The objective of this study was to investigate the drought stress and plant density possibility on water productivity and grain yield of maize (Zea mays L.) (Planting Baghdad 3 synthetic varieties), Field experiment was conducted at Abu Ghraib Research Station (Baghdad) during spring and Autumn seasons of 2016 using a randomized complete block design arranged in split plot with three replications. Three irrigation treatment included: irrigation after depletion 50% of available water (T1), irrigation after depletion 75% of available water (T2) and irrigation after depletion 90% of available water (T3) in the main plots and three plant density which were: 1 seeds hill-1 (D1) giving a uniform plant density of 66666 plants ha-1 , 2 seeds hill1 (D2) giving a uniform plant density of 133332 plants ha-1 and 3 seeds hill-1 (D3) giving a uniform plant density of 266664 plants ha-1 assigned in sub plots. The results showed that the plant density of 66666 plants ha-1 gave highest value for most growth and yield components (day's number to 50% male and female flowering, leaf area, dry matter for root and number of ears per plant) for both seasons, but no significant with plant density of 133332 plants ha-1 . Irrigation at depletion 75% of available water was superior in grain yield and most components of growth, also this treatment not significant compare with irrigation at depletion 50% of available water in all parameter of growth and yield of corn. Irrigation at depletion 75% of available water was saving 21.5 and 12.23% depth of water added compare to irrigation at depletion 50% of available water in spring and autumn season, respectively. The irrigation at depletion 75% of available water gave the highest grain yield 9356 kg ha-1 and plant density D1 gave the highest value 8449 kg ha-1 and not difference with D2 8278 kg ha-1 , but increased compare to D3 treatment.
Zinc oxide (ZnO) nanostructures were synthesized through the hydrothermal method at various conditions growth times (6,7 and 8 hrs.) and a growth temperature (70, 90, and 100 ºC). The prepared ZnO nanostructure samples were described using scanning electron microscopy (SEM) and X-ray diffractometer to distinguish their surface morphologies and crystal structures. The ZnO samples were confirmed to have the same crystal type, with different densities and dimensions (diameter and length). The obtained ZnO nanostructures were used to manufacture gas sensors for NO2 gas detection. Sensing characteristics for the fabricated sensor to NO2 gas were examined at different operating temperatures (180, 200, 220, and 240) ºC with a low gas concentrati
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wind load coefficient
The impact of decorating Fe, Ru, Rh, and Ir metals upon the sensing capability of a gallium nitride nanotube (GaNNT) in detecting chlorine trifluoride (CT) was scrutinized using the density functionals B3LYP and B97D. The interaction of the pristine GaNNT with CT was a physical adsorption with the sensing response (SR) of approximately 6.9. After decorating the above-mentioned metals on the GaNNT, adsorption energy of CT changed from −5.8 to −18.6, −18.9, −19.4, and −20.1 kcal/mol by decorating the Fe, Ru, Rh, and Ir metals into the GaNNT surface, respectively. Also, the corresponding SR dramatically increased to 39.6, 52.3, 63.8, and 106.6. This shows that the sensitivity of the metal-decorated GaNNT (metal@GaNNT) increased by in
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In this paper, an experimental study was conducted to enhance the thermal performance of a double-pass solar air heater (SAH) using phase change material (PCM) for thermal storage at climatic conditions of Baghdad city - Iraq. The double-pass solar air heater integrated with thermal storage system was manufactured and tested to ensure that the air heating reserved after the absence of the sun. The rectangular cavity filled with paraffin wax was used as a latent heat storage and incorporated into the lower channel of solar air heater. Experiments were carried out to evaluate the charging and discharging characteristics of two similar designed solar air collectors with and without using phase change material at a constant
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Strengthening of the existing structures is an important task that civil engineers continuously face. Compression members, especially columns, being the most important members of any structure, are the most important members to strengthen if the need ever arise. The method of strengthening compression members by direct wrapping by Carbon Fiber Reinforced Polymer (CFRP) was adopted in this research. Since the concrete material is a heterogeneous and complex in behavior, thus, the behavior of the confined compression members subjected to uniaxial stress is investigated by finite element (FE) models created using Abaqus CAE 2017 software. The aim of this research is to study experimentally and numerically, the beha
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A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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