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Journal articles on the topic 'Transition-State model'

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Published: 4 June 2021
Last updated: 18 February 2022

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1

Chandra, Shalini, and Raees Ahmad Khan. "Availability state transition model." ACM SIGSOFT Software Engineering Notes 36, no. 3 (May 5, 2011): 1–3. http://dx.doi.org/10.1145/1968587.1968603.

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2

Moulopoulos, K. "Bound-state transition: an analytical model." Journal of Physics: Condensed Matter 12, no. 7 (February 3, 2000): 1285–96. http://dx.doi.org/10.1088/0953-8984/12/7/312.

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3

Sherrod, Michael J., and F. M. Menger. ""Transition-state modeling" does not always model transition states." Journal of the American Chemical Society 111, no. 7 (March 1989): 2611–13. http://dx.doi.org/10.1021/ja00189a040.

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4

Xiang, Hua, Peilin Jiang, Shuang Xiao, Fuji Ren, and Shingo Kuroiwa. "A Model of Mental State Transition Network." IEEJ Transactions on Electronics, Information and Systems 127, no. 3 (2007): 434–42. http://dx.doi.org/10.1541/ieejeiss.127.434.

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5

Shurki, Avital, and Sason Shaik. "The perfectly resonating state: a chemical model for the transition state." Journal of Molecular Structure: THEOCHEM 424, no. 1-2 (February 1998): 37–45. http://dx.doi.org/10.1016/s0166-1280(97)00223-6.

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6

Sinha, Abhijit, Svetlana Stolpner, Abir Mukherjee, and Simon Monckton. "A Precise State Transition Model for Aircraft Navigation." GEOMATICA 68, no. 4 (December 2014): 283–97. http://dx.doi.org/10.5623/cig2014-403.

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This article considers the problem of accurately modeling the kinematic state transition of an Unmanned Aerial Vehicle (UAV). The full 3D range of motion is accurately captured using compact equations for position update derived in this work. This derivation makes use of the independence of the rotation and translation components of a 3D rigid motion. The proposed motion model is transparent to the sensors used in the system; it is particularly useful in GPS-denied environments and can contribute to different aspects of robust navigation, such as accurate state estimation, sensor fault tolerance and sensor bias estimation. Experimental results comparing the performance of the proposed kinematic model with those typically used demonstrate its superiority.
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7

Chung, In-Sang. "Fault Tree Analysis based on State-Transition Model." Journal of the Korea Contents Association 11, no. 10 (October 28, 2011): 49–58. http://dx.doi.org/10.5392/jkca.2011.11.10.049.

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8

Tian, Z., and K. A. Hoo. "Transition control using a state-shared model approach." Computers & Chemical Engineering 27, no. 11 (November 2003): 1641–56. http://dx.doi.org/10.1016/s0098-1354(03)00131-5.

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9

Ramachandran, M. P. "Approximate State Transition Matrix and Secular Orbit Model." International Journal of Aerospace Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/475742.

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The state transition matrix (STM) is a part of the onboard orbit determination system. It is used to control the satellite’s orbital motion to a predefined reference orbit. Firstly in this paper a simple orbit model that captures the secular behavior of the orbital motion in the presence of all perturbation forces is derived. Next, an approximate STM to match the secular effects in the orbit due to oblate earth effect and later in the presence of all perturbation forces is derived. Numerical experiments are provided for illustration.
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10

Tanaka, T. "The state transition model of the substorm onset." Journal of Geophysical Research: Space Physics 105, A9 (September 1, 2000): 21081–96. http://dx.doi.org/10.1029/2000ja900061.

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11

PEDNAULT, EDWIN P. D. "ADL and the State-Transition Model of Action." Journal of Logic and Computation 4, no. 5 (1994): 467–512. http://dx.doi.org/10.1093/logcom/4.5.467.

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12

Lafortune, Stéphane, and Eugene Wong. "A state transition model for distributed query processing." ACM Transactions on Database Systems 11, no. 3 (August 1986): 294–322. http://dx.doi.org/10.1145/6314.6460.

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13

Ariel, G., and E. Vanden-Eijnden. "Testing Transition State Theory on Kac-Zwanzig Model." Journal of Statistical Physics 126, no. 1 (January 3, 2007): 43–73. http://dx.doi.org/10.1007/s10955-006-9165-0.

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14

Komori, Yasuhiro. "Recognizing speech data using a state transition model." Journal of the Acoustical Society of America 115, no. 5 (2004): 1874. http://dx.doi.org/10.1121/1.1757185.

