The catalytic hydrogenation of nitrobenzene to aniline employing nickel impregnated on rutile,anatase,and high surface area titania supports has been investigated.The nickel is present in elemental state as fcc phase ...The catalytic hydrogenation of nitrobenzene to aniline employing nickel impregnated on rutile,anatase,and high surface area titania supports has been investigated.The nickel is present in elemental state as fcc phase on the catalyst as evidenced by X-ray diffraction results.The Ni crystallite size was found to be greater for Ni/anatase.The temperature-programmed reduction results suggest a greater metal-support interaction for Ni/rutile.The observed order of catalytic activity for the hydrogenation of nitrobenzene is Ni/rutile > Ni/anatase > Ni/TiO2.A conversion of 99% was observed for Ni/rutile at 140 oC and hydrogen pressure of 1.96 MPa.Interestingly,aniline is the only product formed which demonstrates the catalytic hydrogenation of nitrobenzene proceeds with atom economy.Both Ni/rutile and Ni/anatase exhibited a better stability than Ni/TiO2.The hydrogenation proceeds with the preferential adsorption of hydrogen on nickel present in the catalyst surface,possibly assisted by TiOx species.展开更多
Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine...Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.展开更多
基金supported by Department of Science and Technology,Government of India
文摘The catalytic hydrogenation of nitrobenzene to aniline employing nickel impregnated on rutile,anatase,and high surface area titania supports has been investigated.The nickel is present in elemental state as fcc phase on the catalyst as evidenced by X-ray diffraction results.The Ni crystallite size was found to be greater for Ni/anatase.The temperature-programmed reduction results suggest a greater metal-support interaction for Ni/rutile.The observed order of catalytic activity for the hydrogenation of nitrobenzene is Ni/rutile > Ni/anatase > Ni/TiO2.A conversion of 99% was observed for Ni/rutile at 140 oC and hydrogen pressure of 1.96 MPa.Interestingly,aniline is the only product formed which demonstrates the catalytic hydrogenation of nitrobenzene proceeds with atom economy.Both Ni/rutile and Ni/anatase exhibited a better stability than Ni/TiO2.The hydrogenation proceeds with the preferential adsorption of hydrogen on nickel present in the catalyst surface,possibly assisted by TiOx species.
文摘Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.
