60 research outputs found

    Gelation mechanism of thermoreversible poly(vinylidene fluoride) gels in glyceryl tributyrate

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    Poly(vinylidene fluoride) (PVF2) gels in glyceryl tributyrate (GTB) with fibrillar morphology in the dried state. The gels are transparent and WAXS results indicate the presence of α-phase PVF2 crystals in the gels. The gelation rate (t-1gel) has been measured by the test tube tilting method and has been analysed with the equation t-1gel ∝ f(c)f(T), where f(c) = concentration function and f(T) = temperature function. At a fixed temperature, the variation of t-1gel with concentration suggests that the nature of the connectedness in this system obeys the three dimensional percolation mechanism. On the other hand, at a fixed concentration, the variation of the gelation rate with temperature suggests that the gelation is a two step concerted process of conformational ordering and crystallization, the former acting as the rate determining step. The formation of fibrillar gels in this system has been attributed to the solvation of the TGTG conformer of PVF2 through compound formation in a 3:1 molar ratio of the monomeric units of PVF2 and GTB

    ESSAYS ON INSTITUTIONS, FINANCIAL DEVELOPMENT, AND ECONOMIC GROWTH

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    A number of recent papers using a linear specification have indicated that private property institutions are a fundamental determinant of growth. In my first paper, I use a semi-nonparametric partially linear model to provide evidence against a linear specification and to support nonlinearities in the relationship. The findings indicate that the exogenous component of private property institutions contributes positively to economic growth for countries in the lower and middle stages of private property institutions and have a negative relationship with economic growth of countries having the highest level of private property institutions. These results are confirmed when using an appropriate parametric specification and estimation by GMM. When using different measures of private property institutions as the 'rule of law' and 'political freedom', the results are consistent. The second paper documents a nonlinear relationship between financial development and income inequality across developing and developed countries, and uncovers the empirical root of this phenomenon. The source is in two parts: there is a close relationship between the level of economic development and the level of financial development across countries; and the impact of financial development on income inequality is contingent on the level of economic development. The 1990s saw considerable economic turbulence due to varying degrees of financial crisis in many countries in Asia and Latin America. In the third paper, I document that a combination of external shocks, weak institutional background and excessive bank lending contributed to the differential responses by countries to financial crisis. Using a version of the models of Bernanke and Gertler (1990) and Jensen and Meckling (1976), the paper builds a theoretical model to show that institutional problems, coupled with external shocks, can affect the capital structure of firms and lead to a choice of projects having low net present value, which carries implications for aggregate investment and growth.. In the empirical counterpart, the study shows that proxies for weak institutions of corporate finance, excessive bank lending and terms of trade shocks played a central role in determining the magnitude of growth and investment collapse as observed in these regions

    Efficient and Scalable Internet Mapping : Record Route Revisited

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    Abstract The IPv4 Record Route option was designed to accurately map the topology between any two nodes on the Internet. The IP protocol design allows only a maximum of nine IP addresses to be accommodated in the record route header field. As nine hops are insufficient to map the current extent of the Internet, the record route technique was replaced by Traceroute and Border Gateway Protocol (BGP) based techniques. These current techniques consume more bandwidth and host resources compared to record route. This paper revives the record route option by proposing various packet-marking schemes to be deployed on routers. The proposed technique also ensures a minimum of computational overhead for both routers and end hosts. It is faster, scalable and consumes lesser bandwidth compared to the Traceroute and BGP techniques

