• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.10-18
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• 2014

Purpose: Among different PET/CT devices which are composed of same PET model but different CT models, SUV, usually used for quantitative evaluation, was measured to assess the accuracy of follow up scans in different PET/CT and confirm that interequipment compatibility is useful in arranging the PET/CT exam appointment. Materials and Methods: Using ACR PET Phantom, PET NEMA IEC Body Phantom, SNM Chest Phantom and Ge-68 cylinder Phantom, $SUV_{mean}$ and $SUV_{max}$ was measured by 3 different models of PET/CT (Discovery 690, Discovery 690Elite and Discovery 710, GE) made in same company. ANOVA was used to evaluate the significant difference in the result. Results: In the result, the average of $SUV_{max}$ was D690 (25 mm-1.82, 16 mm-1.75, 12 mm-1.73, 8 mm-1.44), D690E (25 mm-1.76, 16 mm-1.92, 12 mm-1.78, 8 mm-1.55) and D710 (25 mm-1.84, 16 mm-1.89, 12 mm-1.77, 8 mm-1.61) in ACR Phantom, D690 (25 mm-2.26, 16 mm-2.25, 12 mm-1.92, 8 mm-1.85), D690E (25 mm-2.45, 16 mm-2.25, 12 mm-2.05 8 mm-1.91) and D710(25 mm-2.49, 16 mm-2.20, 1 2mm-2.30, 8 mm-2.05) in PET NEMA IEC Body Phantom, D690-1.04, D690E-1.10 and D710-1.09 in SNM Chest Phantom and D690-0.81, D690E-0.81, D710-0.84 in Ge-68 cylinder Phantom. The differences between average SUV of 4 phantoms were $SUV_{mean}$-1.87%, $SUV_{max}$-2.15%. And also as a result of ANOVA analysis, there was no significant difference statistically. Conclusion: If different models of PET/CT have same specification of PET system, there was no significant difference in $SUV_{mean}$ and $SUV_{max}$ even though they have different CT system. And also differences of $SUV_{mean}$ and $SUV_{max}$ in phantom images were under 5% which many manufacturers recommend. Therefore, follow up scan will be possible using different PET/CT if it has same specification of PET system with the previous PET/CT. This information will enable the accurate comparative analysis when conducting follow up scans and be helpful to schedule PET/CT exam more effectively.

• The Korean Journal of Pesticide Science
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• v.8 no.3
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• pp.151-161
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• 2004

Three dimensional quantitative structure-activity relationships (3D-QSAR) studies for the protox inhibition activities against root and shoot of rice plant (Orysa sativa L.) and barnyardgrass (Echinochloa crus-galli) by a series of new A=3,4,5,6-tetrahydrophthalimino, B=3-chloro-4,5,6,7-tetrahydro-2H-indazolyl and C=3,4-dimethylmaleimino group, and R-group substituted on the phenyl ring in 1-(5-methyl-3-phenylisoxazolin-5-yl)methoxy-2chloro-4-fluorobenzene derivatives were performed using comparative molecular field analyses (CoMFA) methodology with Gasteiger-Huckel charge. Four CoMFA models for the protox inhibition activities against root and shoot of the two plants were generated using 46 molecules as training set and the predictive ability of the each models was evaluated against a test set of 8 molecules. And the statistical results of these models with combination (SIH) of standard field, indicator field and H-bond field showed the best predictability of the protox inhibition activities based on the cross-validated value $r^2_{cv.}$ $(q^2=0.635\sim0.924)$, conventional coefficient $(r^2_{ncv.}=0.928\sim0.977)$ and PRESS value $(0.091\sim0.156)$, respectively. The activities exhibited a strong correlation with steric $(74.3\sim87.4%)$, electrostatic $(10.10\sim18.5%)$ and hydrophobic $(1.10\sim8.30%)$ factors of the molecules. The steric feature of molecule may be an important factor for the activities. We founded that an novel selective and higher protox inhibitors between the two plants may be designed by modification of X-subsitutents for barnyardgrass based upon the results obtained from CoMFA analyses.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Analytical Science and Technology
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• v.31 no.5
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• pp.208-218
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• 2018

Autophagy is a cellular process whereby cytosolic materials or organelles are taken up in a double-membrane vesicle structure known as an autophagosome and transported into a lysosome for degradation. Although autophagy has been studied at the genetic, cellular, or biochemical level, systematic ultrastructural quantitative analysis of autophagosomes during the autophagy process by using transmission electron microscopy (TEM) has not yet been reported. In this study, we performed ultrastructural analysis of autophagosomes in wild-type (WT) mouse embryonic fibroblasts (MEFs) and autophagy essential gene (atg5) knockout (KO) MEFs. First, we performed ultrastructural analysis of autophagosomes in WT MEFs compared to atg5 KO MEFs in basal autophagy or starvation-induced autophagy. Although we observed phagopore, early, late autophagosomes, or autolysosomes in WT MEFs, atg5 KO MEFs had immature autophagosomes that showed incomplete closure. Upon starvation, late autophagosomes accumulated in WT MEFs while the number of immature autophagosomes significantly increased in atg5 KO MEF indicating that atg5 plays an important role in the maturation of autophagosomes. Next, we examined autophagosomes in the cell model expressing polyQ-expanded N-terminal fragment of huntingtin. Our TEM analysis indicates that the number of late autophagosomes was significantly increased in the cells expressing the mutant huntingtin, indicating that improving the fusion of autophagosome with lysosome may be effective to enhance autophagy for the treatment of Huntington's disease. Taken together, the results of our study indicate that ultrastructural and quantitative analysis of autophagosomes using TEM can be applied to various human cellular disease models, and that they will provide an important insight for cellular pathogenesis of human diseases associated with autophagy.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Progress in Medical Physics
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• v.24 no.3
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• pp.162-170
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• 2013

