Testing hypothesis on stability of expected value and variance

The simple samples are independently taken from normal distribution. The two functions of the sample means and sample variances are considered. The density functions of these two statistics have been derived. These statistics can be applied for verifying the hypothesis on stability of expected value and variance of normal distribution considered, e.g., in statistical process control. The critical values for these statistics have been found using numerical integration. The tables with approximated critical values of these statistics have been presented.density function, sample variance, test statistic, numerical integration, statistical process control

Regime variance testing - a quantile approach

This paper is devoted to testing time series that exhibit behavior related to two or more regimes with different statistical properties. Motivation of our study are two real data sets from plasma physics with observable two-regimes structure. In this paper we develop estimation procedure for critical point of division the structure change of a time series. Moreover we propose three tests for recognition such specific behavior. The presented methodology is based on the empirical second moment and its main advantage is lack of the distribution assumption. Moreover, the examined statistical properties we express in the language of empirical quantiles of the squared data therefore the methodology is an extension of the approach known from the literature. The theoretical results we confirm by simulations and analysis of real data of turbulent laboratory plasma

Note on the luminosity distance

We re-derive a formula relating the areal and luminosity distances, entirely in the framework of the classical Maxwell theory, assuming a geometric-optics type condition.Comment: Section 4 revised, misprints correcte

The Social Engagement as a Source of Innovation

Innovation, innovation economy, innovation management are all crucial issues in both theory and practice of management. The purpose of this paper is to provide mechanisms for the use of corporate community involvement in public affairs as a source of innovation for both business organizations and in relation to ways of solving social problems and pursuing public purposes. The use of business engagement in social affairs as a source and inspiration for innovation and the mechanisms of responsible use of that business engagement by community and public organizations were analyzed. Companies have discovered that social problems have their economic side and the involvement in solving the problems of the public sector can strongly stimulate their own business processes. The new paradigm for innovation grows in the field of cooperation between private business and public interest, generating positive and permanent changes for both sides. There is a strong need for the cause social responsiveness and increased social sensitivity, not only on the side of the business but also in public organizations

A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems

The Multi-Objective Mixed-Integer Programming (MOMIP) problem is one of the most challenging. To derive its Pareto optimal solutions one can use the well-known Chebyshev scalarization and Mixed-Integer Programming (MIP) solvers. However, for a large-scale instance of the MOMIP problem, its scalarization may not be solved to optimality, even by state-of-the-art optimization packages, within the time limit imposed on the optimization. If a MIP solver cannot derive the optimal solution within the assumed time limit, it provides the optimality gap, which gauges the quality of the approximate solution. However, for the MOMIP case, no information is provided on the lower and upper bounds of the components of the Pareto optimal outcome. For the MOMIP problem with two and three objective functions, an algorithm is proposed to provide the so-called interval representation of the Pareto optimal outcome designated by the weighting vector when there is a time limit on solving the Chebyshev scalarization. Such interval representations can be used to navigate on the Pareto front. The results of several numerical experiments on selected large-scale instances of the multi-objective, multidimensional 0-1 knapsack problem illustrate the proposed approach. The limitations and possible enhancements of the proposed method are also discussed

The Principles of Implementing Early Recognition Systems in an Organization

In the context of the turbulent environment, contemporary organizations have to work out and implement tools enabling them to handle the turbulence, and primarily, to avoid negative consequences of these processes. The tools are related, among others, to obtaining and providing managers, sufficiently in advance, with adequate management information on the environment. Early Recognition Systems (ERS) are a response to such conditions of the organization functioning and the challenge in respect of information support for decision-making processes. Unfortunately, they are mainly of informalized character, dispersed on various levels and in various functional areas of organizations, and very often based on unconscious, habitual actions, and, in consequence, their advancement and effectiveness are low. Based on the main characteristics of early recognition systems, the article presents the framework procedure of systemic solutions in the area of early recognition, which is supposed to enable formally organized activities within this scope

External bias in the model of isolation of communities

We extend a model of community isolation in the d-dimensional lattice onto the case with an imposed imbalance between birth rates of competing communities. We give analytical and numerical evidences that in the asymmetric two-specie model there exists a well defined value of the asymmetry parameter when the emergence of the isolated (blocked) subgroups is the fastest, i.e. the characteristic time tc is minimal. This critical value of the parameter depends only on the lattice dimensionality and is independent from the system size. Similar phenomenon was observed in the multi-specie case with a geometric distribution of the birth rates. We also show that blocked subgroups in the multi-specie case are absent or very rare when either there is a strictly dominant specie that outnumbers the others or when there is a large diversity of species. The number of blocked species of different kinds decreases with the dimension of the multi-specie system.Comment: 6 pages, 4 figure

A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

A novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for selected model molecules and some molecular systems of importance in biochemistry. The new algorithm is shown to compare favorably with the standard, statically parametrized memetic algorithm