Artículos de revistas: "Time-lag"

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Jusionyte, Ieva. "Time Lag". Journal for the Anthropology of North America 22, n.º 2 (octubre de 2019): 93–95. http://dx.doi.org/10.1002/nad.12101.

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Howard, Amster, T. Walsh y S. W. Dean. "Deflection Lag, Load Lag, and Time Lag of Buried Flexible Pipe". Journal of ASTM International 8, n.º 8 (2011): 102888. http://dx.doi.org/10.1520/jai102888.

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NISHIMURA, Shin-ya, Takashi TAKAHASHI, Satoshi BODA, Takuro NAKAMURA, Yui KANDA, Tsutomu KOBAYASHI y Noriko SAKURAI. "STUDENTS' BEHAVIOURS WITH TIME-LAG AND MOVEMENT-LAG". Journal of Architecture and Planning (Transactions of AIJ) 78, n.º 690 (2013): 1743–50. http://dx.doi.org/10.3130/aija.78.1743.

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4

Franses, Philip Hans. "Time-varying lag cointegration". Journal of Computational and Applied Mathematics 390 (julio de 2021): 113272. http://dx.doi.org/10.1016/j.cam.2020.113272.

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5

Neilson, Brett. "Institution, Time-lag, Globality". Journal of Communication Inquiry 21, n.º 2 (octubre de 1997): 23–34. http://dx.doi.org/10.1177/019685999702100203.

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6

Harker, Lisa. "The poverty time lag". New Economy 8, n.º 2 (junio de 2001): 116–17. http://dx.doi.org/10.1111/1468-0041.00197.

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7

Dunne, Adrian. "Pharmacokinetic lag time estimation". International Journal of Bio-Medical Computing 17, n.º 3-4 (noviembre de 1985): 193–96. http://dx.doi.org/10.1016/0020-7101(85)90022-4.

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8

Ash, Richard y James A. Barrie. "Time lag in diffusion". Journal of Applied Polymer Science 31, n.º 5 (abril de 1986): 1209–18. http://dx.doi.org/10.1002/app.1986.070310506.

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9

Bobrova, N., T. Sorochinskaya, S. Tronina, A. Dembovetskaya, L. Sukhodoeva, I. Levytskyy y N. Trofimova. "«Lag time» in retinoblastoma diagnosis in Ukraine (clinical cases)". Oftalmologicheskii Zhurnal 52, n.º 6 (9 de diciembre de 2014): 5–12. http://dx.doi.org/10.31288/oftalmolzh20146512.

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10

Bobrova, N., N. Sorochinskaya, S. Tronina, A. Dembovetskaya, L. Sukhodoeva, I. Levitskiy y N. Trofimova. "Analysis of «lag time» in the diagnosis of retinoblastoma in Ukraine". Oftalmologicheskii Zhurnal 50, n.º 4 (11 de agosto de 2014): 59–65. http://dx.doi.org/10.31288/oftalmolzh201445965.

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11

Shabanov, Timofey Yu. "Time deals: optimization of lag". Vegetable crops of Russia, n.º 5 (7 de noviembre de 2019): 94–97. http://dx.doi.org/10.18619/2072-9146-2019-5-94-97.

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Relevance. In many ways, a successful purchase and sale transaction is determined by the time it takes to complete it. The search for the best deal time is linked to the optimization of the time lag. The purpose of the study is to optimize the time lag of the agricultural market.Methods. This article considered a role of time in business processes for Russian potato market of producer into period 1998-2018.Results. There was trend of annual reduction about 365 thousand tons (1.2...1.7% of total) per year supply of potatoes on Russian domestic market of producers. Some priority of quantity vs price of producer been revealed. There was inverse proportional function between producers` prices and quantity of potatoes. That is than worse harvest that higher producer`s price. Reducing a supply of potatoes 1 million tons from agricultural market perhaps increase in producer prices by $ 8.5 / t (4...5% on average). Offered concept and hypothesis about linearization of equilibrium of market processes into conditions of time shifts of dynamics their parameters were confirmed. There verified assumption about existence of inter-interval time optimum which this linearization maximal business processes. In course of correlation analysis of dynamics of annual indicators of potato market, it revealed month May of current period as optimal time of producer's offers with price restrictions and diminishing returns, and month September with quantity restrictions and increasing returns. If fixation of producer`s prices situation of deals May more likely, in case of crop failure (limited quantity) in September.

