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Journal articles on the topic 'Stochastic cycle'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Netzer, Corinna, Michal Pasternak, Lars Seidel, Frédéric Ravet, and Fabian Mauss. "Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines." International Journal of Engine Research 21, no. 4 (June 13, 2019): 649–63. http://dx.doi.org/10.1177/1468087419856493.

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Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels’ tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier–Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier–Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier–Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier–Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.

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2

Brandenburg, Axel, and Gustavo Guerrero. "Cycles and cycle modulations." Proceedings of the International Astronomical Union 7, S286 (October 2011): 37–48. http://dx.doi.org/10.1017/s1743921312004619.

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AbstractSome selected concepts of the solar activity cycle are reviewed. Cycle modulations through a stochastic α effect are being identified with limited scale separation ratios. Three-dimensional turbulence simulations with helicity and shear are compared at two different scale separation ratios. In both cases the level of fluctuations shows relatively little variation with the dynamo cycle. Prospects for a shallow origin of sunspots are discussed in terms of the negative effective magnetic pressure instability. Tilt angles of bipolar active regions are discussed as a consequence of shear rather than the Coriolis force.

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3

Bashkirtseva, Irina, and Lev Ryashko. "Stochastic Bifurcations and Noise-Induced Chaos in a Dynamic Prey–Predator Plankton System." International Journal of Bifurcation and Chaos 24, no. 09 (September 2014): 1450109. http://dx.doi.org/10.1142/s0218127414501090.

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We consider the stochastic Truscott–Brindley dynamical model of the interacting populations of prey and predator. We study a new phenomenon of the stochastic cycle splitting. In a zone of Canard cycles, using the stochastic sensitivity function technique, we find a critical value of the parameter corresponding to the supersensitive cycle. In the neighborhood of this critical value, a comparative parametrical analysis of the phenomenon of the stochastic cycle splitting is performed. It is shown that the bifurcation of the stochastic cycle splitting is accompanied by the noise-induced chaotization.

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4

BASHKIRTSEVA, IRINA, LEV RYASHKO, and EUDOKIA SLEPUKHINA. "NOISE-INDUCED OSCILLATING BISTABILITY AND TRANSITION TO CHAOS IN FITZHUGH–NAGUMO MODEL." Fluctuation and Noise Letters 13, no. 01 (March 2014): 1450004. http://dx.doi.org/10.1142/s0219477514500047.

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Stochastic dynamics of the FitzHugh–Nagumo (FHN) neuron model in the limit cycles zone is studied. For weak noise, random trajectories are concentrated in the small neighborhood of the unforced deterministic cycle. As the noise intensity increases, in the Canard-like cycles zone of the FHN model, a bundle of the stochastic trajectories begins to split into two parts. This phenomenon is investigated using probability density functions for the distribution of random trajectories. It is shown that the intensity of noise generating this splitting bifurcation significantly depends on the stochastic sensitivity of cycles. Using the stochastic sensitivity function (SSF) technique, we find a critical value of the parameter corresponding to the supersensitive cycle. For the neighborhood of this critical value, a comparative parametrical analysis of the phenomenon of the stochastic cycle splitting is performed. To predict the splitting bifurcation and estimate a threshold value of the noise intensity, we use a confidence domains method based on SSF. A phenomenon of the noise-induced chaotization is studied. We show that P-bifurcation of the splitting of stochastic cycles implies a D-bifurcation of a noise-induced chaotization.

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5

Melchionna, Andrew. "Stochastic sandpile on a cycle." Journal of Physics A: Mathematical and Theoretical 55, no. 19 (April 12, 2022): 195001. http://dx.doi.org/10.1088/1751-8121/ac61b9.

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Abstract In the stochastic sandpile (SS) model on a graph, particles interact pairwise as follows: if two particles occupy the same vertex, they must each take an independent random walk step with some probability 0 < p < 1 of not moving. These interactions continue until each site has no more than one particle on it. We provide a formal coupling between the SS and the activated random walk models, and we use the coupling to show that for the SS with n particles on the cycle graph Z n , the system stabilizes in O(n 3) time for all initial particle configurations, provided that p(n) tends to 1 sufficiently rapidly as n → ∞.