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15

Lukicheva, I. A., and A. L. Kulikov. "Multi-model power system state estimation based on linear transition models." Vestnik IGEU, no. 1 (February 28, 2021): 31–40. http://dx.doi.org/10.17588/2072-2672.2021.1.031-040.

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Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.
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16

Orowe, Idah. "Three-state transition model with left censoring in vertical transition of HIV." Applied Mathematical Sciences 14, no. 4 (2020): 155–70. http://dx.doi.org/10.12988/ams.2020.912168.

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17

Sinclair, Steve J., Tara Zamin, Paul Gibson-Roy, Joshua Dorrough, Nathan Wong, Vanessa Craigie, Georgia E. Garrard, and Joslin L. Moore. "A state-and-transition model to guide grassland management." Australian Journal of Botany 67, no. 5 (2019): 437. http://dx.doi.org/10.1071/bt18167.

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Grassland ecosystems across the globe have been extensively modified and degraded by agriculture and urban development, leaving conservation managers with a complex set of interacting legacies and opportunities to contend with. We advocate the use of state-and-transition models to assist conservation managers to deal with this complexity. Using a major development and compensation project as a case study (The Melbourne Strategic Assessment under the Australian Environment Protection and Biodiversity Conservation Act 1999), we discuss the uses and limitations of state-and-transition models for conservation management. We define a state-and-transition model for an endangered Australian temperate grassland. Soil and vegetation data are used to evaluate the model and confirm that the assigned states relate to observable agro-ecological patterns. We then discuss the use of this model for several different interacting purposes: as a tool for the simple communication of complex ecological processes; as a tool for landscape stratification to aid the spatial application of management and experimentation; as a framework to set and define conservation objectives; and as an aide for adaptive management.
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18

Krijkamp, Eline M., Fernando Alarid-Escudero, Eva A. Enns, Petros Pechlivanoglou, M. G. Myriam Hunink, Alan Yang, and Hawre J. Jalal. "A Multidimensional Array Representation of State-Transition Model Dynamics." Medical Decision Making 40, no. 2 (January 28, 2020): 242–48. http://dx.doi.org/10.1177/0272989x19893973.

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Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.
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19

Goryachev, E. G., and E. V. Kuzmin. "Ferromagnetic-paramagnetic state transition in the ground state of the Hubbard model." Physics Letters A 131, no. 7-8 (September 1988): 481–85. http://dx.doi.org/10.1016/0375-9601(88)90306-4.

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20

Stanfield, James Ronald, Richard B. Freeman, Robert Topel, and Birgitta Swedenborg. "The Welfare State in Transition: Reforming the Swedish Model." Southern Economic Journal 65, no. 4 (April 1999): 972. http://dx.doi.org/10.2307/1061293.

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21

Himmelstrand, Ulf, Richard B. Freeman, Robert Topel, and Birgitta Swedenborg. "The Welfare State in Transition: Reforming the Swedish Model." Industrial and Labor Relations Review 53, no. 1 (October 1999): 158. http://dx.doi.org/10.2307/2696171.

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22

Amyes, Tina L., and John P. Richard. "Specificity in Transition State Binding: The Pauling Model Revisited." Biochemistry 52, no. 12 (February 4, 2013): 2021–35. http://dx.doi.org/10.1021/bi301491r.

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23

Nilsson, A., K. E. Arzén, and T. F. Petti. "Model-Based Diagnosis - State Transition Events and Constraint Equations." IFAC Proceedings Volumes 25, no. 10 (June 1992): 359–64. http://dx.doi.org/10.1016/s1474-6670(17)50847-2.

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24

Nilsson, A., K. E. Arzén, and T. F. Petti. "Model-based diagnosis-state transition events and constraint equations." Annual Review in Automatic Programming 17 (January 1992): 359–64. http://dx.doi.org/10.1016/s0066-4138(09)91059-x.

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25

Saito, Hiroshi, Ken Takiyama, and Masato Okada. "Estimation of State Transition Probabilities: A Neural Network Model." Journal of the Physical Society of Japan 84, no. 12 (December 15, 2015): 124801. http://dx.doi.org/10.7566/jpsj.84.124801.

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26

Xue-Li, Wang, Wang Chuan-Hui, Tian Zhao-Ming, Yin Shi-Yan, and Yuan Song-Liu. "First-Principles Based Model of Spin-state Phase Transition." Chinese Physics Letters 27, no. 10 (October 2010): 107101. http://dx.doi.org/10.1088/0256-307x/27/10/107101.

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27

Niewieczerzał, Szymon, and Marek Cieplak. "The folding transition state theory in simple model systems." Journal of Physics: Condensed Matter 20, no. 24 (May 29, 2008): 244134. http://dx.doi.org/10.1088/0953-8984/20/24/244134.

