• Safety and Health at Work
• /
• v.10 no.1
• /
• pp.95-102
• /
• 2019

Background: In a cluster-randomized trial, the Safety and Health Involvement For Truck drivers intervention produced statistically significant and medically meaningful weight loss at 6 months (-3.31 kg between-group difference). The current manuscript evaluates the relative impact of intervention components on study outcomes among participants in the intervention condition who reported for a post-intervention health assessment (n = 134) to encourage the adoption of effective tactics and inform future replications, tailoring, and enhancements. Methods: The Safety and Health Involvement For Truck drivers intervention was implemented in a Web-based computer and smartphone-accessible format and included a group weight loss competition and body weight and behavioral self-monitoring with feedback, computer-based training, and motivational interviewing. Indices were calculated to reflect engagement patterns for these components, and generalized linear models quantified predictive relationships between participation in intervention components and outcomes. Results: Participants who completed the full program-defined dose of the intervention had significantly greater weight loss than those who did not. Behavioral self-monitoring, computer-based training, and health coaching were significant predictors of dietary changes, whereas behavioral and body weight self-monitoring was the only significant predictor of changes in physical activity. Behavioral and body weight self-monitoring was the strongest predictor of weight loss. Conclusion: Web-based self-monitoring of body weight and health behaviors was a particularly impactful tactic in our mobile health intervention. Findings advance the science of behavior change in mobile health intervention delivery and inform the development of health programs for dispersed populations.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.9
• /
• pp.3735-3740
• /
• 2015

Context: Insulin-like growth factor peptides play important roles in regulating cell growth, cell differentiation, and apoptosis, and have been demonstrated to promote the development of colorectal cancer (CRC). Objective: To examine the association of insulin-related biomarkers including insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein-3 (IGFBP-3) and C-peptide with CRC risk and assess their relevance in predictive models. Materials and Methods: The odds ratios of colorectal cancer for serum levels of IGF-1, IGFBP-3 and C-peptide were estimated using unconditional logistic regression models in 100 colorectal cancer cases and 100 control subjects. Areas under the receiving curve (AUC) and integrated discrimination improvement (IDI) statistics were used to assess the discriminatory potential of the models. Results: Serum levels of IGF-1 and IGFBP-3 were negatively associated with colorectal cancer risk (OR=0.07, 95%CI: 0.03-0.16, P for trend <.01, OR=0.06, 95%CI: 0.03-0.15, P for trend <.01 respectively) and serum C-peptide was positively associated with risk of colorectal cancer (OR=4.38, 95%CI: 2.13-9.06, P for trend <.01). Compared to the risk model, prediction for the risk of colorectal cancer had substantially improved when all selected biomarkers IGF-1, IGFBP-3 and inverse value of C-peptide were simultaneously included inthe reference model [P for AUC improvement was 0.02 and the combined IDI reached 0.166% (95 % CI; 0.114-0.219)]. Conclusions: The results provide evidence for an association of insulin-related biomarkers with colorectal cancer risk and point to consideration as candidate predictor markers.

• Structural Engineering and Mechanics
• /
• v.70 no.1
• /
• pp.13-31
• /
• 2019

A nonlinear finite element model (FEM) using ATENA-3D software to simulate the axially compressive behavior of circular steel tube confined concrete (CSTCC) columns infilled with ultra high performance concrete (UHPC) was presented in this paper. Some modifications to the material type "CC3DNonlinCementitious2User" of UHPC without and with the incorporation of steel fibers (UHPFRC) in compression and tension were adopted in FEM. The predictions of utimate strength and axial load versus axial strain curves obtained from FEM were in a good agreement with the test results of eighteen tested columns. Based on the results of FEM, the load distribution on the steel tube and the concrete core was derived for each modeled column. Furthermore, the effect of bonding between the steel tube and the concrete core was clarified by the change of friction coefficient in the material type "CC3DInterface" in FEM. The numerical results revealed that the increase in the friction coefficient leads to a greater contribution from the steel tube, a decrease in the ultimate load and an increase in the magnitude of the loss of load capacity. By comparing the results of FEM with experimental results, the appropriate friction coefficient between the steel tube and the concrete core was defined as 0.3 to 0.6. In addition to the numerical evaluation, eighteen analytical models for confined concrete in the literature were used to predict the peak confined strength to assess their suitability. To cope with CSTCC stub and intermediate columns, the equations for estimating the lateral confining stress and the equations for considering the slenderness in the selected models were proposed. It was found that all selected models except for EC2 (2004) gave a very good prediction. Among them, the model of Bing et al. (2001) was the best predictor.

• Steel and Composite Structures
• /
• v.48 no.2
• /
• pp.179-190
• /
• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Steel and Composite Structures
• /
• v.45 no.2
• /
• pp.205-218
• /
• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of Digital Convergence
• /
• v.16 no.8
• /
• pp.169-179
• /
• 2018

Recently, web search query information has been applied in advanced predictive model research. Google dominates the global web search market in the Korean market; however, Naver possesses a dominant market share. Based on this characteristic, this study intends to compare the utility of the Korean web search query information of Google and Naver using predictive models. Therefore, this study develops three time-series predictive models to estimate the youth unemployment rate in Korea using the ARIMA model. Model 1 only used the youth unemployment rate in Korea, whereas Models 2 and 3 added the Korean web search query information of Naver and Google, respectively, to Model 1. Compared to the predictability of the models during the training period, Models 2 and 3 showed better fit compared with Model 1. Models 2 and 3 correlated different query information. During predictive periods 1 (continuous with the training period) and 2 (discontinuous with the training period), Model 3 showed the best performance. During predictive period 2, only Model 3 exhibited a significant prediction result. This comparative study contributes to a general understanding of the usefulness of Korean web query information using the Naver and Google search engines.