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12

Tarasov, V. N. y N. F. Bakhareva. "QUEUEING SYSTEMS WITH TIME LAG". Radio Electronics, Computer Science, Control, n.º 4 (10 de enero de 2022): 49–57. http://dx.doi.org/10.15588/1607-3274-2021-4-5.

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Context. In the queuing theory of a research of the G/G/1 systems are relevant because it is impossible to receive decisions for the average waiting time in queue in a final form in case of arbitrary laws of distributions of an input flow and service time. Therefore, the study of such systems for particular cases of input distributions is important. The problem of deriving solutions for the average waiting time in a queue in closed form for systems with distributions shifted to the right from the zero point is considered. Objective. Getting solutions for the main characteristics of the systems – the average waiting time of requirements in the queue for queuing systems (QS) of type G/G/1 with shifted input distributions. Methods. To solve this problem, we used the classical method of spectral decomposition of the solution of the Lindley integral equation. This method allows to obtaining a solution for the average waiting time for the systems under consideration in a closed form. The method of spectral decomposition of the solution of the Lindley integral equation plays an important role in the theory of systems G/G/1. For the practical application of the results obtained, the well-known method of moments of probability theory is used. Results. For the first time, spectral expansions are obtained for the solution of the Lindley integral equation for systems with delay, which are used to derive formulas for the average waiting time in a queue in closed form. The paper presents the final studies for the remaining eight delay systems. Conclusions. It is shown that in systems with delay, the average waiting time is less than in in the usual systems. The obtained formula for the average waiting time expands and complements the well-known queuing theory incomplete formula for the average waiting time for G/G/1 systems. This approach allows us to calculate the average latency for these systems in mathematical packages for a wide range of traffic parameters. In addition to the average waiting time, such an approach makes it possible to determine also moments of higher orders of waiting time. Given the fact that the packet delay variation (jitter) in telecommunications is defined as the spread of the waiting time from its average value, the jitter can be determined through the variance of the waiting time.

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13

Anderson, M. D., F. Baron y M. C. Bentz. "TLDR: time lag/delay reconstructor". Monthly Notices of the Royal Astronomical Society 505, n.º 2 (19 de mayo de 2021): 2903–12. http://dx.doi.org/10.1093/mnras/stab1394.

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ABSTRACT We present the time lag/delay reconstructor (TLDR), an algorithm for reconstructing velocity delay maps in the maximum a posteriori framework for reverberation mapping. Reverberation mapping is a tomographical method for studying the kinematics and geometry of the broad-line region of active galactic nuclei at high spatial resolution. Leveraging modern image reconstruction techniques, including total variation and compressed sensing, TLDR applies multiple regularization schemes to reconstruct velocity delay maps using the alternating direction method of multipliers. Along with the detailed description of the TLDR algorithm we present test reconstructions from TLDR applied to synthetic reverberation mapping spectra as well as a preliminary reconstruction of the Hβ feature of Arp 151 from the 2008 Lick Active Galactic Nuclei Monitoring Project.

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14

Schacherl, Ugo. "The Relativistic Time Lag Reassessed". Physics Essays 7, n.º 1 (marzo de 1994): 60–68. http://dx.doi.org/10.4006/1.3029114.

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15

Gong, Y. y C. F. N. Cowan. "Equalisation with adaptive time lag". IEE Proceedings - Communications 152, n.º 5 (2005): 661. http://dx.doi.org/10.1049/ip-com:20045305.

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16

Fortunato, Santo. "Growing time lag threatens Nobels". Nature 508, n.º 7495 (abril de 2014): 186. http://dx.doi.org/10.1038/508186a.

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17

KUTO, Yoshinori y Sakae YAMAMOTO. "Operator's characteristics of time lag system". Japanese journal of ergonomics 29, Supplement (1993): 342–43. http://dx.doi.org/10.5100/jje.29.supplement_342.