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6

Luvsantseren, Purevdolgor, Enkhbayar Purevjav, and Khenmedeh Lochin. "Stochastic simulation of cell cycle." Advanced Studies in Biology 5 (2013): 1–9. http://dx.doi.org/10.12988/asb.2013.13001.

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7

Balasubramanian, K., V. Parameswaran, and S. B. Rao. "Characterization of Cycle Stochastic Graphs." Electronic Notes in Discrete Mathematics 15 (May 2003): 36. http://dx.doi.org/10.1016/s1571-0653(04)00520-7.

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8

BASHKIRTSEVA, I., L. RYASHKO, and P. STIKHIN. "NOISE-INDUCED BACKWARD BIFURCATIONS OF STOCHASTIC 3D-CYCLES." Fluctuation and Noise Letters 09, no. 01 (March 2010): 89–106. http://dx.doi.org/10.1142/s0219477510000095.

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We study stochastically forced multiple limit cycles of nonlinear dynamical systems in a period-doubling bifurcation zone. Noise-induced transitions between separate parts of the cycle are considered. A phenomenon of a decreasing of the stochastic cycle multiplicity with a noise intensity growth is investigated. We call it by a backward stochastic bifurcation (BSB). In this paper, for the BSB analysis we suggest a stochastic sensitivity function technique. As a result, a method for the estimation of critical values of noise intensity corresponding to BSB is proposed. The constructive possibilities of this general method for the detailed BSB analysis of the multiple stochastic cycles of the forced Roessler system are demonstrated.

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9

SOWERS, RICHARD B. "STOCHASTIC AVERAGING NEAR LONG HETEROCLINIC ORBITS." Stochastics and Dynamics 07, no. 02 (June 2007): 187–228. http://dx.doi.org/10.1142/s0219493707001974.

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We refine some of the bounds of [10]. There, we considered the effect of diffusive perturbations on a two-dimensional ODE with a heteroclinic cycle. We constructed corrector functions for asymptotically "glueing" together behavior of periodic orbits in the boundary layer near the heteroclinic cycle. Here, we adapt the analysis of [10] to allow for "long" heteroclinic cycles.

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10

Jia, Gaofeng, and Paolo Gardoni. "Stochastic life-cycle analysis: renewal-theory life-cycle analysis with state-dependent deterioration stochastic models." Structure and Infrastructure Engineering 15, no. 8 (March 27, 2019): 1001–14. http://dx.doi.org/10.1080/15732479.2019.1590424.

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11

Pichor, Katarzyna, and Ryszard Rudnicki. "One and two-phase cell cycle models." BIOMATH 8, no. 1 (June 1, 2019): 1905261. http://dx.doi.org/10.11145/j.biomath.2019.05.261.

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In this review paper we present deterministic and stochastic one and two-phase models of the cell cycle. The deterministic models are given by partial differential equations of the first order with time delay and space variable retardation. The stochastic models are given by stochastic iterations or by piecewise deterministic Markov processes. We study asymptotic stability and sweeping of stochastic semigroups which describe the evolution of densities of these processes. We also present some results concerning chaotic behaviour of models and relations between different types of models.

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12

YU. RYAGIN, MIKHAIL, and LEV B. RYASHKO. "THE ANALYSIS OF THE STOCHASTICALLY FORCED PERIODIC ATTRACTORS FOR CHUA'S CIRCUIT." International Journal of Bifurcation and Chaos 14, no. 11 (November 2004): 3981–87. http://dx.doi.org/10.1142/s0218127404011600.

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This report shows the results of sensitivity analysis for Chua's circuit periodic attractors under small disturbances. Sensitivity analysis is based on the quasipotential method. Quasipotential's first approximation in the neighborhood of the limit cycle is defined by the matrix of orbital quadratic form, named stochastic sensitivity function (SSF). SSF is defined for the points of the nonperturbed limit cycle and can be computed using the numerical algorithm. Stochastic sensitivity of the limit cycles for the Chua's circuit period doubling cascade is investigated. The growth of the stochastic sensitivity under transition to chaos is shown.

