• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.126-132
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• 2016

Objectives: This study aims to provide basic data regarding the bacterial total plate count in the atmospheric environment for related studies. Methods: Total plate count and the identification of culturable bacteria in the atmospheric environment in Incheon took place in 2015 using periodic survey. Correlationship analysis was performed between the number of culturable bacteria and environmental elements. In addition, an estimation of novel bacterial species was undertaken using the similarities and phylogenetic tree based on the 16S rRNA gene. Results: The total plate count of culturable bacteria was on average $176CFU/m^3$, and did not exceed $610CFU/m^3$ in the atmospheric environment. Periodic monthly measuring of total plate count was highest in June at $293CFU/m^3$, while the lowest was in July at $125CFU/m^3$. Furthermore, as a result of the identification of culturable bacteria, the genera Arthrobacter and Kocuria were dominant, while novel bacterial taxa that belong to the genera Chryseobacterium and Herbiconiux were separated. Conclusion: The total number of culturable bacteria from the atmospheric environment in Korea is on average $176CFU/m^3$. In addition, the genera Arthrobacter and Kocuria dominate. The presence of novel bacterial taxa are expected in the atmospheric environment, such as belonging to the genera Chryseobacterium and Herbiconiux.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.575-580
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• 2011

With the increasing expectation on personalized medicine, it is getting importance to analyze medical information in molecular biology perspective. Gene expression data are one of representative ones to show the microscopic phenomena of biological activities. In gene expression data analysis, one of major concerns is to identify markers which can be used to predict disease occurrence, progression or recurrence in the molecular level. Existing markers candidate identification methods mainly depend on statistical hypothesis test methods. This paper proposes a search method based decision tree induction to identify candidate markers which consist of multiple genes. The propose method discretizes numeric expression level into three categorical values and allows candidate markers' genes to be expressed by their negation as well as categorical values. It is desirable to have some number of genes to be included in markers. Hence the method is devised to try to find candidate markers with restricted number of genes.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.93-100
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• 2005

This study is to desist and implement an efficient microbial medical image retrieval system based on knowledge and content of them which can make use of more accurate decision on colony as doll as efficient education for new techicians. For this. re first address overall inference to set up flexible search path using rule-base in order U redure time required original microbial identification by searching the fastest path of microbial identification phase based on heuristics knowledge. Next, we propose a color ffature gfraction mtU, which is able to extract color feature vectors of visual contents from a inn microbial image based on especially bacteria image using HSV color model. In addition, for better retrieval performance based on large microbial databases, we present an integrated indexing technique that combines with B+-tree for indexing simple attributes, inverted file structure for text medical keywords list, and scan-based filtering method for high dimensional color feature vectors. Finally. the implemented system shows the possibility to manage and retrieve the complex microbial images using knowledge and visual contents itself effectively. We expect to decrease rapidly Loaming time for elementary technicians by tell organizing knowledge of clinical fields through proposed system.

• Journal of Navigation and Port Research
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• v.40 no.6
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• pp.391-400
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• 2016

A number of maritime accidents, and accident response activities, including the command and control procedures that were implemented at accident scenes, are analyzed to derive useful information about responding to maritime accidents, and to understand how the chain of events developed after the initial accident. In this research, a new concept of a 'risk based accident response support system' is proposed. In order to identify the event chains and associated hazards related to the accident response activities, this study proposes a 'Brainstorming technique for scenario identification', based on the concept of the HAZID technique. A modified version of Event Tree Analysis was used for quantitative risk analysis of maritime accident response activities. PERT/CPM was used to analyze accident response activities and for calculating overall (expected) response activity completion time. Also, the risk based accident response support system proposed in this paper is explained using a simple case study of risk analysis for oil tanker grounding accident response.

• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.515-519
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• 2007

An imperfect fungus Fusiococcum species was isolated from Quercus dentata. A naturally infected Daimyo oak tree was collected and showed elongate wounds on the stem. The fungal cultures were initially white and cottony, and later turned dark gray. Numerous solitary pycnidia were developed on the medium surface, and typically spherical. Yellowish conidial masses were exuded from pycnidia on the culture plates. Conidial masses were swollen and measured as approximately 100 to $300{\mu}m$ in length. It appeared that conidia were usually held together in globose to oval drops. Conidia were hyaline, single-celled (nonseptate), ellipsoid to fusoid, and measured as approximately $8.0{\times}2.7{\mu}m$. Based on these cultural and morphological characteristics, the fungal isolate was identified as a species of Fusicoccum Corda. To preserve and examine fungal spores exuded from pycnidia on the medium surface, a vapor fixation procedure for scanning electron microscopy was employed in this study. The specimens were exposed to the vapor of 2% (v/v) glutaraldehyde and 2% (w/v) osmium tetroxide each for 2 h. With the vapor fixation we obtained excellent retention of conidial masses in this study. The simple and versatile procedure for demonstrating fungal spores and their exudation from fruiting bodies would facilitate characterization of diverse pathological and environmental isolates as they are in native environments.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.83-89
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• 2010

Taxonomic position of 46 Korean isolates of Rhizoctonia solani which were classified into nine intraspecific groups by anastomosis and cultural characteristics was analyzed by randomly amplified polymorphic DNA (RAPD) and sequence analyses of the internal transcribed spacer (ITS) regions of ribosomal DNA. All the isolates within each group showed highly similar band patterns in RAPD. The ITS regions of the isolates within the same groups showed a high level of sequence similarity above 96.0% whereas similarities among different groups were below 94.4%. When compared with several reference strains of R. solani from foreign countries, all the Korean isolates were clustered with the foreign isolates belonging to the same groups in the phylogenetic tree. All six Korean strains of AG-4 were identified as HG-1 out of 3 subgroup of AG-4. We discussed taxonomic position of Korean isolates of R. solani and showed that sequence analysis with ITS regions could be a rapid and useful method for identification of intraspecific group of R. solani.

• KSBB Journal
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• v.32 no.3
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• pp.179-186
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• 2017

This study was undertaken to identify and characterize hemolytic Bacillus cereus isolated from commercial ssam-jang. The physiological and biochemical properties of isolate were first examined. Using the BIOLOG system, the isolate was identified and assigned to B. cereus MH-2. Phylogenetic tree of MH-2 was plotted based on 16S rRNA sequence comparisons. Hemolytic activity was observed around wells of sheep blood agar plates seeded with MH-2 cultures; the zone of hemolysis gradually increased with increasing incubation time of the cultures. Zymographic analysis estimated the molecular weight of the presumed hemolysis-causing molecule to be about 30 kDa. Survival rates of MH-2 cells decreased with increasing NaCl concentrations in the media. The stress shock proteins (e.g., DnaK and GroEL) induced by NaCl were reduced in proportion to the NaCl concentration and exposure period to B. cereus MH-2. Analysis of SDS-PAGE and Western blot revealed that the stress shock proteins, 70-kDa DnaK and 60-kDa GroEL were decreased proportionate to the NaCl concentrations as well as exposure period in exponentially growing cultures. Scanning electron microscopy demonstrated the presence of perforations and irregular rod forms with wrinkled surfaces in cells treated with NaCl.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• Computers and Concrete
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• v.30 no.1
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• pp.43-74
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• 2022

Machine learning technique is recently opening new opportunities to identify the complex shear transfer mechanisms of reinforced concrete (RC) beam members. This study employed 1224 shear test specimens to train decision tree-based machine learning (ML) programs, by which strong correlations between shear capacity of RC beams and key input parameters were affirmed. In addition, shear contributions of concrete and shear reinforcement (the so-called V_c and V_s) were identified by establishing three independent ML models trained under different strategies with various combinations of datasets. Detailed parametric studies were then conducted by utilizing the well-trained ML models. It appeared that the presence of shear reinforcement can make the predicted shear contribution from concrete in RC beams larger than the pure shear contribution of concrete due to the intervention effect between shear reinforcement and concrete. On the other hand, the size effect also brought a significant impact on the shear contribution of concrete (V_c), whereas, the addition of shear reinforcements can effectively mitigate the size effect. It was also found that concrete tends to be the primary source of shear resistance when shear span-depth ratio a/d<1.0 while shear reinforcements become the primary source of shear resistance when a/d>2.0.