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Journal articles on the topic 'Time-varying-potential'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Xiao-Hu Yu, Zhen-Ya He, and Yi-Sheng Zhang. "Time-varying adaptive filters for evoked potential estimation." IEEE Transactions on Biomedical Engineering 41, no. 11 (1994): 1062–71. http://dx.doi.org/10.1109/10.335844.

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2

Özlale, Ümit, and Levent Özbek. "Analyzing time-varying effects of potential output growth shocks." Economics Letters 98, no. 3 (March 2008): 294–300. http://dx.doi.org/10.1016/j.econlet.2007.05.006.

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3

Boovaragavan, Vijayasekaran, and C. Ahmed Basha. "Optimal time-varying potential profile for electro-hydro-dimerization reactions." Journal of Process Control 19, no. 2 (February 2009): 241–46. http://dx.doi.org/10.1016/j.jprocont.2008.04.002.

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4

Cao, Junyi, Wei Wang, Shengxi Zhou, Daniel J. Inman, and Jing Lin. "Nonlinear time-varying potential bistable energy harvesting from human motion." Applied Physics Letters 107, no. 14 (October 5, 2015): 143904. http://dx.doi.org/10.1063/1.4932947.

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5

Witlox, Henk W. M., and Mike Harper. "Modeling of time-varying dispersion for releases including potential rainout." Process Safety Progress 33, no. 3 (November 8, 2013): 265–73. http://dx.doi.org/10.1002/prs.11652.

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6

Rivolta, Giulia. "Potential ECB reaction functions with time-varying parameters: an assessment." Empirical Economics 55, no. 4 (October 13, 2017): 1425–73. http://dx.doi.org/10.1007/s00181-017-1337-z.

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7

Dimeo, Robert M. "Wave packet scattering from time-varying potential barriers in one dimension." American Journal of Physics 82, no. 2 (February 2014): 142–52. http://dx.doi.org/10.1119/1.4833557.

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8

Chiaramello, E., S. Fiocchi, M. Bonato, S. Gallucci, M. Benini, and M. Parazzini. "Cell transmembrane potential in contactless permeabilization by time-varying magnetic fields." Computers in Biology and Medicine 135 (August 2021): 104587. http://dx.doi.org/10.1016/j.compbiomed.2021.104587.

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9

Sabeti, Malihe, and Reza Boostani. "Separation of P300 event-related potential using time varying time-lag blind source separation algorithm." Computer Methods and Programs in Biomedicine 145 (July 2017): 95–102. http://dx.doi.org/10.1016/j.cmpb.2017.04.014.

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10

Brand, Jennie E., and Yu Xie. "11. Identification and Estimation of Causal Effects with Time-Varying Treatments and Time-Varying Outcomes." Sociological Methodology 37, no. 1 (August 2007): 393–434. http://dx.doi.org/10.1111/j.1467-9531.2007.00185.x.

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We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.

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11

Morinaga, Atsuo, Motoyuki Murakami, Keisuke Nakamura, and Hiromitsu Imai. "Scalar Aharonov–Bohm Phase in Ramsey Atom Interferometry under Time-Varying Potential." Atoms 4, no. 3 (August 2, 2016): 23. http://dx.doi.org/10.3390/atoms4030023.

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12

Labrecque, Jeremy A., and Sonja A. Swanson. "Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures." American Journal of Epidemiology 188, no. 1 (September 15, 2018): 231–38. http://dx.doi.org/10.1093/aje/kwy204.

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13

Jaskula, M., and R. Kaszynski. "Using the Parametric Time-Varying Analog Filter to Average-Evoked Potential Signals." IEEE Transactions on Instrumentation and Measurement 53, no. 3 (June 2004): 709–15. http://dx.doi.org/10.1109/tim.2004.827073.

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14

Aydin, Sarper, Sina Arefizadeh, and Ceyhun Eksin. "Decentralized Fictitious Play in Near-Potential Games With Time-Varying Communication Networks." IEEE Control Systems Letters 6 (2022): 1226–31. http://dx.doi.org/10.1109/lcsys.2021.3090651.

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15

Bracken, P., and A. Boyarsky. "A model for calculating the quantum potential for time-varying multi-slit systems." Chaos, Solitons & Fractals 18, no. 1 (September 2003): 45–53. http://dx.doi.org/10.1016/s0960-0779(02)00634-3.

