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| 1 | Morphology and mechanical performance between the skin surface of Rana dybowskii and Bufo gargarizans显示文摘Anuran skin is a typical natural biomaterial with multifunctional features.A specific comparison of mechanical performance and morphology related to them was performed in the skin of Rana dybowskii and Bufo gargarizans using the tensile testing technique and morphological equipment.Rana dybowskii's skin has soft smooth surface covered by polygonal epidermal cells,while the Bufo gargarizans species has tough and uneven skin surface due to numerous verrucae structures.The collagen fibre bundles in lower dermis of Bufo gargarizans have wavelike organisation while the bundles of Rana dybowskii show a parallel arrangement.The mean elastic modulus of Rana dybowskii was nine times higher than that of Bufo gargarizans.This study clarified that the arrangement of collagen fibres play an important role in the strength and elasticity of skin material. | Mo Li Chunyu Du Jili Wang Zibo Gao Xiao Yang Donghui Chen Jin Tong Lili Ren | 2021 | Biosurface and Biotribology2021，7，3： | 0 |
| 2 | ABSORPTION SPECTRA OF 4f ELECTRON TRANSITIONS OF NEODYMIUM COMPLEX WITH 8-HYDROXYQUINOLINE AND OCTYLPHENOL POLY(ETHYLENEGLYCOL)ETHER显示文摘In this paper the absorption spectra of 4f electron transitions of the neodymlum complex with 8-hydroxyquinoline and octylphenol poly(ethyleneglycol)ether have been studied. The marked intensification of the band at low octylphenol poly(ethyleneglycol)ether concentration is found normally at 575 nm, and its resolution into three sharp bands centering at 572, 580 and 584 nm. The absorbances of the absorption maxima are 3.5 (at 572 nm), 7.2 (at 580 nm) and 10.2 (at 584 nm) times greater than that of the chloride. | Nai Xing WANG and Jian Guo GAO Department of Chemistry, Shandong University, Jinan, 250100 *Research Institute of Qilupetro-Chemical Co, Zibo, 255434 | 1992 | Chinese Chemical Letters1992，3，10： | 0 |
| 3 | Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN显示文摘Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution(SISR),there are two underlying limitations to existing methods.First,current supervised learningbased SISR methods for remote sensing satellite imagery do not use paired real sensor data,instead operating on simulated high-resolution(HR)and low-resolution(LR)image-pairs(typically HR images with their bicubic-degraded LR counterparts),which often yield poor performance on real-world LR images.Second,SISR is an ill-posed problem,and the super-resolved image from discriminatively trained networks with l p norm loss is an average of the infinite possible HR images,thus,always has low perceptual quality.Though this issue can be mitigated by generative adversarial network(GAN),it is still hard to search in the whole solution-space and find the best solution.In this paper,we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution.In addition,we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space.Therefore,the super-resolved images have not only high fidelity,but high perceptual quality as well.Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data.Additionally,the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods. | Junwei Wang Kun Gao Zhenzhou Zhang Chong Ni Zibo Hu Dayu Chen Qiong Wu | 2021 | Journal of Remote Sensing2021，，1： | 0 |
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