• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Life Science
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• v.21 no.10
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• pp.1370-1376
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• 2011

To study the functional genomic analysis of a crenachaeon Sulfolobus acidocaldarius, we have constructed an auxotrophic mutant based on pyrEF, which encodes the pyrimidine biosynthetic enzymes orotate phosphoribosyltransferase and orotidine-5'-monophosphate decarboxylase. S. acidocaldarius was shown to be sensitive to 5-fluoroorotic acid (5-FOA), which can be selected for mutations in pyrEF genes within a pyrimidine biosynthesis cluster. Spontaneous 5-FOA-resistant mutants by ultraviolet, KH1U and KH2U, were found to contain two point mutations and a frame shift mutation in pyrE, respectively. Mutations at these sites from KH1U and KH2U decreased the activity of orotate phosphoribosyltransferase encoded by the pyrE gene and blocked the degradation of 5-FOA into toxic 5-FOMP and 5-FUMP that kill the cells. Therefore, KH1U and KH2U were uracil auxotrophs. Transformation of Sulfolobus-Escherichia coli shuttle vector pC bearing pyrEF genes from S. solfataricus P2 into S. acidocaldarius mutant KH2U restored 5-FOA sensitivity and overcame the uracil auxotrophy. This study establishes an efficient genetic strategy towards the systematic knockout of genes in S. acidocaldarius.

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.

• Korean Journal of Plant Resources
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• v.30 no.5
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• pp.571-577
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• 2017

Abscisic acid (ABA) is an important phytohormone involved in abiotic stress tolerance in plants. The group A bZIP transcription factors play important roles in the ABA signaling pathway in Arabidopsis but little is known about their functions in rice. In our current study, we have isolated and characterized a group A bZIP transcription factor in rice, OsABF3 (Oryza sativa ABA responsive element binding factor 3). We examined the expression patterns of OsABF3 in various tissues and time course analysis after abiotic stress treatments such as drought, salinity, cold, oxidative stress, and ABA in rice. Subcellular localization analysis in maize protoplasts using a GFP fusion vector further indicated that OsABF3 is a nuclear protein. Moreover, in a yeast one-hybrid experiment, OsABF3 was shown to bind to ABA responsive elements (ABREs) and its N-terminal region found to be necessary to transactivate a downstream reporter. A homozygous T-DNA insertional mutant of OsABF3 is more sensitive to salinity, drought, and oxidative stress compared with wild type plants ＆ OsABF3OX plants. In addition, this Osabf3 mutant showed a significantly decreased sensitivity to high levels of ABA at germination and post-germination. Collectively, our present results indicate that OsABF3 functions as a transcriptional regulator that modulates the expression of abiotic stress-responsive genes through an ABA-dependent pathway.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1241-1245
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• 2015

Background: Thyroid carcinoma is the most common malignancy of the endocrine organs. Although the majority of thyroid cancer patients experience positive outcomes, anaplastic thyroid carcinoma is considered one of the most aggressive malignancies. Current therapeutic regimens do not confer a significant survival benefit, and new therapies are urgently needed. Oncolytic herpes simplex virus (oHSV) may represent a promising therapy for cancer. In the present study, we investigated the therapeutic effects of a third-generation HSV vector, $G47{\Delta}$, on various human thyroid carcinoma cell lines in vitro. Two subcutaneous (s.c.) models of anaplastic thyroid carcinoma were also established to evaluate the in vivo anti-tumor efficacy of $G47{\Delta}$. Materials and Methods: The human thyroid carcinoma cell line ARO, FRO, WRO, and KAT-5, were infected with $G47{\Delta}$ at different multiplicities of infection (MOIs) in vitro. The survival rates of infected cells were calculated each day. Two s.c. tumor models were established using ARO and FRO cells in Balb/c nude mice, which were intratumorally (i.t.) treated with either $G47{\Delta}$ or mock. Tumor volumes and mouse survival times were documented. Results: $G47{\Delta}$ was highly cytotoxic to different types of thyroid carcinomas. For ARO, FRO, and KAT-5, greater than 30% and 80% of cells were killed at MOI=0.01 and MOI=0.1, respectively on day 5. WRO cells displayed modest sensitivity to $G47{\Delta}$, with only 21% and 38% of cells killed. In the s.c. tumor model, both of the anaplastic thyroid carcinoma cell lines (ARO and FRO) were highly sensitive to $G47{\Delta}$; $G47{\Delta}$ significantly inhibited tumor growth and prolonged the survival of mice bearing s.c. ARO and FRO tumors. Conclusions: The oHSV $G47{\Delta}$ can effectively kill different types of human thyroid carcinomas in vitro. $G47{\Delta}$ significantly inhibited growth of anaplastic thyroid carcinoma in vivo and prolonged animal survival. Therefore, $G47{\Delta}$ may hold great promise for thyroid cancer patients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Journal of Microbiology and Biotechnology
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• v.20 no.11
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• pp.1563-1570
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• 2010

