Optimized multiplexer design and simulation using quantum dot-cellular automata

Recent trends in nano technological field are the exploitation of quantum dot-cellular automata (QCA) as a substitute in advance to existing transistor based CMOS technology to fabricate nano-circuit. Ultra low heat dissipation, faster clocking and high device density make the QCA as a raising research area in nanotechnological field to suppress the FET based circuit. This paper illustrates a simple and basic method to design QCA based 2:1 multiplexer at nanoscale. The proposed 2:1 multiplexer is compared with the previously designed 2:1 multiplexer in account of circuit density, clock zone numbers, amount of QCA cell used to design the circuit and the density consumed by all QCA cell over total density of the circuit is depicted. This comparative analysis has approved the efficiency of the proposed design. The circuit is implemented and proved using QCA designer-2.0.3

PRECISION MEASUREMENTS TO EXPLORE UNDERLYING GEOMETRIES AND INTERACTIONS IN A TRAPPED Ba+ ION

Ph.DDOCTOR OF PHILOSOPH

ANALYSIS OF SECURITY THREATS IN WIRELESS SENSOR NETWORK

ABSTRAC

Fixed-Point Study of Generalized Rational Type Multivalued Contractive Mappings on Metric Spaces with a Graph

The main result of this paper is a fixed-point theorem for multivalued contractions obtained through an inequality with rational terms. The contraction is an F-type contraction. The results are obtained in a metric space endowed with a graph. The main theorem is supported by illustrative examples. Several results as special cases are obtained by specific choices of the control functions involved in the inequality. The study is broadly in the domain of setvalued analysis. The methodology of the paper is a blending of both graph theoretic and analytic methods.This paper has been supported by the Basque Government though Grant T1207-19

Emotion Dynamics of Public Opinions on Twitter

[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-

Electrically doped nanoscale devices using first-principle approach: a comprehensive survey

Doping is the key feature in semiconductor device fabrication. Many strategies have been discovered for controlling doping in the area of semiconductor physics during the past few decades. Electrical doping is a promising strategy that is used for efective tuning of the charge populations, electronic properties, and transmission properties. This doping process reduces the risk of high temperature, contamination of foreign particles. Signifcant experimental and theoretical eforts are demonstrated to study the characteristics of electrical doping during the past few decades. In this article, we frst briefy review the historical roadmap of electrical doping. Secondly, we will discuss electrical doping at the molecular level. Thus, we will review some experimental works at the molecular level along with we review a variety of research works that are performed based on electrical doping. Then we fgure out importance of electrical doping and its importance. Furthermore, we describe the methods of electrical doping. Finally, we conclude with a brief comparative study between electrical and conventional doping methods

Tachyon Condensation and Brane Descent Relations in p-adic String Theory

It has been conjectured that an extremum of the tachyon potential of a bosonic D-brane represents the vacuum without any D-brane, and that various tachyonic lump solutions represent D-branes of lower dimension. We show that the tree level effective action of p-adic string theory, the expression for which is known exactly, provides an explicit realisation of these conjectures.Comment: LaTeX file, epsf, 2 figures, 18 page