Готові списки джерел за темами / Biological dynamic

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Biological dynamic"

Автор: Grafiati
Опубліковано: 11 березня 2023

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Статті в журналах з теми "Biological dynamic"

1

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 and so on. Also introduce the rules of these models, which provide theoretical basis for designing intelligent swarm intelligent system.
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2

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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3

Campelo, F., and A. Hernández-Machado. "Dynamic instabilities in biological membranes." PAMM 7, no. 1 (December 2007): 1121403–4. http://dx.doi.org/10.1002/pamm.200700341.

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4

Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, and Bo Xu. "Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 59–67. http://dx.doi.org/10.1609/aaai.v36i1.19879.

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With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neurons with complex dynamics and learning paradigms with biologically plausible plasticity principles. Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. The population coding at the network scale is integrated with the dynamic neurons coding (containing 2nd-order neuronal dynamics) at the neuron scale towards a powerful spatial-temporal state representation. Extensive experimental results show that our MDC-SAN performs better than its counterpart deep actor network (based on DNNs) on four continuous control tasks from OpenAI gym. We think this is a significant attempt to improve SNNs from the perspective of efficient coding towards effective decision-making, just like that in biological networks.
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5

Gusain, Pooja, Neha Sharma, Tsuyoshi Yoda, and Masahiro Takagi. "1P220 Dynamic Response of Menthol on Thermo-Induced Cell Membrane: More than Receptors(13B. Biological & Artifical membrane: Dynamics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S142. http://dx.doi.org/10.2142/biophys.53.s142_3.

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6

Kinugasa, Tetsuya, and Yasuhiro Sugimoto. "Dynamically and Biologically Inspired Legged Locomotion: A Review." Journal of Robotics and Mechatronics 29, no. 3 (June 20, 2017): 456–70. http://dx.doi.org/10.20965/jrm.2017.p0456.

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[abstFig src='/00290003/01.jpg' width='300' text='Passive dynamic walking: RW03 and Jenkka III' ] Legged locomotion, such as walking, running, turning, and jumping depends strongly on the dynamics and the biological characteristics of the body involved. Gait patterns and energy efficiency, for instance, are known to be greatly affected, not only by travel speed and ground contact conditions but also by body structure such as joint stiffness and coordination, and foot sole shape. To understand legged locomotion principles, we must elucidate how the body’s dynamic and biological characteristics affect locomotion. Efforts should also be made to incorporate these characteristics inventively in order to improve locomotion performance with regard to robustness, adaptability, and efficiency, which realize more refined legged locomotion. For this special issue, we invited our readers to submit papers with approaches to achieving legged locomotion based on dynamic and biological characteristics and studies investigating the effects of these characteristics. In this paper, we review studies on dynamically and biologically inspired legged locomotion.
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Kinugasa, Tetsuya, Koh Hosoda, Masatsugu Iribe, Fumihiko Asano, and Yasuhiro Sugimoto. "Special Issue on Dynamically and Biologically Inspired Legged Locomotion." Journal of Robotics and Mechatronics 29, no. 3 (June 20, 2017): 455. http://dx.doi.org/10.20965/jrm.2017.p0455.

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Legged locomotion, including walking, running, turning, and jumping, strongly depends on the dynamics and biological characteristics of the body involved. Gait patterns and energy efficiency, for example, are known to be greatly affected by not only travel velocity and ground contact conditions but also by body configuration, such as joint stiffness and coordination, as well as foot sole shape. To understand legged locomotion principles, we must clarify how the body’s dynamic and biological characteristics affect locomotion. Effort must also be made to incorporate these characteristics inventively to improve locomotion performance, such as robustness, adaptability, and efficiency, which further refine the legged locomotion. This special issue on “Dynamically and Biologically Inspired Legged Locomotion,” studies on legged locomotion based on dynamic and biological characteristics, covers a wide range of themes, such as a rimless wheel, a design method for a biped based on passive dynamic walking, the analysis of biped locomotion based on passive dynamic walking and dynamically inspired walking, an analysis of gait generation for a triped robot, and quadruped locomotion with a flexible trunk. Since there are interesting papers on legged robots with different numbers of legs, we basically organized the papers based on the number of legs. Studies on “Dynamically and Biologically Inspired Legged Locomotion” are expected to not only realize and improve legged locomotion as engineering, but also to reveal the locomotion mechanism of various creatures as science.
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Marigo, Alessia, and Benedetto Piccoli. "A model for biological dynamic networks." Networks & Heterogeneous Media 6, no. 4 (2011): 647–63. http://dx.doi.org/10.3934/nhm.2011.6.647.

