• The Microorganisms and Industry
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• v.19 no.4
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• pp.32-35
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• 1993

ras는 활성화 형태인 GTP bound form과 비활성화 형태인 GDP bound form의 두 형태로 존재하며 두 형태를 매개하는 regulatory protein들에 의해 그 activity가 조절된다. 또한 ras는 GTP와 GDP에 강한 친화성이 있으며 세포내에는 GTP보다 GDP가 더 많이 있어서 평소에는 ras가 GDP와 결합하고 있다가 활성화될때만 GTP와 결합하는 것으로 추정된다. GDP bound ras는 guanine nucloetide exchange protein(GEP)에 의해 활성화된 GTP bound form으로 전환되며 ras의 기능이 발휘된 후에는 GTPase activating protein(GAP)에 의해 비활성화된다. Yeast의 경우 IRA1과 2의 product가 GAP의 역할을 하는 것으로 알려져 있고 CDC25 gene의 product가 GEP의 기능을 담당하는 것으로 알려져 있다. NF1 gene은 Von Recklinghausen Neurofibromatosis Type I 질병을 가진 환자에게서 발견되었는데 부분적으로 sequencing한 결과에 따르면 yeast의 IRA1/2, mammalian GAP gene product와 protein homology가 높은 것으로 나타났다. Yeast의 경우 IRA1/2 gene의 손실이나 mammalian ras gene의 transformation으로 인한 heat shock sensitivity가 NF1 gene(2,3) 혹은 GAP(4)의 expression으로 suppression된 것으로 보아 NF1이 GAP protein으로서 ras를 불활성화 시킨다는 것이 판명되었다. 결론적으로 ras의 활성은 GTP bound 혹은 GDP bound의 양쪽형태를 이동하면서 조절되는데 이 기능은 GAP과 GEP 또는 그의 유사 protein들에 의해 수행되며 이러한 regulatory protein들은 growth factor, cytokine 그리고 protein kinase 같은 signal에 의해 활성화된다고 생각된다. 본 총설에서는 ras protein의 여러가지 성질보다는 ras의 modification과 관련하여 항암제로 사용할 수 있는 ras에 specific한 약품개발의 가능성과 현재 알려진 ras의 inhibitor를 중심으로 논하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Childhood Kidney Diseases
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• v.9 no.2
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• pp.119-127
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• 2005

Purpose : Podocytes are critical in maintaining the filtration barrier of the glomerulus and are dependent on the integrity of slit diaphragm(SD) proteins including nephrin, p-cadherin, and others. Diabetic proteinuric condition demonstrates defects in SD molecules as well as ultrastructural changes in podocytes. We examined the molecular basis for this alteration of SD molecules especially on P-cadherin as a candidate regulating the modulation of pathogenic changes in the barrier to protein filtration. Methods : To investigate whether high glucose and AGE induce changes in SD, we cultured rat GEpC under normal(5 mM) or high glucose(30 mM) and AGE- or BSA-added conditions and measured the change of P-cadherin expression by Western blotting and RT-PCR. Results : We found that administration of high glucose decreased the P-cadherin production significantly in the presence or absence of AGE by Western blotting. In RT-PCR high glucose with or without AGE also significantly decreased the expression of P-cadherin mRNA compared to those of controls. Such changes were not seen in the osmotic control. Conclusion : We suggest that high glucose with or without AGE suppresses the Production of P-cadherin at the transcriptional level and that these changes nay explain the functional changes of SD in diabetic conditions. (J Korean Soc Pediatr Nephrol 2005;9:119-127)

• Life and Agrochemicals
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• s.265
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• pp.42-45
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• 2011

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.9 no.2
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.9
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• pp.521-528
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• 2004

The demise of the native franchise markets and the emergence of competitive markets in electricity generation service is substantially altering the way that operation and planning activity is conducted and is making it increasingly difficult for market participants such as generation firms to prospect the future electricity markets. Traditional generation expansion planning (GEP) problems which centrally determine the least-cost capacity addition plan that meets forecasted demand within pre-specified reliability criteria over a planning horizon (typically 10 to 20 years) is becoming no more valid in competitive market environments. Therefore, it requires to develop a new methodology for generation investments, which is applicable to the changed electric industry business environments and is able to address the post-privatization situation where individual generation firms seek to maximize their return on generation investments against uncertain market revenues. This paper formulates a new generation expansion planning problem and solve it in a market-oriented manner.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.577-591
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• 2022

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known 𝛼-cut approach, and the Genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, 𝛼. As this is a complicated and time-consuming process, the authors propose utilizing the Gene Expression Programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

• Korean Management Science Review
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• v.17 no.1
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• pp.117-134
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• 2000

Pubilc utility industries including the electric utility industry are facing a new stream of privatization com-petition with the private sector and deregulation. The necewssity to solve now and in the future power supply and demand problems has been increasing through the sophisticated generation expansion plan(GEP) approach con-sidering not only KEPCo's supply-side resources but also outside resources such as non-utility generation(NUG) demand-side management (DSM). Under the environmental situation in the current electric utility industry a new approach is needed to acquire multiple resources competitively. This study presents the development of a modified electric generation expansion analysis system(EGEAS) model with avoided cost based on the existing EGEAS model which is a dynamic program to develope an optimal generation expansion plan for the electric utility. We are trying to find optimal GEP in Korea's case using our modified model and observe the difference for the level of reliabilities such as the reserve margin(RM) loss of load probability(LOLP) and expected unserved energy percent(EUEP) between the existing EGEAS model and our model. In addition we are trying to calculate avoided cost for NUG resources which is a criterion to evaluate herem and test possibility of connection calculation of avoided cost with GEP implementation using our modified model. The results of our case study are as follows. First we were able to find that the generation expansion plan and reliability measures were largely influenced by capacity size and loading status of NUG resources, Second we were able to find that avoided cost which are criteria to evaluate NUG resources could be calculated by using our modified EGEAS model with avoided cost. We also note that avoided costs were calculated by our model in connection with generation expansion plans.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.53-59
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• 2012

This article reviews the current issues of engineering education: engineering creativity, R&D 3.0 and Education 3.0 and comfirms need of refining quality of engineering education through learner-centered hybrid project learning. this article suggests the Global engineering project(GEP) program as an ideal hybrid project learning model that develop student's creativity and convergence capability. GEP program is learnner-centered interdisciplinary program that whole processes are managed by interdisciplinary students team aiming to global engineers who are globally competent and locally relevant so that they can function effectively in any country by local activity in developing country. The program consist of four main parts: 1)pre activity, 2)local activity from abroad, 3)design and producing prototype and 4)participating in the design contest or academic conference with the products. As a result, students' global competence, teamwork skill and capability of creative problem solving are remarkably improved.