• Asian-Australasian Journal of Animal Sciences
• /
• v.24 no.11
• /
• pp.1504-1513
• /
• 2011

It is important to identify genetic interactions related to human diseases or animal traits. Many linear statistical models have been reported but they did not consider genetic interactions. Genotype matrix mapping (GMM) has been developed to identify genetic interactions. This study uses the GMM method to detect superior SNP combinations of the CCDC158 gene that influences average daily gain, marbling score, cold carcass weight and longissimus muscle dorsi area traits in Hanwoo. We evaluated the statistical significance of the major SNP combinations selected by implementing the permutation test of the F-measure. The effect of g.34425+102 A>T (AA), g.8778G>A (GG) and g.4102+36T>G (GT) SNP combinations produced higher performance of average daily gain, marbling score, cold carcass weight and the longissimus muscle dorsi area traits than the effect of a single SNP. GMM is a fast and reliable method for multiple SNP analysis with potential application in marker-assisted selection. GMM may prospectively be used for genetic assessment of quantitative traits after further development.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.729-740
• /
• 2020

The study examines the efficiency of private sector banks in India with the help of Window DEA (Data Envelopment Analysis) for a period from 2005 to 2017. With a window of three years, the period was divided into 11 windows. The study outcomes show that 59.9% of all private sector banks in India operate at more than 0.9 level of efficiency, and there are only three occasions when banks were operating at the efficiency value between 0.6 to 0.7. Further, the consistency in the efficiency scores of the banks has also been analyzed using an efficiency mapping matrix, and the mean efficiency score of the bank in each window is studied. The score of standard deviation was interpreted accordingly for these banks. Banks that are showing the highest efficiency scores also have a higher variance of efficiency scores. There was no bank identified in the matrix that promises high-efficiency ratings with low variability. The study concludes that the analysis of the efficiency mapping matrix indicates that, as a DMU escalates in the efficiency scores, the standard deviation reflecting the risk in overall efficiency scores also tends to rise. The findings complement the concept of higher risk to higher return or greater efficiency.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.2
• /
• pp.102-110
• /
• 2010

Music transcription is extracting pitch (the height of a musical note) and rhythm (the length of a musical note) information from audio file and making a music score. In this paper, we decomposed a waveform into frequency and rhythm components using Non-Negative Matrix Factorization (NMF) and Non-Negative Sparse coding (NNSC) which are often used for source separation and data clustering. And using the subharmonic summation method, fundamental frequency is calculated from the decomposed frequency components. Therefore, the accurate pitch of each score can be estimated. The proposed method successfully performed music transcription with its results superior to those of the conventional methods which used either NMF or NNSC.

• Journal of Microbiology
• /
• v.33 no.2
• /
• pp.95-102
• /
• 1995

A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the chuster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. Theresults show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.

• Health Policy and Management
• /
• v.10 no.4
• /
• pp.99-115
• /
• 2000

This study was conducted to get a resonable set of budget allocation to public health programs. Matrix Delphi technique was used to obtain the logic of study results and eventually to form a human model which could predict opinion of professionals on budget allocation. Thirty-two professionals in academic and governmental area responded to Delphi survey. Questionnaire was developed using matrix formation, and the matrix was formed by 6 decision criteria on budget allocation and 26 public health programs. The decision criteria are as following: size of problem(morbidity), severity of problem, social equity, importance of prevention, technical feasibility and efficiency of programs. Severity of problem dropped out of the model because it had significant correlation with the size of problem. A total score of each program was obtained by weighting the relative importance of each criteria which also were given by survey respondents. These total scores indicate that the most important public health program is vaccination for infants and children in terms of budget allocation. Monitoring communicable diseases, mental health program, and anti-smoking program are the next. In addition, respondents were asked of the desirable budget size of each program. The result was rearranged by multiple regression model using the scores of each decision criteria. In this process, the current budget size of central government was provided to the respondents, and included in the model. h set of desirable budgets modified using tile model was obtained. Considering the current size of budget, tile results of the model is very different from that of the total score. Managing dementia is ranked the first. Health promotion program for the elderly, rehabilitation of the disabled and monitoring communicable diseases are the next. The need to increase the budget of vaccination for the infants and children was not found as so high. The matrix structure in Delphi survey gave us the precise basis to make optimal decision, and made it possible to develop an opinion predicting model. However the plentifulness and diversity of professional opinions were not fully obtained due to the limited number of decision criteria.

