Monthly Joint Operations for the Nakdong Multi-reservoir System in Korea

Abstract

This study applies a state-of-art optimization technique, SSDP/ESP (Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reservoir system in Korea. A rainfall-runoff model, SSARR (Streamflow Synthesis And Reservoir Regulation), is linked to the SSDP/ESP model to provide ESP scenarios for runoff during the next month in the Nakdong River basin. The primary advantage of the SSDP/ESP is that it updates the derived operating policy as new ESP forecasts become available. Another SSDP model that employs historical runoff scenarios (SSDP/Hist) is also developed. The main difference between the two SSDP models is that SSDP/Hist is an off-line model whereas the SSDP/ESP is on-line. The developed operating policies are tested with a simulation model using an object-oriented simulation software, STELLA. The simulation results show that SSDP/ESP is superior to SSDP/Hist with respect to the water supply criterion, although both models perform similarly with respect to the hydroelectric energy production criterion.     

Biofouling control by quorum sensing inhibition and its dependence on membrane surface

Biofouling control by quorum sensing (QS) inhibition and the influence of membrane surface characteristics on biofilm formation and QS inhibition were investigated. Pseudomonas putida isolated from the bio-fouled reverse osmosis (RO) membranes in a real plant was used. Acylase was chosen as a model QS inhibitor. Bacteria on the membrane coupons were quantified with the heterotrophic plate count method. Cell distribution was imaged by a confocal laser scanning microscope. Results showed that biofilm formation on the membrane was reduced by acylase as it inhibits the activity of N-acylhomoserine lactone (AHL) which is a signal molecule of QS. It was also shown that membrane surface characteristics were influential factors affecting bacterial adhesion, biofilm formation, and QS inhibition.     

Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones

The purpose of this study was to develop and apply the neural networks models to estimate daily pan evaporation (PE) for different climatic zones such as temperate and arid climatic zones, Republic of Korea and Iran. Three kinds of the neural networks models, namely multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and support vector machine-neural networks model (SVM-NNM), were used to estimate daily PE. The available climatic variables, consisted of mean air temperature (T_mean), mean wind speed (U_mean), sunshine duration (SD), mean relative humidity (RH_mean), and extraterrestrial radiation (R_a) were used to estimate daily PE using the various input combinations of climate variables. The measurements for the period of January 1985?CDecember 1990 (Republic of Korea) and January 2002?CDecember 2008 (Iran) were used for training and testing the employed neural networks models. The results obtained by SVM-NNM indicated that it performs better than MLP-NNM and GRNNM for estimating daily PE. A comparison was also made among the employed models, which demonstrated the superiority of MLP-NNM, GRNNM, and SVM-NNM over Linacre model and multiple linear regression model (MLRM).     

A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating - ordinality - seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.     

Video gameplay, personality and academic performance

The relationship between video gameplay, video game genre preference, personality, and GPA was investigated in an online correlational study with university students. In addition to administering self-report measures of GPA and personality, we asked three different questions regarding styles of video gameplay. The first asked the average time spent playing video games per week (habitual players), the second asked the total time spent playing favorite video games (selective players), and the third asked the number of different video games played in a year (diverse players). Students who were medium in selective player style (spent 11-50 h) had significantly higher GPAs than students low on selective player style (spent 0-10 h). Students high on habitual playing style (7 or more hours a week) showed significantly lower levels of Conscientiousness compared to students low on habitual playing style (0-1 h a week). Students who were high on the diverse style (i.e., 7 or more games played a year) showed significantly higher Openness scores than students low on the diverse style (0-3 games a year). Finally, several notable relations were found between video game genre preference, GPA, and personality. Results are discussed in terms of the positive implications of video gameplay on academic performance.     

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

Numerical stability of symmetric solitary-wave-like waves of a two-layer fluid—Forced modified KdV equation

Forced internal waves at the interface of a two-layer incompressible fluid in a two-dimensional domain with rigid horizontal boundaries are studied. The lower boundary is assumed to have a small obstruction. We derive a time-dependent forced modified KdV equation when the KdV theory fails and study the stabilities of four types of symmetric time-independent solitary-wave-like solutions numerically.     

I became an attractive person in the virtual world: Users’ identification with virtual communities and avatars

A number of studies have examined virtual worlds, which can facilitate knowledge sharing, education, and enjoyment, among others. However, no study has provided an insightful research model for evaluating virtual worlds. This study suggests that users’ identification with virtual communities and avatars plays a critical role in the construction of attractive virtual worlds. The proposed model measures the level of the user’s identification with virtual communities, through which the user builds his or her trust in other community members. In addition, the study suggests that users’ identification with avatars is an important element of their satisfaction with virtual worlds. The results indicate that users’ identification with virtual communities as well as avatars can enhance their efficacy and trust and thus facilitate their sustained use of virtual services. The results have important theoretical and practical implications.     

The role of self-construal in consumers’ electronic word of mouth (eWOM) in social networking sites: A social cognitive approach

The current study reconceptualized self-construal as a social cognitive indicator of self-observation that individuals employ for developing and maintaining social relationship with others. From the social cognitive perspective, this study investigated how consumers’ self-construal can affect consumers’ electronic word of mouth (eWOM) behavior through two cognitive factors (online community engagement self-efficacy and social outcome expectations) in the context of a social networking site. This study conducted an online experiment that directed 160 participants to visit a newly created online community. The results demonstrated that consumers’ relational view became salient when the consumers’ self-construal was primed to be interdependent rather than independent. Further, the results showed that such interdependent self-construal positively influenced consumers’ eWOM behavioral intentions through their community engagement self-efficacy and their social outcome expectations.