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13

Ambler, Steve, and Alain Paquet. "Stochastic Depreciation and the Business Cycle." International Economic Review 35, no. 1 (February 1994): 101. http://dx.doi.org/10.2307/2527092.

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14

Kalpazidou, S. "Cycle representations of denumerable stochastic matrices." Stochastic Analysis and Applications 16, no. 5 (January 1998): 895–906. http://dx.doi.org/10.1080/07362999808809568.

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15

Kalpazidou, S., and Joel E. Cohen. "Orthogonal cycle transforms of stochastic matrices." Circuits Systems and Signal Processing 16, no. 3 (May 1997): 363–74. http://dx.doi.org/10.1007/bf01246718.

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16

Bhadana, Jyoti, Md Zubbair Malik, and R. K. Brojen Singh. "Universality in stochastic enzymatic futile cycle." Applied Mathematical Modelling 74 (October 2019): 658–67. http://dx.doi.org/10.1016/j.apm.2019.05.008.

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17

Vellela, Melissa, and Hong Qian. "On the Poincaré–Hill cycle map of rotational random walk: locating the stochastic limit cycle in a reversible Schnakenberg model." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 466, no. 2115 (November 11, 2009): 771–88. http://dx.doi.org/10.1098/rspa.2009.0346.

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Recent studies on stochastic oscillations mostly focus on the power spectral analysis. However, the power spectrum yields information only on the frequency of oscillation and cannot differentiate between a stable limit cycle and a stable focus. The cycle flux, introduced by Hill (Hill 1989 Free energy transduction and biochemical cycle kinetics ), is a quantitative measure of the net movement over a closed path, but it is impractical to compute for all possible cycles in systems with a large state space. Through simple examples, we introduce concepts used to quantify stochastic oscillation, such as the cycle flux, the Hill–Qian stochastic circulation and rotation number. We introduce a novel device, the Poincaré–Hill cycle map (PHCM), which combines the concept of Hill’s cycle flux with the Poincaré map from nonlinear dynamics. Applying the PHCM to a reversible extension of an oscillatory chemical system, the Schnakenberg model, reveals stable oscillations outside the Hopf bifurcation region in which the deterministic system contains a limit cycle. Bistable behaviour is found on the small volume scale with high probabilities around both the fixed point and the limit cycle. Convergence to the deterministic system is found in the thermodynamic limit.

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18

HUANG, DONGWEI, HONGLI WANG, and YINGFEI YI. "BIFURCATIONS IN A STOCHASTIC BUSINESS CYCLE MODEL." International Journal of Bifurcation and Chaos 20, no. 12 (December 2010): 4111–18. http://dx.doi.org/10.1142/s0218127410028227.

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We introduce a stochastic business cycle model and study the underlying stochastic Hopf bifurcations with respect to probability densities at different parameter values. Our analysis is based on the calculation of the largest Lyapunov exponent via multiplicative ergodic theorem and the theory of boundary analysis for quasi-nonintegrable Hamiltonian systems. Some numerical simulations of the model are performed.

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19

BASHKIRTSEVA, IRINA, LEV RYASHKO, and PAVEL STIKHIN. "NOISE-INDUCED CHAOS AND BACKWARD STOCHASTIC BIFURCATIONS IN THE LORENZ MODEL." International Journal of Bifurcation and Chaos 23, no. 05 (May 2013): 1350092. http://dx.doi.org/10.1142/s0218127413500922.

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We study the phenomena of stochastic D- and P-bifurcations of randomly forced limit cycles for the Lorenz model. As noise intensity increases, regular multiple limit cycles of this model in a period-doubling bifurcations zone are deformed to be stochastic attractors that look chaotic (D-bifurcation) and their multiplicity is reduced (P-bifurcation). In this paper for the comparative investigation of these bifurcations, the analysis of Lyapunov exponents and stochastic sensitivity function technique are used. A probabilistic mechanism of backward stochastic bifurcations for cycles of high multiplicity is analyzed in detail. We show that for a limit cycle with multiplicity two and higher, a threshold value of the noise intensity which marks the onset of chaos agrees with the first backward stochastic bifurcation.

