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Journal articles on the topic 'Multistep processes'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Hoyer, Patrick. "Multistep replication processes." Current Opinion in Colloid & Interface Science 3, no. 2 (1998): 160–65. http://dx.doi.org/10.1016/s1359-0294(98)80009-5.

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2

Nikol’skii, M. S. "On Linear Multistep Controlled Processes." Moscow University Computational Mathematics and Cybernetics 44, no. 4 (2020): 185–89. http://dx.doi.org/10.3103/s0278641920040032.

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3

Hodgson, P. E. "Multistep processes in nuclear reactions." Acta Physica Hungarica A) Heavy Ion Physics 2, no. 3-4 (1995): 175–97. http://dx.doi.org/10.1007/bf03055106.

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4

Avrigeanu, M., A. N. Antonov, H. Lenske, and I. Şteţcu. "Effective interactions for multistep processes." Nuclear Physics A 693, no. 3-4 (2001): 616–29. http://dx.doi.org/10.1016/s0375-9474(01)00810-7.

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5

Hodgson, P. E. "Multistep processes in nuclear reactions." Contemporary Physics 29, no. 5 (1988): 457–76. http://dx.doi.org/10.1080/00107518808222602.

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6

Shetty, Keerthi S., and Annappa B. "Transcriptional processes: Models and inference." Journal of Bioinformatics and Computational Biology 16, no. 05 (2018): 1850023. http://dx.doi.org/10.1142/s0219720018500233.

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Many biochemical events involve multistep reactions. One of the most important biological processes that involve multistep reaction is the transcriptional process. Models for multistep reaction necessarily need multiple states and it is a challenge to compute model parameters that best agree with experimental data. Therefore, the aim of this work is to design a multistep promoter model which accurately characterizes transcriptional bursting and is consistent with observed data. To address this issue, we develop a model for promoters with several OFF states and a single ON state using Erlang di

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7

LEVINSON, WILLIAM A. "REDUCING APPRAISAL COSTS IN MULTISTEP PROCESSES." Quality Engineering 6, no. 3 (1994): 331–45. http://dx.doi.org/10.1080/08982119408918732.

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8

Demetriou, P., A. Marcinkowski, and B. Mariański. "Multistep processes in charge-exchange reactions." Nuclear Physics A 697, no. 1-2 (2002): 171–82. http://dx.doi.org/10.1016/s0375-9474(01)01243-x.

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9

Szidarovszky, Ferenc, and Ioannis K. Argyros. "On time dependent multistep dynamic processes." Bulletin of the Australian Mathematical Society 43, no. 1 (1991): 51–61. http://dx.doi.org/10.1017/s0004972700028768.

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The discrete time scale Liapunov theory is extended to time dependent, higher order, nonlinear difference equations in a partially ordered topological space. The monotone convergence of the solution is examined and the speed of convergence is estimated.

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10

Longhi, Stefano. "Hexagonal patterns in multistep optical parametric processes." Optics Letters 26, no. 10 (2001): 713. http://dx.doi.org/10.1364/ol.26.000713.

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11

Nazaruk, V. I. "On the multistep processes in the nuclei." European Physical Journal A 39, no. 2 (2009): 249–53. http://dx.doi.org/10.1140/epja/i2008-10699-9.

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12

Greenaway-McGrevy, Ryan. "MULTISTEP PREDICTION OF PANEL VECTOR AUTOREGRESSIVE PROCESSES." Econometric Theory 29, no. 4 (2013): 699–734. http://dx.doi.org/10.1017/s0266466612000679.

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This paper considers the conventional recursive (otherwise known as plug-in) and direct multistep forecasts in a panel vector autoregressive framework. We derive asymptotic expressions for the mean square prediction error (MSPE) of both forecasts as N (cross sections) and T (time periods) grow large. Both the bias and variance of the least squares fitting are manifest in the MSPE. Using these expressions, we consider the effect of model specification on predictor accuracy. When the fitted lag order (q) is equal to or exceeds the true lag order (p), the direct MSPE is larger than the recursive

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13

Argyros, I. K., and F. Szidarovszky. "Some notions of nonstationary multistep iteration processes." Acta Mathematica Hungarica 64, no. 1 (1994): 59–64. http://dx.doi.org/10.1007/bf01873970.

