• Journal of Construction Engineering and Project Management
• /
• v.5 no.4
• /
• pp.1-6
• /
• 2015

Due to unique characteristics of small construction companies, safety management is comprised of complex problems. This paper aims to better understand the complexity and dynamics of safety management in small construction companies. A system dynamics (SD) model was built in order to capture the causal interdependencies between factors at different system levels (regulation, organization, technical and individual) and their effects on safety outcomes. Various tests were conducted to build confidence in the model's usefulness to understand safety problems facing small companies from a system dynamics view. A number of policies were analyzed by changing the value of parameters. The value of a system dynamics approach to safety management in small construction companies is its ability to address joint effects of multiple safety risk factors on safety performance with a systems thinking perspective. By taking into account feedback loops and non-linear relationships, such a system dynamics model provides insights into the complex causes of relatively poor safety performance of small construction companies and improvement strategies.

• Asia pacific journal of information systems
• /
• v.30 no.1
• /
• pp.31-52
• /
• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Kyungpook Mathematical Journal
• /
• v.64 no.3
• /
• pp.371-393
• /
• 2024

Severe Acute Respiratory Syndrome (SARS) is a highly contagious viral disease with high mortality rate. There is no vaccine against SARS, but the spread can be limited by masking or social distancing. In this paper we implement a game-theoretic model of voluntary precautions against SARS. We build on the compartmental ODE model of the 2003 SARS epidemic. We assume that susceptible individuals can mask and/or limit contacts with others in order to decrease their chances of contracting SARS. Since the risk of SARS infection depends on the actions of others, this creates a public goods game. We find the Nash equilibrium, the solution of the game, which is the optimal voluntary level of precautions the individuals should take. We also study the effects of such actions on the spread of SARS and show that the effect significantly depends on the individual cost of the precautions. As soon as the cost rises above a critical threshold, the individuals will have no incentive to use any kind of voluntary precaution.

• Journal of the Ergonomics Society of Korea
• /
• v.36 no.3
• /
• pp.183-196
• /
• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• The Journal of the Korea Contents Association
• /
• v.18 no.3
• /
• pp.81-100
• /
• 2018

This study investigated the influential factor over the intention to buy autonomous driving car by applying extended technology acceptance model (TAM2). To this end, 117 ordinary persons experienced in driving car were analyzed by using SEM(Structural Equation Modeling). Analysis shows that the perceived usefulness and purchase intention is positively affected by social influence and recognized risk. It is found that perceived usefulness is not affected, but purchase intention is positively affected in the case of innovation. On the contrary, analysis shows that driving capability and car playfulness recognized by individual have no influence on the perceived easiness. Although the result that driving capability recognized by individual negatively affects perceived usefulness was not included in the study hypothesis, it was remarkable. Generalizing the above result, it is found that social influence, innovation and recognized risk as variables which affect the intention to buy autonomous car play the role of significant variable. This study is meaningful in that such result can foresee the perception of preliminary accommodators of new technology of the 4th industrial revolution, autonomous driving car.

• Korean Chemical Engineering Research
• /
• v.60 no.3
• /
• pp.356-362
• /
• 2022

Research related to hydrogen technology is being actively conducted around the world. Korea is also making great efforts to develop technology to leap forward as a hydrogen economy powerhouse. In particular, the world's No. 1 hydrogen vehicle penetration rate is proof of this. However, the construction of hydrogen refueling stations is being delayed. The biggest delay factor is the public opposition. As such, policies without public support cannot be successfully implemented and are not sustainable. Therefore, this study intends to analyze the factors affecting the acceptability of hydrogen refueling stations in favor of and against them. As a research method, the basic factors affecting acceptability were identified by reviewing previous studies, and a questionnaire was designed and investigated based on the established factors. The validity and reliability of the questionnaire were verified, and the hypothesis was verified through correlation analysis. And, using structural equation modeling, a factor model was developed on the acceptability of hydrogen refueling stations. As a result of the study, acceptability defined private acceptability and public acceptability. In the case of private acceptability, it was confirmed that the higher the attitude toward the environment, the higher the level of knowledge about the hydrogen charging station, and the lower the degree of feeling the risk of the hydrogen charging station, the higher the acceptability. In the case of public acceptability, it was confirmed that the higher the benefit, the better the attitude toward the environment, and the lower the risk-taking characteristics of the individual, the higher the acceptability. Therefore, in this study, based on the potential factors verified in previous studies, the main factors affecting the acceptance on hydrogen refueling stations were identified. And the acceptance model was developed using structural equation modeling. This study is expected to provide basic data to seek ways to improve the acceptance of public when implementing national policies such as hydrogen refueling stations, and to be used analysis data for scientific communication.