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20

MELNIK, RODERICK V. N., XILIN WEI, and GABRIEL MORENO–HAGELSIEB. "NONLINEAR DYNAMICS OF CELL CYCLES WITH STOCHASTIC MATHEMATICAL MODELS." Journal of Biological Systems 17, no. 03 (September 2009): 425–60. http://dx.doi.org/10.1142/s0218339009002879.

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Cell cycles are fundamental components of all living organisms and their systematic studies extend our knowledge about the interconnection between regulatory, metabolic, and signaling networks, and therefore open new opportunities for our ultimate efficient control of cellular processes for disease treatments, as well as for a wide variety of biomedical and biotechnological applications. In the study of cell cycles, nonlinear phenomena play a paramount role, in particular in those cases where the cellular dynamics is in the focus of attention. Quantification of this dynamics is a challenging task due to a wide range of parameters that require estimations and the presence of many stochastic effects. Based on the originally deterministic model, in this paper we develop a hierarchy of models that allow us to describe the nonlinear dynamics accounting for special events of cell cycles. First, we develop a model that takes into account fluctuations of relative concentrations of proteins during special events of cell cycles. Such fluctuations are induced by varying rates of relative concentrations of proteins and/or by relative concentrations of proteins themselves. As such fluctuations may be responsible for qualitative changes in the cell, we develop a new model that accounts for the effect of cellular dynamics on the cell cycle. Finally, we analyze numerically nonlinear effects in the cell cycle by constructing phase portraits based on the newly developed model and carry out a parametric sensitivity analysis in order to identify parameters for an efficient cell cycle control. The results of computational experiments demonstrate that the metabolic events in gene regulatory networks can qualitatively influence the dynamics of the cell cycle.

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21

Karolewska, Karolina, and Bogdan Ligaj. "Verification of the method of equivalent amplitude determination based on two - parameter fatigue characteristic." MATEC Web of Conferences 182 (2018): 02022. http://dx.doi.org/10.1051/matecconf/201818202022.

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In the most causes the loads which are affected on structural components are various over time and their character changes is stochastic. The stochastic character of operational loads of construction elements in various machine types is depended on many factors, included : work forces variability, environmental conditions, physical properties of components etc. Fatigue life calculation for this type of loads are conducted on the basis of determined sinusoidal cycles set through to use of the cycles counting method. The cycles which are contained to the sinusoidal cycles set are characterized by extensive range of amplitude Sai variation and mean values Smi. Application of Sa-N curve in fatigue life calculations caused disregard of the cycle mean value. This may affect the accuracy of calculations. Taking into account the cycle mean value Sm in the calculations may be realized by determining a substitute cycle with an average value Sm=0 and a substitute amplitude Saz≠Sa.

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22

Posadas, Sergio, and Eugene P. Paulo. "Stochastic Simulation Of A Commander's Decision Cycle." Military Operations Research 8, no. 2 (March 1, 2003): 21–43. http://dx.doi.org/10.5711/morj.8.2.21.

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23

Okabe, Yurie, and Masaki Sasai. "Stable Stochastic Dynamics in Yeast Cell Cycle." Biophysical Journal 93, no. 10 (November 2007): 3451–59. http://dx.doi.org/10.1529/biophysj.107.109991.

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24

Zhang, Yuping, Minping Qian, Qi Ouyang, Minghua Deng, Fangting Li, and Chao Tang. "Stochastic model of yeast cell-cycle network." Physica D: Nonlinear Phenomena 219, no. 1 (July 2006): 35–39. http://dx.doi.org/10.1016/j.physd.2006.05.009.

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25

Felber, S., H. P. Breuer, F. Petruccione, J. Honerkamp, and K. P. Hofmann. "Stochastic simulation of the transducin GTPase cycle." Biophysical Journal 71, no. 6 (December 1996): 3051–63. http://dx.doi.org/10.1016/s0006-3495(96)79499-7.