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14

Zhou, L. Q., Y. C. Zhou, and Y. Pan. "Coating thickness variation during multistep drawing processes." Journal of Materials Science 39, no. 2 (2004): 757–60. http://dx.doi.org/10.1023/b:jmsc.0000011551.99898.26.

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15

Maslov, V. P. "Unbounded probability theory and multistep relaxation processes." Mathematical Notes 93, no. 3-4 (2013): 451–59. http://dx.doi.org/10.1134/s0001434613030115.

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16

Shetty, Keerthi S., and Annappa B. "Clumped-MCEM: Inference for multistep transcriptional processes." Computational Biology and Chemistry 81 (August 2019): 16–20. http://dx.doi.org/10.1016/j.compbiolchem.2019.107092.

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17

Al-Ayed, Omar. "Approaches to Biomass Kinetic Modelling: Thermochemical Biomass Conversion Processes." 1 4, Vol4 (2021): 1–13. http://dx.doi.org/10.48103/jjeci412021.

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Modeling of biomass pyrolysis kinetics is an essential step towards reactors design for energy production. Determination of the activation energy, frequency factor, and order of the reaction is necessary for the design procedure. Coats and Redfern's work using the TGA data to estimate these parameters was the cornerstone for modeling. There are two significant problems with biomass modeling, the first is the determination of the kinetic triplet (Activation energy, Frequency factor, and the order of reaction), and the second is the quantitative analysis of products distribution. Methods used in

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18

Hausman, H. J., S. L. Blatt, R. N. Boyd, et al. "Evidence for multistep processes in proton capture reactions." Physical Review C 31, no. 2 (1985): 660–62. http://dx.doi.org/10.1103/physrevc.31.660.

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19

Lenske, H., H. H. Wolter, and A. Weigel. "Statistical multistep reaction approach for pre-equilibrium processes." Nuclear Physics A 690, no. 1-3 (2001): 267–71. http://dx.doi.org/10.1016/s0375-9474(01)00956-3.

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20

Molina, A., M. López-Tenés, M. M. Moreno, and C. Serna. "Multistep Electrode Processes in Double Potential Step Techniques." Portugaliae Electrochimica Acta 21, no. 4 (2003): 345–59. http://dx.doi.org/10.4152/pea.200304345.

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21

Zhang, Jing-Shang. "A Semiclassical Theory of Multistep Nuclear Reaction Processes." Communications in Theoretical Physics 18, no. 3 (1992): 299–306. http://dx.doi.org/10.1088/0253-6102/18/3/299.

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22

Maslov, V. P. "Unbounded probability theory and multistep relaxation processes, II." Mathematical Notes 93, no. 5-6 (2013): 881–89. http://dx.doi.org/10.1134/s000143461305026x.

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23

Klein, A., C. Gysin, R. Henneck та ін. "π-Nucleus quasi-free scattering and multistep processes". Physics Letters B 187, № 3-4 (1987): 253–56. http://dx.doi.org/10.1016/0370-2693(87)91091-4.

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24

Butusov, Denis, Aleksandra Tutueva, Petr Fedoseev, Artem Terentev, and Artur Karimov. "Semi-Implicit Multistep Extrapolation ODE Solvers." Mathematics 8, no. 6 (2020): 943. http://dx.doi.org/10.3390/math8060943.

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Multistep methods for the numerical solution of ordinary differential equations are an important class of applied mathematical techniques. This paper is motivated by recently reported advances in semi-implicit numerical integration methods, multistep and extrapolation solvers. Here we propose a novel type of multistep extrapolation method for solving ODEs based on the semi-implicit basic method of order 2. Considering several chaotic systems and van der Pol nonlinear oscillator as examples, we implemented a performance analysis of the proposed technique in comparison with well-known multistep

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25

Shukla, Chinmay A., and Amol A. Kulkarni. "Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic." Beilstein Journal of Organic Chemistry 13 (May 19, 2017): 960–87. http://dx.doi.org/10.3762/bjoc.13.97.

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The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the va

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26

Deng, Qiqi, Aimin Chen, Huahai Qiu, and Tianshou Zhou. "Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention." Mathematical Biosciences and Engineering 19, no. 8 (2022): 8426–51. http://dx.doi.org/10.3934/mbe.2022392.

