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For numerical treatment of resin‐containing systems and forecasting of their properties, certain models of branching are needed. In this review, existing theoretical models of systems containing branched structures (polymers, aggregates, etc.) are analyzed and compared. The criteria of selection of the optimal theoretical model comprise chemical and physical problems available for solution, simplicity of such solution, connection between theoretically forecasted and experimental results, and the time needed for computing. It is concluded that, according to these criteria, the optimal (between existing models) is the statistical polymer method.

The purpose of this paper is to contribute to the understanding of how statistical process control (SPC) methodology can be implemented and used in organizational settings.

An action research model was used. Data were collected through formal meeting protocols, interviews and participant observation.

Based on the results of an action research project, the paper emphasizes the need for: top management support with respect to roles such as infrastructural assistance, mentor, critic, financer; creating system validity through the involvement of people with experiential knowledge about the “world” in which SPC should be applied; keeping a small, highly knowledgeable development team with appropriate expertise together during the whole process from beginning to end; keeping the various end‐users in focus but separate and prioritising between their different needs; and working with iterative design methodology.

The paper provides the research field with a unique case of implementing SPC using a computerized administrative data system.

Organizations are given guidelines to use when implementing SPC.

The paper contributes knowledge in an underdeveloped field of research. It may provide a basis for further research and scholarly analysis.

Presents the essentials of heuristic economics. Problems of planning and development, which are complex, ill‐structured and subject to uncertainty, often defy algorithmic mathematical/statistical solutions; hence there is scope for developing a heuristic approach.

The aim of this paper is to present the results from an empirical investigation of Six Sigma in the Indian software industry

The paper begins with a review of literature of Six Sigma and its role in the software industry. The importance of Six Sigma in the software domain is presented, followed by presentation of the results from an empirical investigation of Six Sigma in the Indian software industry

The research reflects the status of Six Sigma application and implementation in the software industry, identifies the commonly used statistical and non statistical and software engineering tools and frameworks used within software business; and determines the critical success factors (CSFs) for a successful Six Sigma initiative in the software/IT industry. The most important factor was management commitment and involvement. Documentation management and suppliers' involvement were found to be the least important factors.

This study was carried out with some boundaries like the number of companies, available resources, time constraints, etc.

This paper dispels the myths concerning the unsuitability of Six Sigma in the software arena. At the same time it highlights the status of Six Sigma implementation in Indian software organizations and the critical success factors for implementation of Six Sigma.

Little research has been carried out in terms of empirical survey relating to the application of Six Sigma in the software industry like that demonstrated in this paper. The paper will be valuable for quality professionals and management personnel in software organizations.

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 purpose of this paper is to investigate the economic‐statistical design of EWMA charts with variable sampling intervals (VSIs) under non‐normality to reduce the process production cycle cost and improve the statistical performance of control charts. The objective is to minimize the cost function by adjusting the control chart parameters which suffice for the statistical restriction.

First, using the Burr distribution to approximate various non‐normal distributions, the economic‐statistical model of the VSI EWMA charts under non‐normality can be developed. Further, the genetic algorithms will be used to search for the optimal values of parameters of the VSI EWMA charts under non‐normality. Finally, a sensitivity analysis is carried out to investigate the effect of model parameters and statistical restriction on the solution of the economic‐statistical design.

The result of sensitivity analysis shows that a large lower bound of average time to signal when the process is in control increases the control limit coefficient, no model parameter significantly affects the short sampling intervals, and so on.

The economic‐statistical design method proposed in this paper can improve the statistical performance of economic design of control charts and the general idea can be applied to other VSI control charts.

To overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.

The need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data.

So far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data.

SeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research.

Individuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection.

On the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.

Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

Corrosion resistance measurements were performed on AISI 316L stainless steel biomaterial samples after three types of treatments: abrasive finishing (MP), standard electropolishing (EP), and magnetoelectropolishing (MEP). The corrosion studies were carried out in Ringer's solution at a room temperature. Potentiodynamic plots obtained were the basis for the analysis of measurement accuracy and uncertainty with the statistical tests results done in Statistica 64/10 software. The results of corrosion studies indicate a significant difference in the breaking potential (Epit) values, dependent on surface treatment. The highest mean values have been obtained on samples after MEP (Epit=961 mV), much lower – after a standard electropolishing EP (Epit=525 mV), and the lowest – after the abrasive treatment MP (Epit=222 mV), all of them measured against a saturated calomel electrode SCE potential. The corrosion results obtained are well correlated with the nanoindentation measurement results (Young's modulus and nanohardness). The paper aims to discuss these issues.

The AISI 316L austenitic stainless steel samples served for the study. There were 11 (MP) and 14 (EP) samples used for each of the treatment, and 31 samples used for magnetoelectropolishing MEP. All polarization measurements were made after one hour immersion in the Ringer's solution. Statistical tests were used to treat the results obtained.

After magnetoelectropolishing MEP130, the pitting corrosion resistance is much better than that after abrasive polishing MP and/or a standard electropolishing EP130. It was proved on a big statistical sample that the pitting corrosion potential Epit after MEP130 is over 1.8 times higher than that after EP130 and over 4.3 times higher than that after MP. The results obtained are in good agreement with the nanoindentation measurement results.

This is an original study of the corrosion resistance of AISI 316L SS in Ringer's solution. The breaking potential Eb obtained is comparable with that of NiTi alloys, not reported anywhere before. The results have been well confirmed statistically (on 31 samples after MEP).