Statistical analysis of geomagnetic field variations during the partial solar eclipse on 2011 January 4 in Turkey

Some geophysical parameters, such as those related to gravitation and the geomagnetic field, could change during solar eclipses. In order to observe geomagnetic fluctuations, geomagnetic measurements were carded out in a limited time frame during the partial solar eclipse that occurred on 2011 January 4 and was observed in Canakkale and Ankara, Turkey. Additionally, records of the geomagnetic field spanning 24 hours, obtained from another observatory （in Iznik, Turkey）, were also analyzed to check for any peculiar variations. In the data processing stage, a polynomial fit, following the application of a running average routine, was applied to the geomagnetic field data sets. Geomagnetic field data sets indicated there was a characteristic decrease at the beginning of the solar eclipse and this decrease can be well-correlated with previous geomagnetic field measurements that were taken during the total solar eclipse that was observed in Turkey on 2006 March 29. The behavior of the geomagnetic field is also consistent with previous observations in the literature. As a result of these analyses, it can be suggested that eclipses can cause a shielding effect on the geomagnetic field of the Earth.

Dynamic thermo-mechanical coupled response of random particulate composites:A statistical two-scale method

This paper focuses on the dynamic thermo-mechanical coupled response of random particulate composite materials. Both the inertia term and coupling term are considered in the dynamic coupled problem. The formulation of the problem by a statistical second-order two-scale （SSOTS） analysis method and the algorithm procedure based on the finite-element difference method are presented. Numerical results of coupled cases are compared with those of uncoupled cases. It shows that the coupling effects on temperature, thermal flux, displacement, and stresses are very distinct, and the micro- characteristics of particles affect the coupling effect of the random composites. Furthermore, the coupling effect causes a lag in the variations of temperature, thermal flux, displacement, and stresses.

GIS Analysis of Spatial Distribution of Crop Incidence

Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.

Dynamic analysis and experimental study of vibro-acoustic system with uncertainties at middle frequencies

To take into account the influence of uncetainties on the dynamic response of the vibro-acousitc structure, a hybrid modeling technique combining the finite element method（FE）and the statistic energy analysis（SEA） is proposed to analyze vibro-acoustics responses with uncertainties at middle frequencies. The mid-frequency dynamic response of the framework-plate structure with uncertainties is studied based on the hybrid FE-SEA method and the Monte Carlo（MC）simulation is performed so as to provide a benchmark comparison with the hybrid method. The energy response of the framework-plate structure matches well with the MC simulation results, which validates the effectiveness of the hybrid FE-SEA method considering both the complexity of the vibro-acoustic structure and the uncertainties in mid-frequency vibro-acousitc analysis. Based on the hybrid method, a vibroacoustic model of a construction machinery cab with random properties is established, and the excitations of the model are measured by experiments. The responses of the sound pressure level of the cab and the vibration power spectrum density of the front windscreen are calculated and compared with those of the experiment. At middle frequencies, the results have a good consistency with the tests and the prediction error is less than 3. 5dB.

Spectral Synthesis via Mean Field approach to Independent Component Analysis

We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis（MF-ICA） in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components（ICs）, and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used：（1） parameter recovery for simulated galaxies,（2） comparison with parameters estimated by other methods, and（3） consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.

Pico–nano bubble column flotation using static mixer-venturi tube for Pittsburgh No.8 coal seam

The flotation process is a particle-hydrophobic surface-based separation technique. To improve the essential flotation steps of collision and attachment probabilities, and reduce the step of detachment probabilities between air bubbles and hydrophobic particles, a selectively designed cavitation venturi tube combined with a static mixer can be used to generate very high numbers of pico and nano bubbles in a flotation column. Fully embraced by those high numbers of tiny bubbles, hydrophobic particles readily attract the tiny bubbles to their surfaces. The results of column flotation of Pittsburgh No. 8 seam coal are obtained in a 5.08 cm ID and 162 cm height flotation column equipped with a static mixer and cavitation venturi tube, using kerosene as collector and MIBC as frother. Design of the experimental procedure is combined with a statistical two-stepwise analysis to determine the optimal operating conditions for maximum recovery at a specified grade. The effect of independent variables on the responses has been explained. Combustible material recovery of 85–90% at clean coal product of 10–11% ash is obtained from feed of 29.6% ash, with a much-reduced amount of frother and collector than that used in conventional column flotation. The column flotation process utilizing pico and nano bubbles can also be extended to the lower limit and upper limit of particle size ranges, minus 75 lm and 300–600 lm, respectively, for better recovery.

