We introduce a method to study anisotropic flow parameter v n as a collective probe to Quark Gluon Plasma in relativistic heavy ion collisions. The emphasis is put on the use of the Fourier expansion of initial spatia...We introduce a method to study anisotropic flow parameter v n as a collective probe to Quark Gluon Plasma in relativistic heavy ion collisions. The emphasis is put on the use of the Fourier expansion of initial spatial azimuthal distributions of participant nucleons in the overlapped region. The coefficients ε n of Fourier expansion are called the spatial anisotropy parameter for the n-th harmonic. We propose that collective dynamics can be studied by v n /ε n . In this paper, we will discuss in particular the second （n = 2） and the fourth （n = 4） harmonics.展开更多
The potential capability of low coherence backscattering(LBS) is explored to determine the anisotropy factor based on azimuthal light backscattering map. The scattering intensity signal measured at azimuthal angle φ=...The potential capability of low coherence backscattering(LBS) is explored to determine the anisotropy factor based on azimuthal light backscattering map. The scattering intensity signal measured at azimuthal angle φ=0° is extracted for analysis. By performing nonlinear regression fitting on the experimental signal to the Henyey-Greenstein phase function, the anisotropy factor is determined. The experiments with tissue phantom consisting of the aqueous suspension of polystyrene microspheres are carried out. The results show that the measured anisotropy factor is well described by Mie theory.展开更多
Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study i...Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study investigates the application of hard and fuzzy clustering algorithms for domain delineation, using geological and geochemical data from two exploration campaigns at the eastern Kahang deposit in central Iran. The dataset includes geological layers (lithology, alteration, and mineral zones), geochemical layers (Cu, Mo, Ag, and Au grades), and borehole coordinates. Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ranged from 3 to 4. The fuzziness and weighting parameters were found to range from 1.1 to 1.3 and 0.1 to 0.3, respectively, based on the evaluation of various hard and fuzzy cluster validity indices. Directional variograms were computed to assess spatial anisotropy, and the anisotropy ellipsoid for each domain was defined to identify the model with the highest level of anisotropic discrimination among the domains. The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the effectiveness of combining clustering techniques with variogram analysis for accurate domain delineation in geostatistical modeling.展开更多
基金Supported by Knowledge Innovation Program of Chinese Academy of Sciences (kjcx2-yw-a14)NSFC (11005083)+3 种基金Sub-topics of 973 for Ministry of Science and Technology (2008CB817707)Key Laboratory of Quark and Lepton Physics (Huazhong Normal University)Ministry of Education, China (QLPL2009P01)Guided Project B, Educational Commission of Hubei Province of China (B20101103)
文摘We introduce a method to study anisotropic flow parameter v n as a collective probe to Quark Gluon Plasma in relativistic heavy ion collisions. The emphasis is put on the use of the Fourier expansion of initial spatial azimuthal distributions of participant nucleons in the overlapped region. The coefficients ε n of Fourier expansion are called the spatial anisotropy parameter for the n-th harmonic. We propose that collective dynamics can be studied by v n /ε n . In this paper, we will discuss in particular the second （n = 2） and the fourth （n = 4） harmonics.
基金supported by the National Natural Science Foundation of China(No.61108086)the Natural Science Foundation of Chongqing(Nos.2011BB5066 and 2012jj A0612)+3 种基金the Chongqing City Science and Technology Plan(No.cstc2012gg-yyjs0572)the Fundamental Research Funds for the Central Universities(Nos.CDJZR10160003 and CDJZR13160008)the China Postdoctoral Science Foundationthe Chongqing Postdoctoral Science Special Foundation of China
文摘The potential capability of low coherence backscattering(LBS) is explored to determine the anisotropy factor based on azimuthal light backscattering map. The scattering intensity signal measured at azimuthal angle φ=0° is extracted for analysis. By performing nonlinear regression fitting on the experimental signal to the Henyey-Greenstein phase function, the anisotropy factor is determined. The experiments with tissue phantom consisting of the aqueous suspension of polystyrene microspheres are carried out. The results show that the measured anisotropy factor is well described by Mie theory.
文摘Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study investigates the application of hard and fuzzy clustering algorithms for domain delineation, using geological and geochemical data from two exploration campaigns at the eastern Kahang deposit in central Iran. The dataset includes geological layers (lithology, alteration, and mineral zones), geochemical layers (Cu, Mo, Ag, and Au grades), and borehole coordinates. Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ranged from 3 to 4. The fuzziness and weighting parameters were found to range from 1.1 to 1.3 and 0.1 to 0.3, respectively, based on the evaluation of various hard and fuzzy cluster validity indices. Directional variograms were computed to assess spatial anisotropy, and the anisotropy ellipsoid for each domain was defined to identify the model with the highest level of anisotropic discrimination among the domains. The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the effectiveness of combining clustering techniques with variogram analysis for accurate domain delineation in geostatistical modeling.