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16

Cai, Jianping, Y. P. Li, and Xiaofeng Wu. "Escape time from potential wells of strongly nonlinear oscillators with slowly varying parameters." Mathematical Problems in Engineering 2005, no. 3 (2005): 365–75. http://dx.doi.org/10.1155/mpe.2005.365.

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The effect of negative damping to an oscillatory system is to force the amplitude to increase gradually and the motion will be out of the potential well of the oscillatory system eventually. In order to deduce the escape time from the potential well of quadratic or cubic nonlinear oscillator, the multiple scales method is firstly used to obtain the asymptotic solutions of strongly nonlinear oscillators with slowly varying parameters, and secondly the character of modulus of Jacobian elliptic function is applied to derive the equations governing the escape time. The approximate potential method, instead of Taylor series expansion, is used to approximate the potential of an oscillation system such that the asymptotic solution can be expressed in terms of Jacobian elliptic function. Numerical examples verify the efficiency of the present method.

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17

Melandso/, Frank, Tore Nitter, Torsten Aslaksen, and Ove Havnes. "The dust direct current self‐bias potential in a time varying plasma sheath." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 14, no. 2 (March 1996): 619–23. http://dx.doi.org/10.1116/1.580155.

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18

Kashevarov, A. V. "A spherical probe with a time-varying potential in a stationary collisional plasma." Technical Physics 46, no. 9 (September 2001): 1088–92. http://dx.doi.org/10.1134/1.1404158.

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19

Chithira, P. R., and Vinita Vasudevan. "Potential Critical Path Selection Based on a Time-Varying Statistical Timing Analysis Framework." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27, no. 6 (June 2019): 1438–49. http://dx.doi.org/10.1109/tvlsi.2019.2893020.

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20

Poncet, Romain, Reika Fukuizumi, and Anne de Bouard. "Vortex solutions in Bose-Einstein condensation under a trapping potential varying randomly in time." Discrete and Continuous Dynamical Systems - Series B 20, no. 9 (November 2015): 2793–817. http://dx.doi.org/10.3934/dcdsb.2015.20.2793.

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21

Lazarus, Arnaud. "Discrete dynamical stabilization of a naturally diverging mass in a harmonically time-varying potential." Physica D: Nonlinear Phenomena 386-387 (January 2019): 1–7. http://dx.doi.org/10.1016/j.physd.2018.08.001.

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22

Haines, Tim, Elena D’Onghia, Benoit Famaey, Chervin Laporte, and Lars Hernquist. "Implications of a Time-varying Galactic Potential for Determinations of the Dynamical Surface Density." Astrophysical Journal 879, no. 1 (July 2, 2019): L15. http://dx.doi.org/10.3847/2041-8213/ab25f3.

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23

Anderson, D., A. Rueda, L. Cagigal, J. A. A. Antolinez, F. J. Mendez, and P. Ruggiero. "Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential." Journal of Geophysical Research: Oceans 124, no. 12 (December 2019): 9209–34. http://dx.doi.org/10.1029/2019jc015312.

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24

Ueno, S., P. Lövsund, and P. Åke Öberg. "Effect of time-varying magnetic fields on the action potential in lobster giant axon." Medical & Biological Engineering & Computing 24, no. 5 (September 1986): 521–26. http://dx.doi.org/10.1007/bf02443969.

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25

Dahiya, Brijender, and Vinod Prasad. "Dynamics of Particle in a Box in Time Varying Potential Due to Chirped Laser Pulse." Journal of Modern Physics 01, no. 06 (2010): 372–78. http://dx.doi.org/10.4236/jmp.2010.16053.

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26

Yang, Jian, and Mi Dong. "Design of a Time-Varying Continuous Control to Solve the Potential Field Local Minima Problem." Applied Mechanics and Materials 130-134 (October 2011): 2465–69. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2465.

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The subject of this paper is the local minima problem (LMP) inherent in potential field methods (PFMs). Firstly, the underlying theoretical basis of LMP is formulated and its theoretical difficulty of control design is analyzed. It is shown that there does not exist a static state feedback control to solve LMP. Then a time-varying continuous control law is proposed to tackle this problem. In particular, challenges of finding continuous control solutions of LMP are discussed and explicit design strategies are then proposed. Moreover, systematic rigorous Lyapunov proof is given to show both global goal convergence provided that the goal is globally reachable and obstacle avoidance of the proposed controls. Simulation results are provided to illustrate the validity and effectiveness.

