Time-varying Multi-regime Models Fitting by Genetic Algorithms

Many time series exhibit both nonlinearity and nonstationarity. Though both features have often been taken into account separately, few attempts have been proposed to model them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying, or piecewise linear threshold modeling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.Nonlinear time series; Nonstationary time series; Threshold model

Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games

We use co-evolutionary genetic algorithms to model the players' learning process in several Cournot models, and evaluate them in terms of their convergence to the Nash Equilibrium. The \social-learning" versions of the two co-evolutionary algorithms we introduce, establish Nash Equilibrium in those models, in contrast to the \individual learning" versions which, as we see here, do not imply the convergence of the players' strategies to the Nash outcome. When players use \canonical co-evolutionary genetic algorithms" as learning algorithms, the process of the game is an ergodic Markov Chain, and therefore we analyze simulation results using the relevant methodology, to find that in the \social" case, states leading to NE play are highly frequent at the stationary distribution of the chain, in contrast to the \individual learning" case, when NE is not reached at all in our simulations; to ftnd that the expected Hamming distance of the states at the limiting distribution from the \NE state" is significantly smaller in the \social" than in the \individual learning case"; to estimate the expected time that the \social" algorithms need to get to the \NE state" and verify their robustness and finally to show that a large fraction of the games played are indeed at the Nash Equilibrium.Genetic Algorithms, Cournot oligopoly, Evolutionary Game Theory, Nash Equilibrium

Determination of sequential best replies in n-player games by Genetic Algorithms

An iterative algorithm for establishing the Nash Equilibrium in pure strategies (NE) is proposed and tested in Cournot Game models. The algorithm is based on the convergence of sequential best responses and the utilization of a genetic algorithm for determining each player's best response to a given strategy profile of its opponents. An extra outer loop is used, to address the problem of finite accuracy, which is inherent in genetic algorithms, since the set of feasible values in such an algorithm is finite. The algorithm is tested in five Cournot models, three of which have convergent best replies sequence, one with divergent sequential best replies and one with \local NE traps"(Son and Baldick 2004), where classical local search algorithms fail to identify the Nash Equilibrium. After a series of simulations, we conclude that the algorithm proposed converges to the Nash Equilibrium, with any level of accuracy needed, in all but the case where the sequential best replies process diverges.Genetic Algorithms, Cournot oligopoly, Best Response, Nash Equilibrium

Multi-regime models for nonlinear nonstationary time series

Nonlinear nonstationary models for time series are considered, where the series is generated from an autoregressive equation whose coe±cients change both according to time and the delayed values of the series itself, switching between several regimes. The transition from one regime to the next one may be discontinuous (self-exciting threshold model), smooth (smooth transition model) or continuous linear (piecewise linear threshold model). A genetic algorithm for identifying and estimating such models is proposed, and its behavior is evaluated through a simulation study and application to temperature data and a financial index.

Smoking as a permissive factor of periodontal disease in psoriasis

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What are patients' expectations of orthodontic treatment: a systematic review

published_or_final_versio

Intraprofessional, team-based treatment planning for oral health students in the comprehensive care clinic

published_or_final_versio

When plants produce not enough or at all: metabolic engineering of flavonoids in microbial hosts

As a result of the discovery that flavonoids are directly or indirectly connected to health, flavonoid metabolism and its fascinating molecules that are natural products in plants, have attracted the attention of both the industry and researchers involved in plant science, nutrition, bio/chemistry, chemical bioengineering, pharmacy, medicine, etc. Subsequently, in the past few years, flavonoids became a top story in the pharmaceutical industry, which is continually seeking novel ways to produce safe and efficient drugs. Microbial cell cultures can act as workhorse bio-factories by offering their metabolic machinery for the purpose of optimizing the conditions and increasing the productivity of a selective flavonoid. Furthermore, metabolic engineering methodology is used to reinforce what nature does best by correcting the inadequacies and dead-ends of a metabolic pathway. Combinatorial biosynthesis techniques led to the discovery of novel ways of producing natural and even unnatural plant flavonoids, while, in addition, metabolic engineering provided the industry with the opportunity to invest in synthetic biology in order to overcome the currently existing restricted diversification and productivity issues in synthetic chemistry protocols. In this review, is presented an update on the rationalized approaches to the production of natural or unnatural flavonoids through biotechnology, analyzing the significance of combinatorial biosynthesis of agricultural/pharmaceutical compounds produced in heterologous organisms. Also mentioned are strategies and achievements that have so far thrived in the area of synthetic biology, with an emphasis on metabolic engineering targeting the cellular optimization of microorganisms and plants that produce flavonoids, while stressing the advances in flux dynamic control and optimization. Finally, the involvement of the rapidly increasing numbers of assembled genomes that contribute to the gene- or pathway-mining in order to identify the gene(s) responsible for producing species-specific secondary metabolites is also considered herein.National Strategic Reference Framework. THALES-TEI CRETE, MIS 380210 Progra

Patients’ expectations from dental implants: a systematic review of the literature

OBJECTIVE: To examine the current literature on the impact of patients’ expectations on treatment outcomes or final patient satisfaction and to identify the theoretical frameworks, study designs and measurement instruments which have been employed to assess patients’ expectations within implant dentistry. METHODS: A structured literature search of four databases Pubmed, Cochrane, Web of Science and PsychINFO was conducted following PRISMA guidelines. Any type of literature published in English discussing the topic of ‘patients expectations’ in oral health were identified and further screened. Studies reporting on expectations regarding dental implants were selected and a narrative review was conducted. RESULTS: The initial search yielded 16707 studies, out of which 1051 ‘potentially effective studies’ were further assessed and final 41 ‘effective studies’ were included [Kappa = 0.76]. Ten observational studies, published from 1999 to 2013, dealt specifically with expectations of dental implants. There was a large degree of heterogeneity among studies in terms of assessment instruments. Expectations relating to aesthetics and function were primarily considered. Among the 10 studies, 8 were classified as quantitative research and 2 as qualitative research. The STROBE quality of reporting scores of the studies ranged from 13.5 to 18.0. Three of the 8 quantitative studies employed a before/after study design (prospective studies) and used visual analogue scales (VAS) to measure patient expectations. CONCLUSIONS: There is a growing interest in patients’ expectations of dental implants. Most studies are cross sectional in nature and the quality of reporting varies considerably. Expectations with respect to aesthetics and function are key attributes considered. The use of visual analogue scales (VAS) provides quantitative assessments of patients’ expectations but the lack of standardization of measures prohibits meta- analyses.published_or_final_versio