Efficiency calibration of neutron detector array for beta decay of exotic nuclei

The research of the in-beam efficiency calibration of Neutron Detector Array of Peking University using N-17 and C-16 beams was introduced in this paper. The efficiency of neutron wall and ball are comparable to the foreign similar devices and neutrons can be detected from low to high energies in high efficiency.

Calibration of an audio-frequency ion trap mass spectrometer

A method for calibration of an audio-frequency (AF) ion trap mass spectrometer is described. The method is proposed to surmount the obstacle that there is a lack of a proper calibrant for mass spectrometers in the mass-to-charge ratio (m/z) range of 10(6) to 10(10). To calibrate such mass spectra, we determine the point of ejection, q(eject), on the stability diagram of the ion trap operated in a mass-selective axial instability mode. This is accomplished by measuring the radial secular frequencies (and therefore, the m/z value) of a single trapped particle using a light scattering method, followed by monitoring the action of particle ejection in real time to obtain the q(eject). A delayed ejection with q(eject) = 0.949 +/- 0.004 is found at a trap driving frequency of Ohm/2pi = 200-600Hz. Theoretical analysis for the origin of the delayed ejection indicates that the delay is predominantly resulted from the existence of multipole components in the fields due to trap imperfections. Inclusion of -3% of the octopole with respect to the basic quadrupole field can satisfactorily account for our observations. An m/z accuracy approaching 0.1% is attainable after proper calibration of the AF ion trap mass spectrometer. (Int J Mass Spectrom 214 (2002) 63-73) (C) 2002 Elsevier Science B.V. All rights reserved.

Calibration of a solid-state copper ion-selective electrode in cupric ion buffers containing chloride

It is shown that near-Nernstian calibration slopes can be obtained with a Cu1.8Se electrode in a range of cupric ion buffers in spite of a high chloride content. Best results are obtained with the ligands ethylenediamine, glycine and histidine. The onset of cupric ion toxicity towards marine organisms falls within the pCu calibration range obtained with glycine, and the Cu1.8Se electrode could, therefore, be useful for monitoring cupric ion activity in bioassays in sea-water media.

Calibration of standard seawater in electrical conductivity

The procedure adopted by the Standard Seawater Service for the calibration of Standard Seawater in electrical conductivity relative to a defined potassium chloride solution is described

A Review of Active Appearance Models

Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research.

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

History repeats itself: The failure of rational choice models in corporate governance

Marnet, Oliver, 'History repeats itself: The failure of rational choice models in corporate governance', Critical Perspectives on Accounting (2005) 18(2) pp.191-210 RAE2008

Scale-wise evolution of rainfall probability density functions fingerprints the rainfall generation mechanism

© 2010 by the American Geophysical Union.The cross-scale probabilistic structure of rainfall intensity records collected over time scales ranging from hours to decades at sites dominated by both convective and frontal systems is investigated. Across these sites, intermittency build-up from slow to fast time-scales is analyzed in terms of heavy tailed and asymmetric signatures in the scale-wise evolution of rainfall probability density functions (pdfs). The analysis demonstrates that rainfall records dominated by convective storms develop heavier-Tailed power law pdfs toward finer scales when compared with their frontal systems counterpart. Also, a concomitant marked asymmetry build-up emerges at such finer time scales. A scale-dependent probabilistic description of such fat tails and asymmetry appearance is proposed based on a modified q-Gaussian model, able to describe the cross-scale rainfall pdfs in terms of the nonextensivity parameter q, a lacunarity (intermittency) correction and a tail asymmetry coefficient, linked to the rainfall generation mechanism.

Conductance of quantum impurity models from quantum monte carlo

The conductance of two Anderson impurity models, one with twofold and another with fourfold degeneracy, representing two types of quantum dots, is calculated using a world-line quantum Monte Carlo (QMC) method. Extrapolation of the imaginary time QMC data to zero frequency yields the linear conductance, which is then compared to numerical renormalization-group results in order to assess its accuracy. We find that the method gives excellent results at low temperature (T TK) throughout the mixed-valence and Kondo regimes but it is unreliable for higher temperature. © 2010 The American Physical Society.

Structuring the Calibration of Qualitative Data as Sets for Qualitative Comparative Analysis (QCA)

Most studies that apply qualitative comparative analysis (QCA) rely on macro-level data, but an increasing number of studies focus on units of analysis at the micro or meso level (i.e., households, firms, protected areas, communities, or local governments). For such studies, qualitative interview data are often the primary source of information. Yet, so far no procedure is available describing how to calibrate qualitative data as fuzzy sets. The authors propose a technique to do so and illustrate it using examples from a study of Guatemalan local governments. By spelling out the details of this important analytic step, the authors aim at contributing to the growing literature on best practice in QCA. © The Author(s) 2012.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.

Numerical solutions of certain nonlinear models in European options on a distributed computing environment

Financial modelling in the area of option pricing involves the understanding of the correlations between asset and movements of buy/sell in order to reduce risk in investment. Such activities depend on financial analysis tools being available to the trader with which he can make rapid and systematic evaluation of buy/sell contracts. In turn, analysis tools rely on fast numerical algorithms for the solution of financial mathematical models. There are many different financial activities apart from shares buy/sell activities. The main aim of this chapter is to discuss a distributed algorithm for the numerical solution of a European option. Both linear and non-linear cases are considered. The algorithm is based on the concept of the Laplace transform and its numerical inverse. The scalability of the algorithm is examined. Numerical tests are used to demonstrate the effectiveness of the algorithm for financial analysis. Time dependent functions for volatility and interest rates are also discussed. Applications of the algorithm to non-linear Black-Scholes equation where the volatility and the interest rate are functions of the option value are included. Some qualitative results of the convergence behaviour of the algorithm is examined. This chapter also examines the various computational issues of the Laplace transformation method in terms of distributed computing. The idea of using a two-level temporal mesh in order to achieve distributed computation along the temporal axis is introduced. Finally, the chapter ends with some conclusions.

Multiple attractors of host-parasitoid models with integrated pest management strategies: eradication, persistence and outbreak

Host-parasitoid models including integrated pest management (IPM) interventions with impulsive effects at both fixed and unfixed times were analyzed with regard to host-eradication, host-parasitoid persistence and host-outbreak solutions. The host-eradication periodic solution with fixed moments is globally stable if the host's intrinsic growth rate is less than the summation of the mean host-killing rate and the mean parasitization rate during the impulsive period. Solutions for all three categories can coexist, with switch-like transitions among their attractors showing that varying dosages and frequencies of insecticide applications and the numbers of parasitoids released are crucial. Periodic solutions also exist for models with unfixed moments for which the maximum amplitude of the host is less than the economic threshold. The dosages and frequencies of IPM interventions for these solutions are much reduced in comparison with the pest-eradication periodic solution. Our results, which are robust to inclusion of stochastic effects and with a wide range of parameter values, confirm that IPM is more effective than any single control tactic.