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26

Medvedev, Georgi S. "Synchronization of coupled stochastic limit cycle oscillators." Physics Letters A 374, no. 15-16 (April 2010): 1712–20. http://dx.doi.org/10.1016/j.physleta.2010.02.031.

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27

Olofsson, Peter, and Thomas O. McDonald. "A stochastic model of cell cycle desynchronization." Mathematical Biosciences 223, no. 2 (February 2010): 97–104. http://dx.doi.org/10.1016/j.mbs.2009.11.003.

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28

Woodhouse, Francis G., Aden Forrow, Joanna B. Fawcett, and Jörn Dunkel. "Stochastic cycle selection in active flow networks." Proceedings of the National Academy of Sciences 113, no. 29 (July 5, 2016): 8200–8205. http://dx.doi.org/10.1073/pnas.1603351113.

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Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

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29

Jenab, Kouroush, Kaveh Salehi Gilani, and Sareh Shafiei Monfared. "Stochastic cycle time analysis in robotic cells." International Journal of Industrial and Systems Engineering 5, no. 2 (2010): 129. http://dx.doi.org/10.1504/ijise.2010.030744.

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30

Itoh, Yoshiaki, and Kei-ichi Tainaka. "Stochastic limit cycle with power-law spectrum." Physics Letters A 189, no. 1-2 (June 1994): 37–42. http://dx.doi.org/10.1016/0375-9601(94)90815-x.

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31

Widianto, Eko, Firman Herdiansyah, Muhammad Burhannudinnur, Suryo Prakoso, and Benyamin Benyamin. "STOCHASTIC POROSITY MODELING IN VOLCANIC RESERVOIR JATIBARANG FORMATION." Journal of Geoscience Engineering & Energy 2, no. 2 (September 1, 2021): 114. http://dx.doi.org/10.25105/jogee.v2i2.9993.

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Jatibarang Formation known as interesting volcanic reservoir in North West Java Basin. The reservoir was characterized by altered and naturally fractured that has significantly producing light oil. The volcanic Jatibarang reservoir consist of 3 volcanic cycles that are cycle 1, cycle 2 and cycle 3 with 16 faults configuration. Total and Fracture porosity modeling was conducted to determine secondary porosity distribution using stochastic method. Lithofacies and property lateral variation were generated to visualize geological model. Total porosity was estimated using formation evaluation. Natural subsurface fracture has been identified by bore hole image (formation micro imager) and geophysical log from several wells in the Jatibarang Field. It has provided both lithology and property reservoir information. Lithofacies tuff and non tuff model has been used as a constrait to distribute pore pressure and bulk modulus. Then, porosity model was distribution using the collocated-co kriging method. The purpose of this study was to determine the distribution of total and fracture porosity of the Jatibarang volcanic reservoir.

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32

Engel, Maximilian, and Christian Kuehn. "A Random Dynamical Systems Perspective on Isochronicity for Stochastic Oscillations." Communications in Mathematical Physics 386, no. 3 (April 8, 2021): 1603–41. http://dx.doi.org/10.1007/s00220-021-04077-z.

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AbstractFor an attracting periodic orbit (limit cycle) of a deterministic dynamical system, one defines the isochron for each point of the orbit as the cross-section with fixed return time under the flow. Equivalently, isochrons can be characterized as stable manifolds foliating neighborhoods of the limit cycle or as level sets of an isochron map. In recent years, there has been a lively discussion in the mathematical physics community on how to define isochrons for stochastic oscillations, i.e. limit cycles or heteroclinic cycles exposed to stochastic noise. The main discussion has concerned an approach finding stochastic isochrons as sections of equal expected return times versus the idea of considering eigenfunctions of the backward Kolmogorov operator. We discuss the problem in the framework of random dynamical systems and introduce a new rigorous definition of stochastic isochrons as random stable manifolds for random periodic solutions with noise-dependent period. This allows us to establish a random version of isochron maps whose level sets coincide with the random stable manifolds. Finally, we discuss links between the random dynamical systems interpretation and the equal expected return time approach via averaged quantities.