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<abstract> <p>Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena.

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27

Gürsoy, Faik, Vatan Karakaya, and B. E. Rhoades. "Some Convergence and Stability Results for the Kirk Multistep and Kirk-SP Fixed Point Iterative Algorithms." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/806537.

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The purpose of this paper is to introduce a new Kirk type iterative algorithm called Kirk multistep iteration and to study its convergence. We also prove some theorems related to the stability results for the Kirk multistep and Kirk-SP iterative processes by employing certain contractive-like operators. Our results generalize and unify some other results in the literature.

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28

Locher, M. P., S. von Rotz, and V. E. Markushin. "Antiproton–nucleon annihilation, multistep processes and the OZI rule." Nuclear Physics A 684, no. 1-4 (2001): 414–16. http://dx.doi.org/10.1016/s0375-9474(01)00440-7.

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29

Krasheninnikov, S. I., and A. Yu Pigarov. "Kinetics of atoms in multistep excitation processes in plasma." Contributions to Plasma Physics 28, no. 4-5 (1988): 345–48. http://dx.doi.org/10.1002/ctpp.2150280412.

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30

Wenes, G., J. N. Ginocchio, A. E. L. Dieperink, and B. Van Der Cammen. "Algebraic treatment of multistep excitation processes in collective nuclei." Nuclear Physics A 459, no. 3-4 (1986): 631–44. http://dx.doi.org/10.1016/0375-9474(86)90165-x.

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31

Pourahmadi, Mohsen. "Alternating projections and interpolation of stationary processes." Journal of Applied Probability 29, no. 4 (1992): 921–31. http://dx.doi.org/10.2307/3214724.

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By using the alternating projection theorem of J. von Neumann, we obtain explicit formulae for the best linear interpolator and interpolation error of missing values of a stationary process. These are expressed in terms of multistep predictors and autoregressive parameters of the process. The key idea is to approximate the future by a finite-dimensional space.

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32

Pourahmadi, Mohsen. "Alternating projections and interpolation of stationary processes." Journal of Applied Probability 29, no. 04 (1992): 921–31. http://dx.doi.org/10.1017/s0021900200043795.

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By using the alternating projection theorem of J. von Neumann, we obtain explicit formulae for the best linear interpolator and interpolation error of missing values of a stationary process. These are expressed in terms of multistep predictors and autoregressive parameters of the process. The key idea is to approximate the future by a finite-dimensional space.

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33

Marcolongo, Juan P., Juan Schmidt, Natalia Levin, and Leonardo D. Slep. "A chemometric approach for determining the reaction quantum yields in consecutive photochemical processes." Physical Chemistry Chemical Physics 19, no. 32 (2017): 21373–81. http://dx.doi.org/10.1039/c7cp03619a.

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34

Ranđelović, Branislav, Saša Nikolić, Aleksandra Milovanović, and Ivana Ilić. "LINEAR RECURRENCE RELATONS AND ORDINARY GENERATING FUNCTIONS APPLIED ON MODELING PROCESSES IN CONTROL THEORY." Facta Universitatis, Series: Automatic Control and Robotics 1, no. 1 (2022): 015. http://dx.doi.org/10.22190/fuacr211223002r.

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In this paper we apply multistep recurrence relations, as one of very simple and useful mathematical models. It is an efficient tool for solving many problems in mathematics, science, and technics. We also use generating functions, as a connection between real number sequences and real functions, and as a very smooth and efficient connection between the discrete mathematics and (continual) mathematical analysis. We present an application of multistep homogenous linear recurrence relations for modelling some processes in the control theory. Further on, we use the ordinary generating function ai

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35

Muravyev, Nikita V., Nobuyoshi Koga, Dmitry B. Meerov, and Alla N. Pivkina. "Kinetic analysis of overlapping multistep thermal decomposition comprising exothermic and endothermic processes: thermolysis of ammonium dinitramide." Physical Chemistry Chemical Physics 19, no. 4 (2017): 3254–64. http://dx.doi.org/10.1039/c6cp08218a.

