• Journal of Korean Society of Forest Science
• /
• v.94 no.6
• /
• pp.481-487
• /
• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Journal of Applied Biological Chemistry
• /
• v.57 no.1
• /
• pp.83-87
• /
• 2014

The effects of combined treatment of aqueous chlorine dioxide ($ClO_2$) and fumaric acid on the microbial growth in fresh-cut paprika were investigated. After the combined treatment, the populations of total aerobic bacteria and inoculated Listeria monocytogenes in the paprika sample were reduced by 0.82 and 1.21 log CFU/g, respectively, compared to those of the control. In addition, after 10 d of storage at $10^{\circ}C$, the populations were decreased by 1.21 and 2.10 log CFU/g, respectively. The predictive model for the populations of total aerobic bacteria and L. monocytogenes in the paprika was applied during storage. The prediction equation using Gompertz model was appropriate, based on the statistical analysis such as accuracy factor and bias factor. These results suggest that the combined treatment of aqueous $ClO_2$ and fumaric acid can be an effective technology for microbial decontamination and it can improve microbial safety by decreasing maximum growth rate and increasing lag time of bacteria in the fresh-cut paprika.

• Journal of Microbiology and Biotechnology
• /
• v.15 no.6
• /
• pp.1323-1329
• /
• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Journal of Information Science Theory and Practice
• /
• v.10 no.2
• /
• pp.45-73
• /
• 2022

The research in the literature review on Internet of Things (IoT) adoption from an individual consumer viewpoint is minimal and has not yet been fully investigated. Therefore, the objectives of this study are to analyze the growth of IoT in recent years and to conduct a weight analysis of the factors that affect acceptance intentions and real usage of IoT-enabled services. For the review, we analyzed 87 publications from 13 conferences and 54 journals published during the period 2014-2020 about consumer adoption of IoT. Following the study, we discovered an unprecedented increase in the number of articles published in the last seven years, which points to an emerging area with an enormous prospect. Furthermore, the weight analysis outcome was associated with the diagrammatic representation in this study. After that, this research developed a generalized consumer IoT adoption model based on the 12 best predictors derived from frequency count and weight analysis, which had the highest predictive power for calculating IoT adoption. This paper further acknowledges the study's theoretical and practical contributions, as well as its shortcomings, and proposes further research directions for future researchers.

• Clinical and Experimental Pediatrics
• /
• v.65 no.2
• /
• pp.90-97
• /
• 2022

Background: Stunting is affected by various factors from mother and child. Previous studies assessed only one or more influencing variables. Unfortunately, nor the significant influence of maternal and child factors nor the indicators contributing to maternal and child factors that affect the stunting incidence have ever been analyzed. Purpose: This study analyzed the effect of maternal and child factors on stunting and the significant indicators that shape the maternal and child factors that impact stunting. Methods: This was a case-control study. Overall, 132 stunted children and 132 nonstunted children in Purbalingga Regency, Central Java Province, participated in the research. Direct interviews and medical record reviews were conducted to assess the studied variables. The research data were tested using the partial least squares structural equation with a formative model. Results: Maternal factors directly affected the occurrence of stunting (t=3.527, P<0.001) with an effect of 30.3%. Maternal factors also contributed a significant indirect effect on stunting through child factors (t=4.762, P<0.001) with an effect of 28.2%. Child factors affected the occurrence of stunting (t=5.749, P<0.001) with an effect of 49.8%. The child factor was influenced by maternal factor with an effect of 56.7% (t=10.014, P<0.001). The moderation analysis results demonstrated that maternal and child factors were moderate predictive variables of stunting occurrence. Conclusion: Child factors have more significant and direct effects on stunting than maternal factors but are greatly affected by them.