Learning Vector Quantization for Classifying Astronomical Objects

The sizes of astronomical surveys in different wavebands are increasing rapidly. Therefore, automatic classification of objects is becoming ever more important. We explore the performance of learning vector quantization (LVQ) in classifying multi-wavelength data. Our analysis concentrates on separating active sources from non-active ones. Different classes of X-ray emitters populate distinct regions of a multidimensional parameter space. In order to explore the distribution of various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxies in the optical, X-ray and infrared bands. We then apply LVQ to classify them with the obtained data. Our results show that LVQ is an effective method for separating AGNs from stars and normal galaxies with multi-wavelength data.

Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm

Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.

Investigation of intergranular bright points from the New Vacuum Solar Telescope

Six high-resolution TiO-band image sequences from the New Vacuum Solar Telescope （NVST） are used to investigate the properties of intergranular bright points （igBPs）. We detect the igBPs using a Laplacian and morphological dilation algorithm （LMD） and automatically track them using a three- dimensional segmentation algorithm, and then investigate the morphologic, photometric and dynamic prop- erties of igBPs in terms of equivalent diameter, intensity contrast, lifetime, horizontal velocity, diffusion index, motion range and motion type. The statistical results confirm previous studies based on G-band or TiO-band igBPs from other telescopes. These results illustrate that TiO data from the NVST are stable and reliable, and are suitable for studying igBPs. In addition, our method is feasible for detecting and track- ing igBPs with TiO data from the NVST. With the aid of vector magnetograms obtained from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, the properties of igBPs are found to be strongly influenced by their embedded magnetic environments. The areal coverage, size and intensity contrast values of igBPs are generally larger in regions with higher magnetic flux. However, the dynamics of igBPs, includ- ing the horizontal velocity, diffusion index, ratio of motion range and index of motion type are generally larger in the regions with lower magnetic flux. This suggests that the absence of strong magnetic fields in the medium makes it possible for the igBPs to look smaller and weaker, diffuse faster, and move faster and further along a straighter path.

A convergent mean shift algorithm to select targets for LAMOST

This paper firstly finds that the Mean Shift Algorithm used by the Observation Control System （OCS） Research Group of the University of Science and Technology of China in Survey Strategy System 2.10 （SSS2.10） to select targets for the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） is not convergent in theory. By carefully studying the mathematical formulation of the Mean Shift Algorithm, we find that it tries to find a point where some objective function achieves its maximum value; the Mean Shift Vector can be regarded as the ascension direction for the objective function. If we regard the objective function as the numerical description for the imaging quality of all targets covered by the focal panel, then the Mean Shift Algorithm can find the place where the imaging quality is the best. So, the problem of selecting targets is equal to the problem of finding the place where the imaging quality is the best. In addition, we also give some effective heuristics to improve computational speed and propose an effective method to assign point sources to the respective fibers. As a result, our program runs fast, and it costs only several seconds to generate an observation.

A Comparison of BBN,ADTree and MLP in separating Quasars from Large Survey Catalogues

We compare the performance of Bayesian Belief Networks （BBN）, Multilayer Perception （MLP） networks and Alternating Decision Trees （ADtree） on separating quasars from stars with the database from the 2MASS and FIRST survey catalogs. Having a training sample of sources of known object types, the classifiers are trained to separate quasars from stars. By the statistical properties of the sample, the features important for classifica- tion are selected. We compare the classification results with and without feature selection. Experiments show that the results with feature selection are better than those without feature selection. From the high accuracy found, it is concluded that these automated methods are robust and effective for classifying point sources. They may all be applied to large survey projects （e.g. selecting input catalogs） and for other astronomical issues, such as the parameter measurement of stars and the redshift estimation of galaxies and quasars.

A fast Stokes inversion technique based on quadratic regression

Stokes inversion calculation is a key process in resolving polarization information on radiation from the Sun and obtaining the associated vector magnetic fields. Even in the cases of simple local thermo- dynamic equilibrium （LTE） and where the Milne-Eddington approximation is valid, the inversion problem may not be easy to solve. The initial values for the iterations are important in handling the case with mul- tiple minima. In this paper, we develop a fast inversion technique without iterations. The time taken for computation is only 1/100 the time that the iterative algorithm takes. In addition, it can provide available initial values even in cases with lower spectral resolutions. This strategy is useful for a filter-type Stokes spectrograph, such as SDO/HMI and the developed two-dimensional real-time spectrograph （2DS）.

