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.
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Afamin, which is a human plasma glycoprotein, a putative multifunctional transporter of hydrophobic molecules and a marker for metabolic syndrome. Afamin concentration have been proposed to have a significant role as a predictor of metabolic disorders. Since NAFLD is associated with metabolic risk factors, e.g., dyslipidemia, insulin resistance and visceral obesity, it is considered as the hepatic manifestation of the metabolic syndrome. The objective of this study is to determine Afamin levels in hypothyroid patients with and without fatty liver disease and compare the results with controls. Also to study the relationship of Afamin level with the Anthropometric and Clinical Features (Age, Gender, BMI and Duration of Hypothyroidism) , Serum
... Show MoreThe aim of this study is to describe the leech species that are used in some of the alternative medicine clinics in Baghdad Province based on morphometric measurements and colouring pattern of the body. A collection of twenty leeches was provided from some clinics. All specimens were identified and described based on standard available keys. The morphometric characteristics and colouring patterns were recorded and the indicated that these leeches were Hirudo orientalis Utevsky & Trontelj, 2005 and H. verbana Carena, 1820.
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This research deals with issues of peaceful coexistence in foreign satellite channels directed in the Arabic language, trying to get acquainted with the most prominent of these topics dealt with the programs subject to analysis and the method of dealing with them and the most used journalistic arts in that.
The research adopted the descriptive approach and the method of content analysis for the purpose of studying the research community represented by the program «Shabab Talk» “Youth Talk” in the German channel Deutsche Welle (DW) and the program «Beina Sam wa Amar» “between Sam and Ammar” in the American free channel, by designing the content analysis form to subject
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Determining risk indicators for dental implants is an essential strategy for preventing peri-implant diseases and effective diagnosis of dental implant success. To investigate the impact of certain potential factors on the osseointegrated dental implant. Eighty-four individuals were included in our study, 50 cases as a patient’s group and 34 participants as a control group. All cases were diagnosed based on certain criteria, 30 (60%) of patients had peri-implantitis, 20 (40%) with severe periimplantitis, 36(72%) were generalized, and 15 (30%) as localized peri-implantitis cases. The study has indicated that 44.7% of dental implants were in the anterior maxilla, followed by (27.3%) posterior maxilla, (17.4%) posterior mandible, and (10.4%)
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