Journal articles: 'Dynamic Systems'

1

Bakal, Chris. "Dynamic systems." Genome Biology 13, no. 1 (2012): 312. http://dx.doi.org/10.1186/gb-2012-13-1-312.

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Sharif, Amir M. "Can systems dynamics be effective in modelling dynamic business systems?" Business Process Management Journal 11, no. 5 (October 2005): 612–15. http://dx.doi.org/10.1108/14637150510619911.

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Sachs, K., S. Itani, J. Fitzgerald, B. Schoeberl, G. P. Nolan, and C. J. Tomlin. "Single timepoint models of dynamic systems." Interface Focus 3, no. 4 (August 6, 2013): 20130019. http://dx.doi.org/10.1098/rsfs.2013.0019.

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Many interesting studies aimed at elucidating the connectivity structure of biomolecular pathways make use of abundance measurements, and employ statistical and information theoretic approaches to assess connectivities. These studies often do not address the effects of the dynamics of the underlying biological system, yet dynamics give rise to impactful issues such as timepoint selection and its effect on structure recovery. In this work, we study conditions for reliable retrieval of the connectivity structure of a dynamic system, and the impact of dynamics on structure-learning efforts. We encounter an unexpected problem not previously described in elucidating connectivity structure from dynamic systems, show how this confounds structure learning of the system and discuss possible approaches to overcome the confounding effect. Finally, we test our hypotheses on an accurate dynamic model of the IGF signalling pathway. We use two structure-learning methods at four time points to contrast the performance and robustness of those methods in terms of recovering correct connectivity.

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Panova, Yulia, and Olli-Pekka Hilmola. "DYNAMIC BOTTLENECKS IN HANDLING AND STORAGE SYSTEMS." Russian Journal of Logistics and Transport Management 2, no. 1 (2015): 11–19. http://dx.doi.org/10.20295/2313-7002-2015-1-11-19.

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Sapaty, P. S. "Spatial grasp model for dynamic distributed systems." Mathematical machines and systems 3 (2021): 3–21. http://dx.doi.org/10.34121/1028-9763-2021-3-21.

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More complex distributed and intelligent systems which relate to economy, ecology, communi-cations, security and defense, and cover both terrestrial and celestial environments are being developed. Their efficient management, especially in dynamic and unpredictable situations, needs serious investigations and development in scientific and technological areas. Their tradi-tional representations as parts operating by certain algorithms and exchanging messages are be-coming inadequate as such systems need much stronger integration to operate as holistic organ-isms pursuing global and often varying goals. This paper is focused on a completely different paradigm for organization and management of large dynamic and distributed systems. This par-adigm extends and transforms the notion of an algorithm for the description of knowledge pro-cessing logic. Moreover, it allows it to exist, propagate and operate as an integral whole in any distributed spaces which may constantly change their volumes and structures. Taking into con-sideration some organizational features related to dangerous viruses, as well as recent pandem-ics, this ubiquitous Spatial Grasp (SG) model is presented in the paper at philosophical and im-plementation levels, together with the introduction of special spatial charts for its exhibition and studies, which extend traditional algorithmic flowcharts towards working directly in dis-tributed spaces. Utilization of this model for the creation of resultant Spatial Grasp Technology and its basic Spatial Grasp Language, already described in details in numerous publications, is briefed as well. Elementary examples of dealing with distributed networks, collective human-robotic behavior, removal of space debris by a constellation of cleaning satellites and simulat-ing the spread of virus and vaccination against it explain SG advantages over traditional system organizations.

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Osipov, G. S. "Intelligent dynamic systems." Scientific and Technical Information Processing 37, no. 5 (December 2010): 259–64. http://dx.doi.org/10.3103/s0147688210050023.

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Tanaka, D. L., J. M. Krupinsky, M. A. Liebig, S. D. Merrill, R. E. Ries, J. R. Hendrickson, H. A. Johnson, and J. D. Hanson. "Dynamic Cropping Systems." Agronomy Journal 94, no. 5 (September 2002): 957–61. http://dx.doi.org/10.2134/agronj2002.9570.

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Power, Mary E. "Engaging Dynamic Systems." BioScience 57, no. 8 (September 1, 2007): 707–9. http://dx.doi.org/10.1641/b570812.

