• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• Journal of Microbiology and Biotechnology
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• v.28 no.11
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• pp.1859-1864
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• 2018

Synthesis of flavonoid glycoside is difficult due to diverse hydroxy groups in flavonoids and sugars. As such, enzymatic synthesis or biotransformation is an approach to solve this problem. In this report, we used stepwise biotransformation to synthesize two quercetin bisglycosides (quercetin 3-O-glucuronic acid 7-O-rhamnoside [Q-GR] and quercetin 3-O-arabinose 7-O-rhamnoside [Q-AR]) because quercetin O-rhamnosides contain antiviral activity. Two sequential enzymatic reactions were required to synthesize these flavonoid glycosides. We first synthesized quercetin 3-O-glucuronic acid [Q-G], and quercetin 3-O-arabinose [Q-A] from quercetin using E. coli harboring specific uridine diphopsphate glycosyltransferase (UGT) and genes for UDP-glucuronic acid and UDP-arabinose, respectively. With each quercetin 3-O-glycoside, rhamnosylation using E. coli harboring UGT and the gene for UDP-rhamnose was conducted. This approach resulted in the production of 44.8 mg/l Q-GR and 45.1 mg/l Q-AR. This stepwise synthesis could be applicable to synthesize various natural product derivatives in case that the final yield of product was low due to the multistep reaction in one cell or when sequential synthesis is necessary in order to reduce the synthesis of byproducts.

• Clinical and Experimental Pediatrics
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• v.53 no.6
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• pp.680-687
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• 2010

Patients with congenital heart diseases (CHD) are confronted with early- and late-onset complications, such as conduction disorders, arrhythmias, myocardial dysfunction, altered coronary flow, and ischemia, throughout their lifetime despite successful hemodynamic and/or anatomical correction. Rhythm disturbance is a well-known and increasingly frequent cause of morbidity and mortality in patients with CHD. Predisposing factors to rhythm disturbances include underlying cardiac defects, hemodynamic changes as part of the natural history, surgical repair and related scarring, and residual hemodynamic abnormalities. Acquired factors such as aging, hypertension, diabetes, obesity, and others may also contribute to arrhythmogenesis in CHD. The first step in evaluating arrhythmias in CHD is to understand the complex anatomy and to find predisposing factors and hemodynamic abnormalities. A practical stepwise approach can lead to diagnosis and prompt appropriate interventions. Electrophysiological assessment and management should be done with integrated care of the underlying heart defects and hemodynamic abnormalities. Catheter ablation and arrhythmia surgery have been increasingly applied, showing increasing success rates with technological advancement despite complicated arrhythmia circuits in complex anatomy and the difficulty of access. Correction of residual hemodynamic abnormalities may be critical in the treatment of arrhythmia in patients with CHD.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.481-488
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• 1999

Recently a removable soil nail is demanded due to problems beyond of economical and engineering purpose. In this study controlled displacement and controlled force field pull-out tests are carried out 7 times in order to evaluate short-term and long-term pull-out characteristics of the removable soil nail. For evaluating application of removable soil nailing system, bending tests of removable soil nails and tensile tests of fixed sockets are carried out. In the removable soil nailing system, the predicted horizontal displacements from FLAC-2D are also compared with the field measurements occurred in stepwise excavation. And approach for the stability analysis of removable soil nailing system after removed is proposed.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.117-124
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• 2013

This study examined that how precisely MFCC, LPC, energy, and pitch related parameters of the speech data, which have been used mainly for voice recognition system could predict the vocal emotion categories as well as dimensions of vocal emotion. 110 college students participated in this experiment. For more realistic emotional response, we used well defined emotion-inducing stimuli. This study analyzed the relationship between the parameters of MFCC, LPC, energy, and pitch of the speech data and four emotional dimensions (valence, arousal, intensity, and potency). Because dimensional approach is more useful for realistic emotion classification. It results in the best vocal cue parameters for predicting each of dimensions by stepwise multiple regression analysis. Emotion categorizing accuracy analyzed by LDA is 62.7%, and four dimension regression models are statistically significant, p<.001. Consequently, this result showed the possibility that the parameters could also be applied to spontaneous vocal emotion recognition.

• ETRI Journal
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• v.20 no.2
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• pp.192-213
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• 1998

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.

• Journal of the Korean Home Economics Association
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• v.35 no.6
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• pp.87-99
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• 1997

This study intends to find the effects of children's stress level and coping behaviors on their stress symptoms. The subjects were 840 4-6th grade children in Seoul. The data were analyzed by frequencies, percentages, means, ANOVA, stepwise regression and Cronbach's α. The regression model explained 46% of children's stress symptoms which were affected by coping behaviors(emotional aggression, positive revaluation, seperation for emotional relaxation) and by stressors(children's social-life, individual factors, school-life).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.365-373
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• 2000

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU.In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.