• The Korean Journal of Physiology and Pharmacology
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• v.71 no.2
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• pp.223-233
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• 2020

The embryoid body test (EBT) is a developmental toxicity test method that measures the size of embryoid bodies (EBs) and the viability of mouse embryonic stem cells (mESCs) and fibroblasts (3T3 cells). The previous pre-validation study confirmed the high accuracy (above 80%) of EBT using 26 coded test chemicals. This second-phase validation study assessed the inter-laboratory reproducibility (5 chemicals in common) and predictive capacity (10 chemicals in each laboratory) test using the coded test chemicals at three laboratories. For the prediction model, the accuracy is increased when more data is accumulated. Therefore, we updated the prediction model and analyzed the results of the second year with the newly created-prediction model. Statistical analysis of the inter-laboratory reproducibility test results indicated that accuracy, sensitivity, and specificity were 87%, 78%, and 100%, respectively. The results of the statistical analysis of the predictive capacity test showed an accuracy of 80%, sensitivity of 78%, and specificity of 81%. In conclusion, the EBT can accurately classify various embryotoxicants within a short period and with relatively little effort. Therefore, EBT can be used as a good way to test developmental toxicity.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.79-86
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• 2006

Many of concurrency control for client-sewer database systems have been proposed and studied in the literature. However, commonly known schemes do not manage to the case of dramatic changes in data contention because the data contention of database systems is changed for each cases. In this paper, we propose o hybrid concurrency control. The proposed scheme uses a optimistic scheme for the first-run transactions and a pessimistic scheme for the second-run transactions. By pre-claiming locks in the second phase, deadlocks which ore possible in pessimistic based concurrency control are prevented in our approach. In addition, the scheme ensures at most one re-execution even if the validation in the optimistic phase fails. By a detailed simulation, this paper examines the behaviors of the Distributed Optimistic Concurrency Control and the proposed scheme. The simulation study shows our scheme outperforms Distributed Optimistic Concurrency Control in our experimental result.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.481-494
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• 2003

Purpose: This study was to develop nursing competencies, sub-competencies and behavior indicators according to the clinical ladder of emergency nurses. Method: index of content validation was used by 21 clinical experts. Results: This study had three phases to develop nursing competencies, sub-competencies and behavior indicators. In first phase: 12 nursing competencies and 33 sub-competencies were developed through the literature review on nursing competency and emergency nurses' job description. The content of 12 competencies and 33 sub-competencies were reviewed by 3 nursing professors. The 12 competencies and 33 sub-competencies were followed: clinical judgement and measures(6 sub-competencies), processing ability of ward works(2 sub-competencies), flexibility(2 sub-competencies), resources management(2 sub-competencies), confidence(3 sub-competencies), cooperation(2 sub-competencies), professional development power(2 sub-competencies), patient service orientation(3 sub-competencies), inclination toward ethical value(5 sub-competencies), influence power(2 sub-competencies), developing others(2 sub-competencies), self control(2 sub-competencies). In second phase, 132 behavior indicators were developed according to nurse clinical ladder: novice, advanced novice, competent, proficient. In Third phase, content validity was examined on 132 behavior indicators by 21 clinical experts. 126 among 132 indicators had over 70% agreement among experts and 6 indicators under 70% were revised. Conclusion: nursing competencies, sub competencies and behavior indicators can be used nurses' clinical performance as well as establishing proper directions for professional growth related to reward system.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1294-1304
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• 2020

Objective: To determine whether T1 mapping could monitor the dynamic changes of injury in myocardial infarction (MI) and be histologically validated. Materials and Methods: In 22 pigs, MI was induced by ligating the left anterior descending artery and they underwent serial cardiovascular magnetic resonance examinations with modified Look-Locker inversion T1 mapping and extracellular volume (ECV) computation in acute (within 24 hours, n = 22), subacute (7 days, n = 13), and chronic (3 months, n = 7) phases of MI. Masson's trichrome staining was performed for histological ECV calculation. Myocardial native T1 and ECV were obtained by region of interest measurement in infarcted, peri-infarct, and remote myocardium. Results: Native T1 and ECV in peri-infarct myocardium differed from remote myocardium in acute (1181 ± 62 ms vs. 1113 ± 64 ms, p = 0.002; 24 ± 4% vs. 19 ± 4%, p = 0.031) and subacute phases (1264 ± 41 ms vs. 1171 ± 56 ms, p < 0.001; 27 ± 4% vs. 22 ± 2%, p = 0.009) but not in chronic phase (1157 ± 57 ms vs. 1120 ± 54 ms, p = 0.934; 23 ± 2% vs. 20 ± 1%, p = 0.109). From acute to chronic MI, infarcted native T1 peaked in subacute phase (1275 ± 63 ms vs. 1637 ± 123 ms vs. 1471 ± 98 ms, p < 0.001), while ECV progressively increased with time (35 ± 7% vs. 46 ± 6% vs. 52 ± 4%, p < 0.001). Native T1 correlated well with histological findings (R² = 0.65 to 0.89, all p < 0.001) so did ECV (R² = 0.73 to 0.94, all p < 0.001). Conclusion: T1 mapping allows the quantitative assessment of injury in MI and the noninvasive monitoring of tissue injury evolution, which correlates well with histological findings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2455-2475
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• 2021

