バイオマスガス化におけるタールの触媒的除去及び水素の生産

筑波大学 (University of Tsukuba)201

Battlefield of global ranking : how do power rivalries shape soft power index building?

International competition over soft power has largely transformed from image promotion and cultural diplomacy to benchmark setting. Benchmarks breed discourses and discourses embody power. The article argues that the soft power index building has turned into a battlefield where different values, norms and development models struggle for legitimacy through quasi-scientific validations. By critically examining the methods employed by two soft power indexes, Portland Soft Power 30 Index and China National Image Global Survey, this article unpacks the mechanisms by which institutions from western and emerging (Brazil, Russia, India, China and South Africa (BRICS)) states embed political values, interests and agendas in the selection of data, indicators and treatments of data. The article finds that while the soft power indexes originating from Western organizations largely normalized liberal values and the current international hierarchy, the Chinese national image survey provides a more self-reflective approach to soft power measurement

The significance of M1 macrophage should be highlighted in peripheral nerve regeneration

Macrophage influences peripheral nerve regeneration. According to the classical M1/M2 paradigm, the M1 macrophage is an inhibitor of regeneration, while the M2 macrophage is a promoter. However, several studies have shown that M1 macrophages are indispensable for peripheral nerve repair and facilitate many critical processes in axonal regeneration. In this review, we summarized the information on macrophage polarization and focused on the activities of M1 macrophages in regeneration. We also provided some examples where the macrophage phenotypes were regulated to help regeneration. We argued that the coordination of both macrophage phenotypes might be effective in peripheral nerve repair, and a more comprehensive view of macrophages might contribute to macrophage-based immunomodulatory therapies

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Metallization of hydrogen by intercalating ammonium ions in metal fcc lattices at low pressure

Metallic hydrogen is capable of showing room temperature superconductivity, but its experimental syntheses are extremely hard. Therefore, it is desirable to reduce the synthesis pressure of metallic hydrogen by adding other chemical elements. However, for most hydrides, the metallization of hydrogen by "chemical precompression" to achieve high-temperature superconductivity still requires relatively high pressure, making experimental synthesis difficult. How to achieve high-temperature superconductivity in the low-pressure range (0-50 GPa) is a key issue for realizing practical applications of superconducting materials. Toward this end, this work proposes a strategy for inserting ammonium ions in the fcc crystal of metals. High-throughput calculations of the periodic table reveal 12 elements which can form kinetically stable and superconducting hydrides at low pressures, where the highest superconducting transition temperatures of AlN2H8, MgN2H8 and GaN2H8 can reach up to 118.40, 105.09 and 104.39 K. Pressure-induced bond length changes and charge transfer reveal the physical mechanism of high-temperature superconductivity, where the H atom continuously gains electrons leading to the transition of H+ ions to atomic H, facilitating metallization of hydrogen under mild pressure. Our results also reveal two strong linear scalar relationships, one is the H-atom charge versus superconducting transition temperature and the other is the first ionization energy versus the highest superconducting transition temperature. Besides, ZnN2H8, CdN2H8, and HgN2H8 were found to be superconductors at ambient pressure, and the presence of interstitial electrons suggests that they are also electrides, whose relatively low work functions (3.03, 2.78, and 3.05 eV) imply that they can be used as catalysts for nitrogen reduction reactions as well

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM

Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation

Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201