• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• 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 Microbiology and Biotechnology
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• v.34 no.6
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• pp.1214-1221
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• 2024

The accumulating evidence substantiates the indispensable role of gut microbiota in modulating the pathogenesis of type 2 diabetes. Uncovering the intricacies of the mechanism is imperative in aiding disease control efforts. Revealing key bacterial species, their metabolites and/or metabolic pathways from the vast array of gut microorganisms can significantly contribute to precise treatment of the disease. With a high prevalence of type 2 diabetes in Inner Mongolia, China, we recruited volunteers from among the Mongolian population to investigate the relationship between gut microbiota and the disease. Fecal samples were collected from the Volunteers of Mongolia with Type 2 Diabetes group and a Control group, and detected by metagenomic analysis and untargeted metabolomics analysis. The findings suggest that Firmicutes and Bacteroidetes phyla are the predominant gut microorganisms that exert significant influence on the pathogenesis of type 2 diabetes in the Mongolian population. In the disease group, despite an increase in the quantity of most gut microbial metabolic enzymes, there was a concomitant weakening of gut metabolic function, suggesting that the gut microbiota may be in a compensatory state during the disease stage. β-Tocotrienol may serve as a pivotal gut metabolite produced by gut microorganisms and a potential biomarker for type 2 diabetes. The metabolic biosynthesis pathways of ubiquinone and other terpenoid quinones could be the crucial mechanism through which the gut microbiota regulates type 2 diabetes. Additionally, certain Clostridium gut species may play a pivotal role in the progression of the disease.

• Journal of Food Hygiene and Safety
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• v.25 no.2
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• pp.162-169
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• 2010

The compulsory beef labelling system has launched from January 1st 2007 by the amended Food Hygiene Law, we were checked the actual conditions of beef origin with a nationwide scale by the Hanwoo differentiation specific test method which was developed by Korea FDA using 90 SNP biomarkers. The test method is useful tool to differentiate the beef origin carrying out the mission of KFDA's annual food safety management guidance. Also we have technically transferred the Hanwoo differentiation specific test method to other institutes as well regional KFDA and established the training program as a regular course in Korea Human Resource Development Institute for Health and Welfare. The beef used in this study were collected according to the 2009 Food safety guidance in roast beef restaurants where business site area greater than 100 $m^2$. Total 216 samples were consisted of 48 samples of the Seoul area and 168 of the region. The monitoring result from restaurants in all the region of Korea showed that 3 of 216 Hanwoo-labelled beefs were found out as a non-Hanwoo (1.3%). This results are gradually deceasing trend compared with 34.0% in 2005, 30.1% in 2006, 3.2% in 2007 and 5.14% in 2008. From these data, the Hanwoo differentiation specific test method on the settlement of the compulsory beef labelling system has an important role. As a outcome of this project, we might be considered the early settlement of the compulsory beef labelling system, technically transferred to other institutes and the establishment of regular training program of the test method.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.2
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• pp.142-152
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• 1995

The combined bottom trawl and hydroacoustic survey was conducted by using the training ship Oshoro Maru belong to Hokkaido University in November 1989-1992 and the training ship Nagasaki Maru belong to Nagasaki University in April 1994 in the East China Sea, respectively. The aim of the investigations was to collect the target strength data of fish school in relation to the biomass estimation of fish in the survey area. The hydroacoustic survey was performed by using the scientific echo sounder system operating at three frequencies of 25, 50 and 100kHz with a microcomputer-based echo integrator. Fish samples were collected by bottom trawling and during the trawl surveys, the openings of otter board and net mouth were measured. The target strength of fish school was estimated from the relationship between the volume back scattering strength for the depth strata of bottom trawling and the weight per unit volume of trawl catches. A portion of the trawl catches preserved in frozon condition on board, the target strength measurements for the defrosted samples of ten species were conducted in the laboratory tank, and the relationship between target strength and fish weight was examined. In order to investigate the effect of swimbladder on target strength, the volume of the swimbladder of white croaker, Argyrosomus argentatus, sampled by bottom trawling was measured by directly removing the gas in the swimbladder with a syringe on board. The results obtained can be summarized as follows: 1.The relationship between the mean volume back scattering strength (, dB) for the depth strata of trawl hauls and the weight(C, $kg/\textrm{m}^3$) per unit volume of trawl catches were expressed by the following equations : 25kHz : = - 29.8+10Log(C) 50kHz : = - 32.4+10Log(C) 100kHz : = - 31.7+10Log(C) The mean target strength estimates for three frequencies of 25, 50 and 100 kHz derived from these equations were -29.8dB/kg, -32.4dB/kg and -31.7dB/kg, respectively. 2. The relationship between target strength and body weight for the fish samples of ten species collected by trawl surveys were expressed by the following equations : 25kHz : TS = - 34.0+10Log($W^{\frac{2}{3}}$) 100kHz : TS = - 37.8+10Log($W^{\frac{2}{3}}$) The mean target strength estimates for two frequencies of 25 and 100 kHz derived from these equations were -34.0dB/kg, -37.8dB/kg, respectively. 3. The representative target strength values for demersal fish populations of the East China Sea at two frequencies of 25 and 100 kHz were estimated to be -31.4dB/kg, -33.8dB/kg, respectively. 4. The ratio of the equivalent radius of swimbladder to body length of white croaker was 0.089 and the volume of swimbladder was estimated to be approximately 10% of total body volume.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Korean Journal of Occupational Health Nursing
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• v.7 no.2
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• pp.143-154
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• 1998