• Journal of Information Technology Services
• /
• v.21 no.3
• /
• pp.63-72
• /
• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Toxicological Research
• /
• v.6 no.2
• /
• pp.205-213
• /
• 1990

The regulation of neurotoxicants has usually been based upon setting reference doses by dividing a no observed adverse effect level (NOAEL) by uncertainty factors that theoretically account for interspecies and intraspecies extraploation of experimental results in animals to humans. Recently, we have proposed a four-step alternative procedure which provides quantitative estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect or biomarker and the dose of chemical administered. The second step is to determine the distribution (variability) of individual measurements of biological effects or their biomarkers about the dose response curve. The third step is to define an adverse or abnormal level of a biological effect or biomarker in an untreated population. The fourth and final step is to combine the information from the first three steps to estimate the risk (proportion of individuals exceeding on adverse or abnormal level of a biological effect or biomarker) as a function of dose. The primary purpose of this report is to enhance the certainty of the first step of this procedure by improving our understanding of the relationship between a biomarker and dose of administered chemical. Several factors which need to be considered include: 1) the pharmacokinetics of the parent chemical, 2) the target tissue concentrations of the parent chemical or its bioactivated proximate toxicant, 3) the uptake kinetics of the parent chemical or metabolite into the target cell(s) and/or membrane interactions, and 4) the interaction of the chemical or metabolite with presumed receptor site(s). Because these theoretical factors each contain a saturable step due to definitive amounts of required enzyme, reuptake or receptor site(s), a nonlinear, saturable dose-response curve would be predicted. In order to exemplify this process, effects of the neurotoxicant, methlenedioxymethamphetamine (MDMA), were reviewed and analyzed. Our results and those of others indicate that: 1) peak concentrations of MDMA and metabolites are ochieved in rat brain by 30 min and are negligible by 24 hr, 2) a metabolite of MDMA is probably responsible for its neurotoxic effects, and 3) pretreatment with monoamine uptake blockers prevents MDMA neurotoxicity. When data generated from rats administerde MDMA were plotted as bilolgical effect (decreases in hippocampal serotonin concentrations) versus dose, a saturation curve best described the observed relationship. These results support the hypothesis that at least one saturable step is involved in MDMA neurotoxicity. We conclude that the mathematical relationship between biological effect and dose of MDMA, the first step of our quantitative neurotoxicity risk assessment procedure, should reflect this biological model information generated from the whole of the dose-response curve.

• Journal of Computing Science and Engineering
• /
• v.7 no.3
• /
• pp.177-186
• /
• 2013

We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk due to individual participation. It has born fruitful results over the past ten years, both in theoretical connections to other fields and in practical applications to real-life datasets. Promises of differential privacy help to relieve concerns of privacy loss, which hinder the release of community-valuable data. This paper covers main ideas behind differential privacy, its interactive versus non-interactive settings, perturbation mechanisms, and typical applications found in recent research.

• The Journal of the Korean life insurance medical association
• /
• v.32 no.2
• /
• pp.33-38
• /
• 2013

Coronary artery diseases are very important agenda in the insurance medicine. Insurance medicine is defined as using medical knowledge for insurance administration such as underwriting, claims, and customer satisfaction. This review article contains review of coronary artery disease in terms of insurance medicine. Estimation of extra-risks for acute myocardial infarction are MR of 349% and EDR of 41‰. In medical underwriting, individual life applicants can be assessed by Framingham's CHD risk assessment model. In claims, medical claims review is a useful method of consulting for claims staffs. Several diagnostic criteria of acute myocardial infarction are introduced in time. The universal definition of myocardial infarction by ESC/ACCF/WHF was demonstrated the most valuable predictor of 10-year mortality. Contents for State-Of-The-Art of the coronary artery disease are current antithrombotics. There are many novel anti-thrombotic agents such as ticagrelol, dabigatran, rivaroxaban, and pegnivacogin.