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Wu, Wu, Feng Wang, and Maw Chang. "Dynamic sensitivity analysis of biological systems." BMC Bioinformatics 9, Suppl 12 (2008): S17. http://dx.doi.org/10.1186/1471-2105-9-s12-s17.

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Cushing, J. M. "Dynamic energy budgets in biological systems." Mathematical Biosciences 137, no. 2 (October 1996): 135–37. http://dx.doi.org/10.1016/s0025-5564(96)00047-8.

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Дисертації з теми "Biological dynamic"

1

McGregor, Juliette Elizabeth. "Imaging dynamic biological processes." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609205.

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2

Reichenbach, Tobias. "Dynamic patterns of biological systems." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-84101.

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Magi, Ross. "Dynamic behavior of biological membranes." Thesis, The University of Utah, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680576.

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Biological membranes are important structural units in the cell. Composed of a lipid bilayer with embedded proteins, most exploration of membranes has focused on the proteins. While proteins play a vital role in membrane function, the lipids themselves can behave in dynamic ways which affect membrane structure and function. Furthermore, the dynamic behavior of the lipids can affect and be affected by membrane geometry. A novel fluid membrane model is developed in which two different types of lipids flow in a deforming membrane, modelled as a two-dimensional Riemannian manifold that resists bending. The two lipids behave like viscous Newtonian fluids whose motion is determined by realistic physical forces. By examining the stability of various shapes, it is shown that instability may result if the two lipids forming the membrane possess biophysical qualities, which cause them to respond differently to membrane curvature. By means of numerical simulation of a simplified model, it is shown that this instability results in curvature induced phase separation. Applying the simplified model to the Golgi apparatus, it is hypothesized that curvature induced phase separation may occur in a Golgi cisterna, aiding in the process of protein sorting.

In addition to flowing tangentially in the membrane, lipids also flip back and forth between the two leaflets in the bilayer. While traditionally assumed to occur very slowly, recent experiments have indicated that lipid flip-flop may occur rapidly. Two models are developed that explore the effect of rapid flip-flop on membrane geometry and the effect of a pH gradient on the distribution of charged lipids in the leaflets of the bilayer. By means of a stochastic model, it is shown that even the rapid flip-flop rates observed are unlikely to be significant inducers of membrane curvature. By means of a nonlinear Poisson- Boltzmann model, it is shown that pH gradients are unlikely to be significant inducers of bilayer asymmetry under physiological conditions.

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4

Waheed, Qaiser. "Molecular Dynamic Simulations of Biological Membranes." Doctoral thesis, KTH, Teoretisk biologisk fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102268.

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Biological membranes mainly constituent lipid molecules along with some proteins and steroles. The properties of the pure lipid bilayers as well as in the presence of other constituents (in case of two or three component systems) are very important to be studied carefully to model these systems and compare them with the realistic systems. Molecular dynamic simulations provide a good opportunity to model such systems and to study them at microscopic level where experiments fail to do. In this thesis we study the structural and dynamic properties of the pure phospholipid bilayers and the phase behavior of phospholipid bilayers when other constituents are present in them. Material and structural properties like area per lipid and area compressibility of the phospholipids show a big scatter in experiments. These properties are studied for different system sizes and it was found that the increasing undulations in large systems effect these properties. A correction was applied to area per lipid and area compressibility using the Helfrich theory in Fourier space. Other structural properties like order of the lipid chains, electron density and radial distribution functions are calculated which give the structure of the lipid bilayer along the normal and in the lateral direction. These properties are compared to the X-ray and neutron scattering experiments after Fourier transform. Thermodynamic properties like heat capacity and heat of melting are also calculated from derivatives of energies available in molecular dynamics. Heat capacity on the other hand include quantum effect and are corrected for that by applying quantum correction using normal mode analysis for a simple as well as ambiguous system like water. Here it is done for SPC/E water model. The purpose of this study is to further apply the quantum corrections on macromolecules like lipids by using this technique. Furthermore the phase behavior of two component systems (phospholipids/cholesterol) is also studied. Phase transition in these systems is observed at different cholesterol concentrations as a function of temperature by looking at different quantities (as an order parameter) like the order of chains, area per molecule and partial specific area. Radial distribution functions are used to look at the in plane structure for different phases having a different lateral or positional order. Adding more cholesterol orders the lipid chains changing a liquid disordered system into a liquid ordered one and turning a solid ordered system into a liquid ordered one. Further more the free energy of domain formation is calculated to investigate the two phasecoexistence in binary systems. Free energy contains two terms. One is bulk freeenergy which was calculated by the chemical potential of cholesterol moleculein a homogeneous system which is favorable for segregation. Second is thefree energy of having an interface which is calculated from the line tension of the interface of two systems with different cholesterol concentration which in unfavorable for domain formation. The size of the domains calculated from these two contributions to the free energy gives the domains of a few nm in size. Though we could not find any such domains by directly looking at our simulations.

