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Journal articles on the topic 'Dynamic communication'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Schiper, André. "Dynamic group communication." Distributed Computing 18, no. 5 (2005): 359–74. http://dx.doi.org/10.1007/s00446-005-0129-4.

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2

Hutton, Pierce, Brett M. Seymoure, Kevin J. McGraw, Russell A. Ligon, and Richard K. Simpson. "Dynamic color communication." Current Opinion in Behavioral Sciences 6 (December 2015): 41–49. http://dx.doi.org/10.1016/j.cobeha.2015.08.007.

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3

Gagie, Travis. "Dynamic asymmetric communication." Information Processing Letters 108, no. 6 (2008): 352–55. http://dx.doi.org/10.1016/j.ipl.2008.07.004.

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4

Wang, Shengfeng, Xin Feng, Ye Wu, and Jinhua Xiao. "Double dynamic scaling in human communication dynamics." Physica A: Statistical Mechanics and its Applications 473 (May 2017): 313–18. http://dx.doi.org/10.1016/j.physa.2017.01.010.

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5

Sher, Itai. "Persuasion and dynamic communication." Theoretical Economics 9, no. 1 (2014): 99–136. http://dx.doi.org/10.3982/te683.

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6

Timothy Coombs, W., and Sherry J. Holladay. "The negative communication dynamic." Journal of Communication Management 11, no. 4 (2007): 300–312. http://dx.doi.org/10.1108/13632540710843913.

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7

Nakazawa, M., K. Suzuki, H. Kubota, E. Yamada, and Y. Kimura. "Dynamic optical soliton communication." IEEE Journal of Quantum Electronics 26, no. 12 (1990): 2095–102. http://dx.doi.org/10.1109/3.64344.

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8

Sano, Ryuji. "Dynamic communication mechanism design." Social Choice and Welfare 57, no. 1 (2021): 163–80. http://dx.doi.org/10.1007/s00355-021-01309-y.

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9

Zhang, Mo, and Hai Shen. "Biological Communication Dynamic Model Research." Applied Mechanics and Materials 556-562 (May 2014): 4975–78. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4975.

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Biological communication behavior is in everywhere, all over the nature, biological system and human society. In simple terms, Swarm intelligence is emerging though information communication and collaboration among some dispersed and simple individuals. Inspired by biological communication behavior, aimed at understanding swarm system collective dynamics behavior, and from the point of system cybernetics, this paper study the relevant biological communication dynamic model, such as the symbiotic model, attractive-repulsive model, external effect model and the multi-population coevolution model

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10

Hiji, Masahiro, Masatoshi Miyazaki, and Hiroshi Nunokawa. "Communication computation model for modeling dynamic human communication." Systems and Computers in Japan 27, no. 12 (1996): 63–72. http://dx.doi.org/10.1002/scj.4690271206.

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11

Ishikawa, Ryuichiro. "Dynamic Game Logic of Communication." Transactions of the Institute of Systems, Control and Information Engineers 32, no. 12 (2019): 429–38. http://dx.doi.org/10.5687/iscie.32.429.

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12

Georgiadis, Leonidas, George Iosifidis, and Leandros Tassiulas. "Dynamic Exchange of Communication Services." ACM SIGMETRICS Performance Evaluation Review 42, no. 3 (2014): 40. http://dx.doi.org/10.1145/2695533.2695548.

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13

Ivanov, Maxim. "Dynamic learning and strategic communication." International Journal of Game Theory 45, no. 3 (2015): 627–53. http://dx.doi.org/10.1007/s00182-015-0474-x.

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14

Margaria, Chiara, and Alex Smolin. "Dynamic communication with biased senders." Games and Economic Behavior 110 (July 2018): 330–39. http://dx.doi.org/10.1016/j.geb.2017.10.017.

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15

Peleg, David. "Communication in concurrent dynamic logic." Journal of Computer and System Sciences 35, no. 1 (1987): 23–58. http://dx.doi.org/10.1016/0022-0000(87)90035-3.

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16

Y.S.V.Raman, Y. S. V. Raman, Dr S. Sri Gowri Dr S.Sri Gowri, and Dr B. Prabharkara Rao. "Performance Enhancement of Dynamic Channel Allocation in Cellular Communication System." Indian Journal of Applied Research 3, no. 7 (2011): 322–25. http://dx.doi.org/10.15373/2249555x/july2013/99.

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17

BERTHOLD, JOST, and RITA LOOGEN. "THE IMPACT OF DYNAMIC CHANNELS ON FUNCTIONAL TOPOLOGY SKELETONS." Parallel Processing Letters 18, no. 01 (2008): 101–15. http://dx.doi.org/10.1142/s0129626408003259.