    Emotion Dynamics of Public Opinions on Twitter

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    [EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). Tweeting India’s Nirbhaya protest: a study of emotional dynamics in an online social movement. Social Movement Studies, 16(4), 447-465. doi:10.1080/14742837.2016.1192457Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). Machine Learning, 50(1/2), 5-43. doi:10.1023/a:1020281327116Araujo, T., Neijens, P., & Vliegenthart, R. (2016). Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands. International Journal of Advertising, 36(3), 496-513. doi:10.1080/02650487.2016.1173765Berger, J. (2011). Arousal Increases Social Transmission of Information. Psychological Science, 22(7), 891-893. doi:10.1177/0956797611413294Bi, B., Tian, Y., Sismanis, Y., Balmin, A., & Cho, J. (2014). Scalable topic-specific influence analysis on microblogs. Proceedings of the 7th ACM international conference on Web search and data mining. doi:10.1145/2556195.2556229Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. doi:10.1016/j.jocs.2010.12.007Chen, W., Wang, C., & Wang, Y. (2010). Scalable influence maximization for prevalent viral marketing in large-scale social networks. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’10. doi:10.1145/1835804.1835934Ding, Z., Jia, Y., Zhou, B., Zhang, J., Han, Y., & Yu, C. (2013). An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs. Lecture Notes in Computer Science, 372-383. doi:10.1007/978-3-642-37401-2_38Ding, Z., Wang, H., Guo, L., Qiao, F., Cao, J., & Shen, D. (2015). Finding Influential Users and Popular Contents on Twitter. Web Information Systems Engineering – WISE 2015, 267-275. doi:10.1007/978-3-319-26187-4_23Feldman Barrett, L., & Russell, J. A. (1998). Independence and bipolarity in the structure of current affect. Journal of Personality and Social Psychology, 74(4), 967-984. doi:10.1037/0022-3514.74.4.967Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390Hillmann, R., & Trier, M. (2012). Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. doi:10.1109/asonam.2012.88Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772751Myers, S. A., Zhu, C., & Leskovec, J. (2012). Information diffusion and external influence in networks. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’12. doi:10.1145/2339530.2339540Nguyen, H. T., Ghosh, P., Mayo, M. L., & Dinh, T. N. (2017). Social Influence Spectrum at Scale. ACM Transactions on Information Systems, 36(2), 1-26. doi:10.1145/3086700Pal, A., & Counts, S. (2011). Identifying topical authorities in microblogs. Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11. doi:10.1145/1935826.1935843Peng, S., Wang, G., & Xie, D. (2017). Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges. IEEE Network, 31(1), 11-17. doi:10.1109/mnet.2016.1500104nmRussell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites. Internet Research, 28(2), 393-418. doi:10.1108/intr-01-2017-0038Silva, A., Guimarães, S., Meira, W., & Zaki, M. (2013). ProfileRank. Proceedings of the 7th Workshop on Social Network Mining and Analysis - SNAKDD ’13. doi:10.1145/2501025.2501033Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29(4), 217-248. doi:10.2753/mis0742-1222290408Vardasbi, A., Faili, H., & Asadpour, M. (2017). SWIM. ACM Transactions on Information Systems, 36(1), 1-33. doi:10.1145/3072652Wang, Y., Li, Y., Fan, J., & Tan, K.-L. (2018). Location-aware Influence Maximization over Dynamic Social Streams. ACM Transactions on Information Systems, 36(4), 1-35. doi:10.1145/3230871Watts, D. J., & Dodds, P. S. (2007). Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34(4), 441-458. doi:10.1086/518527Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010). TwitterRank. Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10. doi:10.1145/1718487.1718520Wolfsfeld, G., Segev, E., & Sheafer, T. (2013). Social Media and the Arab Spring. The International Journal of Press/Politics, 18(2), 115-137. doi:10.1177/1940161212471716Yik, M. S. M., Russell, J. A., & Barrett, L. F. (1999). Structure of self-reported current affect: Integration and beyond. Journal of Personality and Social Psychology, 77(3), 600-619. doi:10.1037/0022-3514.77.3.600Zhang, J., Zhang, R., Sun, J., Zhang, Y., & Zhang, C. (2016). TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter. IEEE/ACM Transactions on Networking, 24(5), 2834-2846. doi:10.1109/tnet.2015.2494059Zhang, Y., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1), 100-119. doi:10.1016/j.ijresmar.2016.07.003Ziegler, C.-N., & Lausen, G. (2005). Propagation Models for Trust and Distrust in Social Networks. Information Systems Frontiers, 7(4-5), 337-358. doi:10.1007/s10796-005-4807-

    The Black Market Exchange Rate in a Developing Economy: The Case of India

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    The existence of black market exchange rates of developing countries is common knowledge. and the Indian rupee has been traded in an unofficial free market for along time (Pick 1975. p. 239). The emergence of the commodities black market in general and the currency black market in particular is the outcome of official control on the free market operation. In the case of the exchange rate, an additional factor is important--a government\u27s perception of the currency\u27s prestige. For example, the political stability of many governments in the developing countries is often at stake when the government decides to devalue currency, even when such a decision is economically sound. the possible political opposition explains partly why most developing countries opt for a fixed exchange rate regime and sticks to the overvalued exchange rate. One strong opinion in favor of a fixed exchange rate is the following: the developing countries cannot follow an independent course in today\u27s world regarding the exchange rate of their currencies. Either they peg their currencies to major world currencies, in which case it is a free floating exchange rate as the base currency floats, or they fix the exchange rate of their currencies vis-a-vis a basket of currencies, in which case it constitutes a managed float. What the developing countries fear most is the speculation with the exchange rate of their currencies. In this case, the country loses valuable foreign exchange
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