QC 20120913

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Jones, E. Y. "Structural and dynamic studies on biological macromolecules." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371551.

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6

Abul-Haija, Yousef Mustafa Yousef. "Dynamic supramolecular hydrogels with adaptive biological functionality." Thesis, University of Strathclyde, 2015. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25997.

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Bunyapaiboonsri, Taridaporn. "Dynamic combinatorial chemistry : Exploration using biological receptors." Université Louis Pasteur (Strasbourg) (1971-2008), 2003. http://www.theses.fr/2003STR13065.

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La chimie combinatoire dynamique a été récemment introduite comme une approche nouvelle et attractive pour générer et cribler un grand nombre de bibliothèques de composés en une seule étape. Basé sur l'interconnexion réversible entre les composés de la bibliothèque, le processus d'auto-ajustement donne accès à la sélection et à l'amplification du meilleur inhibiteur en présence d'une cible. Au cours de cette thèse, nous avons choisi deux cibles biologiques qui nous ont permis d'explorer l'approche de la chimie combinatoire dynamique. La réversibilité du système a été rendue possible en utilisant l'échange de disulfures ou la formation réversible d'acyl hydrazones. Premièrement, une bibliothèque dynamique d'inhibiteurs d'acétylcholinestérase a été générée grâce à l'échange de disulfures. Nous avons observé la réversibilité du système à l'aide de la spectroscopie de RMN. A partir d'un mélange initial de 5 homodisulfures en présence d'un agent réducteur, une bibliothèque contenant 15 composantes a été obtenue. Les composantes de cette bibliothèque ont été mises en évidence par SM-ES et par EC. Deuxièmement, une bibliothèque combinatoire dynamique d'inhibiteurs d'acétylcholinesterase a été générée en se basant sur la formation réversible d'acyl-hydrazones. Le processus pré-équilibré a été utilisé pour obtentir d'une bibliothèque dynamique composée de 66 espèces possibles, à partir de 13 unités de bases. Nous avons ensuite identifié l'inhibiteur très puissant (IC50 et Ki de l'ordre de nM), en utilisant la méthode de la déconvolution dynamique. Finalement, le processus pré-équilibré combiné à la technique de la déconvolution dynamique a été employé pour identifier les inhibiteurs de la HPr kinase/phosphatase. Ainsi, nous avons pu préparer une bibliothèque dynamique constituée de 440 composés possibles, en une seule étape, à partir de 21 unités de bases. Le ligand hétérocyclique bis-cationique s'est révélé un inhibiteur relativement puissant (IC50 de l'ordre de mM)
Dynamic combinatorial chemistry (DCC) has recently been introduced as a new and attractive approach for generating and screening large numbers of library compounds in one step. Based upon the reversible interconnection between library components, the self-adjusting process give access to selection and amplification of the best binder in the presence of a target. In this thesis, two biological targets were chosen to explore the DCC approach. The reversibility of the system was achieved using disulfide interchange or reversible acyl hydrazone formation. Firstly, a dynamic library of acetylcholinesterase inhibitors was generated through disulfide exchange. The reversibility of the system was observed by NMR spectroscopy. Upon scrambling 5 initial homodisulfides in the presence of a reducing agent, a 15-compound library was produced. The library components were analyzed by ESI-MS and CE. Secondly, a dynamic combinatorial library of acetylcholinesterase inhibitors was further generated through reversible acyl hydrazone formation. The pre-equilibrated process was applied to produce a dynamic library composed of 66 possible species, from a set of 13 initial aldehyde and hydrazide building blocks. Using a technique called dynamic deconvolution, a highly potent inhibitor was identified with IC50 in the nanomolar range. Finally, the pre-equilibrated process combined with the dynamic deconvolution technique was further studied to identify HPr kinase/phosphatase inhibitors. From a set of 21 initial aldehyde and hydrazide builiding blocks, a dynamic library of 440 possible compounds was formed in one operation. A bis-cationic heterocyclic ligand was identified as a relatively potent inhibitor, displaying an IC50 in the micromolar range
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8

Romanel, Alessandro. "Dynamic Biological Modelling: a language-based approach." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/368272.