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9

Haykin, Simon. "Cognitive Dynamic Systems." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 33–43. http://dx.doi.org/10.4018/jcini.2011100103.

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The main topics covered in this paper address the following four issues: 1) Distinction between how adaptation and cognition are viewed with respect to each other, 2) With human cognition viewed as the framework for cognition, the following cognitive processes are identified: the perception-action cycle, memory, attention, intelligence, and language. With language being outside the scope of the paper, detailed accounts of the other four cognitive processes are discussed, 3) Cognitive radar is singled out as an example application of cognitive dynamic systems that “mimics” the visual brain; experimental results on tracking are presented using simulations, which clearly demonstrate the information-processing power of cognition, and 4) Two other example applications of cognitive dynamic systems, namely, cognitive radio and cognitive control, are briefly described.

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Haykin, Simon. "Cognitive Dynamic Systems." Proceedings of the IEEE 94, no. 11 (November 2006): 1910–11. http://dx.doi.org/10.1109/jproc.2006.886014.

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Veitch, James, and Robert Laddaga. "Distributed dynamic systems." Communications of the ACM 41, no. 5 (May 1998): 34–36. http://dx.doi.org/10.1145/274946.274953.

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Schwartz, Daniel G. "Dynamic Reasoning Systems." ACM Transactions on Computational Logic 16, no. 4 (November 19, 2015): 1–42. http://dx.doi.org/10.1145/2798727.

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13

Kamenskii, G. A. "Smoothing dynamic systems." Nonlinear Analysis: Theory, Methods & Applications 27, no. 10 (November 1996): 1117–24. http://dx.doi.org/10.1016/0362-546x(95)00123-d.

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Tanaka, D. L., J. M. Krupinsky, M. A. Liebig, S. D. Merrill, R. E. Ries, J. R. Hendrickson, H. A. Johnson, and J. D. Hanson. "Dynamic Cropping Systems." Agronomy Journal 94, no. 5 (2002): 957. http://dx.doi.org/10.2134/agronj2002.0957.

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15

Stefanuk, V. L. "Dynamic expert systems." Kybernetes 29, no. 5/6 (July 2000): 702–9. http://dx.doi.org/10.1108/03684920010333134.

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16

Barboiu, Mihail, and Jean‐Marie Lehn. "Constitutional Dynamic Systems." Israel Journal of Chemistry 53, no. 1‐2 (February 2013): 9–10. http://dx.doi.org/10.1002/ijch.201310003.

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17

SZATKOWSKI, ANDRZEJ. "Generalized dynamical systems: differentiable dynamic complexes and differential dynamic systems." International Journal of Systems Science 21, no. 8 (August 1990): 1631–57. http://dx.doi.org/10.1080/00207729008910481.

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Rodríguez Velásquez, Javier Oswaldo, Jaime Alberto Páez Páez, Sandra Catalina Correa Herrera, Signed Esperanza Prieto Bohórquez, Mario Fernando Castro Fernández, Carlos Enrique Montenegro Marín, and Jairo Augusto Cortes Méndez. "Software for adult cardiac dynamic through dynamic systems." Visión electrónica 13, no. 1 (February 5, 2019): 50–56. http://dx.doi.org/10.14483/22484728.14440.

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The normal and abnormal behavior of an adult heart dynamics and its state of evolution towards one of these two states has been characterized successfully in the context of the theory of dynamic systems and probability. The diagnostic methodology of clinical application designed under these two theories has managed to evaluate in an objective and reproducible way the cardiac dynamics from the values of the frequency of the Holter registers. The automation of this methodology through the design of a software that can be docked in any operating system for PC, and contributes as a diagnostic aid tool to generate more timely responses to the patient's clinical condition. Additionally, the values of the probability of these spaces occupied by the attractor, calculated by the Software, allow using an interface that can be consulted by the specialist to evaluate how far a cardiac dynamic is from normality, analyzing in this way the effectiveness of the treatment.

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19

de la Sen, M., J. L. Malo, and M. J. Gonzalez-Gomez. "Energy Balances in Dynamic Systems Under Unmodelled Dynamics." IFAC Proceedings Volumes 22, no. 18 (November 1989): 65–70. http://dx.doi.org/10.1016/s1474-6670(17)52822-0.