DWT Power Spectrum of the Two-Degree Field Galaxy Redshift Survey

The power spectrum of the two-degree Field Galaxy Redshift Survey （2dFGRS） sample is estimated with the discrete wavelet transform （DWT） method. The DWT power spectra within 0.035 〈 k 〈 2.2 h Mpc^-1 are measured for three volume-limited samples defined in consecutive absolute magnitude bins - 19 - - 18, - 20 - - 19 and - 21 - - 20. We show that the DWT power spectrum can effectively distinguish ACDM models of σ8 = 0.84 and σ8 = 0.74. We adopt maximum likelihood method to perform three-parameter fitting of the bias parameter b, pairwise velocity dispersion σpv and redshift distortion parameterβ = Ωm^0.6/b to the measured DWT power spectrum. The fitting results state that in a σ8 = 0.84 universe the best-fit values of Ωm given by the three samples are mutually consistent within the range 0.28 - 0.36, and the best fitted values of Opv are 398-27^＋35, 475-29^37 and 550 ± 20 km s^-1 for the three samples, respectively. In the model of σ8 = 0.74, our three samples give very different values of Ωm. We repeated the fitting using the empirical formula of redshift distortion. The result of the model of low σ8 is still poor, especially, one of the best-fit values of σpv is as large as 10^3 km s^-1. We also repeated our fitting by incorporating a scale-dependent galaxy bias. This gave a slightly lower value of Ωm. Differences between the models of σ8 = 0.84 and σ8 = 0.74 still exist in the fitting results. The power spectrum of 2dFGRS seems to disfavor models with low amplitude of density fluctuations if the bias parameter is assumed to be scale independent. For the fitting value of Ωm to be consistent with that given by WMAP3, strong scale dependence of the bias parameters is needed.

On the Lorentz factor of superluminal sources

We investigate the properties of features seen within superluminal sources often referred to as components. Our result indicates a fairly strong correlation of r ～ 0.5 for quasars, r ～ 0.4 for galaxies and r ～ 0.7 for BL Lac objects in our sam- ple between component sizes and distances from the stationary core. The assumption of free adiabatic expanding plasma enables us to constrain the Lorentz factor for su- perluminal sources. Our estimated Lorentz factor of y ～ 9-13 for quasars, 7 ～ 7-11 for galaxies and y～ 4 - 9 for BL Lac objects indicates that BL Lacs have the lowest range of Lorentz factors.

Candidate members of star clusters from LAMOST DR2

In this work, we provide 2189 photometrically- and kinematically-selected candidate members of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification： selection along the stellar track in the colormagnitude diagram, i.e., photometric identification, and selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocities from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40% for the sample selected with radial velocity. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated with the LAMOST pipeline. We find that the systematic offsets in radial velocity and metallicity are 0.85 ± 1.26 km s-1and-0.08 ± 0.04 dex, with dispersions of 5.47＋1.16-0.71 km s-1and 0.13＋0.04-0.02 dex, respectively. Finally, we propose that the photometrically-selected candidate members of the clusters covered by the LAMOST footprint should be assigned higher priority so that more candidate stars can be observed.

An investigation of collisions between fiber positioning units in LAMOST

The arrangement of fiber positioning units in the LAMOST focal plane may lead to collisions during the fiber allocation process, To avoid these collisions, a software-based protection system has to abandon some targets located in the overlapping field of adjacent fiber units. In this paper, we first analyze the probability of collisions between fibers and infer their possible reasons. It is useful to solve the problem of collisions among fiber positioning units so as to improve the efficiency of LAMOST. Based on this, a collision handling system is designed by using a master-slave control structure between the micro control unit and microcomputer. Simulated experiments validate that the system can provide real-time inspection and swap information between the fiber unit controllers and the main controller.

A COMPARATIVE STUDY ON SHILLING DETECTION METHODS FOR TRUSTWORTHY RECOMMENDATIONS

Uncovering shilling attackers hidden in recommender systems is very crucial to enhance the robustness and trustworthiness of product recommendation. Many shilling attack detection algorithms have been proposed so far, and they exhibit complementary advantage and disadvantage towards various types of attackers. In this paper, we provide a thorough experimental comparison of several well-known detectors, including supervised C4.5 and NB, unsupervised PCA and MDS, semi-supervised HySAD methods, as well as statistical analysis methods. MovieLens 100K is the most widely-used dataset in the realm of shilling attack detection, and thus it is selected as the benchmark dataset. Meanwhile, seven types of shilling attacks generated by average-filling and random-filling model are compared in our experiments. As a result of our analysis, we show clearly causes and essential characteristics insider attackers that might determine the success or failure of different kinds of detectors.

SEIR-SW, Simulation Model of Influenza Spread Based on the Small World Network

This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.