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27

Kwon, Chulan. "Optimal protocol for maximum work extraction in a feedback process with a time-varying potential." Journal of the Korean Physical Society 71, no. 12 (September 25, 2017): 880–85. http://dx.doi.org/10.3938/jkps.71.880.

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28

Delfour, M. C., and J. P. Zolésio. "Dynamical free boundary problem for an incompressible potential fluid flow in a time-varying domain." Journal of Inverse and Ill-posed Problems 12, no. 1 (February 2004): 1–25. http://dx.doi.org/10.1515/156939404773972743.

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29

Mezeme, M. Essone, and C. Brosseau. "Time-varying electric field induced transmembrane potential of a core-shell model of biological cells." Journal of Applied Physics 108, no. 1 (July 2010): 014701. http://dx.doi.org/10.1063/1.3456163.

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30

Liang, Xihui, Ming J. Zuo, and Tejas H. Patel. "Evaluating the time-varying mesh stiffness of a planetary gear set using the potential energy method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 3 (April 24, 2013): 535–47. http://dx.doi.org/10.1177/0954406213486734.

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Time-varying mesh stiffness is a periodic function caused by the change in the number of contact tooth pairs and the contact positions of the gear teeth. It is one of the main sources of vibration of a gear transmission system. An efficient and effective way to evaluate the time-varying mesh stiffness is essential to comprehensively understand the dynamic properties of a planetary gear set. According to the literature, there are two ways to evaluate the gear mesh stiffness, the finite element method and the analytical method. The finite element method is time-consuming because one needs to model every meshing gear pair in order to know the mesh stiffness of a range of gear pairs. On the other hand, analytical method can offer a general approach to evaluate the mesh stiffness. In this study, the potential energy method is applied to evaluate the time-varying mesh stiffness of a planetary gear set. Analytical equations are derived without any modification of the gear tooth involute curve. The developed equations are applicable to any transmission structure of a planetary gear set. Detailed discussions are given to three commonly used transmission structures: fixed carrier, fixed ring gear and fixed sun gear.

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31

Zuk, Nathaniel, and Bertrand Delgutte. "Neural coding of time-varying interaural time differences and time-varying amplitude in the inferior colliculus." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 544–63. http://dx.doi.org/10.1152/jn.00797.2016.

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Binaural cues occurring in natural environments are frequently time varying, either from the motion of a sound source or through interactions between the cues produced by multiple sources. Yet, a broad understanding of how the auditory system processes dynamic binaural cues is still lacking. In the current study, we directly compared neural responses in the inferior colliculus (IC) of unanesthetized rabbits to broadband noise with time-varying interaural time differences (ITD) with responses to noise with sinusoidal amplitude modulation (SAM) over a wide range of modulation frequencies. On the basis of prior research, we hypothesized that the IC, one of the first stages to exhibit tuning of firing rate to modulation frequency, might use a common mechanism to encode time-varying information in general. Instead, we found weaker temporal coding for dynamic ITD compared with amplitude modulation and stronger effects of adaptation for amplitude modulation. The differences in temporal coding of dynamic ITD compared with SAM at the single-neuron level could be a neural correlate of “binaural sluggishness,” the inability to perceive fluctuations in time-varying binaural cues at high modulation frequencies, for which a physiological explanation has so far remained elusive. At ITD-variation frequencies of 64 Hz and above, where a temporal code was less effective, noise with a dynamic ITD could still be distinguished from noise with a constant ITD through differences in average firing rate in many neurons, suggesting a frequency-dependent tradeoff between rate and temporal coding of time-varying binaural information. NEW & NOTEWORTHY Humans use time-varying binaural cues to parse auditory scenes comprising multiple sound sources and reverberation. However, the neural mechanisms for doing so are poorly understood. Our results demonstrate a potential neural correlate for the reduced detectability of fluctuations in time-varying binaural information at high speeds, as occurs in reverberation. The results also suggest that the neural mechanisms for processing time-varying binaural and monaural cues are largely distinct.

