• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6561-6568
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• 2015

Background: Smokeless tobacco (SLT) has long been realized as an important component of the fight for global tobacco control. It still remains a major problem in countries like India, Bangladesh and Nepal. The objective of this study was to estimate the trends of SLT use in three countries of the SEARO WHO office. Materials and Methods: We used data from national surveys in three countries (Bangladesh, India and Nepal) to estimate trends in prevalence of current SLT use. All available nationally representative data sources were used. Estimates were weighted, age standardized and given along with 95% confidence intervals. Significance of linear trend in prevalence over time was tested using the Cochrane-Armitage test for trend. A p value of less than 0.05 was considered statistically significant. Results: We identified three surveys for Bangladesh, three for India and four for Nepal that met the selection criteria (such as Demographic and Health Surveys, WHO-STEPwise approach to Surveillance and Global Adult Tobacco Surveys). A significantly increasing trend was noticed in the prevalence of current SLT use among Bangladeshi men (20.2% to 23%, p=0.03). In India, a similar significantly increasing trend was seen among men (27.1% to 33.4%, p<0.001) and women (10.1% to 15.7%, p<0.001). In Nepal, there was a no significant trend among both men (39.1% to 31.6%, p=0.11) and women (5.6% to 4.7%, p=0.49). Conclusions: In the study countries SLT use has remained at alarmingly high levels. Usage trends do not show any signs of decline in spite of control efforts. Tobacco control measures should focus more on controlling SLT use.

• Korean Journal of Organic Agriculture
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• v.19 no.1
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• pp.23-38
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• 2011

● The current world is suffering abnormal climate caused by global warming. The main cause of global warming is greenhouse gas such as carbon dioxide. The carbon labeling system and carbon traceability system being pushed ahead in the agricultural sector is the policy for responding to climate change to reduce greenhouse gas emissions. To make this policy more effective and enhanced, the amount of carbon emissions should be calculated based on the kind of crops or the various businesses in the agricultural sector. Therefore, in order to estimate the accurate amount of carbon emissions, it is necessary to establish carbon dioxide emission intensity of various agricultural materials added onto the agriculture, and to calculate the amount of carbon dioxide emission for each crop according to agricultural production. The purpose of this study is to establish the amount of emission, emission per agricultural materials, of agricultural materials being added for crop production as a basic step, and emission intensity which can be used in the future market in order to estimate accurate amount of carbon emission in all the policies being promoted in the agricultural sector. Therefore, in this study, in order to build LCI D/B about organic fertilizers among many organic materials added onto the organic agriculture sector, one leading company in organic fertilizer production was selected and LCA was conducted for this leading company. We had to build the intensity and integrated average concept of intensity upon the two cases once production farmers for their own consumption and farms besides organic fertilizer company were categorized even if it's little amount. But in this study, individually produced organic fertilizers were excluded. Calculated results are following. Carbon emission of mixed expeller cake fertilizer in organic fertilizer was 1,106,966.89kg-$CO^2$ and emission intensity was 0.01606kg-$CO^2$, respectively. Total emission of mixed organic fertilizers was 241,523.2kg-$CO^2$ and emission intensity was 0.01705kg-$CO^2$. And total emission of organic compound fertilizers was 94,592.66kg-$CO^2$ and emission intensity was 0.01769kg-$CO^2$, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.39-48
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• 2011

Global warming causes the climate change and makes severe damage to ecosystem and civilization Carbon dioxide greatly contributes to global warming, thus many studies have been conducted to estimate the forest biomass carbon stock as an important carbon storage. However, more studies are required for the selection and use of technique and remotely sensed data suitable for the carbon stock estimation in Korea In this study, the aboveground forest biomass carbon stocks of Danyang-Gun in South Korea was estimated using $k$NN($k$-Nearest Neighbor) algorithm and regression model, then the results were compared. The Landsat TM and 5th NFI(National Forest Inventory) data were prepared, and ratio images, which are effective in topographic effect correction and distinction of forest biomass, were also used. Consequently, it was found that $k$NN algorithm was better than regression model to estimate the forest carbon stocks in Danyang-Gun, and there was no significant improvement in terms of accuracy for the use of ratio images.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.1
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• pp.39-58
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• 2004