• Gut and Liver
• /
• v.12 no.6
• /
• pp.714-721
• /
• 2018

Background/Aims: Recently reported prognostic models for primary biliary cholangitis (PBC) have been shown to be effective in Western populations but have not been well-validated in Asian patients. This study aimed to compare the performance of prognostic models in Korean patients and to investigate whether inflammation-based scores can further help in prognosis prediction. Methods: This study included 271 consecutive patients diagnosed with PBC in Korea. The following prognostic models were evaluated: the Barcelona model, the Paris-I/II model, the Rotterdam criteria, the GLOBE score and the UK-PBC score. The neutrophil-to-lymphocyte ratio (NLR) was analyzed with reference to its association with prognosis. Results: For predicting liver transplant or death at the 5-year and 10-year follow-up examinations, the UK-PBC score (areas under the receiver operating characteristic curve [AUCs], 0.88 and 0.82) and GLOBE score (AUCs, 0.85 and 0.83) were significantly more accurate in predicting prognosis than the other scoring systems (all p<0.05). There was no significant difference between the performance of the UK-PBC and GLOBE scores. In addition to the prognostic models, a high NLR (>2.46) at baseline was an independent predictor of reduced transplant-free survival in the multivariate analysis (adjusted hazard ratio, 3.74; p<0.01). When the NLR was applied to the prognostic models, it significantly differentiated the prognosis of patients. Conclusions: The UK-PBC and GLOBE scores showed good prognostic performance in Korean patients with PBC. In addition, a high NLR was associated with a poorer prognosis. Including the NLR in prognostic models may further help to stratify patients with PBC.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.15 no.4
• /
• pp.219-233
• /
• 2013

We employed the ecological niche modeling framework using GARP (Genetic Algorithm for Ruleset Production) to model the current and future geographic distribution of Pinus densiflora based on environmental predictor variable datasets such as climate data including the RCP 8.5 emission climate change scenario, geographic and topographic characteristics, soil and geological properties, and MODIS enhanced vegetation index (EVI) at 4 $km^2$ resolution. National Forest Inventory (NFI) derived occurrence and abundance records from about 4,000 survey sites across the whole country were used for response variables. The current and future potential geographic distribution of Pinus densiflora, one of the tree species dominating the present Korean forest was modeled and mapped. Future models under RCP 8.5 scenarios for Pinus densiflora suggest large areas predicted under current climate conditions may be contracted by 2090 showing range shifts northward and to higher altitudes. Area Under Curve (AUC) values of the modeled result was 0.67. Overall, the results of this study were successful in showing the current distribution of major tree species and projecting their future changes. However, there are still many possible limitations and uncertainties arising from the select of the presence-absence data and the environmental predictor variables for model input. Nevertheless, ecological niche modeling can be a useful tool for exploring and mapping the potential response of the tree species to climate change. The final models in this study may be used to identify potential distribution of the tree species based on the future climate scenarios, which can help forest managers to decide where to allocate effort in the management of forest ecosystem under climate change in Korea.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.432-438
• /
• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.55 no.4
• /
• pp.312-318
• /
• 2010

This study was carried out to develop and test a prototype program that recommends the nitrogen topdressing rate using the color digital camera image taken from rice field at panicle initiation stage (PIS). This program comprises four models to estimate shoot N content (PNup) by color digital image analysis, shoot N accumulation from PIS to maturity (PHNup), yield, and protein content of rice. The models were formulated using data set from N rate experiments in 2008. PNup was found to be estimated by non-linear regression model using canopy cover and normalized green values calculated from color digital image analysis as predictor variables. PHNup could be predicted by quadratic regression model from PNup and N fertilization rate at panicle initiation stage with $R^2$ of 0.923. Yield and protein content of rice could also be predicted by quadratic regression models using PNup and PHNup as predictor variables with $R^2$ of 0.859 and 0.804, respectively. The performance of the program integrating the above models to recommend N topdressing rate at PIS was field-tested in 2009. N topdressing rate prescribed for the target protein content of 6.0% by the program were lower by about 30% compared to the fixed rate of 30% that is recommended conventionally as the split application rate of N fertilizer at PIS, while rice yield in the plots top-dressed with the prescribed N rate were not different from those of the plots top-dressed with the fixed N rates of 30% and showed a little lower or similar protein content of rice as well. And coefficients of variation in rice yield and quality parameters were reduced substantially by the prescribed N topdressing. These results indicate that the N rate recommendation using the analysis of color digital camera image is promising to be applied for precise management of N fertilization. However, for the universal and practical application the component models of the program are needed to be improved so as to be applicable to the diverse edaphic and climatic condition.