Dedicated single-photon emission computed tomography (SPECT) systems based on pixelated semiconductors are being developed for studying small animal models of human disease. To clarify the possibility of using a SPECT system with CdTe for a high resolution low-dose small animal imaging, we compared the quality of reconstructed images from pixelated CdTe detector to those from a small SPECT system with NaI(Tl). The CdTe detector was $44.8{\times}44.8$ mm and the pixels were $0.35{\times}0.35{\times}5$ mm. The intrinsic resolution of the detector was 0.35 mm, which is equal to the pixel size. GATE simulations were performed to assess the image quality of both SPECT systems. The spatial resolutions and sensitivities for both systems were evaluated using a 10 MBq $^{99m}Tc$ point source. The quantitative comparison with different injected dose was performed using a voxelized MOBY phantom, and the absorbed doses for each organ were evaluated. The spatial resolution of the SPECT with NaI(Tl) was about 1.54 mm FWHM, while that of the SPECT with a CdTe detector was about 1.32 mm FWHM at 30 mm. The sensitivity of NaI(Tl) based SPECT was 83 cps/MBq, while that of the CdTe detector based SPECT was 116 cps/MBq at 30 mm. The image statistics were evaluated by calculating the CNR of the image from both systems. When the injected activity for the striatum in the mouse brain was 160 Bq/voxel, the CNR of CdTe based SPECT was 2.30 while that of NaI(Tl) based SPECT was 1.85. The CNR of SPECT with CdTe was overall higher than that of the NaI(Tl) based SPECT. In addition, the absorbed dose was higher from SPECT with CdTe than those from NaI(Tl) based SPECT to acquire the same quantitative values. Our simulation results indicated that the SPECT with CdTe detector showed overall high performance compared to the SPECT with NaI(Tl). Even though the validation study is needed, the SPECT system with CdTe detector appeared to be feasible for high resolution low-dose small animal imaging.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.15-15
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• 2015

Aspergillus fumigatus is a major cause of invasive aspergillosis (IA), a significant health issue worldwide with high mortality rates up to 95%. Our lab is interested in how A. fumigatus adapts to low oxygen conditions 'hypoxia', which is one of the important host microenvironments. A. fumigatus SrbA is a basic helix-loop-helix (bHLH) transcriptional regulator and belongs to sterol regulatory element binding protein (SREBP) family members. Loss of SrbA completely blocks growth in hypoxia and results in avirulence in murine models of IA suggesting an essential role of SrbA in hypoxia adaptation and virulence in A. fumigatus. We conducted chromatin immunoprecipitation sequencing (ChIP-seq) with A. fumigatus wild type using a SrbA specific antibody, and 97 genes were revealed as SrbA direct targets. One of the 'SrbA regulons' (AFUB_099590) was a putative bHLH transcriptional regulator whose sequence contained a characteristic tyrosine substitution in the basic portion of the bHLH domain of SREBPs. Therefore, we designated AFUB_099590 SrbB. Further characterization of SrbB demonstrated that SrbB is important for radial growth, biomass production, and biosynthesis of heme intermediates in hypoxia and virulence in A. fumigatus. A series of quantitative real time PCR showed that transcription of several SrbA regulons is coordinately regulated by two SREBPs, SrbA and SrbB in hypoxia. This suggests that SrbA and SrbB have both dependent and independent functions in regulation of genes responsible for hypoxia adaptation in A. fumigatus. Together, our data provide new insights into complicated roles of SREBPs in adaptation of host environments and virulence in pathogenic fungi.

• Molecules and Cells
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• v.38 no.9
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• pp.773-780
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• 2015

3D8 single chain variable fragment (scFv) is a recombinant monoclonal antibody with nuclease activity that was originally isolated from autoimmune-prone MRL mice. In a previous study, we analyzed the nuclease activity of 3D8 scFv and determined that a HeLa cell line expressing 3D8 scFv conferred resistance to herpes simplex virus type 1 (HSV-1) and pseudorabies virus (PRV). In this study, we demonstrate that 3D8 scFv could be delivered to target tissues and cells where it exerted a therapeutic effect against PRV. PRV was inoculated via intramuscular injection, and 3D8 scFv was injected intraperitoneally. The observed therapeutic effect of 3D8 scFv against PRV was also supported by results from quantitative reverse transcription polymerase chain reaction, southern hybridization, and immunohistochemical assays. Intraperitoneal injection of 5 and $10{\mu}g$ 3D8 scFv resulted in no detectable toxicity. The survival rate in C57BL/6 mice was 9% after intramuscular injection of 10 $LD_{50}$ PRV. In contrast, the 3D8 scFv-injected C57BL/6 mice showed survival rates of 57% ($5{\mu}g$) and 47% ($10{\mu}g$). The results indicate that 3D8 scFv could be utilized as an effective antiviral agent in several animal models.