• Asia-Pacific Journal of Business
• /
• v.15 no.2
• /
• pp.71-88
• /
• 2024

Purpose - This study provides an overview of existing research and practices related to market risk premiums(MRP), and empirically estimates the MRP in Korea, particularly using the related option prices. We also seek to improve the current MRP practices and explore alternative solutions. Design/methodology/approach - We present the option price-based MRP estimation method, as proposed by Martin (2017), and implement it within the context of the Korean stock market. We then juxtapose these results with those derived from other methods, and compare the characteristics with those of the United States. Findings - We found that the lower limit of the MRP in the Korean stock market shows a much lower value compared to the US. There seems to be the possibility of a market crash, exchange rate volatility, or a lack of option trading data. We investigated the predictive power of the estimated values and discovered that the weighted average of the results of various methodologies using the Principal Component Analysis (PCA) is superior to the individual method's results. Research implications or Originality - It is required to explore various methods of estimating MRP that are suitable for the Korean stock market. In order to improve the estimation methodology based on option prices, it is necessary to develop the methods using the higher-order(third order or above) moments, or consider additional risk factors such as the possibility of a crash.

• Journal of Integrative Natural Science
• /
• v.10 no.2
• /
• pp.85-94
• /
• 2017

Fibroblast growth factor receptor (FGFR) belongs to the family of receptor tyrosine kinase. They play important roles in cell proliferation, differentiation, development, migration, survival, wound healing, haematopoiesis and tumorigenesis. FGFRs are reported to cause several types of cancers in humans which make it an important drug target. In the current study, HQSAR analysis was performed on a series of recently reported 1H-Pyrazolo [3,4-b]pyridine derivatives as FGFR antagonists. The model was developed with Atom (A) and bond (B) connection (C), chirality (Ch), hydrogen (H) and donor/acceptor (DA) parameters and with different set of atom counts to improve the model. A reasonable HQSAR model ($q^2=0.701$, SDEP=0.654, NOC=5, $r^2=0.926$, SEE=0.325, BHL=71) was generated which showed good predictive ability. The contribution map depicted the atom contribution in inhibitory effect. A contribution map for the most active compound (compound 24) indicated that hydrogen and nitrogen atoms in the side chains of ring B as well as hydrogen atoms in the side chain of ring C and the nitrogen atom in the ring D contributed positively to the activity in inhibitory effect whereas, the lowest active compound (compound 04) showed negative contribution to inhibitory effect. Thus results of our study can provide insights in the designing potent and selective FGFR kinase inhibitors.

• KIISE Transactions on Computing Practices
• /
• v.21 no.1
• /
• pp.46-51
• /
• 2015

The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a prescriptive analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.2
• /
• pp.398-406
• /
• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Information Science Theory and Practice
• /
• v.12 no.1
• /
• pp.60-76
• /
• 2024

Broadband Internet has proven to be vital for economic growth in developed countries. Developing countries have implemented several initiatives to increase their broadband access. However, its full potential can only be realized through adoption and use. With lower-middle-income countries accounting for the majority of the world's unconnected population, this study employs the theory of planned behavior (TPB) to investigate users' intentions to adopt broadband. Rural Tanzania was chosen as a case study. A cross-sectional study was conducted over three weeks, using 155 people from seven villages with the lowest broadband adoption rates. Non-probability voluntary response sampling was used to recruit the participants. Using the TPB constructs: attitude toward behavior (ATB), subjective norms (SN), and perceived behavioral control (PBC), ordinal regression analysis was employed to predict intention. Descriptive statistical analysis yielded mean scores (standard deviation) as 3.59 (0.46) for ATB, 3.34 (0.40) for SN, 3.75 (0.29) for PBC, and 4.12 (0.66) for intention. The model adequately described the data based on a comparison of the model with predictors and the null model, which revealed a substantial improvement in fit (p<0.05). Moreover, the predictors accounted for 50.3% of the variation in the intention to use broadband Internet, demonstrating the predictive power of the TPB constructs. Furthermore, the TPB constructs were all significant positive predictors of intention: ATB (β=1.938, p<0.05), SN (β=2.144, p<0.05), and PBC (β=1.437, p=0.013). The findings of this study provide insight into how behavioral factors influence the likelihood of individuals adopting broadband Internet and could guide interventions through policies meant to promote broadband adoption.