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Iqbal, N., J. Buisson, and Y. Quenec'hdu. "Stability of Dynamic Hybrid Systems With Descriptor Systems as Dynamic Model." IFAC Proceedings Volumes 30, no. 6 (May 1997): 361–67. http://dx.doi.org/10.1016/s1474-6670(17)43391-x.

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21

Verlan, A., and Jo Sterten. "Methods of Complex Dynamic Systems’ Models’ Equivalent Conversion." Mathematical and computer modelling. Series: Technical sciences 1, no. 20 (September 20, 2019): 16–25. http://dx.doi.org/10.32626/2308-5916.2019-20.16-25.

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22

Korolyov, Alexander, and Huiyu Zhou. "Dynamic damper pressure fluctuation in the pumping systems." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 1 (April 27, 2016): 50–60. http://dx.doi.org/10.15276/opu.1.48.2016.07.

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23

Popkov, Y. S. "Oscillations in Dynamic Systems with an Entropy Operator." Nelineinaya Dinamika 19, no. 1 (2023): 0. http://dx.doi.org/10.20537/nd230101.

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This paper considers dynamic systems with an entropy operator described by a perturbed constrained optimization problem. Oscillatory processes are studied for periodic systems with the following property: the entire system has the same period as the process generated by its linear part. Existence and uniqueness conditions are established for such oscillatory processes, and a method is developed to determine their form and parameters. Also, the general case of noncoincident periods is analyzed, and a method is proposed to determine the form, parameters, and the period of such oscillations. Almost periodic processes are investigated, and existence and uniqueness conditions are proved for them as well.

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24

Górecki, H., and M. Zaczyk. "Extremal dynamic errors in linear dynamic systems." Bulletin of the Polish Academy of Sciences: Technical Sciences 58, no. 1 (March 1, 2010): 99–105. http://dx.doi.org/10.2478/v10175-010-0010-x.

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Extremal dynamic errors in linear dynamic systems Two different analytical methods of determining extremal dynamic errors in linear dynamic systems are presented. The main idea of these methods is based on finding certain additional equations. These additional equations are obtained due to the assumption that an extremal point τ obtained from the necessary condition , is also an extremum point with respect to initial conditions, that is, .

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25

Barraza, Manuel, Fernando Matía, and Basil Mohammed Al-Hadithi. "Dynamic Analysis of Fuzzy Systems." Applied Sciences 13, no. 3 (February 2, 2023): 1934. http://dx.doi.org/10.3390/app13031934.

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In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results.

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26

Awrejcewicz, Jan, and José A. Tenreiro Machado. "Entropy in Dynamic Systems." Entropy 21, no. 9 (September 16, 2019): 896. http://dx.doi.org/10.3390/e21090896.

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In order to measure and quantify the complex behavior of real-world systems, either novel mathematical approaches or modifications of classical ones are required to precisely predict, monitor and control complicated chaotic and stochastic processes [...]

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27

Sabry, Khaled, and Jeff Barker. "Dynamic Interactive Learning Systems." Innovations in Education and Teaching International 46, no. 2 (May 2009): 185–97. http://dx.doi.org/10.1080/14703290902843836.

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28

Collings, Patti B. "Fathom: Dynamic Systems Software." American Statistician 55, no. 3 (August 2001): 258–59. http://dx.doi.org/10.1198/tas.2001.s123.

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29

Rabbath, C. A., M. Abdoune, J. Belanger, and K. Butts. "Simulating hybrid dynamic systems." IEEE Robotics & Automation Magazine 9, no. 2 (June 2002): 39–47. http://dx.doi.org/10.1109/mra.2002.1019489.

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30

Thelen, Esther. "Dynamic Systems for Everyone." Contemporary Psychology: A Journal of Reviews 41, no. 10 (October 1996): 1002–3. http://dx.doi.org/10.1037/004523.

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31

Chacon, Edgar, Gisela De Sarrazin, and Ferenc Szigeti. "Pseudo dynamic hybrid systems." Nonlinear Analysis: Theory, Methods & Applications 30, no. 4 (December 1997): 2533–37. http://dx.doi.org/10.1016/s0362-546x(96)00143-5.