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18

Millichap, J. Gordon. "Time Lag to Diagnosis of Stroke". Pediatric Neurology Briefs 16, n.º 12 (1 de diciembre de 2002): 95. http://dx.doi.org/10.15844/pedneurbriefs-16-12-10.

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19

HONMA, Ken-ichi. "Rhythms in Life and Time Lag". TRENDS IN THE SCIENCES 24, n.º 8 (1 de agosto de 2019): 8_20–8_31. http://dx.doi.org/10.5363/tits.24.8_20.

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20

Tarasov, V. N. "Queuing Systems with a Time Lag". INFORMACIONNYE TEHNOLOGII 27, n.º 6 (9 de junio de 2021): 291–98. http://dx.doi.org/10.17587/it.27.291-298.

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The article discusses various queuing systems (QS) formed by four laws of probability distributions: exponential, hyperexponential, Erlang and hyper-Erlang of the second order. These four laws form sixteen different QS. In contrast to the classical theory, this article considers QS with distribution laws shifted to the right from the zero point. Such QS are of type G/G/1 with arbitrary laws of the distribution of intervals between the requirements of the input flow and the service time. As you know, for such systems it is impossible to obtain solutions for the main characteristic of QS the average waiting time in the general case. Therefore, studies of such systems are important for special cases of distribution laws. The article provides an overview of the author's results for the average waiting time in a queue in a closed form for systems with input distributions shifted to the right from the zero point. To solve this problem, the spectral decomposition method for solving the Lindley integral equation was used. In the course of solving the problem, spectral decompositions of the solution of the Lindley integral equation for eight systems were obtained and with their help calculation formulas were derived for the average waiting time in the queue. It is shown that in systems with delay, the average waiting time is shorter than in conventional systems. The obtained calculation formulas for the average waiting time expand and complement the well-known incomplete formula of the queuing theory for the average waiting time for G/G/1 systems. The proposed approach allows us to calculate the average value and moments of higher orders of waiting time for these systems in mathematical packages for a wide range of changes in traffic parameters. Given the fact that the variation in packet delay (jitter) in the telecommunications standard is defined as the spread of waiting time around its average value, the jitter can be determined through the variance of the waiting time.

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21

Kal’yanov, É. V. "Autoparametric delay system with time lag". Technical Physics 52, n.º 8 (agosto de 2007): 963–67. http://dx.doi.org/10.1134/s1063784207080014.

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22

Reshetnyak, M. Yu. "Time-lag effects in Parker’s dynamo". Astronomy Reports 54, n.º 11 (noviembre de 2010): 1047–52. http://dx.doi.org/10.1134/s1063772910110107.

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23

Tschernig, Rolf y Lijian Yang. "Nonparametric Lag Selection for Time Series". Journal of Time Series Analysis 21, n.º 4 (julio de 2000): 457–87. http://dx.doi.org/10.1111/1467-9892.00193.

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24

Shahverdiev, E. M., S. Sivaprakasam y K. A. Shore. "Lag synchronization in time-delayed systems". Physics Letters A 292, n.º 6 (enero de 2002): 320–24. http://dx.doi.org/10.1016/s0375-9601(01)00824-6.

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25

Shneidman, V. A. y E. V. Goldstein. "Nucleation time lag at nano-sizes". Journal of Non-Crystalline Solids 351, n.º 18 (junio de 2005): 1512–21. http://dx.doi.org/10.1016/j.jnoncrysol.2005.03.039.

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26

Delhez, Éric J. M. y Éric Deleersnijder. "Age and the time lag method". Continental Shelf Research 28, n.º 8 (mayo de 2008): 1057–67. http://dx.doi.org/10.1016/j.csr.2008.02.003.

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27

Wu, David T. "The time lag in nucleation theory". Journal of Chemical Physics 97, n.º 4 (15 de agosto de 1992): 2644–50. http://dx.doi.org/10.1063/1.463052.

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28

Pearson, A. E. y Y. A. Fiagbedzi. "An observer for time lag systems". IEEE Transactions on Automatic Control 34, n.º 7 (julio de 1989): 775–77. http://dx.doi.org/10.1109/9.29412.

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29

Du, Sizhen, Guojie Song, Lei Han y Haikun Hong. "Temporal Causal Inference with Time Lag". Neural Computation 30, n.º 1 (enero de 2018): 271–91. http://dx.doi.org/10.1162/neco_a_01028.

