Over the past few decades, there has been an increasing demand for bio-based polymers and resins in industrial applications, due to their potential lower cost and environmental impact compared with petroleum-based counterparts. The present research concerns the synthesis of epoxidized palm oil acrylate (EPOLA) from an epoxidized palm oil product (EPOP) as environmentally friendly material. EPOP was acrylated by acrylic acid via a ring opening reaction. The kinetics of the acrylation reaction were monitored throughout the reaction course and the acid value of the reaction mixture reached 10 mg KOH/g after 16 h, indicating the consumption of the acrylic acid. The obtained epoxy acrylate was investigated intensively by means of FTIR and NMR spectroscopy, and the OPEN ACCESSMolecules 2015, 20 14192 results revealed that the ring opening reaction was completed successfully with an acrylation yield about 82%. The UV free radical polymerization of EPOLA was carried out using two types of photoinitiators. The radiation curing behavior was determined by following the conversion of the acrylate groups. The cross-linking density and the hardness of the cured EPOLA films were measured to evaluate the effect of the photoinitiator on the solid film characteristics, besides, the thermal and mechanical properties were also evaluated.

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Leukemia is a category of cancer that is normally found in blood and bone marrow, and which causes rapid abnormal development in the making of white blood cells than the required amount. The produced white blood cells could be ineffective to fight against harmful infections and can even prejudice or restrict the capability of the bone marrow to generate red blood cells and blood platelets. If this is not diagnosed in the earlier stage, it may start to affect the function of the internal organs and cause death. Normally, entire blood counts image analysis and diagnosis are done manually which is an inaccurate and time-intensive process. In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. Experimental results convey that Hybrid FCM and RF Algorithm attained an accuracy of 99.06%, a sensitivity of 99.4%, and a specificity of 97.8% respectively, and the ROC (Receiver Operating Characteristic) curve shows that the result produced by the Hybrid FCM & RF based Classifier is best suitable in diagnosing the classification of the Acute Leukemia disease. The tool used for developing the proposed method was Matlab R2018 software.