Learning Curve as a Knowledge-based Dynamic Fuzzy Set: A Markov Process Model

In the fuzzy set theory introduced by Zadeh (1965), membership de-gree of a fuzzy set is determined by a static membership function, i.e., it does not change over time. To improve this condition then Wang introduced the dy-namic fuzzy logic. In this concept, the membership degree of a fuzzy set is changing over the time. Intan and Mukaido (2002) introduced the knowledge-based fuzzy set, by means that the membership degree of a set is dependent on the knowledge of a person. Since the knowledge of a person is not static, the knowledge-based fuzzy set can be measured dynamically over time, so that we have the knowledge-based dynamic fuzzy set. In this paper, we approximate the learning process as a knowledge-based dynamic fuzzy set. We consider that the process of learning is dependent on the knowledge of a person from time to time so that we can model the learning process is a Markov process of dynamic knowledge. Additionally, using the triangular fuzzy number, we follow Yabuu-chi et al. (2014), for modelling the time difference in the dynamic knowledge fuzzy set as an autoregressive model of order one

Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge

Organised by: Cranfield UniversityIn this paper applications of logistic S-curve and component logistics are considered in a framework of longterm forecasting of emerging technologies. Several questions and issues are discussed in connection with the presented ways of studying the transition from invention to innovation and further evolution of technologies. First, the features of a simple logistic model are presented and diverse types of competition are discussed. Second, a component logistic model is presented. Third, a hypothesis about the usability of a knowledge growth description and simulation for reliable long-term forecasting is proposed. Some interim empirical results for applying networks of contradictions are given.Mori Seiki – The Machine Tool Compan

Targeting the adaptability of heterogeneous aneuploids

SummaryAneuploid genomes, characterized by unbalanced chromosome stoichiometry (karyotype), are associated with cancer malignancy and drug resistance of pathogenic fungi. The phenotypic diversity resulting from karyotypic diversity endows the cell population with superior adaptability. We show here, using a combination of experimental data and a general stochastic model, that the degree of phenotypic variation, thus evolvability, escalates with the degree of overall growth suppression. Such scaling likely explains the challenge of treating aneuploidy diseases with a single stress-inducing agent. Instead, we propose the design of an “evolutionary trap” (ET) targeting both karyotypic diversity and fitness. This strategy entails a selective condition “channeling” a karyotypically divergent population into one with a predominant and predictably drugable karyotypic feature. We provide a proof-of-principle case in budding yeast and demonstrate the potential efficacy of this strategy toward aneuploidy-based azole resistance in Candida albicans. By analyzing existing pharmacogenomics data, we propose the potential design of an ET against glioblastoma