Bandwidth allocation and pricing problem for a duopoly market

This research discusses the Internet service provider (ISP) bandwidth allocation and pricing problems for a duopoly bandwidth market with two competitive ISPs. According to the contracts between Internet subscribers and ISPs, Internet subscribers can enjoy their services up to their contracted bandwidth limits. However, in reality, many subscribers may experience the facts that their on-line requests are denied or their connection speeds are far below their contracted speed limits. One of the reasons is that ISPs accept too many subscribers as their subscribers. To avoid this problem, ISPs can set limits for their subscribers to enhance their service qualities. This paper develops constrained nonlinear programming to deal with this problem for two competitive ISPs. The condition for reaching the equilibrium between the two competitive firms is derived. The market equilibrium price and bandwidth resource allocations are derived as closed form solutions

A SMIL-Based Catalog Presentation System in Electronic Commerce

Web-based catalog presentations play the key-enabling role in E-commerce in recent years. Existing catalog systems often acquire proprietary platforms, cannot deal with TV-like media objects, or consume network bandwidth inefficiently. With the emergence of advanced technologies of Web and multimedia, such hurdles can be removed. The Synchronized Multimedia Integration Language (SMIL), proposed by W3C allows Web designers to design complicated and vivid multimedia presentations in a declarative manner. These presentations are then rendered on a general-purpose browser by a SMIL player. Since the SMIL specification is quite new to the Internet and E-commerce societies, the functionality and applications of players is limited. In this paper, we propose a novel architecture based on Java JMF technology for tackling with such constraints. The effectiveness of the proposed system is validated through an experiment on product catalog presentations

Collaboration in Bipartite Networks, with an Application to Coauthorship Networks

This paper studies the impact of collaboration on research output. First, we build a micro-founded model for scientific knowledge production, where collaboration between researchers is represented by a bipartite network. The equilibrium of the game incorporates both the complementarity effect between collaborating researchers and the substitutability effect between concurrent projects of the same researcher. Next, we develop a Bayesian MCMC procedure to estimate the structural parameters, taking into account the endogenous matching of researchers and projects. Finally, we illustrate the empirical relevance of the model by analyzing the coauthorship network of economists registered in the RePEc Author Service

The Management of Debris Flow in Disaster Prevention using an Ontology-based Knowledge Management System

In recently years, the government, academia and business have applied different information technologies to disaster prevention and diverse web sites have been developed. Although these web sites provide a large number of data about disaster-prevention, they are knowledge poor in nature. Furthermore, disaster-prevention is a knowledge-intensive task and a potential knowledge management system can overcome the shortcoming of knowledge poor. On the other hand, ontology design plays the key role toward designing a successful knowledge management system. In this paper, we introduce a three-stage life cycle for ontology design for supporting the service of disaster prevention of debris flow and propose a framework of an ontology-based knowledge management system with the KAON API environment. In addition, by appealing to the technology of component reuse, the system is developed at lower cost thus knowledge workers can focus on the design of ontology and knowledge objects. The objectives of the proposed system is to facilitate knowledge accumulation, knowledge reuse and dissemination for the management of disaster prevention. This work is expected to enable the promotion of the traditional disaster management of debris flow towards the so-called knowledge-driven decision support services

A Two-stage Architecture for Stock Price Forecasting by Integrating Self-Organizing Map and Support Vector Regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model

Social networks and collective action in large populations: An application to the Egyptian Arab Spring

We study a dynamic model of collective action in which agents interact and learn through a co-evolving social network. Our approach highlights the importance of communication in this problem and conceives the social network – which is continuously evolving – as the structure through which agents not only interact but also communicate. We consider two alternative scenarios that differ only on how agents form their expectations: while in the “benchmark” context agents are completely informed, in the alternative one their expectations are formed through a combination of local observation and social learning à la DeGroot. We completely characterize the long-run behavior of the system in both cases and show that only in the latter scenario (arguably the most realistic) there is a significant long-run probability that agents eventually achieve collective action within a meaningful time scale. This, we argue, sheds light on the puzzle of how large populations can coordinate on globally desired outcomes. Finally, we illustrate the empirical potential of the model by showing that it can be efficiently estimated for the so-called Egyptian Arab Spring using large-scale cross-sectional data from Twitter

Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution

In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives stemming from interaction benefits on certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interactions are important factors for friendship formation or not. Second, in addition to homophily effects in terms of unobserved characteristics, inclusion of incentive effects in the network formulation also corrects possible friendship selection bias on activity outcomes under network interactions. A theoretical foundation of this unified model is based on a complete information cooperative game. A tractable Bayesian MCMC approach is proposed for the estimation of the model. We apply the model to empirically study American high school students' friendship networks with the Add Health data. We consider two activity variables, GPA and smoking frequency, and find a significant incentive effect from GPA, but not from smoking, on friendship formation. These results suggest that the benefit of interactions in academic learning is an important factor for friendship formation, while the interaction benefit in smoking is not, even though homophily in smoking behavior is important for a smoker to link to other smokers. On the other hand, from the perspective of network interactions, both GPA and smoking frequency are subject to significant positive interaction (peer) effects

Antimicrobial susceptibility and clinical outcomes of Candida parapsilosis bloodstream infections in a tertiary teaching hospital in Northern Taiwan

BackgroundCandida parapsilosis is an emerging non-albicans Candida that is associated with central line-associated infection. C. parapsilosis has higher minimal inhibitory concentration to echinocandin than Candida albicans, and the effects of echinocandin on C. parapsilosis are ambiguous. Therefore, in this study, we aimed to investigate the susceptibility and the correlation between incidence and drug consumption.MethodsThis retrospective study was conducted in a tertiary teaching hospital in northern Taiwan between 2008 and 2012. The Candida species distribution, the correlation between the use of antifungal agents and the incidence of C. parapsilosis bloodstream infection, demographic information, clinical characteristics, mortality rate, and in vitro susceptibility of C. parapsilosis were analyzed.ResultsA total of 77 episodes from 77 patients were included for analysis. The overall 90-day mortality rate was 41.6%. The incidence of C. parapsilosis bloodstream infection showed a moderate positive correlation with the increased defined daily dose of echinocandin. The risk factors associated with mortality included malignancy or a metastatic tumor. Multivariate logistical regression analysis showed that patients with malignancy had higher odds ratios in terms of mortality. The rate of C. parapsilosis resistance to fluconazole was 3%, whereas the susceptibility rate was 95.5%.ConclusionUnderlying comorbidity and malignancy were factors leading to death in patients with C. parapsilosis bloodstream infection. Catheter removal did not influence the mortality rate. The survival rate of patients receiving echinocandin was lower than the group receiving fluconazole. Fluconazole remains the drug of choice to treat C. parapsilosis bloodstream infections