• Computers and Concrete
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• v.22 no.5
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• pp.493-500
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• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

• Korean Journal of Mathematics
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• v.27 no.1
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• pp.1-8
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• 2019

We will deal with an n locus model in which mutation and gene conversion are taken into consideration. Also random partitions of the number n determined by chromosomes with n loci should be investigated. The diffusion process describes the time evolution of distributions of the random partitions. In this paper, we find the probability of distribution of the diffusion process with special diffusion operator $L_1$ and we show that the average probability of genes at different loci on one chromosome can be described by the rate of gene frequency of mutation and gene conversion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.

• Journal of Microbiology
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• v.35 no.3
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• pp.234-238
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• 1997

Conditional lethal mutation nup49-1 of a nuclear pore complex component gene was constructed in Saccharomyces cerevisiae. This mutation deleted one third of the essential NUP49 gene at the carboxy-terminal, but retained 13 repeats of the highly conserved GLFG domain. The nup49-1 mutant strain was viable with a slow-growth phenotype, indicating that the C-terminal is dispensable at normal growth temperature. This strain exhibited both temperature-sensitivity at 37.deg.C and cold-sensitivity at 16.deg.C. Temperature shift experiments revealed that the arrest phenotype at 37.deg.C was random in the cell division cycle. The nup49-1 mutation was tested to be recessive and is expected to be useful for the functional analysis of nuclear pore complex proteins as well as for studies of nuclear transport systems.

• Journal of Microbiology
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• v.34 no.2
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• pp.166-171
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• 1996

Genetic analysis was conducted on newly isolated coliphages form soil by using a RAPD assay. From the initial result, the coliphages were turned out to be different form one another but were closely related to .psi..lambda. due to the fact that they shared the samed RAPD maker in which other T phage testings failed to show. By using the primers EC01 or EC02, a fast genetic mutation of .psi.C1 was found by producing specific RAPD markers on the phages from the first filial progeny to the second filial progeny. When we made a RAPD assay with combined primers (EC01, EC05 and EC08), the genetic mutation was again confirmed in .psi.C1. The assay detection showed mutations in other coliphages such as .psi.C2 and .psi.C3 by revealing specific RAPD bands among different progeny phages, where genetic instability of the coliphages in implied.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4493-4497
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• 2014

Background: Epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) can predict the clinical response to tyrosine kinase inhibitor (TKI) therapy. However, EGFR mutations may be different in primary tumors (PT) and metastatic lymph nodes (MLN). The aim of this study was to compare EGFR mutations between PT and the corresponding MLN in NSCLC patients, and provide some guidelines for clinical treatment using TKI therapy. Materials and Methods: A systematic review and meta-analysis was performed with several research databases. Relative risk (RR) with the 95% confidence interval (CI) were used to investigate the EGFR mutation status between PT and the corresponding MLN. A random-effects model was used. Results: 9 publications involving 707 patients were included in the analysis. It was found that activation of EGFR mutations identified in PT and the corresponding MLN was 26.4% (187/707) and 19.9% (141/707), respectively. The overall discordance rate in our meta-analysis was 12.2% (86/707). The relative risk (RR) for EGFR mutation in PT relative to MLN was 1.33 (95%CI: 1.10-1.60; random-effects model). There was no significant heterogeneity between the studies ($I^2$=5%, p=0.003). Conclusions: There exists a considerable degree of EGFR mutation discrepancy in NSCLC between PT and corresponding MLN, suggesting that tumor heterogeneity might arise at the molecular level during the process of metastasis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.727-730
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• 2016

TSC means classifying time series data based on pattern. Time series data is quite common data type and it has high potential in many fields, so data mining and machine learning have paid attention for long time. In traditional approach, distance and dictionary based methods are quite popular. but due to time scale and random noise problems, it has clear limitation. In this paper, we propose a novel approach to deal with these problems with CNN and data mutation. CNN is regarded as proven neural network model in image recognition, and could be applied to time series pattern recognition by extracting pattern. Data mutation is a way to generate mutated data with different methods to make CNN more robust and solid. The proposed method shows better performance than traditional approach.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5375-5379
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• 2012

Introduction: Published studies on the association between Nijmegen breakage syndrome 1(NBS1) gene polymorphisms and breast cancer risk have been inconclusive, and a meta-analysis was therefore performed for clarification. Methods: Eligible articles were identified by a search of MEDLINE and EMBASE bibliographic databases for the period up to March 2012. The presence of between-study heterogeneity was investigated using the chi-square-based Cochran's Q statistic test. When there was statistical heterogeneity, the random effects model was chosen; otherwise, fixed effects estimates were reported as an alternative approach. Results: A total of 11 eligible articles (14 case-control studies) were identified, nine case-control studies were for the 657del5 mutation (7,534 breast cancer cases, 14,034 controls) and five case-control studies were for the I171V mutation (3,273 breast cancer cases, 4,004 controls). Our analysis results indicated that the 657del5 mutation was associated with breast cancer risk (carriers vs. non-carriers: pooled OR =2.63, 95% CI: 1.76-3.93), whereas the I171V mutation was not (carriers vs. non-carriers: pooled OR =1.52, 95% CI: 0.70-3.28). Conclusion: The present meta-analysis suggests that the 657del5 gene mutation in the NBS1 gene plays a role in breast cancer risk, while the I171V mutation does not exert a significant influence.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.140-140
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• 2000

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. In this paper, we use random process and simulated annealing instead of mutation operator in order to get well tuned fuzzy rules. The key of this approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.81-86
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• 2001

one key goal of software testing is to generate a 'good' test data set, which is consideres as the most difficult and time-consuming task. This paper discusses how genetic algorithns can be used for automatic generation of test data set for software testing. We employ mutation testing to show the effectiveness of genetic algorithms (GAs) in automatic test data generation. The approach presented in this paper is different from other in that test generation process requireas no lnowledge of implementation details of a program under test. In addition, we have conducted some experiments and compared our approach with random testing which is also regarded as a black-box test generation technique to show its effectiveness.