Patient Navigation Program (PNP) is considered as an important implementation of health care systems that can assist in patient's treatment. Due to the feasibility of PNP implementation, a systematic reuse is needed for a wide adoption of PNP computerized system. SPL is one of the promising systematic reuse approaches for creating a reusable architecture to enabled reuse in several similar applications of PNP systems which has its own variations with other applications. However, stakeholder decision making which result from the imprecise, uncertain, and subjective nature of architecture selection based on quality attributes (QA) further hinders the development of the product line architecture. Therefore, this study aims to propose a quality-driven approach using Multi-Criteria Decision Analysis (MCDA) techniques for Software Product Line Architecture (SPLA) to have an objective selection based on the QA of stakeholders in the domain of PNP. There are two steps proposed to this approach. First, a clear representation of quality is proposed by extending feature model (FM) with QA feature to determine the QA in the early phase of architecture selection. Second, MCDA techniques were applied for architecture selection based on objective preference for certain QA in the domain of PNP. The result of the proposed approach is the implementation of the PNP system with SPLA that had been selected using MCDA techniques. Evaluation for the approach is done by checking the approach's applicability in a case study and stakeholder validation. Evaluation on ease of use and usefulness of the approach with selected stakeholders have shown positive responses. The evaluation results proved that the proposed approach assisted in the implementation of PNP systems.

• Safety and Health at Work
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• v.13 no.3
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• pp.364-371
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• 2022

Background: Respiratory masks can provide healthcare workers with protection from biological hazards when they have good performance. There is a direct relationship between the visual specifications of a mask and its efficacy; thus, the aim of this study was to develop tools for qualitative assessment of the performance of masks used by healthcare workers. Methods: A mixed-methods design was used to develop a qualitative assessment tool for medical face masks (MFM) and particle filtering half masks (PFHM). The development of domains and items was undertaken using observation and interviews, the opinions of an expert panel, and a review of texts and international standards. The second phase evaluated the psychometric properties of tools. Finally, the validated Mask Qualitative Assessment Tools (MQAT) were used to assess six samples from 10 brands of the two types of masks. Results: MQAT-MFM and MQAT-PHFM shared 42 items across seven domains: "cleanliness," "design," "marking, labeling and packaging," "mask layers," "mask strap," "materials and construction," and "nose clip." MQAT-MFM included one additional item. MQAT-PHFM included another nine items associated with an eighth "Practical Performance" domain, and the valve version had another additional "Exhalation Valve" domain and six items. The evaluation indicated 80% compliance for MFM and 71% compliance for PFHM. "Marking, labeling and packaging" and "Layers" were associated with the least compliance in both types of masks and should be checked carefully for defining mask quality. Conclusion: MQAT can be used for immediate screening and initial assessment of MFM and PHFM through appearance, simple tools, and visual inspection.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korean Journal of School Psychology
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• v.17 no.2
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• pp.165-180
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• 2020