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32

Russell, David W. "Dynamic Systems & Chaos." IFAC Proceedings Volumes 31, no. 29 (October 1998): 6. http://dx.doi.org/10.1016/s1474-6670(17)38316-7.

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33

Satake, Akiharu, and Yoshiaki Kobuke. "Dynamic supramolecular porphyrin systems." Tetrahedron 61, no. 1 (January 2005): 13–41. http://dx.doi.org/10.1016/j.tet.2004.10.073.

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34

Leman, M. "Music and dynamic systems." Interface 19, no. 1 (January 1990): 1. http://dx.doi.org/10.1080/09298219008570552.

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35

Martin, Clyde, K. P. Loucks, and Bijoy K. Ghosh. "Homogeneous dynamic systems theory." IFAC Proceedings Volumes 32, no. 2 (July 1999): 1410–15. http://dx.doi.org/10.1016/s1474-6670(17)56239-4.

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36

Polyakov, N. L., and M. V. Shamolin. "On Dynamic Aggregation Systems." Journal of Mathematical Sciences 244, no. 2 (November 28, 2019): 278–93. http://dx.doi.org/10.1007/s10958-019-04619-w.

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37

Albeverio, S., A. Khrennikov, B. Tirozzi, and S. De Smedt. "p-adic dynamic systems." Theoretical and Mathematical Physics 114, no. 3 (March 1998): 276–87. http://dx.doi.org/10.1007/bf02575441.

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38

Dumortier, Ir Frits. "Control and dynamic systems." Automatica 28, no. 3 (May 1992): 650–52. http://dx.doi.org/10.1016/0005-1098(92)90194-k.

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39

Štecha, J. "Modeling of dynamic systems." Automatica 32, no. 6 (June 1996): 946–47. http://dx.doi.org/10.1016/0005-1098(96)89429-5.

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40

Popescu, Theodor D. "Modeling of dynamic systems." Control Engineering Practice 3, no. 6 (June 1995): 897–98. http://dx.doi.org/10.1016/0967-0661(95)90022-5.

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41

Charbonnier, F., H. Alla, and R. David. "Discrete-event dynamic systems." IEEE Transactions on Control Systems Technology 7, no. 2 (March 1999): 175–87. http://dx.doi.org/10.1109/87.748144.

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42

Hide, R. "Chaos in Dynamic Systems." Physics Bulletin 37, no. 9 (September 1986): 390. http://dx.doi.org/10.1088/0031-9112/37/9/034.

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43

Ligęza, Antoni. "Dynamic backward reasoning systems." Artificial Intelligence 43, no. 2 (May 1990): 127–52. http://dx.doi.org/10.1016/0004-3702(90)90083-c.

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44

Heitmann, Stewart, and Michael Breakspear. "Putting the “dynamic” back into dynamic functional connectivity." Network Neuroscience 2, no. 2 (June 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; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.

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45

Adler, Rasmus, Frank Elberzhager, Rodrigo Falcão, and Julien Siebert. "Defining and Researching “Dynamic Systems of Systems”." Software 3, no. 2 (May 1, 2024): 183–205. http://dx.doi.org/10.3390/software3020009.

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Digital transformation is advancing across industries, enabling products, processes, and business models that change the way we communicate, interact, and live. It radically influences the evolution of existing systems of systems (SoSs), such as mobility systems, production systems, energy systems, or cities, that have grown over a long time. In this article, we discuss what this means for the future of software engineering based on the results of a research project called DynaSoS. We present the data collection methods we applied, including interviews, a literature review, and workshops. As one contribution, we propose a classification scheme for deriving and structuring research challenges and directions. The scheme comprises two dimensions: scope and characteristics. The scope motivates and structures the trend toward an increasingly connected world. The characteristics enhance and adapt established SoS characteristics in order to include novel aspects and to better align them with the structuring of research into different research areas or communities. As a second contribution, we present research challenges using the classification scheme. We have observed that a scheme puts research challenges into context, which is needed for interpreting them. Accordingly, we conclude that our proposals contribute to a common understanding and vision for engineering dynamic SoS.

