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.
This work describes the enhancement of phenol red decolorization through immobilizing of laccase in chitosan and enzyme recycling. Commercial laccase from white rot fungus, Trametesversicolor (Tvlac), was immobilizedin to freshly prepared chitosan beads by using glutaraldehyde as a cross linker. Characterization of prepared chitosan was confirmed by FTIR and scanning electron microscope (SEM). Tvlac (46.2 U/mL) immobilized into chitosan beads at 0.8 % glutaraldehyde (v/v) within 24 hrs. Synthetic (HBT) and natural (vanillin) mediators were used to enhance dye decolorizoation. It was found that 89 % of phenol red was decolorized by chitosan beads within 180 min. in the absence of enzyme and mediator, while decolorization percenta
... Show MoreThe purpose of this paper is to examine absorbance for the removal of the Red Congo using wheat husk as a biological pesticide. Several experiments have been conducted with the aim of configuring breakthrough data in a fluidized bed reactor. The minimum fluidized velocities of the bed were found to be 0.031 mm/s for mish sizes of (250) µm diameter with study the mass transfer be calculated KL values. The results showed a well-fitting with the experimental data. Different operating conditions were selected: bed height (2, 5 and 10) cm, flow rate (90, 100and 120) ml/sec and particle diameter (250, 600, 1000) µm. The breakthrough curves were plotted for Congo Red, Values showed that the lower the bed, the lower the number of ad
... Show MoreThis study investigated the shear performance of concrete beams with GFRP stirrups vs. traditional steel stirrups. Longitudinal glass fiber‐reinforced polymer (GFRP) bars were used to doubly reinforce the tested beams at both the top and bottom of their cross sections. To accomplish this, several stirrup spacings were provided. Eight beam specimens, measuring 300 × 250 × 2400 mm, were used in an experimental program to test under a two‐point concentrated load with an equal span‐to‐depth ratio until failure. Four beams in Group I have standard mild steel stirrups of 8 mm diameter, while four beams in Group II have GFRP stirrups with the same adopted diameter. The difference betwe
In this study, silver nanoparticles (AgNPs) were synthesized using a cold plasma technique and a plasma jet. They were then used to explore how photothermal treatment may be used to treat lung cancer (A549) and normal cells (REF) <i>in vitro</i>. The anti-proliferative activity of these nanoparticles was studied after A549 cells were treated with (AgNPs) at various concentrations (100%, 50%, or 25%) and exposure times (6 or 8 min) of laser after 1 h or 24 h from exposed AgNPs. The highest growth inhibition for cancer cells is (75%) at (AgNPs) concentration (100%) and the period of exposure to the laser is (8 min). Particle size for the prepared samples varied according to the diameter o
... Show MoreBreast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer. This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients. The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study. The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there were
... Show MoreStick-slip is kind of vibration which associated with drilling operation in around the bottom hole assembly (BHA) due to the small clearance between drill string & the open hole and due to the eccentric rotating of string. This research presents results of specific experimental study that was run by using two types of drilling mud (Fresh water Bentonite & Polymer), with/without Nanoparticle size materials of MgO in various ratios and computes the rheological properties of mud for each concentration [Yield point, plastic viscosity, Av, PH, filter loss (30 min), filter cake, Mud Cake Friction, Friction Factor]. These results then were used to find a clear effects of Nanoparticle drilling mud rheology on stick - slip strength by sev
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