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Parallel functional programs with implicit communication often generate purely hierarchical communication topologies during execution: communication only happens between parent and child processes. Hence, messages between siblings must be passed via the parent causing inefficiencies that can be avoided by direct communication between arbitrary processes. The Eden parallel functional language provides dynamic channels to implement arbitrary communication topologies. This paper analyses the impact of dynamic channels on Eden's topology skeletons, i.e. skeletons which define process topologies su

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18

A, Sasi, and Santhiya P. "Dynamic Location Modelling in 3D Beamforming for 5G Mobile Communications." Computer Engineering and Applications Journal 4, no. 1 (2015): 43–50. http://dx.doi.org/10.18495/comengapp.v4i1.116.

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Mobile communication system is designed to provide reliable communication with more number of services and with low cost among multiple users. Due to limited frequency spectrum and resources, mobile communication requires more development in case of both establishing communication and maintenance in service quality. To fulfill these requirements, 5G mobile communication is being developed to provide high quality reliable communication and quality of service, by using beamforming model. As the trend of next generation mobile communication, 3D directional transmission is considered to give enhan

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19

Hilbert, Martin, and David Darmon. "Large-Scale Communication is More Complex and Unpredictable with Automated Bots." Journal of Communication 70, no. 5 (2020): 670–92. http://dx.doi.org/10.1093/joc/jqaa021.

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Abstract Automated communication bots follow deterministic local rules that either respond to programmed instructions or learned patterns. On the microlevel, their automated and reactive behavior makes certain parts of the communication dynamic more predictable. Studying communicative turns in the editing history of Wikipedia, we find that on the macrolevel, the overall emergent communication process becomes both more complex and less predictable. The increased presence of bots is the main explanatory variable for these seemingly contradictory tendencies. In short, individuals introduce bots t

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20

De Lahaut, Damien Gautier, and CÉcile Germain. "A Static Approach for Compiling Communications in Paranel Scientific Programs." Scientific Programming 4, no. 4 (1995): 291–305. http://dx.doi.org/10.1155/1995/397320.

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On most massively parallel architectures, the actual communication performance remains much less than the hardware capabilities. The main reason for this difference lies in the dynamic routing, because the software mechanisms for managing the routing represent a large overhead. This article presents experimental studies on benchmark programs concerning scientific computing; the results show that most communication patterns in application programs are predictable at compile-time. An execution model is proposed that utilizes this knowledge such that predictable communications are directly compil

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21

Tang, Xiongyan, and Peida Ye. "Comparison of dynamic soliton communication and path-averaged soliton communication." Fiber and Integrated Optics 13, no. 3 (1994): 261–70. http://dx.doi.org/10.1080/01468039408202235.

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22

Zhu, Chun Jiang, Tan Zhu, Kam-Yiu Lam, Song Han, and Jinbo Bi. "Communication-Optimal Distributed Dynamic Graph Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5957–64. http://dx.doi.org/10.1609/aaai.v33i01.33015957.

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We consider the problem of clustering graph nodes over large-scale dynamic graphs, such as citation networks, images and web networks, when graph updates such as node/edge insertions/deletions are observed distributively. We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models. Given a graph with n nodes that is observed at s remote sites over time [1,t], the two proposed algorithms have communication costs Õ(ns) and Õ(n + s) (Õ hides a polylogarithmic factor), almost matching their lower bounds, Ω(ns) and

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23

Kobayashi, Mikito, and Satoshi Tojo. "Agent Communication for Dynamic Belief Update." Transactions of the Japanese Society for Artificial Intelligence 24 (2009): 314–21. http://dx.doi.org/10.1527/tjsai.24.314.

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24

Dolev, Shlomi, Roberto Segala, and Alexander Shvartsman. "Dynamic load balancing with group communication." Theoretical Computer Science 369, no. 1-3 (2006): 348–60. http://dx.doi.org/10.1016/j.tcs.2006.09.020.

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25

Bhawuk, Dharm P. S. "Intercultural Communication in a Dynamic Environment." Psychology and Developing Societies 21, no. 2 (2009): 161–81. http://dx.doi.org/10.1177/097133360902100201.

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26

Gupta, P. K. Das. "Dynamic Interconnection Techniques for Parallel Communication." Defence Science Journal 44, no. 2 (1994): 113–18. http://dx.doi.org/10.14429/dsj.44.4158.

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27

Dolev, S., and J. L. Welch. "Crash resilient communication in dynamic networks." IEEE Transactions on Computers 46, no. 1 (1997): 14–26. http://dx.doi.org/10.1109/12.559799.

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28

Lang, Annie. "Dynamic Human-Centered Communication Systems Theory." Information Society 30, no. 1 (2014): 60–70. http://dx.doi.org/10.1080/01972243.2013.856364.

