumiyia wastewater treatment plant (WWTP) on the quality of Diyala river. The model of the artificial neural network (ANN) and factor analysis (FA) based on Nemerow pollution index (NPI). To define important water quality parameters for North Al-Rustumiyia for the line(F2), the Nemerow Pollution Index was introduced. The most important parameters of assessment of water variation quality of wastewater were the parameter used in the model: biochemical oxygen demand (BOD), chemical oxygen dem
Polymorphisms in the genes of G-protein subunit beta 3 (GNB3); rs5443, tryptophan hydroxylase 1 (TPH1); rs211105 and rs4537731, tryptophan hydroxylase 2 (TPH2); rs4570625 and sodium voltage-gated channel alpha subunit 5 (SCN5A); rs1805124, have known to cause the abnormalities in the gastrointestinal tract that are implicated to irritable bowel syndrome (IBS) predisposition. Upfront genetic polymorphism genotyping in IBS-related gene polymorphisms will help to intervene and guide the decision-making in the management of IBS patients. This study aimed to develop a genotyping method to detect the respective polymorphisms using nested allele-specific multiplex polymerase chain reaction (NASM-PCR). A combi
... Show MoreSeveral efforts have been made to study the behavior of Total Electron Content (TEC) with many types of geomagnetic storm, the purpose of this research is to study the disturbances of the ionosphere through the TEC parameter during strong, severe and great geomagnetic storms and the validity of International Reference Ionosphere IRI model during these kinds of storms. TEC data selected for years 2000-2013 (descending solar cycle 23 to ascending cycle 24), as available from koyota Japan wdc. To find out the type of geomagnetic storms the Disturbance storm time (Dst) index was selected for the years (2000-2013) from the same website. Data from UK WDC have been taken for the solar indices sunspots number (SSN), radio flux (F10.7) and ionosp
... Show MoreA New Mannich base [N-(4-morpholinomethyl)-1,8-naphthalimide] (L), was synthesized and characterized by C.H.N analysis, FTIR, UV-Vis and 1HNMR spectral analysis. Metal ion complexes of (L) with Pt(IV), Rh(III), Ru(III) and Pd(II) ions were prepared and characterized by FT-IR, and UV-Vis spectroscopy, elemental analysis (C.H.N), flame atomic absorption techniques as well as magnetic susceptibility and conductivity measurements. The results showed that metal ion complexes for all complexes were found in [1:2] [M:L] ratio except for Pd(II) complex which was found in [1:1] [M:L] ratio. Hyperchem-8 program has been used to predict structural geometries of the (L) and it's complexes in gas phase. The electrostatic potential (EP) of the (L) was
... Show MoreCeramic coating compose from a ceramic mixture (MgO, Al2O3) and metall (Al-Ni) were produced by Thermal Spray Technique. The mixed ratio of used materials Al:Ni (50%) and 40% of Al2O3 and 10% MgO. This mixture was spray on a stainless steel substrate of type (316 L) by using thermal spray with flame method and at spraying distances (8, 12, 16 and 20) cm, then the prepared films were treated by laser and thermal treatment. After that performing a hardness and adhesion tests were eximined. The present study shows that the best value of the thermal treatment is 1000 ℃ for 30 mint; the optimum spray distance is 12 cm and most suitable laser is 500 mJ where the microscopic and mechanical character
... Show MoreThis paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi