• Journal of Radiation Protection and Research
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• v.28 no.1
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• pp.59-63
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• 2003

Purpose : Measurement of transmission dose is useful for in vivo dosimetry of QA purpose. The objective of this study is to develope an algorithm for estimation of tumor dose using measured transmission dose for open radiation field. Materials and Methods : Transmission dose was measured with various field size (FS), phantom thickness (Tp), and phantom chamber distance (PCD) with a acrylic phantom for 6 MV and 10 MV X-ray Source to chamber distance (SCD) was set to 150 cm. Measurement was conducted with a 0.6 cc Farmer type ion chamber. Using measured data and regression analysis, an algorithm was developed for estimation of expected reading of transmission dose. Accuracy of the algorithm was tested with flat solid phantom with various settings. Results : The algorithm consisted of quadratic function of log(A/P) (where A/P is area-perimeter ratio) and tertiary function of PCD. The algorithm could estimate dose with very high accuracy for open square field, with errors within ${\pm}0.5%$. For elongated radiation field, the errors were limited to ${\pm}1.0%$. Conclusion : The developed algorithm can accurately estimate the transmission dose in open radiation fields with various treatment settings.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Journal of Environmental Health Sciences
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• v.22 no.4
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• pp.82-90
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• 1996

This study was performed to find the optimum mixing ratio of paper waste in composting of mixture of swine feces and newspaper. Using the experimental setting of aeration rate which was found in the experiment carried out priorly, and moisture contents reported in other literature, just the initial C:N ratios were differentiated by mixing different amount of newspaper with the same amount of swine feces. This study was carried out by operating 4 experimental cornposting reactors of bench scale for 3 weeks. The followings are the conclusions that were derived from this study. 1. During composting reaction, the C:N ratio of each cornposter was decreased. Degree of decrease was in order of run 3, run 2, run 4, and run 1 of which initial C:N ratio was 30, 25, 35, and 20 respectively. All of the final composts were found to be completed composting reaction. 2. Ash contents of each reactor increased rapidly in order of run 3, run 2, run 4, and run 1. The absolute values of quadratic effect coefficients of each second order regression function was 0.059, 0.038, 0.032, and 0.030 respectively. Ash contents evolution trend had a linear correlation with the C:N ratio trend. (r=-0.96932, p<0.05) 3. The range of highest temperatures reached during composting was 47.2-53.5$\circ$C. Those were not significantly different from one another. Thermophilic temperatures were maintained in the range of 48-108 hours. 4. Contents of heavy metal detected in the final compost were lower than those of Korean and European standards. 5. Concentration range of Nitrogen in the final compost was 1.11-2.27%, and that of phosphorus was 8.40-10.70 mg/kg. 6. The optimum C:N ratio which has been proposed without the consideration of types of bulking agents should be re-examined. Biodegradabilities of each bulking agents was thought to be important factor when determining the optimum initial C:N ratio for cornposting.

• Journal of Environmental Health Sciences
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• v.47 no.2
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• pp.175-181
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• 2021

Objectives: The correlation between the size of a leak hole, the volume of the leakage, and the range of influence was investigated for a hydrochloric acid tank-lorry. Methods: For the case of a tank-lorry chemical accident, KORA (Korea Off-site Risk Assessment Supporting Tool) was used to predict the leak rate and the range of influence according to the size of the leak hole. The correlation was studied using R. Results: As a result of analyzing the leak rate change according to the leak hole size in a 35% hydrochloric acid tank-lorry, as the size of the leak hole increased from 1 to 100 mm, the leak rate increased from 0.008 to 83.94 kg/sec, following the power function. As a result of calculating the range of influence under conditions ranging from 1 to 100 mm in size and 10 to 60 minutes of leakage time, it was found that the range spanned from a minimum of 5.4 m to a maximum of 307.9 m. As a result of multiple regression analysis using R, the quadratic function model best explained the correlation between the size of the leak hole, the leak time, and the range of influence with an adjected coefficient of determination of 0.97 and a root mean square error of 22.33. Conclusion: If a correlation database for the size of a leak hole is accumulated for various substances and under various conditions, the amount of leakage and the range of influence can easily be calculated, facilitating field response activities.