It is very important that predict the inelastic seismic behavior exactly for seismic performance evaluation of a building in the performance based seismic design. Evaluation method of seismic performance based on the pushover analysis reflected in PBSE was developed by some researchers. For the evaluation of inelastic global and local seismic responses by pushover analysis exactly. lateral load distribution should be adjusted and reflected the dynamic characteristics of structural system and various seismic ground motions. And performance point should be determined based on the evaluation of reasonable deformation capacity of a building more exactly. An effective method based on the improved the adaptive lateral load distribution and the equivalent responses of a multistory building is proposed in this study to efficiently estimate the accurate inelastic seismic responses. The proposed method can be used to evaluate the seismic performance for the global inelastic behavior of a building and to accurately estimate its local inelastic seismic responses. In order to demonstrate the accuracy and validity of this method, inelastic seismic responses estimated by the proposed method are compared with those obtained from other analytical methods.

• Smart Structures and Systems
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• v.15 no.3
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• pp.665-682
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• 2015

Structural identification or St-Id is 'the parametric correlation of structural response characteristics predicted by a mathematical model with analogous characteristics derived from experimental measurements'. This paper describes a St-Id exercise on Humber Bridge that adopted a novel two-stage approach to first calibrate and then validate a mathematical model. This model was then used to predict effects of wind and temperature loads on global static deformation that would be practically impossible to observe. The first stage of the process was an ambient vibration survey in 2008 that used operational modal analysis to estimate a set of modes classified as vertical, torsional or lateral. In the more recent second stage a finite element model (FEM) was developed with an appropriate level of refinement to provide a corresponding set of modal properties. A series of manual adjustments to modal parameters such as cable tension and bearing stiffness resulted in a FEM that produced excellent correspondence for vertical and torsional modes, along with correspondence for the lower frequency lateral modes. In the third stage traffic, wind and temperature data along with deformation measurements from a sparse structural health monitoring system installed in 2011 were compared with equivalent predictions from the partially validated FEM. The match of static response between FEM and SHM data proved good enough for the FEM to be used to predict the un-measurable global deformed shape of the bridge due to vehicle and temperature effects but the FEM had limited capability to reproduce static effects of wind. In addition the FEM was used to show internal forces due to a heavy vehicle to to estimate the worst-case bearing movements under extreme combinations of wind, traffic and temperature loads. The paper shows that in this case, but with limitations, such a two-stage FEM calibration/validation process can be an effective tool for performance prognosis.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• Journal of Korea Trade
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• v.24 no.8
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• pp.153-170
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• 2020

Purpose - As the US-China trade war intensifies and lasts long time, there is growing concern about its potential effects on the global economy. In particular, for the countries like Korea that have a large economic dependence on the economy of the two countries, the US-China trade war may have a great repercussion in many ways. The aim of this paper is to investigate the global productivity and market structure implications of the US-China trade war for Korea, as well as for other surrounding countries and regions. Design/methodology - In this paper, we develop a full multi-country/region multi-sector computable general equilibrium (CGE) model of global trade incorporating heterogeneous workers and firms in individual skill levels and used technologies. We then calibrate the model using a global Social Accounting Matrix (SAM) dataset extracted from the recently released GTAP 10 Database, and assess the potential effects of the US-China trade war on the aggregate real productivity and the market structure for Korea, as well as for other surrounding countries and regions. Findings - We show that the US-China trade war may largely affect the aggregate productivity in each sector in each country/region, as well as the global market structure through entry and exit of firms, which results finally in considerable changes in the industrial comparative advantage of each country/region. Though the effects are diverse sector by sector, the results show that Korea may also be affected significantly: concerning the real productivity implications, it is shown that the machinery industry may be affected the most negatively; on the other hand, it is shown that the number of exporting firms may decrease the most in the other transports industry. Originality/value - As the US-China trade war intensifies, many studies have tried to estimate the possible implications, and for this usually the CGE models have largely been used as the standard tool for evaluating the impacts of changes in trade policies. Standard CGE models, however, cannot be used to assess the global productivity and market structure implications due to the symmetric and simplified base assumptions. This paper is the first to analyze and quantify the possible impacts of the US-China trade war on the aggregate productivity and global market structure using a CGE model incorporating endogenous skill-technology assignment of heterogeneous workers and firms.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.341-354
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• 2018

