• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.17-23
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• 2016

Wireless sensor-actuator networks (WSANs) enhance the existing wireless sensor networks (WSNs) by equipping sensor nodes with an actuator. The actuators work with the sensor nodes and perform application-specific operations. The WSAN systems have several applications such as disaster relief, intelligent building, military surveillance, health monitoring, and infrastructure security. These applications require capability of reliable data transfer to act responsively and accurately. Biologically inspired modeling techniques have received considerable attention for achieving robustness, scalability, and adaptability, while retaining individual simplicity. In this paper, an epidemic-inspired algorithm for data dissemination with delay constraints while minimizing energy consumption in WSAN is proposed. The steady states and system stability are analyzed using control theory. Also, simulation results indicate that the proposed scheme provides desirable dissemination delay and energy saving.

• Journal of Microbiology and Biotechnology
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• v.24 no.5
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• pp.704-713
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• 2014

The highly pathogenic avian influenza A (HPAI) viruses of the H5N1 subtype infect poultry and have also been spreading to humans. Although new antiviral drugs and vaccinations can be effective, rapid detection would be more efficient to control the outbreak of infections. In this study, a phage-display library was applied to select antibody fragments for HPAI strain A/Hubei/1/2010. As a result, three clones were selected and sequenced. A hemagglutinin inhibition assay of the three scFvs revealed that none exhibited hemagglutination inhibition activity towards the H5N1 virus, yet they showed a higher binding affinity for several HPAI H5N1 strains compared with other influenza viruses. An ELISA confirmed that the HA protein was the target of the scFvs, and the results of a protein structure simulation showed that all the selected scFvs bound to the HA2 subunit of the HA protein. In conclusion, the three selected scFVs could be useful for developing a specific detection tool for the surveillance of HPAI epidemic strains.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• Korean Journal of Veterinary Research
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• v.49 no.4
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• pp.329-334
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• 2009

Aujeszky's disease (AD) is a respiratory, infectious viral illness associated with high mortality, especially in neonatal piglets and has frequently been considered an economically important disease in many endemic countries. Although AD is still occurring in a geographically defined region in Korea, little attention has been paid to the economics of AD. In this study, partial budget technique was used to develop a simulation model to measure financial losses following the disease epidemic in a swine operation utilizing stochastic or deterministic parameters from the literatures and the index case herd of AD occurred in 2005, where available and applicable. For the infected case herd with a 12500-pig, the total economic loss for this operation was estimated to be about 199 million Korean won (95% confidence interval [CI] 148,645,000-250,741,000). Given net loss due to death of a pig at sow level was 119,000 won, total loss for the case herd with 1200 sows accounted for 143 million won (95% CI 92,599,000-193,729,000). The net loss of the death of one pig at growing and fattening level resulted in loss of 46,000 won (95% CI 40,000-53,000) and 126,000 won (95% CI 122,000-131,000), respectively. Taking into account for the number of pigs raised in the case herd, total loss amounted to 8 million won (95% CI 7,167,000-9,347,000) and 12 million won (95% CI 11,959,000-12,891,000), for growers and fatteners, respectively, assuming 63% of saved feed intake when a pig dies halfway through the respective period. Under the model's assumptions, suckling pig mortality was the major factors of loss in estimating the economic consequences (approximately 71.8% of the total loss). The high economic losses of a herd infected with AD suggest that the effective and region-specific control measures should be implemented in disease endemic foci.