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Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past evidence would take some time to cause a future effect instead of an immediate response. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. In this letter, we propose to learn the causal relations as well as the lag among different time series simultaneously from data. Specifically, we develop a probabilistic decomposed slab-and-spike (DSS) model to perform the inference by applying a pair of decomposed spike-and-slab variables for the model coefficients, where the first variable is used to estimate the causal relationship and the second one captures the lag information among different temporal variables. For parameter inference, we propose an efficient expectation propagation (EP) algorithm to solve the DSS model. Experimental results conducted on both synthetic and real-world problems demonstrate the effectiveness of the proposed method. The revealed time lag can be well validated by the domain knowledge within the real-world applications.

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Citra, Anindya, Budi Utomo y Hendrian Dwikoloso Soebagjo. "Longer Lag Time in Early-Stage Retinoblastoma". Folia Medica Indonesiana 58, n.º 2 (5 de junio de 2022): 103–7. http://dx.doi.org/10.20473/fmi.v58i2.24975.

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Highlights: Patients with IRSS I stage have the longest lag time than IVB stage. Lag time is no significant with stage retinoblastoma. Abstract: Retinoblastoma is a rare neoplasm disease that occurs in children, generally under the age of two. Retinoblastoma is more prevalent in developing countries and is often associated with a late diagnosis. Such delays can lead to a poor prognosis. The time from the appearance of symptoms of retinoblastoma (onset) to the time of diagnosis is called lag time. Early diagnosis of retinoblastoma by paying attention to factors such as age, clinical symptoms, and laterality can help improve retinoblastoma survival rates, especially in developing countries. The purpose of this study was to analyze the relationship between the lag time to the stage of retinoblastoma patients at Dr. Soetomo General Academic Hospital, Surabaya, Indonesia. This study was a retrospective analytical observational study using secondary data from retinoblastoma patients at the Ophthalmology Outpatient Unit at Dr. Soetomo General Academic Hospital, Surabaya, from January 2014 to December 2018. The data were analyzed using Kendall's tau-C test. No significant correlation was found between lag time and stage (p = 0.339). Patients with International Retinoblastoma Staging System (IRSS) I stage had the longest lag time (on average 28 months), and patients with stage IVB had the shortest lag time (on average four months). There was no correlation between lag time and retinoblastoma stage. However, there was a trend of patients with early stages delaying hospital visits, while patients with advanced stages in earlier to the hospital.

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31

Watt, W. Edgar y K. C. Ander Chow. "A general expression for basin lag time". Canadian Journal of Civil Engineering 12, n.º 2 (1 de junio de 1985): 294–300. http://dx.doi.org/10.1139/l85-031.

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Basin lag time is an important variable in the calculation of peak discharge resulting from a specified precipitation input. For the case of ungauged basins, the lag time must be estimated, normally from one or more expressions relating lag time to basin physical characteristics. A number of such expressions exist but each was developed for a particular range of basin size and geographic region. To overcome the problem of using an expression that may not be appropriate, a tentative general expression for basin lag time has been developed using data from basins representative of many regions in North America and ranging in area from 0.5 ha to 5840 km2. The tentative expression has only one basin characteristic, basin length divided by the square root of basin slope, [Formula: see text], and applies to natural basins with minimal effective lake and swamp storage. When more data become available, the expression will be modified to include the effects of urban development and significant storage. Key words: lag time, peak discharge, prediction equation, ungauged basins.

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32

Dikheel, Tahir R. y Alaa Q. Yaseen. "Robust Lag Weighted Lasso for Time Series Model". Journal of Modern Applied Statistical Methods 19, n.º 1 (17 de junio de 2021): 2–15. http://dx.doi.org/10.22237/jmasm/1608553500.

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The lag-weighted lasso was introduced to deal with lag effects when identifying the true model in time series. This method depends on weights to reflect both the coefficient size and the lag effects. However, the lag weighted lasso is not robust. To overcome this problem, we propose robust lag weighted lasso methods. Both the simulation study and the real data example show that the proposed methods outperform the other existing methods.