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33

Mircea, Gabriela, Mihaela Neamt¸u, and Dumitru Opris. "The Kaldor–Kalecki stochastic model of business cycle." Nonlinear Analysis: Modelling and Control 16, no. 2 (April 25, 2011): 191–205. http://dx.doi.org/10.15388/na.16.2.14105.

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This paper is concerned with the deterministic and the stochastic delayed Kaldor–Kalecki nonlinear business cycle models of the income. They will take into consideration the investment demand in the form suggested by Rodano. The existence of the Hopf bifurcation is studied and the direction and the local stability of the Hopf bifurcation is also taken into consideration. For the stochastic model, the dynamics of the mean values and the square mean values of the model’s variables are set. Numerical examples are given to illustrate our theoretical results.

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34

Carroll, Michael S., and Jan-Marino Ramirez. "Cycle-by-cycle assembly of respiratory network activity is dynamic and stochastic." Journal of Neurophysiology 109, no. 2 (January 15, 2013): 296–305. http://dx.doi.org/10.1152/jn.00830.2011.

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Rhythmically active networks are typically composed of neurons that can be classified as silent, tonic spiking, or rhythmic bursting based on their intrinsic activity patterns. Within these networks, neurons are thought to discharge in distinct phase relationships with their overall network output, and it has been hypothesized that bursting pacemaker neurons may lead and potentially trigger cycle onsets. We used multielectrode recording from 72 experiments to test these ideas in rhythmically active slices containing the pre-Bötzinger complex, a region critical for breathing. Following synaptic blockade, respiratory neurons exhibited a gradient of intrinsic spiking to rhythmic bursting activities and thus defied an easy classification into bursting pacemaker and nonbursting categories. Features of their firing activity within the functional network were analyzed for correlation with subsequent rhythmic bursting in synaptic isolation. Higher firing rates through all phases of fictive respiration statistically predicted bursting pacemaker behavior. However, a cycle-by-cycle analysis indicated that respiratory neurons were stochastically activated with each burst. Intrinsically bursting pacemakers led some population bursts and followed others. This variability was not reproduced in traditional fully interconnected computational models, while sparsely connected network models reproduced these results both qualitatively and quantitatively. We hypothesize that pacemaker neurons do not act as clock-like drivers of the respiratory rhythm but rather play a flexible and dynamic role in the initiation and stabilization of each burst. Thus, at the behavioral level, each breath can be thought of as de novo assembly of a stochastic collaboration of network topology and intrinsic properties.

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35

Lin, Jie, and Debbie A. Niemeier. "An exploratory analysis comparing a stochastic driving cycle to California's regulatory cycle." Atmospheric Environment 36, no. 38 (December 2002): 5759–70. http://dx.doi.org/10.1016/s1352-2310(02)00695-7.

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36

Adams, Fred C., and Anthony M. Bloch. "Hill's equation with small fluctuations: Cycle to cycle variations and stochastic processes." Journal of Mathematical Physics 54, no. 3 (March 2013): 033511. http://dx.doi.org/10.1063/1.4795351.

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37

Ghouchanian, Kamal, Kiarash Ghassaban, and Mojtaba Jokar. "Optimization of the Capsule Production Stochastic Cycle Time." Research in Economics and Management 2, no. 4 (July 18, 2017): 21. http://dx.doi.org/10.22158/rem.v2n4p21.

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<p><em>Manufacturing speed is one of the most important factors in pharmaceutical production, since the drug excipient is sensitive and its exposure to light and temperature should be controlled. Therefore, by minimizing the manufacturing cycle time, the quality of product can be improved. This process also results in minimizing the cost of manufacturing such as working hours, human resource, energy consumption and overhead cost while increasing the system productivity. In this study, using a stochastic dynamic programming method, the stochastic manufacturing cycle time of pharmaceutical product in a plant with process layout and concurrent machines is minimized. The result of this study has been compared to simulation modeling of the process.</em></p>

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38

Agrawal, Prachi, Talari Ganesh, and Ali Wagdy Mohamed. "Application of Water Cycle Algorithm to Stochastic Fractional Programming Problems." International Journal of Swarm Intelligence Research 13, no. 1 (January 2022): 1–21. http://dx.doi.org/10.4018/ijsir.2022010101.

