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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Objective: To determine the effectiveness of the Nursing Management Education Program upon the Nurse
Managers' Knowledge about planning in Nursing Management at Baghdad City Hospitals.
Methodology: A Quasi-experimental study in which a purposive "non-probability" sample of (50) Nurse
Managers was selected from Baghdad City Hospitals. These Managers were divided into two equal groups of
(25) Nurse Managers for each (study and control ones). A questionnaire was developed as a tool for data
collection. A pilot study was carried out to test the reliability of the questionnaire for the period from April 2nd to
June 20th 2007. The pre-post test approach was applied to the study and control groups as mean for the quasiexperime
A fixed firefighting system is a key component of fire safeguarding and reducing fire danger. It is installed as a permanent component in a structure to protect the entire or a portion of the building and its contents. The study aims to review the previous studies that deal with the evaluation of fire safety measures and their use in resolving problems associated with fire threats in buildings. For this reason, a number of previous studies in this field were reviewed compared with the NFPA code. The findings revealed that regulatory developments over the last several decades had created an atmosphere conducive to innovation. This has resulted in a growth in the number of fixed firefighting system types now obtainable. Th
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Asthma is a chronic respiratory disorder of airways characterized by inflammation, hyperresponsiveness, inflammatory cell infiltration, mucous secretion, and remodelling. Ammi majus is medicinal plant belong to family of Apiaceous which has anti-inflammatory and antioxidant activities. This study designed to investigate of anti-asthmatic activity of alcoholic extract of Ammi majus in improvement of asthma. Forty-eight healthy female mice divided to six groups Group I: the negative control group (distal water only), Group II: Positive control group (ovalbumin group), Group III: Ammi majus (64 mg/kg/day) with sensitization, Group IV:Ammi majus (128 mg/kg/day) w
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The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti
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