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33

Gonzalez, Joseph V., Bryan M. Trout, Rodney M. Stuck y Lori A. Vrbos. "Time analysis for screw application: Traditional lag technique versus self-tapping lag technique". Journal of Foot and Ankle Surgery 36, n.º 6 (noviembre de 1997): 422–24. http://dx.doi.org/10.1016/s1067-2516(97)80093-2.

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34

Ristic, Ratko. "Lag time on torrential catchments in Serbia". Bulletin of the Faculty of Forestry, n.º 87 (2003): 51–65. http://dx.doi.org/10.2298/gsf0387051r.

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Maximal discharge Qmax(p) is the basic input data for designing longitudinal and transversal objects in torrential beds. Calculation of maximal discharge Qmax, on unstudied catchments, is carrying out by usage the theory of synthetic unit hidrograph and SCS methodology for separation effective rain Pe by total rainfall Pb. One of the basic time characteristics of torrential floods was analyzed: lag time tp. Lag time tp was determined on the basis of processing of hydrographs and deriving representative unit hydrographs, on 93 control profiles. Dominant physical-geography characteristics were determined: magnitude of the catchment area A, length of the catchment area along the main stream L, the distance from the centroid of the catchment area to the outlet profile Lc, average slope of the river bed Iu, average slope of terrain on the catchment area Isr. Lag time and dominant physical-geography characteristics were the basis for deriving some equations with regional significance (tp=f(L, Lc, Iu); tp=f(L, Lc, Iu, Isr)). The relation between lag time tp and time of concentration Tc was determined (tp=f(Tc)).

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35

Okuda, Shounosuke, Tetsunobu Murakami, Masamichi Sakai y Shougo Sekine. "Starting time lag of neon lamp discharging". JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 74, Appendix (1990): 43. http://dx.doi.org/10.2150/jieij1980.74.appendix_43.

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36

Gierz, Kristine, Kayoung Park y Peihua Qiu. "Non-parametric treatment time-lag effect estimation". Statistical Methods in Medical Research 31, n.º 1 (16 de noviembre de 2021): 62–75. http://dx.doi.org/10.1177/09622802211032693.

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In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

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37

Folmar, Norman D. y Arthur C. Miller. "Development of an Empirical Lag Time Equation". Journal of Irrigation and Drainage Engineering 134, n.º 4 (agosto de 2008): 501–6. http://dx.doi.org/10.1061/(asce)0733-9437(2008)134:4(501).

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38

Pettitt. "Time Lag and Elizabeth Gaskell's Transatlantic Imagination". Victorian Studies 54, n.º 4 (2012): 599. http://dx.doi.org/10.2979/victorianstudies.54.4.599.

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39

Zhang, Yilin, Zhenyu Cheng y Qingsong He. "Time lag analysis of FDI spillover effect". International Journal of Emerging Markets 15, n.º 4 (1 de noviembre de 2019): 629–50. http://dx.doi.org/10.1108/ijoem-03-2019-0225.

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Purpose For the developing countries involving in the Belt and Road Initiative (BRI) with China as the main source of foreign development investment (FDI) and development as the top priority, it appears to attract more and more attention on how to make the best use of China’s outward foreign development investment. However, the contradictory evidence in the previous studies of FDI spillover effect and the remarkable time-lag feature of spillovers motivate us to analyze the mechanism of FDI spillover effect. The paper aims to discuss this issue. Design/methodology/approach The mechanism of FDI spillovers and the unavoidable lag effect in this process are empirically analyzed. Based on the panel data from the Belt and Road developing countries (BRDCs) and China’s direct investments (CDIs) from 2003 to 2017, the authors establish a panel vector autoregressive model, employing impulse response function and variance decomposition analysis, together with Granger causality test. Findings Results suggest a dynamic interactive causality mechanism. First, CDI promotes the economic growth of BRDCs through technical efficiency, human capital and institutional transition with combined lags of five, nine and eight years. Second, improvements in the technical efficiency and institutional quality promote economic growth by facilitating the human capital with integrated delays of six and eight years. Third, China’s investment directly affects the economic growth of BRDCs, with a time lag of six years. The average time lag is about eight years. Originality/value Based on the analysis on the mechanism and time lag of FDI spillovers, the authors have shown that many previous articles using one-year lagged FDI to examine the spillover effect have systematic biases, which contributes to the research on the FDI spillover mechanism. It provides new views for host countries on how to make more effective use of FDI, especially for BRDCs using CDIs.

