• IE interfaces
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• v.12 no.3
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• pp.401-405
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• 1999

We present a simulation model to generate the spatial distribution of mobiles in cellular communication network. Three types of spatial distributions are considered; biased, random, and ratio-based distributions. This study also points out and corrects the critical errors performed by Das and Morgera(1997) in getting random location of mobiles. By applying a simple path loss model, the effects of our correction on the signal-to-interference(SIR) ratio are discussed. The numerical results indicate that the variation of SIR in the Das's biased distribution is larger than that of other distributions. As compared with the random distribution, the average SIR error of the biased distribution is 91.1%.

• Communications of the Korean Mathematical Society
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• v.33 no.3
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• pp.919-928
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• 2018

In this paper, we consider the admissible control problem for the linear systems by using the solution of the Hausdorff moment problem. In addition, we consider the admissible control problem for SIR epidemic model.

• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.27-48
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• 2021

We extend and modify the canonical epidemiology model of Eichenbaum, Rebelo and Trabandt (2020) to investigate the general equilibrium effects of COVID-19 spread in the Korean economy when vaccine, treatment and social distancing are available. Particularly, we develop a SIR-macro model which considers possibility of moral inattention of the overconfident agents through which people is more likely to be infected. Our model implies that people's decision to cut back on consumption and work reduces the severity of the epidemic and thus exacerbate the size of the economic recession caused by the epidemic. Another finding is that the average 13 weeks to develop the vaccine and treatment will lead to 2% drop of consumption.

• Journal of Preventive Medicine and Public Health
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• v.51 no.1
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• pp.33-40
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• 2018

Objectives: The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods: This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The $Besag-York-Molli{\acute{e}}$ (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results: The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96), not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94) and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68) were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range) and mean (standard deviation) values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01) and 1.05 (1.31), respectively. Conclusions: Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in at-risk areas.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.301-312
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• 2015

The purpose of this paper is to establish the epidemic model to explain the process of disease spread. The process of disease spread can be classified into two types: deterministic process and stochastic process. Most studies supposed that the process follows the deterministic process and established the model using the ordinary differential equation. In this article, we try to build the disease spread prediction model based on the SIR (Suspectible - Infectious - Recovered) model. we first estimated the model parameters using least squared method and applied to a deterministic model using ordinary differential equation. we also applied to a stochastic model based on Gillespie algorithm. The methods introduced in this paper are applied to the data on the number of cases of malaria every week from January 2001 to March 2003, released by Korea Centers for Disease Control and Prevention. As a result, we conclude that our model explains well the process of disease spread.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6151-6158
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• 2013

Radio frequency ablation (RFA) is an effective means of achieving local control of liver cancer. It is a particularly suitable mode of therapy for small and favorably located tumors. However, local progression rates are substantially higher for large tumors (>3.0 cm). In the current study, we report on a mathematical model based on geometric optimization to treat large liver tumors. A database of mathematical models relevant to the configuration of liver cancer was also established. The specific placement of electrodes and the frequency of ablation were also optimized. In addition, three types of liver cancer lesion were simulated by computer guidance incorporating mathematical models. This approach can be expected to provide a more effective and rationale mechanism for employing RFA in the therapy of hepatic carcinoma.

• ETRI Journal
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• v.33 no.3
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• pp.320-325
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• 2011

This paper considers the effects of simultaneous correlated multipath fading and shadowing on the performances of a signal-to-interference ratio (SIR)-based dual-branch selection combining (SC) diversity receiver. This analysis includes the presence of cochannel interference. A generalized fading/shadowing channel model in an interference-limited correlated fading environment is modeled by generalized-K distribution. Closed-form expressions are obtained for probability density function and cumulative distribution function of the SC output SIR, as well as for the outage probability. Based on this, the influence of various fading and shadowing parameter values and the correlation level on the outage probability is examined.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.19 no.4
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• pp.229-235
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• 2016

Purpose: To study whether breastfeeding and breastfeeding status during gluten introduction influences the age at diagnosis of celiac disease (CD). In addition to study, whether the timing of gluten introduction influences the age at diagnosis of CD. Methods: It was a hospital based observational study. Total 198 patients diagnosed with CD as per modified European Society of Pediatric Gastroenterology, Hepatology and Nutrition (2012) criteria, aged between 6 months to 6 years were included. Detail history taken with special emphasis on breastfeeding and age of gluten introduction. Standard statistical methods used to analyze the data. Results: $Mean{\pm}standard$ deviation age of onset and diagnosis of CD in breastfed cases was $2.81{\pm}1.42$ years and $3.68{\pm}1.55$ years respectively as compared to $1.84{\pm}1.36$ years and $2.70{\pm}1.65$ years respectively in not breastfed cases (p<0.05). Those who had continued breastfeeding during gluten introduction and of longer duration had significantly delayed onset of disease. The age at onset of CD was under one year in 40.42% of the cases, who had started gluten before 6 months of age compared to only 12.58% of those who had started gluten later (p<0.001). The proposed statistical model showed that two variables, i.e., breast feeding status during gluten introduction and age at gluten introduction positively influencing the age at diagnosis of CD. Conclusion: Delayed gluten introduction to infant's diet along with continuing breastfeeding, delays symptomatic CD. However, it is not clear from our study that these infant feeding practices provide permanent protection against the disease or merely delays the symptoms.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.