• Korean Journal of Culture and Social Issue
• /
• v.23 no.4
• /
• pp.557-585
• /
• 2017

In this research, it was examined whether predicting the ripple effects of events influences decision-making difficulty. In addition, it was examined whether perceived accountability for decision-making results mediates the relation above. In Study 1, participants were presented with policy decision-making vignettes and were asked to report on the ripple effects of their policy decisions as well as on the difficulty of making the decision. Consistent with the hypothesis, the bigger the expected ripple effects, the greater difficulty participants felt in making policy decisions. In Study 2, ripple effect magnitudes were experimentally manipulated such that participants were led to predict big ripple effects in one condition and relatively small ripple effects in another condition. It was investigated whether participants predicting bigger ripple effects would perceive decision-making to be more difficult than participants predicting smaller ripple effects. Whether this relation would be mediated by perceived personal accountability for the results of decision-making was also examined. Consistent with expectations, it was found that in the moral domains of Harm/care, Fairness/reciprocity, and Ingroup/loyalty, participants predicting bigger ripple effects reported more difficult decision-making than their counterparts. The relation above was mediated by perceived personal accountability for decision-making results only in the domain of Ingroup/loyalty. In combination, these results showed that bigger predicted ripple effects contributed to greater decision-making difficulty. In addition, participants felt more responsible for the results of their decisions when predicting bigger ripple effects, which led them to feel greater decision-making difficulty in the domain of Ingroup/loyalty. The implications of these results and future directions for research are discussed.

• Journal of Korea Water Resources Association
• /
• v.55 no.1
• /
• pp.23-32
• /
• 2022

This paper performs two-dimensional numerical simulation of surface water-groundwater coupled flow and solute transport to investigate the effect of the hyporheic exchange at the sediment-water interface (SWI) on surface solute transport. For the impermeable bed case in the absence of hyporheic flow, the trapping effect of flow recirculation associated with the ripple bed controls the shape of breakthrough curves (BTCs). However, the permeable bed case with hyporheic flow stimulates the extended tailing of the BTCs more significantly due to the elevated concentration of the BTC tailing resulting from slow hyporheic velocity. Also, the increased bottom pressure at the SWI with an increase in surface velocity shortens the BTC tailing because of increasing hyporheic velocity. These results infer that hyporhiec flow is critically important in predicting solute residence times in surface water.

• Journal of Advanced Research in Ocean Engineering
• /
• v.2 no.3
• /
• pp.99-111
• /
• 2016

Seabed affected by scouring, sedimentation, and siltation occurrences often cause exposure, which induces risks to existing structures or crude oil or gas pipeline buried subsea. In order to prevent possible risks, more economical structure installation methodology is proposed in this study by predicting and managing the risk. Also, the seabed does not only consist of sandy material, but clayey soil is also widespread, and the effect of undrained shear strength should be considered, and by cyclic environmental load, pore water pressure will occur in the seabed, which reduces shear strength and allows particles to move easily. Based on previous research regarding sedimentation or erosion, the average value of external environmental loads should be applied; for scouring, a 100-year period of environmental conditions should be applied. Also, sedimentation and erosion are mainly categorized by the bed load and suspended load; also, they are calculated as the sum of bed load and suspended load, which can be obtained from the movement of particles caused by sedimentation or erosion.

• Agribusiness and Information Management
• /
• v.7 no.1
• /
• pp.1-9
• /
• 2015

The purpose of this study is to develop a growth prediction model that can predict growth and development information influencing the production of citrus fruits: the growth model algorithm that can predict floral leaf ratio, number of fruit sets, fruit width, and overweight depending on the main period of growth and development with consideration of the applied weather factors. Every year, large scale of manpower was mobilized to investigate the production of outdoor-grown citrus fruits, but it was limited to recycling the data without an observation supporting system to systemize the database. This study intends to create a systematical database based on the basic data obtained through the observation supporting system in application of an algorithm according to the accumulated long term data and prepare a base for its continuous improvement and development. The importance of the observed data is increasingly recognized every year, and the citrus fruit observation supporting system is important for utilizing an effective policy and decision making according to various applications and analysis results through an interconnection and an integration of the investigated statistical data. The citrus fruit is a representative crop having a great ripple effect in Jeju agriculture. An early prediction of the growth and development information influencing the production of citrus fruits may be helpful for decision making in supply and demand control of agricultural products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.207-209
• /
• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.