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29

Folhadela Benevides, Mario Roberto, and Isaque Macalam Saab Lima. "Dynamic Epistemic Logic with Communication Actions." Electronic Notes in Theoretical Computer Science 344 (August 2019): 67–82. http://dx.doi.org/10.1016/j.entcs.2019.07.005.

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30

Heitmann, Stewart, and Michael Breakspear. "Putting the “dynamic” back into dynamic functional connectivity." Network Neuroscience 2, no. 2 (2018): 150–74. http://dx.doi.org/10.1162/netn_a_00041.

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The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; general

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31

Ingold, Tim. "Communication and communion." Behavioral and Brain Sciences 25, no. 5 (2002): 627–28. http://dx.doi.org/10.1017/s0140525x02290111.

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Shanker & King's (S&K's) dynamic systems approach converges with developments in social anthropological studies of communication which were long ago anticipated in the writings of Volosinov and Schutz. Following a review of these writings, this commentary suggests that a dynamic systems approach should distinguish communion from communication. It concludes with a remark on the evolutionary implications of the approach.

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32

Kopeikin, Andrew N., Sameera S. Ponda, Luke B. Johnson, and Jonathan P. How. "Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams." Unmanned Systems 01, no. 01 (2013): 41–58. http://dx.doi.org/10.1142/s2301385013500039.

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A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment info

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33

Buchenberg, Sebastian, Florian Sittel, and Gerhard Stock. "Time-resolved observation of protein allosteric communication." Proceedings of the National Academy of Sciences 114, no. 33 (2017): E6804—E6811. http://dx.doi.org/10.1073/pnas.1707694114.

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Allostery represents a fundamental mechanism of biological regulation that is mediated via long-range communication between distant protein sites. Although little is known about the underlying dynamical process, recent time-resolved infrared spectroscopy experiments on a photoswitchable PDZ domain (PDZ2S) have indicated that the allosteric transition occurs on multiple timescales. Here, using extensive nonequilibrium molecular dynamics simulations, a time-dependent picture of the allosteric communication in PDZ2S is developed. The simulations reveal that allostery amounts to the propagation of

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34

Liu, Mei, Shuai Li, Xiaodi Li, Long Jin, Chenfu Yi, and Zhiguan Huang. "Intelligent Controllers for Multirobot Competitive and Dynamic Tracking." Complexity 2018 (October 21, 2018): 1–12. http://dx.doi.org/10.1155/2018/4573631.

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This paper focuses on the problem of target tracking using k fittest robots in a group of n mobile robots with n>k. We present centralized and distributed coordination models with all-to-all and limited communications, respectively. For the case of all-to-all communication between robots, theoretical analysis is presented to prove the exponential stability of the whole system. In real applications of robotic networks, a robot may only be allowed to exchange information with a limited number of neighbors. In such a limited communication case where centralized quantity is not available, a con

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35

Nitschke, Paula, and Patrick Donges. "Intentional and emergent strategies: Analyzing the motivational and structural dynamics in online communications of political interest organizations." Public Relations Inquiry 7, no. 3 (2018): 225–41. http://dx.doi.org/10.1177/2046147x18794998.

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This article focuses on the motivational and structural dynamics of the online communication of political interest organizations. Instead of describing political interest organizations as fully rational players, we develop a theoretical framework that establishes an alternative view of political interest organizations by characterizing them as actors that are also dependent on their institutional environment. The basic assumption of this framework is that there is no unidirectional relationship between motives and structures and the online communication activities. Instead, there is a dynamic

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36

Burgin, Mark. "Triadic Structures in Interpersonal Communication." Information 9, no. 11 (2018): 283. http://dx.doi.org/10.3390/info9110283.

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Communication, which is information exchange between systems, is one of the basic information processes. To better understand communication and develop more efficient communication tools, it is important to have adequate and concise, static and dynamic, structured models of communication. The principal goal of this paper is explication of the communication structures, formation of their adequate mathematical models and description of their dynamic interaction. Exploring communication in the context of structures and structural dynamics, we utilize the most fundamental structure in mathematics,

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37

Cui, Xiang, Xiaowen Li, and Bei Wang. "Communication Optimization Technology Based on Network Dynamic Performance Model." Mathematical Problems in Engineering 2020 (October 15, 2020): 1–13. http://dx.doi.org/10.1155/2020/8890721.

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This work analyses different communication modes in applications of supercomputing, proposes a communication dynamic performance model based on topology awareness, and realizes the prototype system of all-to-all communication and stencil communication optimization based on this model. Basic tests on the optimization of all-to-all communication and stencil communication were carried out on the Sunway TaihuLight System, and this achieved obvious optimization results. Several applications, including molecular dynamics simulation and turbulence simulation, have been optimized and tested. The avera

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38

Kashima, Yoshihisa, Andrew Perfors, Vanessa Ferdinand, and Elle Pattenden. "Ideology, communication and polarization." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1822 (2021): 20200133. http://dx.doi.org/10.1098/rstb.2020.0133.