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46

Pervin, Lawrence A. "A Dynamic Systems Approach to Personality." European Psychologist 6, no. 3 (September 2001): 172–76. http://dx.doi.org/10.1027//1016-9040.6.3.172.

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David Magnusson has been the most articulate spokesperson for a holistic, systems approach to personality. This paper considers three concepts relevant to a dynamic systems approach to personality: dynamics, systems, and levels. Some of the history of a dynamic view is traced, leading to an emphasis on the need for stressing the interplay among goals. Concepts such as multidetermination, equipotentiality, and equifinality are shown to be important aspects of a systems approach. Finally, attention is drawn to the question of levels of description, analysis, and explanation in a theory of personality. The importance of the issue is emphasized in relation to recent advances in our understanding of biological processes. Integrating such advances into a theory of personality while avoiding the danger of reductionism is a challenge for the future.

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Zahariev, E. V. "Earthquake dynamic response of large flexible multibody systems." Mechanical Sciences 4, no. 1 (February 20, 2013): 131–37. http://dx.doi.org/10.5194/ms-4-131-2013.

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Abstract. In the paper dynamics of large flexible structures imposed on earthquakes and high amplitude vibrations is regarded. Precise dynamic equations of flexible systems are the basis for reliable motion simulation and analysis of loading of the design scheme elements. Generalized Newton–Euler dynamic equations for rigid and flexible bodies are applied. The basement compulsory motion realized because of earthquake or wave propagation is presented in the dynamic equations as reonomic constraints. The dynamic equations, algebraic equations and reonomic constraints compile a system of differential algebraic equations which are transformed to a system of ordinary differential equations with respect to the generalized coordinates and the reactions due to the reonomic constraints. Examples of large flexible structures and wind power generator dynamic analysis are presented.

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48

Martínez-Marín, Sindy, Nataly Puello-Pereira, and David Ovallos-Gazabon. "Cluster Competitiveness Modeling: An Approach with Systems Dynamics." Social Sciences 9, no. 2 (February 7, 2020): 12. http://dx.doi.org/10.3390/socsci9020012.

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This study makes a systemic review to cluster and create a competitiveness relationship considering a systems dynamics approach. A dynamic hypothesis was constructed to validate what factors increase a cluster’s level of competitiveness, through causal analysis. Then, the causal diagram that validates the dynamic H0 hypothesis was constructed in Vensim PLE systems®. Literature review shows the evolution of the cluster system according to the current needs of the market, and emphasizes the need for new approaches and models that capture the complexity and dynamics of this system, allowing the understanding of its structure and the evaluation of the contribution of factors and capabilities to cluster competitiveness. It highlights the usefulness of systems dynamics as a simulation methodology for dynamic and complex systems, and establishes itself as a growing line of research applied to various systems of study. Dynamic hypothesis H0 was validated using the causal diagram, reaching the conclusion that innovation, productive management, financial management, organizational management, commercial management, and cluster management factors positively increase the cluster competitiveness level. From structure analysis, the behavior is associated to the archetype “Path Dependence”, usual in growing industrial markets.

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Smith, Jeremy C., Pan Tan, Loukas Petridis, and Liang Hong. "Dynamic Neutron Scattering by Biological Systems." Annual Review of Biophysics 47, no. 1 (May 20, 2018): 335–54. http://dx.doi.org/10.1146/annurev-biophys-070317-033358.

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Dynamic neutron scattering directly probes motions in biological systems on femtosecond to microsecond timescales. When combined with molecular dynamics simulation and normal mode analysis, detailed descriptions of the forms and frequencies of motions can be derived. We examine vibrations in proteins, the temperature dependence of protein motions, and concepts describing the rich variety of motions detectable using neutrons in biological systems at physiological temperatures. New techniques for deriving information on collective motions using coherent scattering are also reviewed.

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Qu, Zu-Qing, and R. Panneer Selvam. "Dynamic Superelement Modeling Method for Compound Dynamic Systems." AIAA Journal 38, no. 6 (June 2000): 1078–83. http://dx.doi.org/10.2514/2.1070.

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Journal articles on the topic 'Dynamic Systems'

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