• Smart Structures and Systems
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• v.6 no.5_6
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• pp.595-618
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• 2010

This paper describes the application of a wireless sensor network to a 31 meter-tall medieval tower located in the city of Trento, Italy. The effort is motivated by preservation of the integrity of a set of frescoes decorating the room on the second floor, representing one of most important International Gothic artworks in Europe. The specific application demanded development of customized hardware and software. The wireless module selected as the core platform allows reliable wireless communication at low cost with a long service life. Sensors include accelerometers, deformation gauges, and thermometers. A multi-hop data collection protocol was applied in the software to improve the system's flexibility and scalability. The system has been operating since September 2008, and in recent months the data loss ratio was estimated as less than 0.01%. The data acquired so far are in agreement with the prediction resulting a priori from the 3-dimensional FEM. Based on these data a Bayesian updating procedure is employed to real-time estimate the probability of abnormal condition states. This first period of operation demonstrated the stability and reliability of the system, and its ability to recognize any possible occurrence of abnormal conditions that could jeopardize the integrity of the frescos.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.119-124
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• 2022

Recently a method of constructing architectural products using additive manufacturing technology has been proposed. The additive manufacturing technology automates the construction process and it can secure the safety of workers. In addition, due to the high implementation efficiency of atypical shapes, the applicability to the manufacturing process of buildings and infrastructure is drawing attention. Additive manufacturing technology has the ability of satisfying computer-based construction automation, resource management and construction period prediction which is required in the modern construction industry. However, the industrial application is still limited by insufficient data, standards, regulations, and operating methods. In this study, in order to analyze the applicability of architectural additive manufacturing technology, we manufacture each architectural product with two additive manufacturing systems. In addition, we apply an application of each building product into an appropriate manufacturing system through the AM production decision model. And identify problems in the manufacturing process through empirical experiments. As a result, we propose an extended additive production decision model to improve the quality of building products.

• Economic and Environmental Geology
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• v.53 no.3
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• pp.321-336
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• 2020

The Headquarters for Earthquake Research Promotion (HERP) represents the upper-most level of Japan's earthquake disaster prevention governance. Its policy committee establishes the national earthquake investigation research promotion plan. The earthquake investigation committee of HERP collects survey geo-data and evaluates the research results of each earthquake disaster prevention agency. The establishment of an earthquake-related geo-resilience research strategy is both necessary and desirable for Korea. The concept of geo-resilience entails the ability to improve disaster resilience through the application of research results and the convergence of geoscience with science and technology (S&T) including the humanities and social sciences. The achievement of geo-resilience requires a national long-term roadmap and strategy for earthquake prediction research, the development of earthquake disaster prediction and prevention technology, Geo-ICT convergence technology development, implementation of a geocyber physics system (Geo-CPS), the use of geo-mimetics, and geoscientific R&D as it relates to local communities. Through such efforts, the national research institutes of Korea will be able to develop earthquake prediction capacities in relevant fields, reinforce proactive response capabilities, enhance community-level confidence in geodata and its research results, foster next-generation geoscientific manpower, and expand geoscientific infrastructure.

• Journal of Radiation Protection and Research
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• v.28 no.4
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• pp.311-319
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• 2003

We developed a grid-based Gaussian plume model to evaluate tracer release data measured at Young Gwang nuclear site in 1996. Downwind distance was divided into every 10m from 0.1km to 20km, and crosswind distance was divided into every 10m centering released point from -5km to 5km. We determined dispersion factors, ${\sigma}_y\;and\;{\sigma}_z$ using Pasquill-Gifford method computed by atmospheric stability. Forecasting ability of the grid-based Gaussian plume model was better at the 3km away from the source than 8km. We confirmed that dispersion band must be modified if receptor is far away from the source, otherwise P-G method is not appropriate to compute diffusion distance and diffusion strength in case of growing distance. So, we developed an empirical equation using linear programming. An objective function was designed to minimize sum of the absolute value between observed and computed values. As a result of application of the modified dispersion equation, prediction ability was improved rather than P-G method.

