• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.1-8
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• 2009

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. The optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement is generally accepted. The Bayesian parametric inference of model using log Poisson execution time employ tool of Markov chain(Gibbs sampling and Metropolis algorithm). In a numerical example by T1 data was illustrated. make out estimating software optimal release time from the maximum likelihood estimation and Bayesian parametric estimation.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.161-170
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• 1994

This paper presents the performance and problems in analysis method and testing system of Electronic Speckle Pattern Interfermetry(ESPI) method, in measuring two-dimensional in- plane displacement. The analysis result of measurement by ESPI is quite comparable to that of measurement by strain gauge method. This implieds that the method of ESPI is a very effective tool in non-contact two-dimensional in-plane strain analysis. But there is a controversial point, measurement error. This error is discussed to be affected not by ESPI method itself, but by its analysis scheme of the interference fringe, where the first-order interpolation has been applied to the points of strain measured. Further development of advanced first-order interpolation method is being undertaken for the more precise in-plane strain measurement.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.611-624
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• 2023

Soil properties make it attractive as a building material due to its mechanical strength, aesthetically appearance, plasticity, and low cost. However, it is frequently necessary to improve and stabilize the soil mechanical properties with binders. Soil-cement is applied for purposes ranging from housing to dams, roads and foundations. Unconfined compression strength (UCS) and split tensile strength (CD) are essential mechanical parameters for ascertaining the aptitude of soil-cement for a given application. However, quantifying these parameters requires specimen preparation, testing, and several weeks. Methodologies that allowed accurate estimation of mechanical parameters in shorter time would represent an important advance in order to ensure shorter deliverable timeline and reduce the amount of laboratory work. In this work, an extensive campaign of UCS and CD tests was carried out in a sandy soil from the Leiria region (Portugal). Then, using the machine learning tool Neural Pattern Recognition of the MATLAB software, a prediction of these two parameters based on six input parameters was made. The results, especially those obtained with resource to a Bayesian regularization-backpropagation algorithm, are frankly positive, with a forecast success percentage over 90% and very low root mean square error (RMSE).

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.279-295
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• 2024

Purpose: This study aimed to develop a scale to measure hospital nurses' silence behavior and examine its validity and reliability. Methods: A total of 52 preliminary items on hospital nurses' silence behavior were selected using a content validity test by seven experts on 53 candidate items derived from a literature review and in-depth interviews with 14 nurses. A total of 405 hospital nurses participated in a psychometric testing. Data analysis comprised item analysis, exploratory and confirmatory factor analyses, and convergent and discriminant validity tests. Pearson's correlation coefficient was used for assessing concurrent validity, and Cronbach's alpha was used for the reliability test. Results: The final scale consisted of nine factors with 31 items, exhibiting acceptable model fit indices, convergent validity, and discriminant validity. The score of the entire scale was positively correlated with the 'Organizational Silence Scale (OSS)-the issues on which nurses remain silent' (r = .60, p < .001) and 'OSS-the reasons why nurses remain silent' (r = .68, p < .001). Cronbach's α of the scale was .92, and α of each subscale ranged from .71 to .90. Conclusion: The Hospital Nurses' Silence Behavior Scale is a useful tool for assessing multifaceted silence behavior among nurses. It can provide basic data for developing better communication strategies among nurses and other hospital staff.

• BMB Reports
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• v.57 no.1
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• pp.50-59
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• 2024

The application of gene engineering in livestock is necessary for various reasons, such as increasing productivity and producing disease resistance and biomedicine models. Overall, gene engineering provides benefits to the agricultural and research aspects, and humans. In particular, productivity can be increased by producing livestock with enhanced growth and improved feed conversion efficiency. In addition, the application of the disease resistance models prevents the spread of infectious diseases, which reduces the need for treatment, such as the use of antibiotics; consequently, it promotes the overall health of the herd and reduces unexpected economic losses. The application of biomedicine could be a valuable tool for understanding specific livestock diseases and improving human welfare through the development and testing of new vaccines, research on human physiology, such as human metabolism or immune response, and research and development of xenotransplantation models. Gene engineering technology has been evolving, from random, time-consuming, and laborious methods to specific, time-saving, convenient, and stable methods. This paper reviews the overall trend of genetic engineering technologies development and their application for efficient production of genetically engineered livestock, and provides examples of technologies approved by the United States (US) Food and Drug Administration (FDA) for application in humans.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.360-367
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• 2024

The purpose of this study was to examine the role of parental support in mitigating the impact of discrimination experiences on depression among children and adolescents. This focus was based on the premise that experiences of discrimination can negatively affect peer relationships as well as behavioral and emotional development in youths who spend a significant amount of time with their peers at school. We aimed to explore the mediating role of parental support and to present policy and practical recommendations from a social welfare perspective. For this purpose, data from the "2020 Survey on the Human Rights of Children and Adolescents" was utilized, involving 9,000 students ranging from 4th to 6th grade in elementary school, grade in middle school, and grade in high school from across the country. The study was conducted targeting these groups. For research analysis, frequency analysis using SPSS 26.0 was employed to calculate the general characteristics of the study subjects and the reliability coefficient of the testing tool. And regression analysis was conducted to verify the mediating effect of parental support on the impact of discrimination experiences on depression. The analysis revealed that there were 4,473 males (51.9%) and 4,150 females (48.1%), and that experiences of discrimination had a negative effect on depression (B=311, P<0.001). It was found that the more frequent the experiences of discrimination, the higher the level of depression, and the more a youth experienced discrimination, the greater the psychological depression they endured.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.130-135
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• 2024

The Stable diffsuion AI tool is popular among designers because of its flexible and powerful image generation capabilities. However, due to the diversity of its AI models, it needs to spend a lot of time testing different AI models in the face of different design plans, so choosing a suitable general AI model has become a big problem at present. In this paper, by comparing the AI images generated by two different Stable diffsuion models, the advantages and disadvantages of each model are analyzed from the aspects of the matching degree of the AI image and the prompt, the color composition and light composition of the image, and the general AI model that the generated AI image has an aesthetic sense is analyzed, and the designer does not need to take cumbersome steps. A satisfactory AI image can be obtained. The results show that Playground V2.5 model can be used as a general AI model, which has both aesthetic and design sense in various style design requirements. As a result, content designers can focus more on creative content development, and expect more groundbreaking technologies to merge generative AI with content design.

• Korean Journal of Materials Research
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• v.34 no.2
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• pp.63-71
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• 2024

Slipchip offers advantages such as high-throughout, low cost, and simple operation, and therefore, it is one of the technologies with the greatest potential for high-throughput, single-cell, and single-molecule analyses. Slipchip devices have achieved remarkable advances over the past decades, with its simplified molecular diagnostics gaining particular attention, especially during the COVID-19 pandemic and in various infectious diseases scenarios. Medical testing based on nucleic acid amplification in the Slipchip has become a promising alternative simple and rapid diagnostic tool in field situations. Herein, we present a comprehensive review of Slipchip device advances in molecular diagnostics, highlighting its use in digital recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), and polymerase chain reaction (PCR). Slipchip technology allows users to conduct reliable droplet transfers with high-throughput potential for single-cell and molecule analyses. This review explores the device's versatility in miniaturized and rapid molecular diagnostics. A complete Slipchip device can be operated without special equipment or skilled handling, and provides high-throughput results in minimum settings. This review focuses on recent developments and Slipchip device challenges that need to be addressed for further advancements in microfluidics technology.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.