Two-color resonant two-photon mass-analyzed threshold ionization （MATI） spectroscopy was used to record the vibrationally resolved cation spectra of the selected rotamers of p- ethoxyphenol. The adiabatic ionization...Two-color resonant two-photon mass-analyzed threshold ionization （MATI） spectroscopy was used to record the vibrationally resolved cation spectra of the selected rotamers of p- ethoxyphenol. The adiabatic ionization energies of the trans and cis rotamers are determined to be 61565±5 and 61670±5 cm^-1, which are less than that of p-methoxyphenol by 645 and 643 cm^-1, respectively. Analysis on the MATI spectra of the selected rotamers of p-ethoxyphenol cation shows that the relative orientation of the ethoxy group has little effect on the in-plane ring vibrations. The low-frequency OC2H5 bending vibrations appear to be active for both forms of the cation.展开更多
An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques ha...An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.展开更多
文摘Two-color resonant two-photon mass-analyzed threshold ionization （MATI） spectroscopy was used to record the vibrationally resolved cation spectra of the selected rotamers of p- ethoxyphenol. The adiabatic ionization energies of the trans and cis rotamers are determined to be 61565±5 and 61670±5 cm^-1, which are less than that of p-methoxyphenol by 645 and 643 cm^-1, respectively. Analysis on the MATI spectra of the selected rotamers of p-ethoxyphenol cation shows that the relative orientation of the ethoxy group has little effect on the in-plane ring vibrations. The low-frequency OC2H5 bending vibrations appear to be active for both forms of the cation.
文摘An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.