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36

Harsanyi, Antal, and Graham Sandford. "Fluorine gas for life science syntheses: green metrics to assess selective direct fluorination for the synthesis of 2-fluoromalonate esters." Green Chemistry 17, no. 5 (2015): 3000–3009. http://dx.doi.org/10.1039/c5gc00402k.

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37

Bloemendal, Victor R. L. J., Mathilde A. C. H. Janssen, Jan C. M. van Hest, and Floris P. J. T. Rutjes. "Continuous one-flow multi-step synthesis of active pharmaceutical ingredients." Reaction Chemistry & Engineering 5, no. 7 (2020): 1186–97. http://dx.doi.org/10.1039/d0re00087f.

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38

Martin, Cristina, Santanu Bhattacharyya, Amitava Patra, and Abderrazzak Douhal. "Single and multistep energy transfer processes within doped polymer nanoparticles." Photochem. Photobiol. Sci. 13, no. 9 (2014): 1241–52. http://dx.doi.org/10.1039/c4pp00086b.

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39

Lv, Hao, Aimei Liu, Jufang Tong, et al. "Multistep ion exchange processes of gradient refractive index rod lens." Optics Letters 36, no. 1 (2010): 28. http://dx.doi.org/10.1364/ol.36.000028.

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40

Bonn, Daniel, and Noushine Shahidzadeh. "Multistep crystallization processes: How not to make perfect single crystals." Proceedings of the National Academy of Sciences 113, no. 48 (2016): 13551–53. http://dx.doi.org/10.1073/pnas.1616536113.

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41

Ing, Ching-Kang. "Selecting optimal multistep predictors for autoregressive processes of unknown order." Annals of Statistics 32, no. 2 (2004): 693–722. http://dx.doi.org/10.1214/009053604000000148.

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42

Chen, Niya, Zheng Qian, and Xiaofeng Meng. "Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/461983.

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Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summ

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43

Conforti, M., F. Baronio, C. De Angelis, M. Marangoni, and G. Cerullo. "Theory and experiments on multistep parametric processes in nonlinear optics." Journal of the Optical Society of America B 28, no. 4 (2011): 892. http://dx.doi.org/10.1364/josab.28.000892.

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44

Gottesfeld, Shimshon. "(Invited) Multistep Electrocatalytic Processes in Fuel Cells: A Comprehensive Analysis." ECS Meeting Abstracts MA2020-02, no. 41 (2020): 2688. http://dx.doi.org/10.1149/ma2020-02412688mtgabs.

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45

Clifford, John N., Emilio Palomares, Md K. Nazeeruddin, Ravindranathan Thampi, Michael Grätzel, and James R. Durrant. "Multistep Electron Transfer Processes on Dye Co-sensitized Nanocrystalline TiO2Films." Journal of the American Chemical Society 126, no. 18 (2004): 5670–71. http://dx.doi.org/10.1021/ja049705h.

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46

von Ardenne, Manfred. "Fundamentals of combating cancer metastasis by oxygen multistep immunostimulation processes." Medical Hypotheses 17, no. 1 (1985): 47–65. http://dx.doi.org/10.1016/0306-9877(85)90019-2.

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47

Proietti, Tommaso. "Direct and iterated multistep AR methods for difference stationary processes." International Journal of Forecasting 27, no. 2 (2011): 266–80. http://dx.doi.org/10.1016/j.ijforecast.2010.05.014.

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48

Alonso, C. E., M. I. Gallardo, M. Lozano, and A. Vitturi. "Algebraic description of multistep processes in very-heavy ion reactions." Nuclear Physics A 540, no. 1-2 (1992): 261–74. http://dx.doi.org/10.1016/0375-9474(92)90203-v.

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49

Liao, Jen-Che, and Wen-Jen Tsay. "OPTIMAL MULTISTEP VAR FORECAST AVERAGING." Econometric Theory 36, no. 6 (2020): 1099–126. http://dx.doi.org/10.1017/s0266466619000434.

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This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasednes

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50

Kang, Shin Min, Ljubomir B. Ćirić, Arif Rafiq, Faisal Ali, and Young Chel Kwun. "Faster Multistep Iterations for the Approximation of Fixed Points Applied to Zamfirescu Operators." Abstract and Applied Analysis 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/464593.

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