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28

Lenevich, Stepan, Juhua Xu, Ayako Hosokawa, Christopher J. Cramer, and Mark D. Distefano. "Transition State Analysis of Model and Enzymatic Prenylation Reactions." Journal of the American Chemical Society 129, no. 18 (May 2007): 5796–97. http://dx.doi.org/10.1021/ja069119j.

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29

Wang, H., L. Capolungo, B. Clausen, and C. N. Tomé. "A crystal plasticity model based on transition state theory." International Journal of Plasticity 93 (June 2017): 251–68. http://dx.doi.org/10.1016/j.ijplas.2016.05.003.

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30

Richardson, John, and Margaret R. Zarnosky. "A state transition model of United States congressional information." Journal of Government Information 21, no. 1 (January 1994): 25–35. http://dx.doi.org/10.1016/1352-0237(94)90038-8.

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31

Martinec, Tomislav, Stanko Škec, Nikola Horvat, and Mario Štorga. "A state-transition model of team conceptual design activity." Research in Engineering Design 30, no. 1 (January 2019): 103–32. http://dx.doi.org/10.1007/s00163-018-00305-1.

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32

Zhou, Yi-zhou, Yi Ren, Lin-lin Liu, Zheng Ma, and Zi-li Wang. "Binary logic state transition oriented formal general reliability model." Journal of Shanghai Jiaotong University (Science) 20, no. 4 (July 30, 2015): 482–88. http://dx.doi.org/10.1007/s12204-015-1654-3.

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33

Kloor, H., and J. M. Honig. "Quantum State Model of the Verwey Transition in Magnetite." Journal of Solid State Chemistry 148, no. 1 (November 1999): 135–42. http://dx.doi.org/10.1006/jssc.1999.8398.

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34

Schneidewind, Norman. "Investigation of State Transition Model for Predicting Software Reliability." Journal of Aerospace Computing, Information, and Communication 4, no. 7 (July 2007): 918–32. http://dx.doi.org/10.2514/1.31984.

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35

Devaraj, Vimalathithan, and Biplab Bose. "Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition." Journal of Clinical Medicine 8, no. 7 (June 26, 2019): 911. http://dx.doi.org/10.3390/jcm8070911.

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Epithelial to Mesenchymal Transition (EMT) is a multi-state process. Here, we investigated phenotypic state transition dynamics of Epidermal Growth Factor (EGF)-induced EMT in a breast cancer cell line MDA-MB-468. We have defined phenotypic states of these cells in terms of their morphologies and have shown that these cells have three distinct morphological states—cobble, spindle, and circular. The spindle and circular states are the migratory phenotypes. Using quantitative image analysis and mathematical modeling, we have deciphered state transition trajectories in different experimental conditions. This analysis shows that the phenotypic state transition during EGF-induced EMT in these cells is reversible, and depends upon the dose of EGF and level of phosphorylation of the EGF receptor (EGFR). The dominant reversible state transition trajectory in this system was cobble to circular to spindle to cobble. We have observed that there exists an ultrasensitive on/off switch involving phospho-EGFR that decides the transition of cells in and out of the circular state. In general, our observations can be explained by the conventional quasi-potential landscape model for phenotypic state transition. As an alternative to this model, we have proposed a simpler discretized energy-level model to explain the observed state transition dynamics.
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36

Williams, Ian H. "Catalysis: transition-state molecular recognition?" Beilstein Journal of Organic Chemistry 6 (November 3, 2010): 1026–34. http://dx.doi.org/10.3762/bjoc.6.117.

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The key to understanding the fundamental processes of catalysis is the transition state (TS): indeed, catalysis is a transition-state molecular recognition event. Practical objectives, such as the design of TS analogues as potential drugs, or the design of synthetic catalysts (including catalytic antibodies), require prior knowledge of the TS structure to be mimicked. Examples, both old and new, of computational modelling studies are discussed, which illustrate this fundamental concept. It is shown that reactant binding is intrinsically inhibitory, and that attempts to design catalysts that focus simply upon attractive interactions in a binding site may fail. Free-energy changes along the reaction coordinate for SN2 methyl transfer catalysed by the enzyme catechol-O-methyl transferase are described and compared with those for a model reaction in water, as computed by hybrid quantum-mechanical/molecular-mechanical molecular dynamics simulations. The case is discussed of molecular recognition in a xylanase enzyme that stabilises its sugar substrate in a (normally unfavourable) boat conformation and in which a single-atom mutation affects the free-energy of activation dramatically.
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37

Xu, Bin, Naohide Yamagishi, Makoto Suzuki, and Masayuki Goto. "Language-Independent Word Acquisition Method Using a State-Transition Model." Industrial Engineering and Management Systems 15, no. 3 (September 30, 2016): 224–30. http://dx.doi.org/10.7232/iems.2016.15.3.224.