The purpose of this research is to suggest basic materials for the practical infection precaution program to protect health care workers from hospital infection by grasping their compliance level of Universal Precautions and examining the factors affecting them. The number of the health care workers we studied were 486, including the doctors, the nurses, and the lab technicians who were working in a university hospital. The period of this research was from Aug. 18th, 1997 to Aug. 30th, 1997. The method of the study was to measure the compliance level of Universal Precautions with the item of "Universal Precautions" established by CDC in 1987, and examine the questionnaire of 52 questions dividing related factors into socio-populational, individual, socio-psychological and organizational management ones. The data was analyzed by t-test. ANOVA, and chi-square test. The results were as follows : 1. An the compliance level of Universal Precautions, hand washing had the highest score(85.4%), and doctors(18.9%), nurses(44.0%), and lab technicians(7.6%), had a low compliance level in the safe handling of an injection syringe, and item not to handle patients and their samples when the subject suffered from dermatitis or injury had the lowest score of 17.1%. 23.3% of them said that they wear protection gown, goggles and mask. 2. Female's Compliance level of Universal Precautions Was higher than male. 3. The health care workers who had high recognition on Universal Precautions got significantly higher compliance level of Universal Precautions than those have low recognition on Universal Precautions(P<0.001). 4. The health care workers experienced a needle stick injury had a significantly higher compliance level of Universal Precautions than those who had not(P<0.000). 5. The health care workers who had infection protection education got a significantly higher compliance level of Universal Precautions than those who didn't(P<0.000). 6. The health care workers who had a firm belief in the effect of Universal Precautions got a higher compliance level of Universal Precautions than those who didn't. 7. The health care workers who had less conflicts between treating patient arid protecting them-selves got a higher compliance level of Universal Precautions than others with many conflicts. 8. The health care workers who had a high score in organizational management factors got a significantly higher compliance level of Universal Precautions than those with a low score(P<0.000). 9. Only 16.9 percent of the all respondents(82 in number) answered that they knew well or a little about the Universal Precautions, which is very low rate of recognition. 10. The variables which affected the score in organizational management factors were age, sex, education period, work experience, the kind of work, recognition on Universal Precautions, the experience of needle stick injury, revealing dangerous circumstance related to infection, and training on precaution again infection. According to the result above, compliance level of Universal Precautions showed high correlation with sex, the recognition on Universal Precautions, the experience of needle stick injury, training on precaution against infection, the belief in the effect of Universal Precautions, the recognition degree of conflicts and organizatinal management factors. These results could be used as the basic materials for the developing infection protection programs. Also, There should have a systematic training course to elevate a effective compliance level of Universal Precautions as well as the manageeent of infection protection programs.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of the Korean Society of Radiology
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• v.6 no.5
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• pp.421-426
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• 2012

The purpose of this study was to investigate the knowledge of dental hygiene students of the quality assurance of dental radiation such quality assurance and related educational experiences in an effort to accelerate the preparation of a curriculum for systematic quality-assurance. The subjects in this study were 453 dental hygiene students who participated in dental radiography courses. Varied statistical analyses such as frequency analyses, reliability, chi-square, and independent samples t-tests were conducted on the data collected, using SPSS 12.0. Scheffe test was also used after one-way ANOVA as post-hoc tests. Results showed that (a) the students' acknowledge level of Radiographic Quality Assurance was $7.71{\pm}1.7$ out of 12 on average. The more theory and practical classes students took, the higher points they got (p<0.001); (b) Most of the students experienced 1-3 lessons out of 13 in practical training and 26.3% of students did not take any practical lesson. ;(c)Students who did not take any practical training got 7.20 points out of 13, students who took 1-3 lessons got 7.84 points out of 13, students who took 4-5 lessons got 7.87 points out of 13, and students who took more than 6 lessons got 8.14 points out of 13 on average. The more practical classes they took the higher acknowledge level they were. Therefore it needs to provide adequate practical lessons to them.

• Information Systems Review
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• v.18 no.4
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• pp.17-42
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• 2016

Information security incidents caused by authorized insiders are increasing in financial firms, and this increase is particularly increased by outsourced contractors. With the increase in outsourcing in financial firms, outsourced contractors having authorized right has become a threat and could violate an organization's information security policy. This study aims to analyze the differences between own employees and outsourced contractors and to determine the factors affecting the violation of information security policy to mitigate information security incidents. This study examines the factors driving employees to violate information security policy in financial firms based on the theory of planned behavior, general deterrence theory, and information security awareness, and the moderating effects of employee type between own employees and outsourced contractors. We used 363 samples that were collected through both online and offline surveys and conducted partial least square-structural equation modeling and multiple group analysis to determine the differences between own employees (246 samples, 68%) and outsourced contractors (117 samples, 32%). We found that the perceived sanction and information security awareness support the information security policy violation attitude and subjective norm, and the perceived sanction does not support the information security policy behavior control. The moderating effects of employee type in the research model were also supported. According to the t-test result between own employees and outsourced contractors, outsourced contractors' behavior control supported information security violation intention but not subject norms. The academic implications of this study is expected to be the basis for future research on outsourced contractors' violation of information security policy and a guide to develop information security awareness programs for outsourced contractors to control these incidents. Financial firms need to develop an information security awareness program for outsourced contractors to increase the knowledge and understanding of information security policy. Moreover, this program is effective for outsourced contractors.