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.
With a goal to identify, and ultimately removing from the oil fraction, the carcinogenic components, an oil fraction oil has been analyzed into a main three hydrocarbon groups, paraffins, aromatics, and polycyclic saturates. A multi-stage adsorption apparatus has been used. Four units of 300 g alumina each seems to be sufficient for removing the polynuclear aromatics from 75 g of an oil fraction boiling between 365-375 °C from Qurna crude oil. The usefulness of the ternary diagram for analyzing the oil fraction to the three hydrocarbons groups has been studied and verified. An experimentally based linear relationship of density and refractive index was established to enable of identifying the composition of an oil fraction using th
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An experimental analysis was included to study and investigate the mass transport behavior of cupric ions reduction as the main reaction in the presence of 0.5M H2SO4 by weight difference technique (WDT). The experiments were carried out by electrochemical cell with a rotating cylinder electrode as cathode. The impacts of different operating conditions on mass transfer coefficient were analyzed such as rotation speeds 100-500 rpm, electrolyte temperatures 30-60 , and cupric ions concentration 250-750 ppm. The order of copper reduction reaction was investigated and it shows a first order reaction behavior. The mass transfer coefficient for the described system was correlated with the aid of dimensionless groups as fo
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In this work, pure Polypyrrole (PPy) and Polypyrrole (PPy)/Graphene (GN) was synthesized by in-situ polymerization in different weight percentages (0.1, 0.3, 0.5, 1, 3 and 5 wt.% (g)) of GN nano particles using chemical oxidation method at room temperature. The FTIR, SEM and electrical properties were studies for the nano composites. The result show that when concentration of GN Nano particle increase, the electrical conductivity increased and the graphene sheets were merging to form a continuous area of the GN through the polypyrrole base material. The FTIR spectra shows that the characteristics absorption peaks of polypyrrole that is, 1546.80, 1463.87 and 3400.27 cm-1(stretching vibration in the pyrrol
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Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that
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Genus Salix is among family Salicaceae, distributing in the northern hemisphere. It is represented in Egypt by two species (Salix mucronata and Salix tetrasperma). The classification of Salix at the generic and infra-generic levels is still outstanding. We have agreed to list the Egyptian species of this genus. We collected them during field trips to most Egyptian habitats; fresh and herbarium specimens were subjected to taxonomic revision based on morphological characters; scanning electron microscope (SEM) for pollen grains; isozyme analysis using esterase and peroxidase enzymes and genetic diversity using random amplified polymorphic DNA (RAPD). We recorded that both sexes of S.
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Most cultivars of sorghum have low field emergence as common problem. Three experiments were carried out at Agriculture College, Baghdad University. First experiment was conducted in laboratory according to Complete Randomized Design (CRD) with four replicates. Second and third experiments were conducted at field according to Randomized Complete Block Design (RCBD) during the spring and the fall seasons of 2015, respectively, with four replicates for each one. Same two factors were studied at each experiment. First factor was seed priming by seed soaking for 10 hours in distilled water, solvents of GA3, KCl and thiamine (300, 40 and 30 mg*L-1, respectively) and non-primed seed. Second factor was three cultivars of sorghum (Inqath, Kafier an
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This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions, (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear
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