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Systems biology investigates the interactions and relationships among the components of biological systems to understand how they globally work. The metaphor “cells as computations†, introduced by Regev and Shapiro, opened the realm of biological modelling to concurrent languages. Their peculiar characteristics led to the development of many different bio-inspired languages that allow to abstract and study specific aspects of biological systems. In this thesis we present a language based on the process calculi paradigm and specifically designed to account for the complexity of signalling networks. We explore a new design space for bio-inspired languages, with the aim to capture in an intuitive and simple way the fundamental mechanisms governing protein-protein interactions. We develop a formal framework for modelling, simulating and analysing biological systems. An implementation of the framework is provided to enable in-silico experimentation.
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9

Cavallo, Antonio. "Four dimensional particle tracking in biological dynamic processes." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964904667.

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Lewis, Mark A. "Analysis of dynamic and stationary biological pattern formation." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276976.

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Книги з теми "Biological dynamic"

1

Aon, M. A., and S. Cortassa. Dynamic Biological Organization. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2.

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2

Hannon, Bruce, and Matthias Ruth. Modeling Dynamic Biological Systems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05615-9.

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3

Ruth, Matthias, and Bruce Hannon. Modeling Dynamic Biological Systems. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0651-4.

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Matthias, Ruth, ed. Modeling dynamic biological systems. New York: Springer, 1997.

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5

R, Carson Ewart, ed. Mathematical modelling of dynamic biological systems. 2nd ed. Letchworth, Hertfordshire, England: Research Studies Press, 1985.

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6

Rao, Vadrevu Sree Hari, and Ponnada Raja Sekhara Rao. Dynamic Models and Control of Biological Systems. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0359-4.

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M, Harris-Warrick Ronald, ed. Dynamic biological networks: The stomatogastric nervous system. Cambridge, Mass: MIT Press, 1992.

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8

Rao, Vadrevu Sree Hari. Dynamic models and control of biological systems. Dordrecht: Springer, 2009.

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9

S. A. L. M. Kooijman. Dynamic energy and mass budgets in biological systems. 2nd ed. Cambridge, UK: Cambridge University Press, 2000.

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10

Aon, M. A. Dynamic biological organization: Fundamentals as applied to cellular systems. London: Chapman & Hall, 1997.

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Частини книг з теми "Biological dynamic"

1

Bloomfield, Victor A. "Biological Applications." In Dynamic Light Scattering, 363–416. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2389-1_10.

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Aon, M. A., and S. Cortassa. "General concepts." In Dynamic Biological Organization, 3–43. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_1.

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Aon, M. A., and S. Cortassa. "Spatio-temporal coordination of cellular energetics and metabolism during development." In Dynamic Biological Organization, 361–90. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_10.

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Aon, M. A., and S. Cortassa. "Cell growth and differentiation from the perspective of dynamics and thermodynamics of cellular and subcellular processes." In Dynamic Biological Organization, 391–429. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_11.

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Aon, M. A., and S. Cortassa. "Dynamic coupling and spatio–temporal coherence in cellular systems." In Dynamic Biological Organization, 430–84. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_12.

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Aon, M. A., and S. Cortassa. "Conclusions and outlook: models, facts and biocomplexity." In Dynamic Biological Organization, 485–97. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_13.

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Aon, M. A., and S. Cortassa. "Dynamic organization in cellular systems." In Dynamic Biological Organization, 44–72. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_2.

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Aon, M. A., and S. Cortassa. "Rhythms as a fundamental property of biological systems." In Dynamic Biological Organization, 73–103. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_3.

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Aon, M. A., and S. Cortassa. "Symmetry in dynamic biological organization." In Dynamic Biological Organization, 104–44. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_4.

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Aon, M. A., and S. Cortassa. "Dynamic organization in biologically oriented artificial systems." In Dynamic Biological Organization, 145–76. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5828-2_5.

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Тези доповідей конференцій з теми "Biological dynamic"

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Kozhevnikov, Nikolai M. "Biological materials for dynamic holography." In International Conference on Advanced Optical Materials and Devices, edited by Edgar A. Silinsh, Arthur Medvids, Andrejs R. Lusis, and Andris O. Ozols. SPIE, 1997. http://dx.doi.org/10.1117/12.266850.

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Olivo-Marin, Jean-Christophe. "MOVIE CRUNCHING IN BIOLOGICAL DYNAMIC IMAGING." In Proceedings of the Conference CSB 2006. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2006. http://dx.doi.org/10.1142/9781860947575_0007.

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Ay, Ferhat, Thang N. Dinh, My T. Thai, and Tamer Kahveci. "Finding Dynamic Modules of Biological Regulatory Networks." In 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.31.

