An investigation was conducted to suggest relations for estimating yield and properties of the improved light lubricating oil fraction produced from furfural extraction process by using specified regression.

Mass transfer in mixer-settler has been studied. Mass transfer coefficient of continuous phase, mass transfer coefficient of dispersed phase and the overall mass transfer coefficient extraction of light lubes oil distillate fraction by furfural are calculated in addition to all physical properties of individual components and the extraction mixtures.

The effect of extraction variables were studied such as extraction temperature which ranges from 70 to 110°C and solvent to oil ratio which ranges from 1:1 to 4:1 (wt/wt

The present work was conducted in the fields of Al-Sewarah and Kurkok stations which belong to the State Board of Agricultural Researches, Ministry of Agriculture, Iraq during the growing season of 2018. The goal of the study was to test the effects of the application of cyanobacteria  (Anabaena circinalis and Nostoc commune) alone or in combination with reducing the dose of chemical fertilizers (CFs), which consisted of diammonium phosphate (DAP) and urea (46% nitrogen), on growth, yield and yield components of wheat cv. IPA99. Application of 50%  and 100% of CFs without cyanobacteria as well as control (without cyanobacteria and CFs) were also included in this study for comparison.

The resul

This study was carried out at University of Baghdad - College of Agricultural Engineering Sciences - Research Station B during the autumn season 2019-2020, in order to evaluate the effect of Ozone and the foliar application of coconut water and moringa extract on the growth of broccoli plant grown in modified NFT film technology. A factorial experiment (2*5) was carried out within Nested Design with three replicates. The ozone treatment was distributed into the main plots which consisted of oxygen (O2) and ozone (O3). The foliar application of organic nutrients were distributed randomly within each replicate including five treatments, which were the control treatment (T0), Coconut water with two concentrations of 50 (T1) and 100 ml.

    Using a reduction of TRIM simulation data, the sputtering yield behaviour of Zinc target bombard by heavy Xenon ions plasma is studied. The sputtering yield as a function of Zinc layer width, Xenon ion number, energy of ions, and the angle of ion incidence are calculated and illustrated graphically. The corresponding energy loss due to ionization, vacancies and phonons, are graphically shown and discussed. Further, we fit the calculations and expressions for fitted curves are presented with its coefficients.

Wheat (Triticum aestivum L.) is one of the Poaceae family (Gramineae). Is momentous for human nutrition, and the stresses can affect strongly on the phenotype characteristics of the plant. The aim of this study was to determine how electric shock on germinated grain (for 2.5, 5, and 7.5 mins.) and heat shock at ( 35 oC, 40 oC and 45 oC) applied after phase out the radical length of 2-5 mm, from the grain of two wheat Varieties: “Baghdad 1” and “Babylon 113”. The electric shock for 2.5 mins., lead to delay of flowering, from day to 50 % flowering was 93 day, as well as, gave lowest value of plant height 64.5cm and lowest spike length was 10.7 cm. While The highest flag leaf area was obtained by electric shock for 5 mins. was 56.6

     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea