| 2 | Study on the synthesis of spin labeled poly(styrene-co-maleic acid)s and their segmental motion显示文摘Study of the segmental mobility of polymer chains is important when the polymer is used as drag reduction agents of crude oil.Electron spin resonance(ESR)spectroscopy can provide important information about segmental dynamics of polymer chains,which is related to their microenvironment.In this article,we employed an amphiphilic polymer to study the effect of hydrophilic/hydrophobic balance of the polymers on the segmental motion of polymer chains.Poly(styrene-co-maleic acid)(PSMA)was spin labeled with 4-amino TEMPO radicals by increasing the concentration of radical moiety on the polymer chains.The PSMA and spin labeled-PSMAs(SL-PSMAs)were characterized by nuclear magnetic resonance(NMR),Fourier transform infrared(FT-IR)spectroscopy,Cyclic voltammetry(CV)and ESR techniques.Inter-polymer complexes(IPCs)of SL-PSMA-2 were prepared by employing polyethylene glycols(PEGs)of varying molecular weights.The results showed that the increased hydrophobic interactions of nitroxide radicals on the SLPSMAs’chains reduced the rotational mobility of spin labels and the random coil-toglobular transition of polymer chains occurred at higher pH value for SL-PSMAs,which showed a slow motion component in the ESR spectra of SL-PSMAs.Further,by increasing the molecular weight of PEGs in IPCs the complexation was increased,which also reduced the rotational motion of spin labels due to interpolymer hydrogen bonding causing a slow motion component in the ESR spectra.©2021 The Authors. | Kaleem-Ur-Rahman Naveed Li Wang Haojie Yu Qian Zhang Wei Xiong Raja Summe Ullah Ahsan Nazir Muhammad Usman Shah Fahad Amin Khan Md Alim Uddin Di Shen | 2022 | Magnetic Resonance Letters2022，2，2： | 1 |
| 4 | An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification显示文摘Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique. | Ahsan Aziz Muhammad Attique Usman Tariq Yunyoung Nam Muhammad Nazir Chang-Won Jeong Reham R.Mostafa Rasha H.Sakr | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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