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.
Radioactive elements were identified in samples of imported coffee consumed in the province of Basra using gamma spectrometry SAM940TM. It is a scintillation detector of NaI(Tl) crystal and the dimensions of 2×2 inch. We have identified specific concentration As(Bq/kg) and annual effective dose D(Sv/y) for radioactive elements (_^40)K, (_^131)I, (_^134)Cs and (_^137)Cs. The estimated average effective dose for adults from coffee samples were found to be 0.037mSv/y, 88.434nSv/y, 46.909nSv/y, 27.212nSv/y for ((_^40)K,(_^131)I,(_^134)Cs,(_^137)Cs) respectively. The present results of the study revealed that the radioactivity was relatively low in the coffee and within the permissiblelimit.
Reconstruction project management in the cities of Mosul, Anbar, and Tikrit, in Iraq still faces major obstacles that impede the comprehensive performance of these projects. It is thus necessary to improve the arising challenge estimation in the implementation of reconstruction projects and evaluate their components: time, cost, quality, and scope. This study used the Analytical Hierarchy Process (AHP) to prioritize major and minor criteria in the influential causes of challenges and formulate a mathematical model to help decision-makers estimate them. Using the Super Decisions software, the final results indicated that changes in scope reached 40.8%, which is the greatest difficulty, followed by changes in cost at 27.6%, changes in
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The foreguts of a total of 515 fish of Chondrostoma regium (Heckel, 1843) (locally: Bala’aot Malloky) were studied. These fish were collected from Tigris River at Salah Al-Deen Province (between Al-Hagag & Yathrib) for 20 months between March and October of the next year. Detritus, plant in origin materials (19.6%, 23.0% & 24.9%); green and blue green algae, mostly Cladophora, Cosmarium and Merismpedia sp. (17.1%, 12.9% & 12.2%) and diatoms, mostly Diatoma, Chanathes, Amphora and Cyulbella sp. (16.9%, 8.8% & 8.2%) were the main food categories taken by these fishes according to occurrence (O%), volumetric methods (V%) and ranking index (R%). Debris (not part of the diet) took 45.3% of the studied fish foreguts by volume. Detritus was also
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The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed a great competence of the proposed WELM compared to the ELM.
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HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
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This study was conducted in 2018, at Technical College of Applied Sciences, Sulaimani Polytechnic University, Kurdistan Region-Iraq. The aim of the study was to determinate nutritional compositions and some elemental contents in marketable white button mushroom (Agaricus bisporus) that is collected in local markets of Kurdistan Region-Iraq. Five different samples (A: Penjwen product fresh, B: Sulaimani product fresh, C: American caned, D: Valencia Netherlandcaned and E: Erbil product fresh) were collected to be observed. The elements were analyzed by Atomic Absorption Spectrometry methods, and their chemical compassions were determined, too. The collected data were analyzed by One Way ANOVA. The highest fat, protein, fiber and d
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This study was conducted in 2018, at Technical College of Applied Sciences, Sulaimani Polytechnic University, and Kurdistan Region-Iraq. The aim of the study was to determinate nutritional compositions and some elemental contents in marketable white button mushroom (Agaricus bisporus) that is collected in local markets of Kurdistan Region-Iraq. Five different samples (A: Penjwen product fresh, B: Sulaimani product fresh, C: American caned, D: Valencia Netherlandcaned and E: Erbil product fresh) were collected to be observed. The elements were analyzed by Atomic Absorption Spectrometry methods, and their chemical compassions were determined, too. The collected data were analyzed by One Way ANOVA. The highest fat, protein, fiber and dry matte
... Show MoreCurrently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of
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Limestones have considerable commercial importance because they are used as building stones and are widely used for flooring and interior and exterior facings. On the other hand, the reserve calculation reveals the economic effectiveness of the investigation. This study aims to calculate the reserve of the middle Miocene limestone for engineering purposes. The limestone beds of the Nfayil Formation in Central Iraq have been studied over 15 outcrop sections. The Nfayil bed has an average thickness of about 1.64 m, while the overburden has an average of about 0.93 m. The average bulk density of limestone is 2.1 gm/cm3 . Kriging and triangulation method has been adopted and used in the calculation and assessment of reserve. The industrial laye
... Show MoreBackground: Energy drinks are non alcoholic beverages which contain stimulant drugs chiefly caffeine and marketed as mental and physical stimulators. Consumption of energy drinks is popular practice among college students as they are exposed to academic stress. Caffeine which is the main constituent of energy drinks could become an addictive substance or cause intoxication. Objectives: This study aims to assess the prevalence of energy drinks consumption among medical students of alkindy college of Medicine.Type of the study: A cross sectional study.Methods: It was performed at alkindy medical college on March 2016. A total number of 600 students were contacted to participate in this study. A self administered questionnaire was used to c
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