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To define synthetic indices of changes of vegetation coverage on salt marshes of the Venice lagoon that might be used to model changes of the entire basin.

Remote sensing data from the satellite sensor QuickBird were processed to retrieve vegetation coverage in different seasons, i.e. different phenological stages of the halophytic vegetation. These changes have been described by means of landscape metrics (LSM's) and statistical moments.

LSM's selected have been useful to describe the dominant shapes of vegetation coverage, while the low variations occurred were only partially explained. The first four statistical moments have been only partially functional to the description of temporal variation of the patches.

These indicators, whose performances have been evaluated by the present work, could be used to detect larger variations on longer period and on wider spatial scale. In this way it could also be possible to easily detect the effects of changes on the hydrodynamic regime that may occur in relation with new projected interventions on the Venice lagoon.

This approach has never been applied to salt marshes of the Venice lagoon. The remote sensing data, in particular LSM's and statistical moments applied together, might be valuable tools to detect shape changes in the delicate environment of salt marshes and suggest possible remedies for problems of managing the Venice lagoon.

The purpose of this study is to estimate the convergence order of the Aumann–Serrano Riskiness Index.

This study uses the equivalent relation between the Aumann–Serrano Riskiness Index and the moment generating function and aggregately compares between each two statistical moments for statistical significance. Thus, this study enables to find the convergence order of the index to its stable value.

This study finds that the first-best estimation of the Aumann–Serrano Riskiness Index is reached in no less than its seventh statistical moment. However, this study also finds that its second-best approximation could be achieved with its second statistical moment.

The implications of this research support the standard deviation as a statistically sufficient approximation of Aumann–Serrano Riskiness Index, thus strengthening the CAPM methodology for asset pricing in the financial markets.

This research sheds a new light, both in theory and in practice, on understanding of the risk’s structure, as it may improve accuracy of asset pricing.

To establish a statistical formulation of robust design optimization and to develop a fast optimization algorithm for the solution of the statistical design problem.

Existing formulations and methods for statistical robust design are reviewed and compared. A consistent problem formulation in terms of statistical parameters of the involved variables is introduced. A novel algorithm for statistical optimization is developed. It is based on the unscented transformation, a fast method for the propagation of random variables through nonlinear functions. The prediction performance of the unscented transformation is demonstrated and compared with other methods by means of an analytical test function. The validity of the proposed approach is shown through the design of the superconducting magnetic energy storage device of the TEAM workshop problem 22.

Provides a consistent formulation of statistical robust design optimization and an efficient and accurate method for the solution of practical problems.

The proposed approach can be applied to all kinds of design problems and allows to account for the inevitable effects of tolerances and parameter variations occuring in practical realizations of designed devices.

The study aims to identify the prospects and challenges associated with current practices regarding digital data sets management in university libraries in Pakistan.

A cross-sectional survey approach was used to collect the data from library and information science (LIS) professionals working in public sector university libraries in Pakistan. A four-part questionnaire was used to collect the data from the respondents. The collected data from 371 participants were analyzed using a statistical package for social sciences (SPSS-24 version) and analysis of moment structure (AMOS-24).

LIS professionals are better placed to support digital data management practices, such as finding, collecting, assessing and analyzing digital data sets and making digital data publicly discoverable and accessible via open access. In spite of this, a lack of leadership support, interest and cooperation among university departments and the absence of a data management plan, policies and procedures were reported as significant challenges.

To meet the needs of data users, LIS professionals must become knowledgeable about managing and reusing digital data sets. Due to the demands of the information society, university librarians need to learn about data-centric practices that can enhance research outputs and provide new insights.

This research paper is extracted from a PhD dissertation to present a contemporary picture of library data management services and the challenges LIS professionals face to provide possible solutions.

The purpose of this paper is to provide the results of investigation of multi‐moment statistical characteristics of chaos and apply them to improve the accuracy of nonlinear algorithms for chaos filtering for real‐time applications.

The approach to find multi‐moment statistical properties of chaos‐multi‐moment cumulant (covariance) functions of higher order is a generalization of the previously proposed (by the authors) “degenerated cumulant equations” method. Those multi‐moment cumulants functions are applied in the generalization of the Stratonovich‐Kushner equations (SKE) for the optimum algorithm of nonlinear filtering of chaos as well as for synthesis of the quasi‐optimum algorithms.

Results are presented to investigate the multi‐moment statistical properties of chaos and formulate the theoretical background for synthesis of multi‐moment optimum and quasi‐optimum algorithms for nonlinear filtering of chaos with the improved accuracy in the presence of additive white noise.

The paper presents new theoretical results of the statistical description of chaos, previously partially reported only from experimental studies. A novel approach for chaos filtering is also presented. The proposed approach is dedicated to further improvement of the filtering accuracy for the case of low (less than one) SNR scenarios and is important for implementation in real‐time processing. As an important practical example, the new modified EKF algorithm is proposed with the rather opportunistic characteristics of the filtering fidelity together with algorithm complexity – practically the same as the “classic” one‐moment EKF algorithm.