• BMB Reports
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• v.39 no.5
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• pp.560-570
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• 2006

Mistletoe (Viscum album) lectins, which are classified as a type II ribosome-inactivating protein (RIP) due to their unique biological function and the potential medical and therapeutic application in cancer cells, receive a rising attention. The heterodimeric glycoproteins contain the A-chain with catalytic activity and the B-chain with sugar binding properties. In recent years, studies involving the lectins from the white berry European mistletoe (Viscum album) and the yellow berry Korean mistletoe (Viscum album coloratum) have been described. However, the detailed mechanism in exerting unique cytotoxic effect on cancer cells still remains unclear. Here, we aim to understand and define the molecular basis and biological effects of the type II RIPs, through the studies of the recombinant Korean mistletoe lectin. To this end, we expressed, purified the recombinant Korean mistletoe lectin (rKML), and investigated its molecular characteristics in vitro, its cytotoxicity and ability to induce apoptotic cell death in cancer cells. To gain structural basis for its catalytic activity and sugar binding properties, we performed homology modeling studies based on the high degree of sequence identity and conserved secondary structure prediction between Korean and European, Himalayan mistletoe lectins, and Ricin.

• Food Science of Animal Resources
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• v.32 no.4
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• pp.448-457
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• 2012

The predictive ability for off-flavor development and quality change of ground beef was evaluated using a microbial time temperature indicator (TTI). Quality indices such as off-flavor detection (OFD) time, color, pH, volatile basic nitrogen (VBN), aerobic mesophilic bacteria (AMB) counts, and lactic acid bacteria (LAB) counts were measured during storage at 5, 10, 15, and $25^{\circ}C$, respectively. Arrhenius activation energies (Ea) were estimated for temperature dependence. The Ea values for TTI response (changes in titratable acidity (TA)), VBN, AMB counts, LAB counts, and freshness, which is defined based on OFD time for quality indices of ground beef, were 106.22 kJ/mol, 58.98 kJ/mol, 110.35 kJ/mol, 116.65 kJ/mol, and 92.73 kJ/mol, respectively. The Ea of microbial TTI was found to be closer to those of the AMB counts, LAB counts, and freshness. Therefore, AMB counts, LAB counts, and freshness could be predicted accurately by the microbial TTI response due to their Ea similarity. The microbial TTI exhibited consistent relationships between its TA change and corresponding quality indices, such as AMB counts, LAB counts, and freshness, regardless of storage temperature. Conclusively, the results established that the developed microbial TTI can be used in intelligent packaging technology for representing some selected quality indices of ground beef.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.113-117
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• 2022

Additive manufacturing (AM, also known as 3D printing) technology is digitalized technology, making it easy to predict and manage quality and also, have design freedom ability. With these advantages, AM technology is applied to various industries. In particular, a method of manufacturing buildings and infrastructure with AM technology is being proposed to the construction industry. However, the application of AM technology is restricted due to problems such as insufficient history and quality of technology, lack of construction management methods, and certification of manufacturing products. Therefore, the manufacture of architectural products is implemented with indirect AM technology. In particular, it manufactures formwork using AM and injecting building materials to implement the architectural product. In this study, hybrid type material extrusion AM is used to manufacture large-sized formwork and implement building products. Moreover, we identify factors that can predict productivity and economic feasibility in the additive manufacturing process. As a result, design optimization results are proposed to reduce the production cost and time of architecture buildings.

• Journal of The Korean Society of Clinical Toxicology
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• v.12 no.1
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• pp.14-21
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• 2014

Purpose: The objective of this study was to develop a new scoring tool that is comprehensively applicable and predicts fatality within 24 h of intoxication. Methods: This was a cohort study conducted in two emergency medical centers from 2011 to 2012. We identified factors associated with severe/fatality. Through a discriminant analysis, we devised the aBIG (age, Base deficit, Infection, and Glasgow coma scale) score. To compare the ability of aBIG to predict intoxication severity with that of previous scoring systems such as APACHE II, MODS, SAPS IIe, and SOFA, we determined the receiver operating characteristic curves of each variable in predicting severe-to-fatal toxicity. Results: Compared with the mild/moderate toxicity group (n=211), the severe/fatal group (n=143) had higher incidences of metabolic acidosis, infection, serious mental change, QTc prolongation and hepato-renal failure. Age, base deficit, infection-WBC count, and Glasgow Coma Scale were independently associated with severe/fatal poisoning. These variables were combined into the poisoning "aBIG" score [$0.28{\times}$Age group+$0.38{\times}WBC$ count/$10^3+0.52{\times}$Base deficit+$0.64{\times}$(15-GCS)], which were each calculated to have an area under the curve of 0.904 (95% confidence interval: 0.868-0.933). The aBIG poisoning score had an equivalent level of severity predictability as APACHE II and a superior than MODS, SOFA, and SAPS IIe. Conclusion: We developed a simplified scoring system using the four variables of age, base deficit, infected leukocytosis, and GCS. The poisoning aBIG score was a simple method that could be performed rapidly on admission to evaluate severity of illness and predict fatal severity in patients with acute intoxications.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.