• Korean Chemical Engineering Research
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• v.46 no.5
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• pp.915-921
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• 2008

In most PEM fuel cell research, effects of cell geometry, physical properties of component such as membrane, carbon cloth, catalyst, etc. and water transport phenomena are key issues. The scope of these research was limited to single cell and stack except BOP(Balance of plant) of fuel cell. The research fouced on the fuel cell system usually neglect to consider detailed transport phenomena in the cell. The research of the fuel cell system was interested in a system performance and system dynamics. In this paper, the effect of the anode recirculation is calculated using the 2D steady-state model. For this work, 2D steady-state modeling and experiments are performed. For convenience of modifying of model equation, not commercial pakage but the in-house algorithm was used in simulation. For an vehicle industry, the analysis of the anode recirculation system helps the optimization of operating condition of the fuel cell.

• Genomics & Informatics
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• v.8 no.3
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• pp.150-158
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• 2010

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

• Korean Journal of Chemical Engineering
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• v.35 no.12
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• pp.2327-2335
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• 2018

Fluid catalytic cracking (FCC) is an important chemical process that is widely used to produce valuable petrochemical products by cracking heavier components. However, many difficulties exist in modeling the FCC process due to its complexity. In this study, a dynamic process model of a FCC process is suggested and its structural observability is analyzed. In the process modeling, yield function for the kinetic model of the riser reactor was applied to explain the product distribution. Hydrodynamics, mass balance and energy balance equations of the riser reactor and the regenerator were used to complete the modeling. The process model was tested in steady-state simulation and dynamic simulation, which gives dynamic responses to the change of process variables. The result was compared with the measured data from operating plaint. In the structural analysis, the system was analyzed using the process model and the process design to identify the structural observability of the system. The reactor and regenerator unit in the system were divided into six nodes based on their functions and modeling relationship equations were built based on nodes and edges of the directed graph of the system. Output-set assignment algorithm was demonstrated on the occurrence matrix to find observable nodes and variables. Optimal locations for minimal addition of measurements could be found by completing the whole output-set assignment algorithm of the system. The result of this study can help predict the state more accurately and improve observability of a complex chemical process with minimal cost.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.4
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• pp.275-287
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• 2000

Fermentation strategies for recombinant protein production in Pichia pastoris have been investigated and are reviewed here. Characteristics of the expression system, such as phenotypes and carbon utilization, are summarized. Recently reported results such as growth model establishment, app58lication of a methanol sensor, optimization of substrate feeding strategy, DOstat controller design, mixed feed technology, and perfusion and continuous culture are discussed in detail.

• Genomics & Informatics
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• v.3 no.2
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• pp.47-51
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• 2005

Molecular mechanisms of biological processes are typically represented as 'pathways' that have a graph­analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and the semantics is embedded implicitly. The kinds of interactions that connect biological entities are likewise diverse. Consequently, how to model or process pathway data is not a trivial issue. In this review article, we give an overview of the challenges in pathway database development by taking the INOH project as an example.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Journal of Ecology and Environment
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• v.46 no.4
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• pp.292-303
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• 2022

Background: Climate change significantly influences the geographical distribution of plant species worldwide. Selecting indicator species allows for better-informed and more effective ecosystem management in response to climate change. The Korean Peninsula is the northernmost distribution zone of warm temperate evergreen broad-leaved (WTEB) species in Northeast Asia. Considering the ecological value of these species, we evaluated the current distribution range and future suitable habitat for 13 WTEB tree species designated as climate-sensitive biological indicator species. Results: Up-to-date and accurate WTEB species distribution maps were constructed using herbarium specimens and citizen science data from the Korea Biodiversity Observation Network. Current northern limits for several species have shifted to higher latitudes compared to previous records. For example, the northern latitude limit for Stauntonia hexaphylla is higher (37° 02' N, Deokjeokdo archipelago) than that reported previously (36° 13' N). The minimum temperature of the coldest month (Bio6) is the major factor influencing species distribution. Under future climate change scenarios, suitable habitats are predicted to expand toward higher latitudes inland and along the western coastal areas. Conclusions: Our results support the suitability of WTEB trees as significant biological indicators of species' responses to warming. The findings also suggest the need for consistent monitoring of species distribution shifts. This study provides an important baseline dataset for future monitoring and management of indicator species' responses to changing climate conditions in South Korea.

• Animal cells and systems
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• v.5 no.4
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• pp.313-317
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• 2001

The sequential assessment model describes a fight between two conspecific as an ongoing statistical sampling process, which makes it possible to predict fight length or repetition number of a behavioral element depending on relative RHP (resource holding potential: e.g. weight or fighting ability). We staged contests between males of common freshwater gobies to test some predictions of this model. Fights proceeded in a consistent sequence of phases. Most contests began with two contestants adopting lateral display, and then escalated to intense physical contacts. The length of contests was negatively correlated with weight difference between the contestants. The duration of complete phases was, however, independent of weight, and the prior information gained during complete phases did not appear to affect subsequent phases of the fight. Our results show that the contests of common freshwater gobies are well predicted by the sequential assessment model.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.210-215
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• 2011

Holographic quantitative structure-activity relationships (HQSAR) is a useful tool to correlates structures with their biological activities. HQSAR is a two dimensional (2D) QSAR methodology, which generates QSAR equations through 2D fingerprint and correlates it with biological activity. Here, we report a 2D-QSAR model for a series of fifty-one 3,4-dihydroxychalcones derivatives utilizing HQSAR methodology. We developed HQSAR model with 6 optimum numbers of components (ONC), which resulted in cross-validated correlation coefficient ($q^2$) of 0.855 with 0.283 standard error of estimate (SEE). The non-cross-validated correlation coefficient (r2) with 0.966 indicates the model is predictive enough for analysis. Developed HQSAR model was binned in to a hologram length of 257. Atomic contribution map revealed the importance of dihydroxy substitution on phenyl ring.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.60-64
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• 1995

The conventional neural network models are a parody of biological neural structures, and have very slow learning. In order to emulate some dynamic functions, such as learning and adaption, and to better reflect the dynamics of biological neurons, M.M. Gupta and D.H. Rao have developed a 'dynamic neural model'(DNU). Proposed neural unit model is to introduce some dynamics to the neuron transfer function, such that the neuron activity depends on internal states. Integrating an dynamic elementry processor within the neuron allows the neuron to act dynamic response Numerical examples are presented for a model system. Those case studies showed that the proposed DNU is so useful in practical sense.