As a tiered system of supports, School-Wide Positive Behavior Support (SWPBS) is an evidence-based practice in the educational system of Korea. An important aspect of SWPBS is the ongoing progress monitoring and evaluation of implementation fidelity. This study aimed to develop and validate the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC). The preliminary KT3-FC consisted of a 37-item, 6-factor checklist. In the first phase of the study, 10 experts reported that the range of content validity of the KT3-FC was adequate. In the second phase of the study, 185 teachers (52 men and 133 women) who implemented SWPBS completed the KT3-FC, Individualized Supports Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. An exploratory factor analysis resulted in a 5-factor structure, with 20 items, instead of 37 items, consisting of: (a) progress monitoring and evaluation of the individualized supports, (b) provision of supports by aligning and integrating mental health and SWPBS, (c) crisis management planning, (d) problem behavior assessment, and (e) establishment of individualized support team. The internal consistency of the KT3-FC was good (full scale α = .950, sub-factor α = .888 ~ .954). In addition, the KT3-FC showed good convergent validity, having statistically significant correlations with the Individualized Support Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and the PBS Effectiveness Scale. Finally, the confirmatory factor analysis showed that the 5-factor model of the KT3-FC had some good model fits, indicating that the newly developed fidelity measure could be a reliable and valid tool to assess the implementation of Tier 3 supports in Korean schools. Accordingly, the KT3-FC could contribute to implement SWPBS as an evidence-based behavioral intervention for Korean students.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.1
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• pp.135-146
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• 2000

The purpose of this study was to develope, based on the Nursing Intervention Classification (NIC) system. a set of standardized nursing interventions which had been validated. and their associated activities. for use with nursing diagnoses related to home health care for women who have had a caesarian delivery and for their newborn babies. This descriptive study for instrument development had three phases: first. selection of nursing diagnoses. second, validation of the preliminary home health care interventions. and third, application of the home care interventions. In the first phases, diagnoses from 30 nursing records of clients of the home health care agency at P. medical center who were seen between April 21 and July 30. 1998. and from 5 textbooks were examined. Ten nursing diagnoses were selected through a comparison with the NANDA (North American Nursing Diagnosis Association) classification In the second phase. using the selected diagnoses. the nursing interventions were defined from the diagnoses-intervention linkage lists along with associated activities for each intervention list in NIC. To develope the preliminary interventions five-rounds of expertise tests were done. During the first four rounds. 5 experts in clinical nursing participated. and for the final content validity test of the preliminary interventions. 13 experts participated using the Fehring's Delphi technique. The expert group evaluated and defined the set of preliminary nursing interventions. In the third phases, clinical tests were held at in a home health care setting with two home health care nurses using the preliminary intervention list as a questionnaire. Thirty clients referred to the home health care agency at P. medical center between October 1998 and March 1999 were the subjects for this phase. Each of the activities were tested using dichotomous question method. The results of the study are as follows: 1. For the ten nursing diagnoses. 63 appropriate interventions were selected from 369 diagnoses interventions links in NlC., and from 1.465 associated nursing activities. From the 63 interventions. the nurses expert group developed 18 interventions and 258 activities as the preliminary intervention list through a five-round validity test 2. For the fifth content validity test using Fehring's model for determining lCV (Intervention Content Validity), a five point Likert scale was used with values converted to weights as follows: 1=0.0. 2=0.25. 3=0.50. 4=0.75. 5=1.0. Activities of less than O.50 were to be deleted. The range of ICV scores for the nursing diagnoses was 0.95-0.66. for the nursing interventions. 0.98-0.77 and for the nursing activities, 0.95-0.85. By Fehring's method. all of these were included in the preliminary intervention list. 3. Using a questionnaire format for the preliminary intervention list. clinical application tests were done. To define nursing diagnoses. home health care nurses applied each nursing diagnoses to every client. and it was found that 13 were most frequently used of 400 times diagnoses were used. Therefore. 13 nursing diagnoses were defined as validated nursing diagnoses. Ten were the same as from the nursing records and textbooks and three were new from the clinical application. The final list included 'Anxiety', 'Aspiration. risk for'. 'Infant behavior, potential for enhanced, organized'. 'Infant feeding pattern. ineffective'. 'Infection'. 'Knowledge deficit'. 'Nutrition, less than body requirements. altered', 'Pain'. 'Parenting'. 'Skin integrity. risk for. impared' and 'Risk for activity intolerance'. 'Self-esteem disturbance', 'Sleep pattern disturbance' 4. In all. there were 19 interventions. 18 preliminary nursing interventions and one more intervention added from the clinical setting. 'Body image enhancement'. For 265 associated nursing activities. clinical application tests were also done. The intervention rate of 19 interventions was from 81.6% to 100%, so all 19 interventions were in c1uded in the validated intervention set. From the 265 nursing activities. 261(98.5%) were accepted and four activities were deleted. those with an implimentation rate of less than 50%. 5. In conclusion. 13 diagnoses. 19 interventions and 261 activities were validated for the final validated nursing intervention set.