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32

Wilson, Jeffrey L., J. B. O. Caughman, Phi Long Nguyen, and D. N. Ruzic. "Measurements of time varying plasma potential, temperature, and density in a 13.56 MHz radio‐frequency discharge." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 7, no. 3 (May 1989): 972–76. http://dx.doi.org/10.1116/1.575830.

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33

Hagmann, Mark J. "Efficient numerical methods for solving the Schr�dinger equation with a potential varying sinusoidally with time." International Journal of Quantum Chemistry 56, S29 (February 25, 1995): 289–95. http://dx.doi.org/10.1002/qua.560560832.

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34

Xuan, Heng-Nong, and Miao Zuo. "Matter-Wave Solitons in Two-Component Bose—Einstein Condensates with Tunable Interactions and Time Varying Potential." Communications in Theoretical Physics 56, no. 6 (December 2011): 1035–40. http://dx.doi.org/10.1088/0253-6102/56/6/11.

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35

Niu, Jie, Xingdong Liang, and Xin Zhang. "Time-Varying Kelvin Wake Model and Microwave Velocity Observation." Sensors 20, no. 6 (March 12, 2020): 1575. http://dx.doi.org/10.3390/s20061575.

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In the synthetic aperture radar (SAR) imaging of ship-induced wakes, it is difficult to obtain the Doppler velocity of a Kelvin wake due to the lack of time-varying wake models and suitable radar equipment. The conventional Kelvin wake investigation based on the static Kelvin wake model failed to reflect time-varying characteristics, which are significant in the application of the Kelvin wake model. Therefore, a time-varying Kelvin wake model with consideration of geometric time-varying characteristics and the hydrodynamic equation is proposed in this paper, which reflects the wake’s time-varying change lacking in the conventional Kelvin wake investigation. The Doppler velocity measurement, measured by a specially designed radar, can be exploited to verify the time-varying model by the comparison of velocity fields. Ground-based multi-input multi-output (MIMO) millimeter wave radar imaging through the simultaneous switching of transceiver channels was used to obtain the Doppler velocity for the first time. Finally, promising results have been achieved, which are in good agreement with our proposed model in consideration of the experimental scene. The proposed time-varying model and radar equipment provide velocity measurements for the Kelvin wake observation, which contains huge application potential.

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36

de la Sen, M. "Global Stability of Polytopic Linear Time-Varying Dynamic Systems under Time-Varying Point Delays and Impulsive Controls." Mathematical Problems in Engineering 2010 (2010): 1–33. http://dx.doi.org/10.1155/2010/693958.

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This paper investigates the stability properties of a class of dynamic linear systems possessing several linear time-invariant parameterizations (or configurations) which conform a linear time-varying polytopic dynamic system with a finite number of time-varying time-differentiable point delays. The parameterizations may be timevarying and with bounded discontinuities and they can be subject to mixed regular plus impulsive controls within a sequence of time instants of zero measure. The polytopic parameterization for the dynamics associated with each delay is specific, so that(q+1)polytopic parameterizations are considered for a system withqdelays being also subject to delay-free dynamics. The considered general dynamic system includes, as particular cases, a wide class of switched linear systems whose individual parameterizations are timeinvariant which are governed by a switching rule. However, the dynamic system under consideration is viewed as much more general since it is time-varying with timevarying delays and the bounded discontinuous changes of active parameterizations are generated by impulsive controls in the dynamics and, at the same time, there is not a prescribed set of candidate potential parameterizations.

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37

Mala, N., and A. R. Sudamani Ramaswamy. "Passivity Analysis of Markovian Jumping Neural Networks with Leakage Time-Varying Delays." Journal of Computational Methods in Physics 2013 (July 18, 2013): 1–17. http://dx.doi.org/10.1155/2013/172906.

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This paper is concerned with the passivity analysis of Markovian jumping neural networks with leakage time-varying delays. Based on a Lyapunov functional that accounts for the mixed time delays, a leakage delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). The mixed delays includes leakage time-varying delays, discrete time-varying delays, and distributed time-varying delays. By employing a novel Lyapunov-Krasovskii functional having triple-integral terms, new passivity leakage delay-dependent criteria are established to guarantee the passivity performance. This performance not only depends on the upper bound of the time-varying leakage delay but also depends on the upper bound of the derivative of the time-varying leakage delay . While estimating the upper bound of derivative of the Lyapunov-Krasovskii functional, the discrete and distributed delays should be treated so as to appropriately develop less conservative results. Two numerical examples are given to show the validity and potential of the developed criteria.