Normal transformation is often required in structural reliability analysis to convert the non-normal random variables into independent standard normal variables. The existing normal transformation techniques, for example, Rosenblatt transformation and Nataf transformation, usually require the joint probability density function (PDF) and/or marginal PDFs of non-normal random variables. In practical problems, however, the joint PDF and marginal PDFs are often unknown due to the lack of data while the statistical information is much easier to be expressed in terms of statistical moments and correlation coefficients. This study aims to address this issue, by presenting an alternative normal transformation method that does not require PDFs of the input random variables.

The new approach, namely, the Hermite polynomial normal transformation, expresses the normal transformation function in terms of Hermite polynomials and it works with both uncorrelated and correlated random variables. Its application in structural reliability analysis using different methods is thoroughly investigated via a number of carefully designed comparison studies.

Comprehensive comparisons are conducted to examine the performance of the proposed Hermite polynomial normal transformation scheme. The results show that the presented approach has comparable accuracy to previous methods and can be obtained in closed-form. Moreover, the new scheme only requires the first four statistical moments and/or the correlation coefficients between random variables, which greatly widen the applicability of normal transformations in practical problems.

This study interprets the classical polynomial normal transformation method in terms of Hermite polynomials, namely, Hermite polynomial normal transformation, to convert uncorrelated/correlated random variables into standard normal random variables. The new scheme only requires the first four statistical moments to operate, making it particularly suitable for problems that are constraint by limited data. Besides, the extension to correlated cases can easily be achieved with the introducing of the Hermite polynomials. Compared to existing methods, the new scheme is cheap to compute and delivers comparable accuracy.

The paper attempts to establish the connection between structural reliability and structural optimization for the particular case of plastic structures. Along this line, the paper outlines a reliability‐based optimization approach to design plastic structures with uncertain interdependent strengths and acted on by random interdependent loads. The importance of such interdependencies, and of some of the other statistical parameters used as input data in probabilistic computations, is demonstrated by several examples of sensitivity studies on both the probability of collapse failure as well as the reliability‐based optimum solution.

Using a convenient tail-risk measure of performance, this paper aims to explore the extent to which incorporating higher statistical moments such as an assets skewness and kurtosis, provides further insight into the potential benefits of asset-class diversification within the realm of Islamic finance.

The authors use Engle’s (2002) DCC-GARCH model to study the dynamic conditional correlations between asset classes. Furthermore, the authors use the modified value-at-risk (Favre and Galeano, 2002), which incorporates higher statistical moments, to measure the performance of portfolios during both crisis and bullish regimes.

The most important finding relates to the estimation of portfolio tail-risk. In particular, the authors find that using a standard two-moment value-at-risk (VaR) measure, which assumes normally distributed returns, rather than a four-moment VaR, which incorporates an asset skewness and kurtosis, can lead to a substantial underestimation of portfolio risk during the most extreme market conditions.

This paper contributes to the extremely limited research considering higher-moments within the realm of Islamic portfolio-management. The results suggest that Islamic portfolio managers should remain cognisant of the skewness and kurtosis parameters of their assets. Ignoring higher-moments could induce misleading inferences and would, therefore, constitute imprudent risk-management.

The purpose of this paper is to find an accurate, efficient and easy-to-implement point estimate method (PEM) for the statistical moments of random systems.

First, by the theoretical and numerical analysis, the approximate reference variables for the frequently used nine types of random variables are obtained; then by combining with the dimension-reduction method (DRM), a new method which consists of four sub-methods is proposed; and finally, several examples are investigated to verify the characteristics of the proposed method.

Two types of reference variables for the frequently used nine types of variables are proposed, and four sub-methods for estimating the moments of responses are presented by combining with the univariate and bivariate DRM.

In this paper, the number of nodes of one-dimensional integrals is determined subjectively and empirically; therefore, determining the number of nodes rationally is still a challenge.

Through the linear transformation, the optimal reference variables of random variables are presented, and a PEM based on the linear transformation is proposed which is efficient and easy to implement. By the numerical method, the quasi-optimal reference variables are given, which is the basis of the proposed PEM based on the quasi-optimal reference variables, together with high efficiency and ease of implementation.

It is well known that precision of mass unbalance measurement of sphere is determined by perturbing moments in contactless suspension systems. Therefore, estimating perturbing moment level in those systems is important and necessary to meet the requirements of a specified precision of mass unbalance measurement.

In this paper, probability‐theoretic method is employed to determine probability characteristics of the perturbing moments in both electrostatic suspension system and gas suspension system by statistically estimating of the quality of sphere surface. As a result, the relationship between probability characteristics of the perturbing moment and statistical estimates of the quality of sphere surface is established. It is expressed as a simple formula by specifying the correlation function of random field of sphere surface.

Numerical experiments of the perturbing moment calculation in the two suspension systems show that for sphere with small correlation coefficient α, it is better to use gas suspension system for mass unbalance measurement. While for sphere with large correlation coefficient α, electrostatic suspension system is more suitable for its perturbing moment are less than gas suspension system.

Knowledge of this relationship is of great theoretic and practical importance, since the probabilistic depiction of the perturbing moments, and hence the accuracy of the mass unbalance measurement systems can be estimated without a detailed roundness measurement of the sphere surface.