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38

Huang, Yong Sheng, and Hua Mei Du. "State Transition Model of Green Logistical Network in Manufacturing Engineering." Applied Mechanics and Materials 340 (July 2013): 255–58. http://dx.doi.org/10.4028/www.scientific.net/amm.340.255.

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With the developing of the technologies in the field of the Internet of things, it is much possible to achieve more information about things scattered in a certain zone. Based on the Internet of things, the information zing and modelling of the logistical network are efficient methods for logistical services. In this paper, an approach for the state transition model of logistical network is put forward, in which the sets of key elements and their states, and as well as the correlations between and among the key elements with certain states are used as components to express the states and states transition of the logistical network.
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39

Ema, Tadashi, Ryoichi Okada, Minoru Fukumoto, Masahito Jittani, Mikiko Ishida, Kenji Furuie, Kunihiro Yamaguchi, Takashi Sakai, and Masanori Utaka. "Transition-state model for subtilisin-catalyzed transesterifications of secondary alcohols." Tetrahedron Letters 40, no. 23 (June 1999): 4367–70. http://dx.doi.org/10.1016/s0040-4039(99)00750-9.

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40

Cateau, Hideyuki, and Shigeru Tanaka. "1301 A state-transition model for LTP and LTD induction." Neuroscience Research 28 (January 1997): S166. http://dx.doi.org/10.1016/s0168-0102(97)90447-8.

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41

FUJII, Shinji, Kei SENDA, and Syusuke MANO. "Acceleration of Reinforcement Learning by Estimating State Transition Probability Model." Transactions of the Society of Instrument and Control Engineers 42, no. 1 (2006): 47–53. http://dx.doi.org/10.9746/sicetr1965.42.47.

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42

Corey, E. J., David Barnes-Seeman, Thomas W. Lee, and Steven N. Goodman. "A transition-state model for the mikami enantioselective ene reaction." Tetrahedron Letters 38, no. 37 (September 1997): 6513–16. http://dx.doi.org/10.1016/s0040-4039(97)01517-7.

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43

de Araújo, João M., Cesar I. N. Sampaio Filho, and Francisco G. B. Moreira. "Noise induced phase transition in theS-state block voter model." Physica A: Statistical Mechanics and its Applications 508 (October 2018): 642–49. http://dx.doi.org/10.1016/j.physa.2018.05.133.

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44

Xiaolan, Peng, Xie Lun, Liu Xin, and Wang Zhiliang. "Emotional state transition model based on stimulus and personality characteristics." China Communications 10, no. 6 (June 2013): 146–55. http://dx.doi.org/10.1109/cc.2013.6549266.

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45

López, Dardo R., L. Cavallero, M. A. Brizuela, and M. R. Aguiar. "Ecosystemic structural-functional approach of the state and transition model." Applied Vegetation Science 14, no. 1 (January 14, 2011): 6–16. http://dx.doi.org/10.1111/j.1654-109x.2010.01095.x.

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46

Greenhill, Paul G., and Robert G. Gilbert. "Recombination reactions: variational transition state theory and the Gorin model." Journal of Physical Chemistry 90, no. 14 (July 1986): 3104–6. http://dx.doi.org/10.1021/j100405a014.

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47

Zhou, Carol L. Ecale. "S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition." Bioinformatics and Biology Insights 10 (January 2016): BBI.S38194. http://dx.doi.org/10.4137/bbi.s38194.

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48

Sintov, Avishai, Andrew S. Morgan, Andrew Kimmel, Aaron M. Dollar, Kostas E. Bekris, and Abdeslam Boularias. "Learning a State Transition Model of an Underactuated Adaptive Hand." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 1287–94. http://dx.doi.org/10.1109/lra.2019.2894875.

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49

Chen, Danmei, and Z. R. Yang. "Kinetic phase transition of the three-state-vector Potts model." Physical Review B 49, no. 17 (May 1, 1994): 11910–14. http://dx.doi.org/10.1103/physrevb.49.11910.

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50

Yuan, Xiao-Ping, and Zhi-Gang Zheng. "Ground-State Transition in a Two-Dimensional Frenkel—Kontorova Model." Chinese Physics Letters 28, no. 10 (October 2011): 100507. http://dx.doi.org/10.1088/0256-307x/28/10/100507.

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