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This paper presents an application of Water Cycle algorithm (WCA) in solving stochastic programming problems. In particular, Linear stochastic fractional programming problems are considered which are solved by WCA and solutions are compared with Particle Swarm Optimization, Differential Evolution, and Whale Optimization Algorithm and the results from literature. The constraints are handled by converting constrained optimization problem into an unconstrained optimization problem using Augmented Lagrangian Method. Further, a fractional stochastic transportation problem is examined as an application of the stochastic fractional programming problem. In terms of efficiency of algorithms and the ability to find optimal solutions, WCA gives highly significant results in comparison with the other metaheuristic algorithms and the quoted results in the literature which demonstrates that WCA algorithm has 100% convergence in all the problems. Moreover, non-parametric hypothesis tests are performed and which indicates that WCA presents better results as compare to the other algorithms.

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39

Li, Longbiao. "A Micromechanical Fatigue Limit Stress Model of Fiber-Reinforced Ceramic-Matrix Composites under Stochastic Overloading Stress." Materials 13, no. 15 (July 24, 2020): 3304. http://dx.doi.org/10.3390/ma13153304.

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Fatigue limit stress is a key design parameter for the structure fatigue design of composite materials. In this paper, a micromechanical fatigue limit stress model of fiber-reinforced ceramic-matrix composites (CMCs) subjected to stochastic overloading stress is developed. The fatigue limit stress of different carbon fiber-reinforced silicon carbide (C/SiC) composites (i.e., unidirectional (UD), cross-ply (CP), 2D, 2.5D, and 3D C/SiC) is predicted based on the micromechanical fatigue damage models and fatigue failure criterion. Under cyclic fatigue loading, the fatigue damage and fracture under stochastic overloading stress at different applied cycle numbers are characterized using two parameters of fatigue life decreasing rate and broken fiber fraction. The relationships between the fatigue life decreasing rate, stochastic overloading stress level and corresponding occurrence applied cycle number, and broken fiber fraction are analyzed. Under the same stochastic overloading stress level, the fatigue life decreasing rate increases with the occurrence applied cycle of stochastic overloading, and thus, is the highest for the cross-ply C/SiC composite and lowest for the 2.5D C/SiC composite. Among the UD, 2D, and 3D C/SiC composites, at the initial stage of cyclic fatigue loading, under the same stochastic overloading stress, the fatigue life decreasing rate of the 3D C/SiC is the highest; however, with the increasing applied cycle number, the fatigue life decreasing rate of the UD C/SiC composite is the highest. The broken fiber fraction increases when stochastic overloading stress occurs, and the difference of the broken fiber fraction between the fatigue limit stress and stochastic overloading stress level increases with the occurrence applied cycle.

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Sanders, Sara, Kunaal Joshi, Petra Anne Levin, and Srividya Iyer-Biswas. "Beyond the average: An updated framework for understanding the relationship between cell growth, DNA replication, and division in a bacterial system." PLOS Genetics 19, no. 1 (January 5, 2023): e1010505. http://dx.doi.org/10.1371/journal.pgen.1010505.

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Our understanding of the bacterial cell cycle is framed largely by population-based experiments that focus on the behavior of idealized average cells. Most famously, the contributions of Cooper and Helmstetter help to contextualize the phenomenon of overlapping replication cycles observed in rapidly growing bacteria. Despite the undeniable value of these approaches, their necessary reliance on the behavior of idealized average cells masks the stochasticity inherent in single-cell growth and physiology and limits their mechanistic value. To bridge this gap, we propose an updated and agnostic framework, informed by extant single-cell data, that quantitatively accounts for stochastic variations in single-cell dynamics and the impact of medium composition on cell growth and cell cycle progression. In this framework, stochastic timers sensitive to medium composition impact the relationship between cell cycle events, accounting for observed differences in the relationship between cell cycle events in slow- and fast-growing cells. We conclude with a roadmap for potential application of this framework to longstanding open questions in the bacterial cell cycle field.

