To find out a simple and efficient equation to estimate maize ear grain weight on farm (in situ), twenty three maize crosses along with two synthetics were grown in the field. On the experimental farm of the Dept. of Field Crop Sci., College of Agric., Univ. of Baghdad, seeds of twenty five maize genotypes were grown in the fall season of 2013 with three replicates. At dough stage of the kernels, five naked ears of each experimental units were measured for length and maximum diameter. This will sum up 125 ears of the trial. The volumes of ears were calculated as cylinder (length× r2× 3.1416). Grain weight of all ears were determined after harvesting and drying to 15% grain moisture. A constant was calculated by dividing ear grain weight by each ear volume. Estimated ear grain weights were tested against observed by applying correlation coefficient and it was found to be positive and highly significant (r= 0.998**). The observed and estimated values of ear grain weights were tested by t-test. The two means of observed and estimated ear grain weights were fit to 0.89 probability of t-value. The final equation to estimate ear grain weight in situ is= r2× L× 0.94, where r is radius of ear and L is ear length. However, in case of super hybrids of high ear fertility and kernel filling, estimated ear grain weight will be= r2× L.
Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups. Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials.
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Abstract: The aim of this study was to evaluate the effect of bone density value in Hounsfield unit derived from cone beam computed tomography (CBCT), and implant dimensions in relation to implant stability parameters namely the resonance frequency analysis and the insertion torque (IT) value. It included 24 patients who received 42 dental implants (DI). The bone density of the planned implant site was preoperatively measured using cone beam computed tomography. The implant stability was measured using Osstell implant stability quotient (ISQ). The ISQ values were recorded immediately postoperatively and after 16 weeks. The IT value was categorized as 35 N/cm or > 35 N/cm. The mean (standard deviation) primary stability was 79.58 (5.27) ISQ,
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The aim of this study is to propose reliable equations to estimate the in-situ concrete compressive strength from the non-destructive test. Three equations were proposed: the first equation considers the number of rebound hummer only, the second equation consider the ultrasonic pulse velocity only, and the third equation combines the number of rebound hummer and the ultrasonic pulse velocity. The proposed equations were derived from non-linear regression analysis and they were calibrated with the test results of 372 concrete specimens compiled from the literature. The performance of the proposed equations was tested by comparing their strength estimations with those of related existing equations from literature. Comparis
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre
... Show MoreThe manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co
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Background: The frontal sinus area can be used as a diagnostic aid to recognize mouth breather subjects. The aims of this study were to determine the gender difference in each group, to compare the frontal sinus area between mouth breather and nasal breather group, and to verify the presence of correlation between the frontal sinus area and the cephalometric skeletal measurements used in this study. Materials and Methods: Cephalometric radiographs were taken for 60 adults (30 mouth breathers and 30 nasal breathers) age range (18-25), for each group 15 males and 15 females, in the orthodontic clinic in the college of Dentistry at Baghdad University. The control group (nasal breather) with skeletal class I and ANB angle ranged between 2-4º,
... Show MoreEFFECT OF SPRAYING IRON AND ZINC CONCENTRATIONS IN GRAIN AND LEAF CONTENT FOR TWO VARIETIES OF WHEAT CROP