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38

Qiu, Panghe, Zhiyuan Ye, Zhichen Bai, Xin Liu, and Su Bo. "Computational Ghost Imaging with Multiplexed Time-Varying Signals." International Journal of Optics 2020 (July 18, 2020): 1–8. http://dx.doi.org/10.1155/2020/4109612.

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This study proposes two methods of optical watermarking based on multiplexed time-varying signals for computational ghost imaging using the Hadamard matrices. The proposed methods can realize image fusion and dual optical encryption. The time-varying signal is encoded into a specific Hadamard coefficient in advance and hidden in the light source of the transmitting end as a multiplicative factor or loaded at the receiving end as an additive factor. Theory and experiments confirm the feasibility of this scheme. Moreover, the scheme is highly scalable and has potential applications in multispectral single-pixel imaging.

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39

KENGNE, E., and S. T. CHUI. "EXACT SOLUTIONS IN ONE-DIMENSIONAL BOSE–EINSTEIN GAS IN A TIME-VARYING TRAP POTENTIAL AND ATOMIC SCATTERING LENGTH." International Journal of Modern Physics B 21, no. 07 (March 20, 2007): 1043–50. http://dx.doi.org/10.1142/s0217979207036813.

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In this paper we discuss the solutions of the cubic nonlinear Schrödinger equation with variable coefficients that model a trapped, quasi-one-dimensional Bose–Einstein condensate with time-varying potential and time-varying atomic scattering length. By applying the theory of Jacobian elliptic functions, we propose a new ansatz to find two new families of solutions. We also obtained regions in the parameters space in which the found families of solutions exist.

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40

Wu, Hao, Yong Huang, Yu Sheng Quan, and Pu Xin Shi. "New Method of Determiner of the Position of Lightning Stroke Point by Using Dynamic Analysis." Applied Mechanics and Materials 675-677 (October 2014): 253–56. http://dx.doi.org/10.4028/www.scientific.net/amm.675-677.253.

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This paper summarize the existing method that can explain the lightning shield failing and put forward a new method to analyze the development of the leader and the upward leader. By using time-varying electromagnetic field analytical method. Solve the dynamic potential at the streamer zone in the bottom of leader. Give a simulation analysis for the dynamic potential and find the trend grow in exponential form. Deduce the electric field and the magnetic field from the dynamic potential in time-varying electromagnetic field.

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41

Ning, Jia, Yi Tang, and Bingtuan Gao. "A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors." Applied Sciences 7, no. 11 (November 3, 2017): 1132. http://dx.doi.org/10.3390/app7111132.

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42

Johnson, J. A., S. L. Bowker, K. Richardson, and C. A. Marra. "Time-varying incidence of cancer after the onset of type 2 diabetes: evidence of potential detection bias." Diabetologia 54, no. 9 (July 12, 2011): 2263–71. http://dx.doi.org/10.1007/s00125-011-2242-1.

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43

Varga, Peter, Erik Grafarend, and Johannes Engels. "Relation of Different Type Love–Shida Numbers Determined with the Use of Time-Varying Incremental Gravitational Potential." Pure and Applied Geophysics 175, no. 5 (March 22, 2017): 1643–48. http://dx.doi.org/10.1007/s00024-017-1532-z.

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44

Guo, Wei, Tao Xu, Keming Tang, Jianjiang Yu, and Shuangshuang Chen. "Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction." Mathematical Problems in Engineering 2018 (May 31, 2018): 1–22. http://dx.doi.org/10.1155/2018/6195387.