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Shaked, Natan T., Matthew T. Rinehart, and Adam Wax. "Dynamic Quantitative Phase Microscopy of Biological Cells." In Conference on Lasers and Electro-Optics. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/cleo.2009.cfa4.

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Wu, Cheng-Tao, Shinq-Jen Wu, and Jyh-Yeong Chang. "Inverse Aspect of Optimization for Dynamic Biological Pathway." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.146.

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Tahmassebi, Amirhessam, Behshad Mohebali, Lisa Meyer-Baese, Philip Philip Solimine, Katja Pinker, and Anke Meyer-Baese. "Determining driver nodes in dynamic signed biological networks." In Smart Biomedical and Physiological Sensor Technology XVI, edited by Brian M. Cullum, Eric S. McLamore, and Douglas Kiehl. SPIE, 2019. http://dx.doi.org/10.1117/12.2519550.

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Wang, Charles C. N., David A. Hecht, Han C. W. Hsiao, Phillip C. Y. Sheu, and Jeffrey J. P. Tsai. "Describing Dynamic Biological Systems in SPDL and SCDL." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.56.

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Sendra, G. H., J. C. Salerno, C. Weber, H. J. Rabal, R. Arizaga, and M. Trivi. "Biological specimens analysis using dynamic speckle spectral bands." In Optical Metrology, edited by Heidi Ottevaere, Peter DeWolf, and Diederik S. Wiersma. SPIE, 2005. http://dx.doi.org/10.1117/12.612606.

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Afonina, S., A. Rondi, D. Kiselev, L. Bonacina, and J. P. Wolf. "Label free optimal dynamic discrimination of biological macromolecules." In SPIE LASE, edited by Alexander Heisterkamp, Peter R. Herman, Michel Meunier, and Stefan Nolte. SPIE, 2013. http://dx.doi.org/10.1117/12.2002467.

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Rastgoftar, Hossein, and Suhada Jayasuriya. "Alignment as Biological Inspiration for Control of Multi Agent Systems." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6141.

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In this paper, we develop a framework for evolution of a multi agent systems (MAS) under local perception. The idea of this paper comes from natural biological swarms where agents adjust their behavior based on individual perception of the behavior of its neighbors. Most available engineered swarms rely on local communication where an individual agent needs exact state information of its adjacent agents to evolve. We consider agents of a MAS to be particles of a continuum (deformable Body) transforming under a homogenous mapping. Homogenous transformations have the property that two crossing straight lines in an initial configuration translate as two different crossing straight lines. We will consider this feature of homogenous mappings to show how certain desired objectives can be achieved by agents of a swarm by preserving alignment among nearby agents. We show that evolution of a MAS under this alignment strategy can be achieved where agents don’t need to know the exact positions of the adjacent agents nor do they need peer to peer communication.
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Звіти організацій з теми "Biological dynamic"

1

Rabitz, Herschel, and Robert Levis. MURI: Optimal Quantum Dynamic Discrimination of Chemical and Biological Agents. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada498514.

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2

Cummings, Molly E. Biological Response to the Dynamic Spectral-Polarized Underwater Light Field. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada541131.

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Cummings, Molly E., Samir Ahmed, Heidi Dierssen, Alexander Gilerson, William F. Gilly, George Kattawar, Brad Seibel, and James Sullivan. Biological Response to the Dynamic Spectral-Polarized Underwater Light Field. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598460.

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4

Cummings, Molly E. Biological Response to the Dynamic Spectral-Polarized Underwater Light Field. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557141.

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5

Timlin, Jerilyn Ann, Howland D. T. Jones, Aaron M. Collins, Anne M. Ruffing, Kylea Joy Parchert, Christine Alexandra Trahan, Omar Fidel Garcia, et al. From benchtop to raceway : spectroscopic signatures of dynamic biological processes in algal communities. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1055623.

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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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Ahring, Birgitte K., Nitin S. Baliga, James R. Frederickson, Samuel Kaplan, Himadri B. Pakrasi, Joel G. Pounds, Imran shah, et al. Biological Interactions and Dynamics Science Theme Advisory Panel (BID-STAP). Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1089109.

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Zurada, Jacek M., Andy G. Lozowski, and Mykola Lysetskiy. Modeling of Spatial and Temporal Dynamics in Biological Olfactory Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada472796.

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Singh, Rajesh, Marshall Richmond, Pedro Romero-Gomez, Cynthia Rakowski, and John Serkowski. Validation of Computational Fluid Dynamics Simulations for Biological Performance Assessment in Hydropower units. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1798166.

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