• Journal of Internet Computing and Services
• /
• v.20 no.1
• /
• pp.1-10
• /
• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.3057-3075
• /
• 2020

End-to-end network delay plays an vital role in distributed services. This delay is used to measure QoS (Quality-of-Service). It would be beneficial to know all node-pair delay information, but unfortunately it is not feasible in practice because the use of active probing will cause a quadratic growth in overhead. Alternatively, using the measured network delay to estimate the unknown network delay is an economical method. In this paper, we adopt the state-of-the-art matrix completion technology to better estimate the network delay from limited measurements. Although the number of measurements required for an exact matrix completion is theoretically bounded, it is practically less helpful. Therefore, we propose an online adaptive sampling algorithm to measure network delay in which statistical leverage scores are used to select potential matrix elements. The basic principle behind is to sample the elements with larger leverage scores to keep the traits of important rows or columns in the matrix. The amount of samples is adaptively decided by a proposed stopping condition. Simulation results based on real delay matrix show that compared with the traditional sampling algorithm, our proposed sampling algorithm can provide better performance (smaller estimation error and less convergence pressure) at a lower cost (fewer samples and shorter processing time).

• Asian Journal of Innovation and Policy
• /
• v.5 no.3
• /
• pp.251-274
• /
• 2016

Practical methodology for categorizing collaborative disciplines or research in a quantitative manner is presented by developing a Correlation Matrix of Major Disciplines (CMMD) using bibliometric data collected between 2009 and 2014. First, 21 major disciplines in science and engineering are defined based on journal publication frequency. Second, major disciplines using a comparing discipline correlation matrix is created and correlation score using CMMD is calculated based on an analyzer function that is given to the matrix elements. Third, a correlation between the major disciplines and 14 research fields using CMMD is calculated for validation. Collaborative researches are classified into three groups by partially accepting the definition of pluri-discipline from peer review manual, European Science Foundation, inner-discipline, inter-discipline and cross-discipline. Applying simple categorization criteria identifies three groups of collaborative research and also those results can be visualized. Overall, the proposed methodology supports the categorization for each research field.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2002.06a
• /
• pp.5-23
• /
• 2002

Motivation: Protein-protein interaction plays a critical role in the biological processes. The identification of interacting proteins by bioinformatical methods can provide new lead In the functional studies of uncharacterized proteins without performing extensive experiments. Results: Protein-protein interactions are predicted by a computational algorithm based on the weighted scoring system for domain interactions between interacting protein pairs. Here we propose potential interaction domain (PID) pairs can be extracted from a data set of experimentally identified interacting protein pairs. where one protein contains a domain and its interacting protein contains the other. Every combinations of PID are summarized in a matrix table termed the PID matrix, and this matrix has proposed to be used for prediction of interactions. The database of interacting proteins (DIP) has used as a source of interacting protein pairs and InterPro, an integrated database of protein families, domains and functional sites, has used for defining domains in interacting pairs. A statistical scoring system. named "PID matrix score" has designed and applied as a measure of interaction probability between domains. Cross-validation has been performed with subsets of DIP data to evaluate the prediction accuracy of PID matrix. The prediction system gives about 50% of sensitivity and 98% of specificity, Based on the PID matrix, we develop a system providing several interaction information-finding services in the Internet. The system, named PreDIN (Prediction-oriented Database of Interaction Network) provides interacting domain finding services and interacting protein finding services. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PreDIN system. This system can be also used as a new tool for functional prediction of unknown proteins.

• Journal of Korean Clinical Nursing Research
• /
• v.23 no.2
• /
• pp.189-201
• /
• 2017

Purpose: The aim of this study was to develop the nursing management core task and competency matrix for hospital unit managers. The perceived level of importance and performance of identified core competencies by unit managers were also investigated. Methods: Literature review and expert survey identified nursing management core task and competencies. Subsequently, the core task and competency matrix was developed and validated by expert panel. A survey of 196 nurse managers from 3 cities identified perceived importance and performance of core competiences. Results: Thirty-eight nursing management core task and thirty-seven nursing management core competencies were identified comprising five categories; Clinical practice knowledge, Evidence-based practice, Employee development, Strategic planning and Initiative. Based on the core task and competencies, the task and competency matrix for unit managers was developed. In the analysis of importance and performance of core competencies, the mean score of importance ($3.50{\pm}0.30$) was higher than the mean score of performance ($3.03{\pm}0.34$). Conclusion: The development of core task and competencies for unit managers in hospitals provides a guide for the development and evaluation of programs designed to increase competence of unit managers.