• Korean Journal of Fisheries and Aquatic Sciences
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• v.33 no.5
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• pp.455-462
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• 2000

The objective of this study was to examine the physicochemical characteristics of coagulation reaction between loess and red tide organisms (RTO) and its feasibility, in developing a technology for the removal of RTO bloom in coastal sea. The physicochemical characteristics of loess were examined for a particle size distribution, surface characteristics by scanning electron microscope, zeta potential, and alkalinity and pH variations in sea water. Two kinds of RTO that were used in this study, Cylindrothen closterium and Skeietonema costatum, were sampled in Masan bay and were cultured in laboratory. Coagulation experiments were conducted using various concentrations of loess, RTO, and a jar tester. The supernatant and RTO culture solution were analyzed for pH, alkalinity, RTO cell number. A negative zeta potential of loess increased with increasing pH at $10^(-3)M$ NaCl solution and had -71.3 mV at pH 9.36. Loess had a positive zeta potential of +1,8 mV at pH 1.98, which resulted in a characteristic of material having an amphoteric surface charge. In NaCl and $CaCl_2$, solutions, loess had a decreasing negative zeta potential with increasing $Na^+\;and\;Ca^(+2)$ ion concentration and then didn't result in a charge reversal due to not occurring specific adsorption for $Na^+$ ion while resulted in a charge reversal due to occurring specific adsorption for $Ca^(+2)$ ion. In sea water, loess and RTO showed the similar zeta potential values of -112,1 and -9.2 mV, respectively and sea sand powder showed the highest zeta potential value of -25.7 mV in the clays. EDLs (electrical double-layers) of loess and RTO were extremely compressed due to high concentration of salts included in sea water, As a result, there didn't almost exist EDL repulsive force between loess and RTO approaching each other and then LVDW (London-yan der Waals) attractive force was always larger than EDL repulsive force to easily form a floe. Removal rates of RTO exponentially increased with increasing a loess concentration. The removal rates steeply increased until $800 mg/l$ of loess, and reached $100{\%}$ at 6,400 mg/l of loess. Removal rates of RTO exponentially increased with increasing a G-value. This indicated that mixing (i.e., collision among particles) was very important for a coagulation reaction. Loess showed the highest RTO removal rates in the clays.

• The Korean Journal of Petroleum Geology
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• v.8 no.1_2 s.9
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• pp.1-43
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• 2000

A comparison study for understanding a stratigraphic response to tectonic evolution of sedimentary basins in the Yellow Sea and adjacent areas was carried out by using an integrated stratigraphic technology. As an interim result, we propose a stratigraphic framework that allows temporal and spatial correlation of the sedimentary successions in the basins. This stratigraphic framework will use as a new stratigraphic paradigm for hydrocarbon exploration in the Yellow Sea and adjacent areas. Integrated stratigraphic analysis in conjunction with sequence-keyed biostratigraphy allows us to define nine stratigraphic units in the basins: Cambro-Ordovician, Carboniferous-Triassic, early to middle Jurassic, late Jurassic-early Cretaceous, late Cretaceous, Paleocene-Eocene, Oligocene, early Miocene, and middle Miocene-Pliocene. They are tectono-stratigraphic units that provide time-sliced information on basin-forming tectonics, sedimentation, and basin-modifying tectonics of sedimentary basins in the Yellow Sea and adjacent area. In the Paleozoic, the South Yellow Sea basin was initiated as a marginal sag basin in the northern margin of the South China Block. Siliciclastic and carbonate sediments were deposited in the basin, showing cyclic fashions due to relative sea-level fluctuations. During the Devonian, however, the basin was once uplifted and deformed due to the Caledonian Orogeny, which resulted in an unconformity between the Cambro-Ordovician and the Carboniferous-Triassic units. The second orogenic event, Indosinian Orogeny, occurred in the late Permian-late Triassic, when the North China block began to collide with the South China block. Collision of the North and South China blocks produced the Qinling-Dabie-Sulu-Imjin foldbelts and led to the uplift and deformation of the Paleozoic strata. Subsequent rapid subsidence of the foreland parallel to the foldbelts formed the Bohai and the West Korean Bay basins where infilled with the early to middle Jurassic molasse sediments. Also Piggyback basins locally developed along the thrust. The later intensive Yanshanian (first) Orogeny modified these foreland and Piggyback basins in the late Jurassic. The South Yellow Sea basin, however, was likely to be a continental interior sag basin during the early to middle Jurassic. The early to middle Jurassic unit in the South Yellow Sea basin is characterized by fluvial to lacustrine sandstone and shale with a thick basal quartz conglomerate that contains well-sorted and well-rounded gravels. Meanwhile, the Tan-Lu fault system underwent a sinistrai strike-slip wrench movement in the late Triassic and continued into the Jurassic and Cretaceous until the early Tertiary. In the late Jurassic, development of second- or third-order wrench faults along the Tan-Lu fault system probably initiated a series of small-scale strike-slip extensional basins. Continued sinistral movement of the Tan-Lu fault until the late Eocene caused a megashear in the South Yellow Sea basin, forming a large-scale pull-apart basin. However, the Bohai basin was uplifted and severely modified during this period. h pronounced Yanshanian Orogeny (second and third) was marked by the unconformity between the early Cretaceous and late Eocene in the Bohai basin. In the late Eocene, the Indian Plate began to collide with the Eurasian Plate, forming a megasuture zone. This orogenic event, namely the Himalayan Orogeny, was probably responsible for the change of motion of the Tan-Lu fault system from left-lateral to right-lateral. The right-lateral strike-slip movement of the Tan-Lu fault caused the tectonic inversion of the South Yellow Sea basin and the pull-apart opening of the Bohai basin. Thus, the Oligocene was the main period of sedimentation in the Bohai basin as well as severe tectonic modification of the South Yellow Sea basin. After the Oligocene, the Yellow Sea and Bohai basins have maintained thermal subsidence up to the present with short periods of marine transgressions extending into the land part of the present basins.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is