In this study predication of crop coefficient (Kc) values through growing season for cucumber plant was conducted. A field experiment was carried out at AL Yusufiyah Township, in the Governorate of Baghdad, (latitude: 33°09' N, longitude: 44°24' E, and altitude: 34 m) in medium loam soil. The plant was cultivated inside the greenhouse under subsurface trickle irrigation system with soil water retention technology (SWRT) during the growing season 2017. Crop coefficient values were guessed from the direct method of measurements of daily crop evapotranspiration, while reference evapotranspiration was obtained from Agricultural Meteorology Project - Station of Baghdad - Abu-Ghraib. The obtained results were showed that crop coefficient values for initial, mid and late of season growing stages were: 0.1, 1.29, and 0.9, respectively. Comparison with other local work was showed similar values of Kc. Although the type of irrigation and utilizing new techniques of saving the applied water which reduced the ETc value by about 132 % comparing with other, the reference evapotranspiration value was plays an effecting role in calculating the Kc values.
Many image processing and machine learning applications require sufficient image feature selection and representation. This can be achieved by imitating human ability to process visual information. One such ability is that human eyes are much more sensitive to changes in the intensity (luminance) than the color information. In this paper, we present how to exploit luminance information, organized in a pyramid structure, to transfer properties between two images. Two applications are presented to demonstrate the results of using luminance channel in the similarity metric of two images. These are image generation; where a target image is to be generated from a source one, and image colorization; where color information is to be browsed from o
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The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana
... Show MoreIn this paper, we show that for the alternating group An, the class C of n- cycle, CC covers An for n when n = 4k + 1 > 5 and odd. This class splits into two classes of An denoted by C and C/, CC= C/C/ was found.
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Objective: To compare the efficacy and safety of isosorbide mononitrate (IMN) versus misoprostol used to induce labour for overdue pregnancy.
Setting: A prospective randomized clinical study conducted at AL-Elwiya Maternity Teaching Hospital in Baghdad from Jan. 2008 to Dec. 2008.
Method: One hundred and fifty women with over due pregnancy (past date and posterm pregnancy) referred for induction of labour with Bishop scores <_ 5 were randomly allocated to receive either forty mg isosorbide mononitrate (IMN) tablet as a single vaginal dose (n=75) or fifty mcg misoprostol vaginally (n=75) every six hrs for a maximum of three doses. Amniotomy and/or oxytocin infusion is considered when Bishop scores frankly progressed (augmentation
This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4
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