The Esterification kinetics of acetic acid with ethanol in the presence of sulfuric acid as a homogenous catalyst was studied with isothermal batch experiments at 50-60°C and at a different molar ratio of ethanol to acetic acid [EtOH/Ac]. Investigation of kinetics of the reaction indicated that the low of [EtOH/Ac] molar ratio is favored for esterification reaction, this is due to the reaction is catalyzed by acid. The maximum conversion, approximately 80% was obtained at 60°C for molar ratio of 10 EtOH/Ac. It was found that increasing temperature of the reaction, increases the rate constant and conversion at a certain mole ratio, that is due to the esterification is exothermic. Activity coefficients were calculated using UNIFAC program. Results showed deviation in activation energy in the non-ideal system of about 20% this is due to the polarities of water and ethanol compared to the non-polar ethyl acetate this dissimilarity leading to strong non- ideal behavior. The homogenous reaction has been described with simple power-law model. The chemical equilibrium combustion calculated form the kinetic model in agreement with the measured chemical equilibrium.
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Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.
This was a cross‐sectional study based on electronic survey data that was collected in Iraq during December first‐19th, 2020. The electronic surve
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Chronic kidney disease is one of the leading public health problems that affect millions of women and men worldwide.
This study aims to examine the effect of deep breathing to reduce discomfort amongst patient undergoing haemodialysis (HD).
This randomised controlled experimental study was conducted consisted of 108 patients (54 in each group) who undergoing HD in hospitalised adults’ patients between November 2024 an
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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