To construct a highly sensitive detection system for endocrine disruptors (EDs), we have compared the activity of promoters with the n-alkane-inducible cytochrome P450 gene (ALK1), isocitrate lyase gene (ICL1), ribosomal protein S7 gene (RPS7), and the translation elongation factor-1${\alpha}$ gene (TEF1) for the heterologous gene in Yarrowia lipolytica. The promoters were introduced into the upstream of the lacZ or hERa reporter genes, respectively, and the activity was evaluated by ${\beta}$-galactosidase assay for lacZ and Western blot analysis for hER${\alpha}$. The expression analysis revealed that the ALK1 and ICL1 promoters were induced by n-decane and by EtOH, respectively. The constitutive promoter of RPS7 and TEF1 showed mostly a high level of expression in the presence of glucose and glycerol, respectively. In particular, the TEF1 promoter showed the highest ${\beta}$-galactosidase activity and a significant signal by Western blotting with the anti-estrogen receptor, compared with the other promoters. Moreover, the detection system was constructed with promoters linked to the upstream of the expression vector for the hER${\alpha}$ gene transformed into the Y. lipolytica with a chromosome-integrated lacZ reporter gene under the control of estrogen response elements (EREs). It was indicated that a combination of pTEF1p-hER${\alpha}$ and CXAU1-2XERE was the most effective system for the $E_2$-dependent induction of the ${\beta}$-galactosidase activity. This system showed the highest ${\beta}$-galactosidase activity at $10^{-6}\;M\;E_2$, and the activity could be detected at even the concentration of $10^{-10}\;M\;E_2$. As a result, we have constructed a strongly sensitive detection system with Y. lipolitica to evaluate recognized/suspected ED chemicals, such as natural/synthetic hormones, pesticides, and commercial chemicals. The results demonstrate the utility, sensitivity, and reproducibility of the system for identifying and characterizing environmental estrogens.

• Korean Journal of Microbiology
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• v.30 no.5
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• pp.360-365
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• 1992

Clinical isolates of 8 ofloxacin resistant Staphylococcus auresu (ORSA) were subjected to MIC test, Southern analysis on gyrA locus and nucleotide sequence analysis of 290 bp of gyrA gene (gyrA-290) spanning amino acid 26 to 121 in order to understand the mechanism of quinolone resistance in Staphylococcus aureus. ORSAs showed highlevel resistance against quinolones (8-250 fold increase of MICs) and also significant resistance agianst ${\beta}-lactams$ (2-32 fold increase of MICs). However, ORSs did not show any change in sensitivity agianst vancomycin. Southern analysis of ORSAs with HindIII, PstI and AluI revealed RFLPs on gyrA locus. In order to further analyze the gyrA gene, gyrA-290 was amplified by PCR and cloned to pTZ vector. Subsequent nucleic acid sequence analysis of gyrA-290 demonstrated a point mutation of C to T resulting amino acid change of Ser-84 to Leu-84 in all 8 ORSA strains. The substitution at 84th amino acid of tyrase A might confer one mechanism of high level quinolone resistance in Staphylococcus aureus.