• Interdisciplinary Bio Central
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• v.1 no.3
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• pp.11.1-11.5
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• 2009

One of the main issues of molecular evolution is to reveal the principles dictating protein evolutionary rates. A traditional hypothesis posits that protein evolutionary rates are mostly determined by the average functional importance of amino acids in a given protein. Thus the correlations of evolutionary rates with different variables such as PPI, gene essentiality and expression abundance have been studied to test the traditional hypothesis. Recently, mRNA expression abundance among the variables has drawn much attention, not only because it shows relatively strong correlation with protein evolutionary rates, but also because of the controversies surrounding an alternative hypothesis against the traditional one. Here, I will give an overview over the traditional hypothesis, and summarize the different variables that have been found to correlate with protein evolutionary rates. Then I will introduce pros and cons on the two different hypotheses.

• The Plant Pathology Journal
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• v.35 no.5
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• pp.498-507
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• 2019

Odontoglossum ringspot virus (ORSV) is a member of the genus Tobamovirus. It is one of the most prevalent viruses infecting orchids worldwide. Earlier studies reported the genetic variability of ORSV isolates from Korea and China. However, the evolutionary rate, timescale, and phylogeographical analyses of ORSV were unclear. Twenty-one coat protein (CP) gene sequences of ORSV were determined in this study, and used them together with 145 CP sequences obtained from GenBank to infer the genetic diversities, evolutionary rate, timescale and migration of ORSV populations. Evolutionary rate of ORSV populations was $1.25{\times}10^{-3}nucleotides/site/y$. The most recent common ancestors came from 30 year ago (95% confidence intervals, 26-40). Based on CP gene, ORSV migrated from mainland China and South Korea to Taiwan island, Germany, Australia, Singapore, and Indonesia, and it also circulated within east Asia. Our study is the first attempt to evaluate the evolutionary rates, timescales and migration dynamics of ORSV.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.301-308
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• 1997

The learning speed of the neural networks, the most important factor in applying to real problems, greatly depends on the learning rate of the networks, Three approaches-empirical, deterministic, and stochastic ones-have been proposed to date. We proposed a new learning-rate selection algorithm using an evolutionary programming search scheme. Even though the performance of our method showed better than those of the other methods, it was found that taking much time for selecting evolutionary learning rates made the performance of our method degrade. This was caused by using static intervals (called static windows) in order to update learning rates. Out algorithm with static windows updated the learning rates showed good performance or didn't update the learning rates even though previously updated learning rates shoved bad performance. This paper introduce a window control scheme to avoid such problems. With the window control scheme, our algorithm try to update the learning ra es only when the learning performance is continuously bad during a specified interval. If previously selected learning rates show good performance, new algorithm will not update the learning rates. This diminish the updating time of learning rates greatly. As a result, our algorithm with the window control scheme show better performance than that with static windows. In this paper, we will describe the previous and new algorithm and experimental results.

• Korean Journal of Psychosomatic Medicine
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• v.30 no.2
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• pp.99-111
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• 2022

Akey factor in evolution is reproduction, which is also a major concern in medicine. Evolutionists have proposed many theories and hypotheses to explain the low fertility rates of modern industrial societies, which are contrary to maximization of biological fitness. Given that childbirth is the most significant factor affecting reproductive fitness, it is likely that a variety of psychological modules related to childbirth behavior and intention evolved over time. Several evolutionary psychological modules have been proposed in relation to reproduction, including sexual desire, status-seeking, a need for nurturing, and the desire for children. Previously adaptive psychological modules may now be expressed maladaptively due to the discrepancy between the Environment of Evolutionary Adaptedness (EEA) and the environment of modern industrial society. Several evolutionary ecological factors influence childbirth intention in modern society, including individual personality factors, childhood life history experiences, and socioecological factors throughout reproductive life. By focusing on mental, social, and ecological factors, this review examines several hypothetical models relating to evolutionary psychological factors and childbirth decisions in modern industrial society, as well as a possible explanation for the low birth rate.

• Journal of Astronomy and Space Sciences
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• v.13 no.1
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• pp.1-8
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• 1996

CCD time series photometry was performed for two Oosterhoff type II field RR Lyrae stars, SV Eri and XX And, to construct light curves in B and V bands. The maximum light times from our observations were combined with those in the literature to construct phase shift diagrams, from which we determined the period change rates ${\beta}$. We obtained large positive period change rates for these stars, which are expected from current evolutionary models if these stars are evolving rapidly from blue to red toward the end of core helium burning phase.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.277-286
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• 1998

This paper investigates the problem of determining optimal replacement policies for equipment subject to failures with cyclic rates. In many situations, the system failures depend on the operating environmental conditions that vary on time, usually with periodical manners. We use nonhomogeneous Poisson processes whose rate functions exhibit cyclic behavior as well as a long-term evolutionary trend to model the stochastic process of the failures when the rate of occurrence of the failures varies periodically, for example from day to day or between seasons. In this study, we compare optimal policies under the nonhomogeneous process with/without a cyclic component in the failure rate function. The analytical results for various situations are presented along with numerical examples using simulated data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.312-326
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• 2021

The prey-predator model is one of the most influential mathematical models in ecology and evolutionary biology. In this study, we considered a modified prey-predator model, which describes the rate of change for each species. The effects of modifications to the classical prey-predator model are investigated here. The conditions required for the existence of the first integral and the stability of the fixed points are studied. In particular, it is shown that the first integral exists only for a subset of the model parameters, and the phase portraits around the fixed points exhibit physically relevant phenomena over a wide range of the parameter space. The results show that adding coupling terms to the classical model widely expands the dynamics with great potential for applicability in real-world phenomena.

• Genomics & Informatics
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• v.21 no.2
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• pp.27.1-27.7
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• 2023

Recombination events complicate the evolutionary history of populations and species and have a significant impact on the inference of isolation-with-migration (IM) models. However, several existing methods have been developed, assuming no recombination within a locus and free recombination between loci. In this study, we investigated the effect of recombination on the estimation of IM models using genomic data. We conducted a simulation study to evaluate the consistency of the parameter estimators with up to 1,000 loci and analyze true gene trees to examine the sources of errors in estimating the IM model parameters. The results showed that the presence of recombination led to biased estimates of the IM model parameters, with population sizes being more overestimated and migration rates being more underestimated as the number of loci increased. The magnitude of the biases tended to increase with the recombination rates when using 100 or more loci. On the other hand, the estimation of splitting times remained consistent as the number of loci increased. In the absence of recombination, the estimators of the IM model parameters remained consistent.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.347-355
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• 2020

An Isolation-with-Migration (IM) model is used to estimate extant population sizes, the splitting time of populations split away from their common ancestral populations, and migration rates between the extant populations. An evolutionary model such as IM models is estimated by analyzing DNA sequences sampled from the extant populations in the model. When a true model includes an unsampled 'ghost' population without data, the unsampled population is often ignored from the evolutionary model to infer. In this paper, we conduct a simulation study to investigate the effect of an unsampled population on the estimation of the size of the sampled population. When there exists an unsampled population that shares migrations with the sampled population, the size estimation of the sampled population was biased. However, the size estimation was improved if an evolutionary model, including the unsampled population, was estimated.