Competing fusion and quasifission reaction mechanisms in the production of superheavy nuclei

Within the framework of a dinuclear system model, a new master equation is constructed and solved, which includes the relative distance of nuclei as a new dynamical variable in addition to the mass asymmetry variable so that the nucleon transfer, which leads to fusion and the evolution of the relative distance, which leads to quasifission (QF) are treated simultaneously in a consistent way. The QF mass yields and evaporation residual cross sections to produce superheavy nuclei are systematically investigated under this framework. The results fit the experimental data well. It is shown that the Kramers formula gives results of QF, which agree with those by our diffusion treatment, only if the QF barrier is high enough. Otherwise some large discrepancies occur.

Influence of entrance channels on the formation of superheavy nuclei in massive fusion reactions

Within the framework of the dinuclear system (DNS) model, the production cross sections of superheavy nuclei Hs (Z=108) and Z=112 combined with different reaction systems are analyzed systematically. It is found that the mass asymmetries and the reaction Q values of the projectile target combinations play a very important role on the formation cross sections of the evaporation residues. Both methods to obtain the fusion probability by nucleon transfer by solving a set of microscopically derived master equations along the mass asymmetry degree of freedom (ID) and distinguishing protons and neutrons of fragments (2D) are compared with each other and also with the available experimental data. (C) 2010 Elsevier B.V. All rights reserved.

Heat capacity and enthalpy of fusion of penconazole (C13H15C12N3)

Low-temperature heat capacities of penconazole (C13H15Cl2N3) were precisely measured with an automated adiabatic calorimeter over the temperature rang from 78 to 364 K. The sample was observed to melt at 332.38 +/- 0.06 K. The molar enthalpy and entropy of fusion of the compound were determined to be 33580 +/- 11 J mol(-1), 101.03 +/- 0.02 J mol(-1) K-1, respectively. Further research of the melting process for this compound was carried out by means of differential scanning calorimetry (DSC) technique. The result was in agreement with that obtained from the measurements of heat capacities. (C) 2003 Elsevier B.V. All rights reserved.

Heat capacity and enthalpy of fusion of pyrimethanil laurate (C24H37N3O2)

Low-temperature heat capacities of pyrimethanil laurate (C24H37N3O2) were precisely measured with an automated adiabatic calorimeter over the temperature range between T = 78 K and T = 340 K. The sample was observed to melt at (321.52 +/- 0.04) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (67244 +/- 11) J (.) mol(-1), (209.28 +/- 0.02) J (.) mol(-1) (.) K-1, (0.9943 +/- 0.0004) mass fraction, respectively. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (322.264 +/- 0.006) K. (C) 2004 Elsevier Ltd. All rights reserved.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Chinese Academy of Sciences ; National Science Foundation of China [41071059]; National Key Technology R&D Program of China [2008BAK50B06-02]; National Basic Research Program of China [2010CB950900, 2010CB950704]; Natural Sciences and Engineering Research Council of Canada

Local histogram based geometric invariant image watermarking

Compared with other existing methods, the feature point-based image watermarking schemes can resist to global geometric attacks and local geometric attacks, especially cropping and random bending attacks (RBAs), by binding watermark synchronization with salient image characteristics. However, the watermark detection rate remains low in the current feature point-based watermarking schemes. The main reason is that both of feature point extraction and watermark embedding are more or less related to the pixel position, which is seriously distorted by the interpolation error and the shift problem during geometric attacks. In view of these facts, this paper proposes a geometrically robust image watermarking scheme based on local histogram. Our scheme mainly consists of three components: (1) feature points extraction and local circular regions (LCRs) construction are conducted by using Harris-Laplace detector; (2) a mechanism of grapy theoretical clustering-based feature selection is used to choose a set of non-overlapped LCRs, then geometrically invariant LCRs are completely formed through dominant orientation normalization; and (3) the histogram and mean statistically independent of the pixel position are calculated over the selected LCRs and utilized to embed watermarks. Experimental results demonstrate that the proposed scheme can provide sufficient robustness against geometric attacks as well as common image processing operations. (C) 2010 Elsevier B.V. All rights reserved.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions

Feature-based image watermarking schemes, which aim to survive various geometric distortions, have attracted great attention in recent years. Existing schemes have shown robustness against rotation, scaling, and translation, but few are resistant to cropping, nonisotropic scaling, random bending attacks (RBAs), and affine transformations. Seo and Yoo present a geometrically invariant image watermarking based on affine covariant regions (ACRs) that provide a certain degree of robustness. To further enhance the robustness, we propose a new image watermarking scheme on the basis of Seo's work, which is insensitive to geometric distortions as well as common image processing operations. Our scheme is mainly composed of three components: 1) feature selection procedure based on graph theoretical clustering algorithm is applied to obtain a set of stable and nonoverlapped ACRs; 2) for each chosen ACR, local normalization, and orientation alignment are performed to generate a geometrically invariant region, which can obviously improve the robustness of the proposed watermarking scheme; and 3) in order to prevent the degradation in image quality caused by the normalization and inverse normalization, indirect inverse normalization is adopted to achieve a good compromise between the imperceptibility and robustness. Experiments are carried out on an image set of 100 images collected from Internet, and the preliminary results demonstrate that the developed method improves the performance over some representative image watermarking approaches in terms of robustness.

Local Feature Based Geometric-Resistant Image Information Hiding

Watermarking aims to hide particular information into some carrier but does not change the visual cognition of the carrier itself. Local features are good candidates to address the watermark synchronization error caused by geometric distortions and have attracted great attention for content-based image watermarking. This paper presents a novel feature point-based image watermarking scheme against geometric distortions. Scale invariant feature transform (SIFT) is first adopted to extract feature points and to generate a disk for each feature point that is invariant to translation and scaling. For each disk, orientation alignment is then performed to achieve rotation invariance. Finally, watermark is embedded in middle-frequency discrete Fourier transform (DFT) coefficients of each disk to improve the robustness against common image processing operations. Extensive experimental results and comparisons with some representative image watermarking methods confirm the excellent performance of the proposed method in robustness against various geometric distortions as well as common image processing operations.