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Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular

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39

Leuchtmann, Lorena, Anne Milek, Katharina Bernecker, et al. "Temporal dynamics of couples’ communication behaviors in conflict discussions: A longitudinal analysis." Journal of Social and Personal Relationships 36, no. 9 (2018): 2937–60. http://dx.doi.org/10.1177/0265407518806582.

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Negative and positive conflict communication predicts long-term relationship satisfaction. However, some studies show harmful effects and others show beneficial effects of negative conflict communication on long-term relationship satisfaction. One reason for the heterogeneous results might be that most studies focused on aggregated behaviors across a conflict interaction but neglected the temporal dynamics within such an interaction. This study examined whether individual initial levels and temporal trajectories of negative and positive communications predict long-term relationship satisfactio

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40

Ge, Yongquan, Chengxin Yu, Xiaodong Liu, and Ronghui Wang. "Monitoring dynamic deformation of communication tower using photography dynamic monitoring system." IOP Conference Series: Earth and Environmental Science 558 (September 5, 2020): 022061. http://dx.doi.org/10.1088/1755-1315/558/2/022061.

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41

Shin, Dong-Yun, and Young-Kil Kim. "Dynamic QoS Mechanism for supporting VoIP Service in Tactical Communication Environment." Journal of the Korean Institute of Information and Communication Engineering 16, no. 9 (2012): 2078–83. http://dx.doi.org/10.6109/jkiice.2012.16.9.2078.

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42

Yang, Ning Ou, Ying Liang, Chun Yue Huang, and He Geng Wei. "Dynamic Analysis of Communication Cabinet Structure Design." Advanced Materials Research 199-200 (February 2011): 1113–17. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.1113.

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Two communication cabinet finite element analysis(FEA) models with different cross-sectional structure vertical columns were set up. Based on the two communication cabinet FEA models, modal analysis was carried out by using the subspace method; the first 6 order natural frequencies and vibration modes were obtained. Harmonic response analysis was also carried out; the displacement response of the communication cabinet structure under external loading was determined. The dynamic performance comparison of the two communication cabinets with different cross-sectional structure vertical columns wa

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43

Padhariya, Nilesh, and Kshama Raichura. "MobPrice: Dynamic Data Pricing for Mobile Communication." Journal of information and communication convergence engineering 13, no. 2 (2015): 86–96. http://dx.doi.org/10.6109/jicce.2015.13.2.086.

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44

CHONG, DENNIS, and JAMES N. DRUCKMAN. "Dynamic Public Opinion: Communication Effects over Time." American Political Science Review 104, no. 4 (2010): 663–80. http://dx.doi.org/10.1017/s0003055410000493.

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We develop an approach to studying public opinion that accounts for how people process competing messages received over the course of a political campaign or policy debate. Instead of focusing on the fixed impact of a message, we emphasize that a message can have variable effects depending on when it is received within a competitive context and how it is evaluated. We test hypotheses about the effect of information processing using data from two experiments that measure changes in public opinion in response to alternative sequences of information. As in past research, we find that competing me

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45

Okajima, Hiroshi, Kenji Sawada, and Nobutomo Matsunaga. "Dynamic Quantizer Design Under Communication Rate Constraints." IEEE Transactions on Automatic Control 61, no. 10 (2016): 3190–96. http://dx.doi.org/10.1109/tac.2015.2509438.

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46

Xia, Yongxiang, and David J. Hill. "Dynamic Braess's Paradox in Complex Communication Networks." IEEE Transactions on Circuits and Systems II: Express Briefs 60, no. 3 (2013): 172–76. http://dx.doi.org/10.1109/tcsii.2013.2240912.

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47

Fuentes, Miguel, and Hernán Miguel. "Dynamic landscapes to model communication and learning." Scientiae Studia 14, no. 1 (2016): 65. http://dx.doi.org/10.11606/s1678-31662016000100005.

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48

Mahmoud, M. S., N. M. Alyazidi, and A. W. A. Saif. "Dynamic feedback control over unreliable communication channels." IMA Journal of Mathematical Control and Information 31, no. 2 (2013): 195–216. http://dx.doi.org/10.1093/imamci/dnt006.

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49

Mense, Mark G. "Dynamic Communication Tools for Veterinary Pathology (eVetPath)." Veterinary Pathology 46, no. 1 (2009): 1. http://dx.doi.org/10.1354/vp.46-1-1.

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50

Manning, A. D. "Dynamic and static communication in electronic media." IEEE Transactions on Professional Communication 42, no. 4 (1999): 301–5. http://dx.doi.org/10.1109/47.807969.

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