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40

&NA;. "Lag-Time to Publication in Plastic Surgery". Annals of Plastic Surgery 71, n.º 6 (diciembre de 2013): 684. http://dx.doi.org/10.1097/01.sap.0000438395.47675.f4.

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41

Podobnik, B., D. Wang, D. Horvatic, I. Grosse y H. E. Stanley. "Time-lag cross-correlations in collective phenomena". EPL (Europhysics Letters) 90, n.º 6 (1 de junio de 2010): 68001. http://dx.doi.org/10.1209/0295-5075/90/68001.

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Gomes, Ana Cristina R. y Gonçalo C. Cardoso. "The lag-time constraint for behavioural plasticity". Proceedings of the Royal Society B: Biological Sciences 287, n.º 1926 (29 de abril de 2020): 20200525. http://dx.doi.org/10.1098/rspb.2020.0525.

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Environmental instability (i.e. environments changing often) can select fixed phenotypes because of the lag time of plastically adapting to environmental changes, known as the lag-time constraint. Because behaviour can change rapidly (e.g. switching between foraging strategies), the lag-time constraint is not considered important for behavioural plasticity. Instead, it is often argued that responsive behaviour (i.e. behaviour that changes according to the environment) evolves to cope with unstable environments. But proficiently performing certain behaviours may require time for learning, for practising or, in social animals, for the group to adjust to one's behaviour. Conversely, not using certain behaviours for a period of time can reduce their level of performance. Here, using individual-based evolutionary simulations, we show that environmental instability selects for fixed behaviour when the ratio between the rates of increase and reduction in behavioural performance is below a certain threshold; only above this threshold does responsive behaviour evolve in unstable environments. Thus, the lag-time constraint can apply to behaviours that attain high performance either slowly or rapidly, depending on the relative rate with which their performance decreases when not used. We discuss these results in the context of the evolution of reduced behavioural plasticity, as seen in fixed personality differences.

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43

Brauer, F. "Time lag in disease models with recruitment". Mathematical and Computer Modelling 31, n.º 4-5 (febrero de 2000): 11–15. http://dx.doi.org/10.1016/s0895-7177(00)00016-9.

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44

Mahmoud, Magdi S. "Frequency-domain robustness of time-lag systems". IFAC Proceedings Volumes 32, n.º 2 (julio de 1999): 3395–99. http://dx.doi.org/10.1016/s1474-6670(17)56579-9.

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45

Gluck, Paul y Bernd Saering. "No Time Lag in the Photoelectric Effect". Physics Teacher 48, n.º 5 (mayo de 2010): 285–86. http://dx.doi.org/10.1119/1.3393052.

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46

Bisnovatyi-Kogan, G. S. y F. Giovannelli. "Time lag in transient cosmic accreting sources". Astronomy & Astrophysics 599 (28 de febrero de 2017): A55. http://dx.doi.org/10.1051/0004-6361/201628810.

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47

Lee, David T., Jason LaCombe, Christina K. Chung, Ahlia Kattan y Gordon K. Lee. "Lag-Time to Publication in Plastic Surgery". Annals of Plastic Surgery 71, n.º 4 (octubre de 2013): 410–14. http://dx.doi.org/10.1097/sap.0b013e31824bd75c.

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48

FIAGBEDZI, Y. A. y A. E. PEARSON. "Exponential state observer for time-lag systems". International Journal of Control 51, n.º 1 (enero de 1990): 189–204. http://dx.doi.org/10.1080/00207179008934056.

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49

Eden, O. B. y V. Saha. "Lag time for children with brain tumours." Archives of Disease in Childhood 75, n.º 3 (1 de septiembre de 1996): 266. http://dx.doi.org/10.1136/adc.75.3.266.

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Edrei, I. y M. Gitterman. "Time lag in nucleation: Monte Carlo simulations". Journal of Physics A: Mathematical and General 19, n.º 16 (11 de noviembre de 1986): 3279–86. http://dx.doi.org/10.1088/0305-4470/19/16/026.

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