The one of the principles described in ICH E9 is that only results obtained from pre-specified statistical methods in a protocol are regarded as confirmatory evidence. However, in multi-regional clinical trials, even when results obtained from pre-specified statistical methods in protocol are significant, it does not guarantee that the test treatment is approved by regional regulatory agencies. In other words, there is no so-called global approval, and each regional regulatory agency makes its own decision in the face of the same set of data from a multi-regional clinical trial. Under this situation, there are two natural methods a regional regulatory agency can use to estimate the treatment effect in a particular region. The first method is to use the overall treatment estimate, which is to extrapolate the overall result to the region of interest. The second method is to use regional treatment estimate. If the treatment effect is completely identical across all regions, it is obvious that the overall treatment estimator is more efficient than the regional treatment estimator. However, it is not possible to confirm statistically that the treatment effect is completely identical in all regions. Furthermore, some magnitude of regional differences within the range of clinical relevance may naturally exist for various reasons due to, for instance, intrinsic and extrinsic factors. Nevertheless, if the magnitude of regional differences is relatively small, a conventional method to estimate the treatment effect in the region of interest is to extrapolate the overall result to that region. The purpose of this paper is to investigate the effects produced by this type of extrapolation via estimations, followed by hypothesis testing of the treatment effect in the region of interest. This paper is written from the viewpoint of regional regulatory agencies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.401-408
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• 2015

Conventional model updating methods for the structures have used global structural responses which are modal parameters obtained through vibration measurements. Although models updated by modal parameters estimate global structural responses accurately, they have difficulties to predict local responses for safety assesment of structural members. The safety of structural members in the structures has been evaluated through the stress estimation based on strain measurements. Thus, this study additionally uses measured strain responses of structural members to perform model updating besides modal parameters. In the proposed method, the objective functions are set to the differences of the global and local responses obtained from updated model and measurement and those functions are minimized by NSGA-II, one of the multi-objective optimization techniques. The strain responses predicted from updated model are used for safety assessment of the steel frame structures. The proposed method are verified by numerical and experimental studies through the impact hammer tests for a steel frame specimen.

• Atmosphere
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• v.25 no.1
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• pp.169-177
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• 2015

Vertical distribution of particle mass concentrations was estimated from 8-year elastic-backscatter lidar and sky radiometer data, and from ground-level PM10 concentrations measured in Seoul. Lidar ratio and mass extinction efficiency were determined from aerosol optical depth (AOD) and ground-level PM10 concentrations, which were used as constraints to estimate particle mass concentration. The mean lidar ratio (with standard deviation) and mass extinction efficiency for the entire 8-year study period were $60.44{\pm}23.17$ sr and $3.69{\pm}3.00m^2g^{-1}$, respectively. The lidar ratio did not vary significantly with the ${\AA}ngstr{\ddot{o}}m$ exponent (less than ${\pm}10%$); however, the mass extinction efficiency decreases to $1.82{\pm}1.67m^2g^{-1}$ (51% less than the mean value) when the ${\AA}ngstr{\ddot{o}}m$ exponent is less than 0.5. This result implies that the particle mass concentration from lidar measurements can be underestimated for dust events. Seasonal variation of the particle mass concentration estimated from lidar measurements for the boundary layer, was quite different from ground-level PM10 measurements. This can be attributable to an inhomogeneous vertical distribution of aerosol in the boundary layer.