• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.105-112
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• 2022

The current study is at the forefront of examining the theory of principal-agent framework and financing constraints to explain the level of corporate innovation. To boost the firm's level of innovation, this study uses corporate governance and corporate performance as driving factors. The study's secondary goal is to give information on the parallel relationship between corporate governance and the level of corporate innovation. This study used a two-step least square (TSLS) regression analysis to examine such a simultaneous association using secondary data from Indonesian listed businesses from 2000 to 2021, which totaled around 1,910 observations. This study uses the Principal Component Analysis (PCA) tool to test cumulative variances of potential corporate governance indicators such as the total commissioner of the firm (TCOM), total independent commissioner of the firm (INDPCOM), the proportion of institutional ownership (INSOWN), total female commissioner (FEMCOM), CEO duality (CEODUAL), and type of the firm (SOE). As a result, PCA reveals that four of these variables, omitting CEODUAL and SOE, were a corporate governance construct. Furthermore, the study discovered that the amount of firm innovation and corporate governance are related.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.201-211
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• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.

• Korean Journal of Food Science and Technology
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• v.37 no.3
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• pp.431-437
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• 2005

Overall experiments were planned by central composite design, and results were analyzed by response surface methodology (RSM) to determine effects of three independent variables, temperature ($X_{1}$), extraction time ($X_{3}$), and pressure ($X_{3}$), on yield of sesame oil extract (Y). Regression equation model optimized by response surface analysis was: Y (sesame oil) = $-3.89+0.07X_{1}+0.03X_{2}+0.0006X_{3}-0.0007X_{1}^{2}-0.0002X_{2}X_{1}-0.00008X_{2}^{2}+0.000004X_{3}X_{1}+0.0000009X_{3}X_{2}-0.00000009X_{3}^{2}$. According to RSM analysis, optimum extracting conditions of temperature, time, and pressure were $45.89^{\circ}C$, 131.89 min, and 34228.41 kPa, respectively, and statistical maximum yield of sesame oil was 96.27%. Fatty acid composition of sesame oil showed sesame oil extracted by Supereritical Fluid $CO_{2}$ contained lower levels of palmitic, stcaric, and oleic acids and higher levels or palmitoleic and linoleic acids than commercial sesame oil. Commercial and extracted sesame oils were analyzed by electronic nose composed of 12 different metal oxide sensors. Obtained data were interpreted by statistical method of MANOVA. Sensitivities of sensors from electronic nose were analysed by principal component analysis. Proportion of first principal component was 99.92%. All sesame oils showed different odors (p < 0.05).

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.435-450
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• 2019

There is ample research on the Korea-China-Japan trilateral relationship in various directions that includes geopolitical aspects. There still exist escalated tensions in politics and diplomacy despite the remarkable growth in the economic and cultural exchanges between these countries. This study presents a way of representing national identity based on survey results via principal component analysis, and investigates if these national identities can be related to conflict and cooperation among the three countries. The results show that the attachment to the nation does not affect the conflicts between the countries and that a more friendly awareness of other countries tends to give a positive effect to cooperation between countries.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.905-913
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• 2011

This study was conducted to characterize the discharge feature of pollutant load from the upper and lower watershed influencing on the water quality of South Han River using the empirical equation and Factor Analysis. The results of regression analysis between flow rate and pollutant load were as follows. In the streams of the upper watershed of South Han river, $BOD_5$ and $COD_{Mn}$ were increased as the flow rate was increased. Also, steep increases in SS and TP were observed with positive correlation with the flow rate while change in TN was slightly shown. On the other hand, in the streams of the lower watershed of South Han river, $BOD_5$ was negatively correlated with the flow rate, being decreased with the increase in the flow rate. However, changes in $COD_{Mn}$, TN, SS, and TP showed a similar trend with those observed in the upper watershed. With Factor Analysis of the water quality and various components, it was appeared that the flow rate, SS, and TP were significantly correlated each other and they were indicated as the principal component influencing on water quality in the streams of the upper watershed. In contrast, $BOD_5$, $COD_{Mn}$ and TOC were significantly correlated each other and they were included as the principal pollution component of the streams in the lower watershed. From these results, it was conclusive that the upper watershed of South Han River was mainly affected by non point source pollutants while the lower watershed was influenced by point source pollutants from the developed areas.

• Journal of Microbiology and Biotechnology
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• v.34 no.4
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• pp.958-968
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• 2024

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.1-9
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• 2016

A correlation analysis between raw water turbidity at two wide-area water treatment plants and hydrological data was conducted for efficient water supply, design and management of water treatment plant. Both correlation analysis and principal component analysis were conducted using hydrological time series data such as inflow discharge, outflow discharge, and rainfall at dam basin of intake station of wide-area water treatment plants. And, forecasting of change in turbidity was conducted using regression equation for turbidity prediction. The raw water turbidity of two water treatment plants was strongly related to time series of discharge. The raw water turbidity of Chungju water treatment plant is strongly related to outflow discharge at Chungju dam (0.708). Whereas, the raw water turbidity of Wabu water treatment plant is strongly related to inflow discharge at Paldang dam (0.805). Similar trends between turbidity forecasting result using regression equation and calculation result using estimation equation on Korea water supply facilities standard were obtained. The result of this study can provide basic data for construction and management of water treatment plant.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.338-357
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• 2012

This study investigated any possible financial attributes of the CDS spreads of a firm belonging to financial industries headquartered in the Republic of Korea. There were few studies on this issue, especially for the firms located in emerging capital markets. Coupled with the models such as a multiple regression and a principal component analysis(PCA), this research has identified that only two explanatory variables such as SLOPE and INTER3 (i.e. interaction effect between the BETA and the SLOPE) consistently showed their statistically significant influence on the CDS spreads through the 'selected' model without and with applying a stepwise regression procedure for the robustness. Given the rapid developments of sophisticated financial derivatives, this study may suggest a valuable insight to foreign and domestic investors to identify the possible determinants of CDS spreads at the firm- and/or the industry-level.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.8
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• pp.870-876
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• 2005

The multiple regression models which have two factors(population and commercial area) have been used to forecast the water demand in the future. But, the coefficient of population had a negative value because proper regional classification wasn't performed, and it is not reasonable because the population must be a positive factor. So, the regional classification was performed by principal component and cluster analysis to solve the problem. 6 regional characters were transformed into 4 principal components, and the areas were divided into two groups according to cluster analysis which had 4 principal components. The new regression models were made by each group, and the problem was solved. And, the future water demands were estimated by three scenarios(Active, moderate, and passive one). The increase of water demand ore $89.034\;m^3/day$ in active plat $49,077\;m^3/day$ in moderate plan, and $19,996\;m^3/day$ in passive plan. The water supply ability as scenarios is enough in water treatment plant, however, 2 reservoirs among 4 reservoirs don't have enough retention time in all scenarios.