Hybrid XML Data Model Architecture for Efficient Document Management

XML has been known as a document standard in representation and exchange of data on the Internet, and is also used as a standard language for the search and reuse of scattered documents on the Internet. The issues related to XML are how to model data on effective and efficient management of semi-structured data and how to actually store the modeled data when implementing a XML contents management system. Previous researches on XML have limitations in (1) reproduction of XML documents from the stored data, (2) retrieval of XML sub-graph from search, (3) supporting only top-down search, not full-search, and (4) dependency of data structure on XML documents. The purpose of this paper is to present a hybrid XML data model architecture for the storage and search of XML document data. By representing both data and structure views of XML documents, this new XML data model technique overcomes the limitations of previous researches on data model for XML documents as well as the existing database systems such as relational and object-oriented data model

Molecular characterization of two glutathione peroxidase genes of Panax ginseng and their expression analysis against environmental stresses

Glutathione peroxidases (GPXs) are a group of enzymes that protect cells against oxidative damage generated by reactive oxygen species (ROS). GPX catalyzes the reduction of hydrogen peroxide (H2O2) or organic hydroperoxides to water or alcohols by reduced glutathione. The presence of GPXs in plants has been reported by several groups, but the roles of individual members of this family in a single plant species have not been studied. Two GPX cDNAs were isolated and characterized from the embryogenic callus of Panax ginseng. The two cDNAs had an open reading frame (ORF) of 723 and 681 bp with a deduced amino acid sequence of 240 and 226 residues, respectively. The calculated molecular mass of the matured proteins are approximately 26.4 kDa or 25.7 kDa with a predicated isoelectric point of 9.16 or 6.11, respectively. The two PgGPXs were elevated strongly by salt stress and chilling stress in a ginseng seedling. In addition, the two PgGPXs showed different responses against biotic stress. The positive responses of PgGPX to the environmental stimuli suggested that ginseng GPX may help to protect against environmental stresses

A Multi-institutional Study on Histopathological Characteristics of Surgically Treated Renal Tumors: the Importance of Tumor Size

PURPOSE: The incidence of accidentally detected small renal tumors is increasing throughout the world. In this multi-institutional study performed in Korea, histopathological characteristics of contemporarily surgically removed renal tumors were reviewed with emphasis on tumor size. MATERIALS and METHODS: Between January 1995 and May 2005, 1,702 patients with a mean age of 55 years underwent surgical treatment at 14 training hospitals in Korea for radiologically suspected malignant renal tumors. Clinicopathological factors and patient survival were analyzed. RESULTS: Of the 1,702 tumors, 91.7% were malignant and 8.3% were benign. The percentage of benign tumors was significantly greater among those 4cm (4.5%) (p or = T3 was significantly less among tumors 4cm (26.8%) (p or = 3 was also significantly less among tumors 4cm (50.9%) (p < 0.001). The 5-year cancer-specific survival rate was 82.7%, and T stage (p < 0.001), N stage (p < 0.001), M stage (p = 0.025), and Fuhrman's nuclear (p < 0.001) grade were the only independent predictors of cancer-specific survival. CONCLUSION: In renal tumors, small tumor size is prognostic for favorable postsurgical histopathologies such as benign tumors, low T stages, and low Fuhrman's nuclear grades. Our observations are expected to facilitate urologists to adopt function-preserving approach in the planning of surgery for small renal tumors with favorable predicted outcomes.ope

E-Commerce Liability and Security Breaches in Mobile Payment for e-Business Sustainability

This study investigates liability issues in electronic transactions when security or privacy breaches occur. As data is transferred using various devices, such as PCs, mobile phones, tablets, sensors, smart meters, and cars, and various architecture, such as the cloud, IoT, as well as in well-defined network structures in electronic commerce, privacy and security breaches happen. These have become a major hindrance to the development and use of commercial activities on the Internet. There have been many security breach cases, such as those of Target Corporation&#8217;s security and payment system (2013), eBay&#8217;s cyberattack (2014), Uber&#8217;s hacking incident (2016), Facebook&#8217;s personal data use and privacy breach (2018), and many others. Therefore, when a dispute regarding electronic transactions arises between a customer and a firm, the allocation of liability is very important for the sustainability of e-businesses. Many cases show that firms are held liable for those incidents. However, the liability allocation rule tends to vary slightly from country to country depending on the application areas. EU countries seem to favor customers. In the United States, there are actually no uniform federal laws relating to business cybersecurity. Also, in the case of cryptocurrency, liability tends to lie with customers. Why is the ruling different? In this regard, this paper analyses the legal framework for security and privacy breaches for sustainable e-businesses. In particular, this paper focuses on the optimal liability in terms of enhancing social welfare when considering both sides&#8212;the customer and the firm (or service provider). This paper shows that liability can be generally imposed on the firm&#8217;s side when the possibility of security or privacy breaches is high, and the customer&#8217;s loss is relatively large. However, the liability depends on the customer&#8217;s attitude towards risk, customer&#8217;s losses, and the efficiency of security investment

Cloud Services and Pricing Strategies for Sustainable Business Models: Analytical and Numerical Approaches

Previous studies have introduced different potential pricing strategies for cloud services. However, not much research has been done comparing subscription pricing and pay-per-use pricing, which are commonly used pricing schemes. Also, there are very few studies which analyze a two-part tariff pricing scheme for cloud services, even though this option may increasingly attract service providers as the cloud market becomes more competitive and the profit margin grows narrower. Previous research has focused on firms&rsquo; profitability rather than social welfare due to the limitations of free services. This study uses theoretical and numerical analysis to compare the social welfare and profitability of three pricing schemes commonly used by firms: subscription pricing, pay-per-use pricing, and two-part tariff pricing. It shows that the pay-per-use pricing is the best solution from the perspective of social welfare, which contrasts with the conclusion of a previous study stating that social welfare is maximized under a two-part tariff. This paper also shows that the two-part tariff is the most profitable pricing scheme for firms

The Effect of Cash Incentive Projects on the Social Value Performances of Social Enterprises: An Empirical Analysis of SK’s Social Progress Credit in Korea

Social enterprises seek to maximize benefits to society and the environment while obtaining profits. Social enterprises are increasing in number; however, their size and growth rates are very small. In addition, many social enterprises face difficulties in obtaining profits through social activities that generate social value, even though they are supported by government policy. Previous research has focused on the relationship between social performance and financial performance, compensation, and policy making, as well as the effect of incentives on social performance within organizations. To our knowledge, there is lack of empirical research on cash incentives for activities that generate social value. This paper analyzes the behavior of companies with regard to fostering a social enterprise ecosystem and a cash incentive system for social enterprises. In particular, we investigate the relationship between SK’s cash incentive system, which is called social progress credit (SPC), and the activities of social enterprises, and we examine which social value activities are affected by a cash incentive system. Furthermore, through empirical analysis, this paper analyzes how the amount of cash for incentives is determined by specific social activities, such as social service performance, employment performance, environmental performance, and social ecosystem performance, as well as by the size of the social enterprise and its financial performance (i.e., revenue and net profit). The results show that employment performance is the most important factor for incentive payments, reflecting the social atmosphere and government policy in Korea, and that it can be a simpler measurement of performance than other social performance measures. Moreover, the results show that there is a significant positive (+) relationship between incentive payments and financial performance, such as sales and net profit of social enterprises. In addition, it was found that more incentives were paid to small social enterprises with higher sales growth

Geometric Case Based Reasoning for Stock Market Prediction

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p &lt; 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error

A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions

In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model