How to select combination operators for fuzzy expert systems using CRI

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI

A fuzzy set preference model for market share analysis

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction)

An experimental methodology for a fuzzy set preference model

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems

Synthesis and Characterization of Superparamagnetic Silica--Homopolypeptide Composite Particles

Hierarchical construction and characterization of core-shell composite particles of interest are presented. The research described shows interest in new types of polymers that provide chirality and responsiveness which have been ignored for so long. The general synthetic design described in this document can be readily used as a model for other systems. Core-shell composite systems of interest consist of silica or silica-coated cobalt cores grafted with a homopolypeptide shell, mostly poly(epsilon-carbobenzyloxy-L-lysine) or poly(gamma-benzyl-L-glutamate). Core particles were decorated with amino groups through a silylation reaction. The amino groups initiated the polymerization (with attachment) of N-carboxyanhydride (NCA) monomers, resulting in a homopolypeptide shell. Homopolypeptide-grafted particles were considered as hierarchical systems due to the two levels of responsiveness. The first level of responsiveness is due to the superparamagnetic nature of the core and the second level of responsiveness is through the thermally responsive polypeptide shell. Both levels of response were confirmed with several characterization methods. Characterization by electron microscopy and dynamic light scattering (DLS) confirmed the uniformity of the particles. Magnetic properties of the silica-coated cobalt core and polypeptide-grafted particles were investigated with a superconducting quantum interference device (SQUID). DLS and digital polarimeter demonstrated helix-coil transitions of the particles through continuous heating and cooling cycles in m-creso

Privacy, Security and Politics:Current Issues and Future Prospects

Overview Individual privacy and national security have been regarded as notions with a conflicting impact. As seen in the UK general election 2017, security has taken a prominent role on the Conservative Party agenda while public perceptions on privacy were split. This article reviews the election manifestos of three political parties on privacy and security. We use the pre-election YouGov survey of 2017 UK respondents to understand the views of the public by age groups and gender. While there is general support for legislation aimed at strengthening national security and crime prevention, such as the Investigatory Powers Act 2016, the younger segment of the UK population is increasingly concerned with the infringement of their privacy (both in traditional and online settings). These contrasting views may explain the outcome of the general election in 2017, and offer open questions for legislators