• Advances in nano research
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• v.10 no.1
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• pp.77-90
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• 2021

In the present study, novel chitosan coated magnetic magnetite (Fe3O4) nanoparticles were successfully biosynthesized from mushroom, Agaricus campestris, extract. The obtained bio-nanocomposite material was used to investigate ultra-fast and highly efficient for removal of Ni2+ ions in a fixed-bed column. Chitosan was treated as polyelectrolyte complex with Fe3O4 nanoparticles and a Fungal Bio-Nanocomposite Material (FBNM) was derived. The FBNM was characterized by using X-Ray Diffractometer (XRD), Scanning Electron Microscopy-Energy Dispersive X-Ray Spectroscopy (SEM-EDS), Fourier Transform Infrared spectra (FTIR) and Thermogravimetric Analysis (TGA) techniques and under varied experimental conditions. The influence of some important operating conditions including pH, flow rate and initial Ni2+ concentration on the uptake of Ni2+ solution was also optimized using a synthetic water sample. A Central Composite Design (CCD) combined with Response Surface Modeling (RSM) was carried out to maximize Ni2+ removal using FBNM for adsorption process. A regression model was derived using CCD to predict the responses and analysis of variance (ANOVA) and lack of fit test was used to check model adequacy. It was observed that the quadratic model, which was controlled and proposed, was originated from experimental design data. The FBNM maximum adsorption capacity was determined as 59.8 mg g-1. Finally, developed method was applied to soft drinks to determine Ni2+ levels. Reusability of FBNM was tested, and the adsorption and desorption capacities were not affected after eight cycles. The paper suggests that the FBNM is a promising recyclable nanoadsorbent for the removal of Ni2+ from various soft drinks.

• Journal of Ginseng Research
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• v.45 no.2
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• pp.236-245
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• 2021

Background: Red ginseng (RG) extract, especially ginsenoside Rg1 and Rb1 fractions has been reported to have antithrombotic activities. However, gastric instability and low intestinal permeability are considered to be obstacles to its oral administration. We hypothesized that stability, permeability, and activities of RG might be improved by encapsulation within nanoparticles (NPs) prepared with antithrombotic coating materials. Methods: RG-loaded chitosan (CS) NPs (PF-NPs) were prepared by complex ionic gelation with the antithrombotic wall materials, polyglutamic acid (PGA), and fucoidan (Fu). The concentrations of PGA (mg/mL, X₁) and Fu (mg/mL, X₂) were optimized for the smallest particle size by response surface methodology. Antithrombotic activities of RG and PF-NPs were analyzed using ex vivo and in vivo antiplatelet activities, in vivo carrageenan-induced mouse tail, and arteriovenous shunt rat thrombosis models. Results: In accordance with a quadratic regression model, the smallest PF-NPs (286 ± 36.6 nm) were fabricated at 0.628 mg/mL PGA and 0.081 mg/mL Fu. The inhibitory activities of RG on ex vivo and in vivo platelet aggregation and thrombosis in in vivo arteriovenous shunt significantly (p < 0.05) increased to approximately 66.82%, 35.42%, and 38.95%, respectively, by encapsulation within PF-NPs. For an in vivo carrageenan-induced mouse tail thrombosis model, though RG had a weaker inhibitory effect, PF-NPs reduced thrombus significantly due to the presence of PGA and Fu. Conclusion: PF-NPs contributed to improve the activities of RG not only by nanoencapsulation but also by antithrombotic coating materials. Therefore, PG-NPs can be suggested as an efficient delivery system for oral administration of RG.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• The Journal of Bigdata
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• v.6 no.2
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• pp.109-124
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• 2021

In this paper, the structural characteristics of Industry-University cooperation networks are analyzed using network analysis. Recent studies have shown that technological cooperation and joint research has a positive effect on R&D performance. In order to boost innovation performance, various types of cooperative activities and governmental policy supports for major R&D stakeholders(i.e. universities, laboratories, etc.) are provided. However, despite these efforts, the outcome is still insufficient, so it is time to prepare for a plan to build an innovative network to strengthen university-centered Industry-University cooperation activities. Specifically, this study builds the networks according to the form of Industry-University cooperations(i.e. patent, paper, joint research, and technology transfer), and different types of Industry-University cooperation networks are analyzed from a statistical viewpoint by using QAP correlation and regression analyses. The analysis results show that joint research network is closely related to paper network, and is related to other Industry-University cooperation networks. This study is expected to shed a light on supporting innovation activities such as establishing Industry-University cooperation strategies and discovering cooperative partners necessary for creating new growth engines for universities.

• Clinical and Experimental Pediatrics
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• v.48 no.1
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• pp.75-80
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• 2005

Purpose : Internal tibial torsion is prevalent in East Asian countries such as Korea and Japan, where sitting on the floor is common behavior. Internal tibial torsion or excessive lateral tibial torsion may cause esthetical, functional, or psychological problems and also may induce degenerative arthritis in older age. The purpose of this study is to measure the tibial torsion in children of the Jeju area. Methods : Tibial torsion was measured in 1,042 lower extremities of 521 children from one to 12 years of age. The values of transmalleolar angles were analyzed for each age group divided by 6 months. Quadratic and linear regression models were used to fit patterns of changes in mean values of transmalleolar angles. The age at seven, which provides the highest coefficient of determination for quadratic regression analysis, was used as a cut-off point to fit different statistical models. Results : The mean transmalleolar angle was $0.10{\pm}5.79^{\circ}$ in all children,$ 0.90{\pm}5.49^{\circ}$ in males, and $-0.80{\pm}5.97^{\circ}$ in females. The value was $4.25{\pm}4.04$ in 1 year of age, gradually decreased to the lowest level of $-1.98^{\circ}$ in four years and seven months of age, increased again with age until it reached $0.67{\pm}1.10^{\circ}$ at seven years of age, and stayed at that level thereafter. Conclusion : Internal tibial torsion in infancy is known to correct spontaneously in the normal developing process. But in this study, the mean transmalleolar angle in children of Jeju area annually decreased after one year of age; to the lowest angle at four years and seven months of age; increased again gradually to the age of seven; and persisted in that level, about $10^{\circ}$ less than western children, not correcting further thereafter. These findings suggest tibial torsion might be caused by lifestyle, especially sitting on feet. To prevent abnormalities of joints and gaits, early diagnosis of tibial torsion in childhood and posture correction or early treatment when needed, seems to be necessary.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.1
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• pp.70-77
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• 2016

The object of this study was to evaluate the effects of climatic elements on potato yield and create a model for estimating the potato yield. We used 35 yield data of Sumi variety produced in mulching cultivation from 17 regions over 11 years. According to the results, some climatic elements showed significant level of correlation coefficient with marketable yield of potato. Totally 22 items of climatic elements appeared to be significant. Especially precipitation for 20 days after planting (Prec_1 & 2), relative humidity during 11~20 days after planting (RH_2), precipitation for 20 days before harvest (Prec_9 & 10), sunshine hours during 50~41 days before harvest (SH_6) and 20 days before harvest (SH_9 & 10), and days of rain during 10 days before harvest (DR_10) were highly significant in quadratic regression analysis. 22 items of predicted yield ($Y_i=aX_i{^2}+bX_i+c$) were induced from the 22 items of climatic elements (step 1). The correlations between the predicted yields and marketable yield were stepwised using SPSS, statistical program, and we selected a model (step 2), in which 4 items of independent variables ($Y_i$) were used. Subsequently the $Y_i$ were replaced with the equation in step 1, $aX_i{^2}+bX_i+c$. Finally we derived the model to predict the marketable yield of potato as below. $$Y=-336{\times}DR_-10^2+854{\times}DR_-10-0.422{\times}Prec_-9^2+43.3{\times}Prec_-9\\-0.0414{\times}RH_-2^2+46.2{\times}RH_-2-0.0102{\times}Prec_-2^2-7.00{\times}Prec_-2-10039$$.