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Many real world applications are of time-varying nature and an online learning algorithm is preferred in tracking the real-time changes of the time-varying system. Online sequential extreme learning machine (OSELM) is an excellent online learning algorithm, and some improved OSELM algorithms incorporating forgetting mechanism have been developed to model and predict the time-varying system. But the existing algorithms suffer from a potential risk of instability due to the intrinsic ill-posed problem; besides, the adaptive tracking ability of these algorithms for complex time-varying system is still very weak. In order to overcome the above two problems, this paper proposes a novel OSELM algorithm with generalized regularization and adaptive forgetting factor (AFGR-OSELM). In the AFGR-OSELM, a new generalized regularization approach is employed to replace the traditional exponential forgetting regularization to make the algorithm have a constant regularization effect; consequently the potential ill-posed problem of the algorithm can be completely avoided and a persistent stability can be guaranteed. Moreover, the AFGR-OSELM adopts an adaptive scheme to adjust the forgetting factor dynamically and automatically in the online learning process so as to better track the dynamic changes of the time-varying system and reduce the adverse effects of the outdated data in time; thus it tends to provide desirable prediction results in time-varying environment. Detailed performance comparisons of AFGR-OSELM with other representative algorithms are carried out using artificial and real world data sets. The experimental results show that the proposed AFGR-OSELM has higher prediction accuracy with better stability than its counterparts for predicting time-varying system.

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45

JONES, PRESTON. "ELECTROMAGNETIC RADIATION FROM TEMPORAL VARIATIONS IN SPACE–TIME AND PROGENITORS OF GAMMA RAY BURST AND MILLISECOND PULSARS." International Journal of Modern Physics D 16, no. 11 (November 2007): 1871–77. http://dx.doi.org/10.1142/s0218271807011164.

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A time varying space–time metric is shown to be a source of electromagnetic radiation even in the absence of charge sources. The post-Newtonian approximation is used as a realistic model of the connection between the space–time metric and a time-varying gravitational potential. Rapid temporal variations in the metric from the coalescence of relativistic stars are shown to be likely progenitors of gamma ray burst and millisecond pulsars.

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46

Meschede, Henning. "Analysis on the demand response potential in hotels with varying probabilistic influencing time-series for the Canary Islands." Renewable Energy 160 (November 2020): 1480–91. http://dx.doi.org/10.1016/j.renene.2020.06.024.

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47

Sakaguchi, Shosei. "Estimation of time-varying average treatment effects using panel data when unobserved fixed effects affect potential outcomes differently." Economics Letters 146 (September 2016): 82–84. http://dx.doi.org/10.1016/j.econlet.2016.07.021.

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48

Sun, Kun, Bo Tian, Wen-Jun Liu, Yan Jiang, Qi-Xing Qu, and Pan Wang. "Soliton dynamics and interaction in the Bose–Einstein condensates with harmonic trapping potential and time-varying interatomic interaction." Nonlinear Dynamics 67, no. 1 (March 1, 2011): 165–75. http://dx.doi.org/10.1007/s11071-011-9969-6.

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49

Hong, Guanglei, and Stephen W. Raudenbush. "Causal Inference for Time-Varying Instructional Treatments." Journal of Educational and Behavioral Statistics 33, no. 3 (September 2008): 333–62. http://dx.doi.org/10.3102/1076998607307355.

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The authors propose a strategy for studying the effects of time-varying instructional treatments on repeatedly observed student achievement. This approach responds to three challenges: (a) The yearly reallocation of students to classrooms and teachers creates a complex structure of dependence among responses; (b) a child’s learning outcome under a certain treatment may depend on the treatment assignment of other children, the skill of the teacher, and the classmates and teachers encountered in the past years; and (c) time-varying confounding poses special problems of endogeneity. The authors address these challenges by modifying the stable unit treatment value assumption to identify potential outcomes and causal effects and by integrating inverse probability of treatment weighting into a four-way value-added hierarchical model with pseudolikelihood estimation. Using data from the Longitudinal Analysis of School Change and Performance, the authors apply these methods to study the impact of “intensive math instruction” in Grades 4 and 5.

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Zhang, Xin, Huashan Liu, Yiyuan Zheng, Yuqing Sun, Wuneng Zhou, Yiming Gan, and Guo Wei. "Exponential stability for Markovian neutral stochastic systems with general transition probabilities and time-varying delay." Transactions of the Institute of Measurement and Control 41, no. 2 (April 19, 2018): 350–65. http://dx.doi.org/10.1177/0142331218757859.

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Abstract:

This paper discusses the problem of exponential stability for Markovian neutral stochastic systems with general transition probabilities and time-varying delay. Based on non-convolution type multiple Lyapunov functions and stochastic analysis method, we obtain the conditions which are independent to any decay rate of the exponential stability for uncertain transition probabilities neutral stochastic systems with time-varying delay. Finally, two examples are presented to illustrate the effectiveness and potential of the proposed results.

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