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Hu, Hui Bin, Li Jun Cao, Shu Xiao Chen, and Xin Wen Cao. "Stochastic Fatigue Reliability Analysis for Torsion Shaft of Military Tracked Vehicles." Applied Mechanics and Materials 543-547 (March 2014): 199–202. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.199.

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There are coupled cases of high-cycle fatigue and low-cycle fatigue in torsion shaft of military tracked vehicles. To accurately analyze the stochastic fatigue reliability of torsion shaft, a new kind of decoupling method for high-cycle fatigue and low-cycle fatigue was firstly put forward. Probability fatigue accumulation damage theory and nominal stress method were combined to analyze high-cycle fatigue. Random response surface method was adopted to fit the life distribution function for low-cycle fatigue. To obtain the high-cycle and low-cycle stochastic fatigue reliability, probability fatigue accumulation damage theory and local stress and strain method were used. Then, composite damages of torsion shaft under high-cycle fatigue and low-cycle fatigue could be achieved based on probability fatigue accumulation damage thoery.

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42

Brockwell, Peter J. "Stochastic models in cell kinetics." Journal of Applied Probability 25, A (1988): 91–111. http://dx.doi.org/10.2307/3214149.

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We discuss the role of stochastic processes in modelling the life-cycle of a biological cell and the growth of cell populations. Results for multiphase age-dependent branching processes have proved invaluable for the interpretation of many of the basic experimental studies of the life-cycle. Moreover problems from cell kinetics, in particular those related to diurnal rhythm in cell-growth, have stimulated research into ‘periodic' renewal theory, and the asymptotic behaviour of populations of cells with periodic death rate.

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Brockwell, Peter J. "Stochastic models in cell kinetics." Journal of Applied Probability 25, A (1988): 91–111. http://dx.doi.org/10.1017/s0021900200040286.

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We discuss the role of stochastic processes in modelling the life-cycle of a biological cell and the growth of cell populations. Results for multiphase age-dependent branching processes have proved invaluable for the interpretation of many of the basic experimental studies of the life-cycle. Moreover problems from cell kinetics, in particular those related to diurnal rhythm in cell-growth, have stimulated research into ‘periodic' renewal theory, and the asymptotic behaviour of populations of cells with periodic death rate.

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44

Pichór, Katarzyna, and Ryszard Rudnicki. "Applications of stochastic semigroups to cell cycle models." Discrete & Continuous Dynamical Systems - B 24, no. 5 (2019): 2365–81. http://dx.doi.org/10.3934/dcdsb.2019099.

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Clarida, R. H. "Aggregate Stochastic Implications of the Life Cycle Hypothesis." Quarterly Journal of Economics 106, no. 3 (August 1, 1991): 851–67. http://dx.doi.org/10.2307/2937930.

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Vitturi, S. "Stochastic model of the Profibus DP cycle time." IEE Proceedings - Science, Measurement and Technology 151, no. 5 (September 1, 2004): 335–42. http://dx.doi.org/10.1049/ip-smt:20040668.

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47

Kim, Juram, and Changyong Lee. "Stochastic service life cycle analysis using customer reviews." Service Industries Journal 37, no. 5-6 (April 26, 2017): 296–316. http://dx.doi.org/10.1080/02642069.2017.1316379.

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Holmes, James M., and Patricia A. Hutton. "A Stochastic Monopsony Theory of the Business Cycle." Economic Inquiry 43, no. 1 (January 2005): 206–19. http://dx.doi.org/10.1093/ei/cbi014.

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Mininni, P. D., D. O. Gómez, and G. B. Mindlin. "Stochastic Relaxation Oscillator Model for the Solar Cycle." Physical Review Letters 85, no. 25 (December 18, 2000): 5476–79. http://dx.doi.org/10.1103/physrevlett.85.5476.

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Hogan, Arthur M. B., and David Nickerson. "Insurance Contracts, Stochastic Regulation and the Insurance Cycle." IFAC Proceedings Volumes 33, no. 16 (July 2000): 559–62. http://dx.doi.org/10.1016/s1474-6670(17)39694-5.

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