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Journal articles on the topic 'Stochastic simulation technique (SST)'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Viseur, Sophie. "Turbidite reservoir characterization : object-based stochastic simulation meandering channels." Bulletin de la Société Géologique de France 175, no. 1 (2004): 11–20. http://dx.doi.org/10.2113/175.1.11.

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Abstract Stochastic imaging has become an important tool for risk assessment and has successfully been applied to oil field management. This procedure aims at generating several possible and equiprobable 3D models of subsurface structures that enhance the available data set. Among these stochastic simulation techniques, object-based approaches consist of defining and distributing objects reproducing underground geobodies. A technical challenge still remains in object-based simulation. Due to advances in deep water drilling technology, new hydrocarbon exploration has been opened along the Atlan

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Rodionov, Alexander, Alexander Zhuchkov, and Victoria Pekut. "The Features of the Technique of Practical Training on “Fundamentals of Simulation of Automated Systems» Discipline." NBI Technologies, no. 1 (August 2019): 21–24. http://dx.doi.org/10.15688/nbit.jvolsu.2019.1.4.

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The paper deals with the content and methods of practical training in “Fundamentals of Simulation of Automated Systems”discipline. The relevance of the work is due to ever-growing requirements for the design of protected information systems, which are a class (subsystem) of automated systems. The level of mathematical training of students, and especially undergraduates, is significantly different. This leads to the need for the careful study of methods of lectures and especially practical training in the discipline. With multiple simulations of the network system, it is possible to accumulate

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Aragon-Calvo, M. A. "Smooth stochastic density field reconstruction." Monthly Notices of the Royal Astronomical Society 503, no. 1 (2021): 557–62. http://dx.doi.org/10.1093/mnras/stab403.

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ABSTRACT We introduce a method for generating a continuous, mass-conserving and high-order differentiable density field from a discrete point distribution such as particles or haloes from an N-body simulation or galaxies from a spectroscopic survey. The method consists on generating an ensemble of point realizations by perturbing the original point set following the geometric constraints imposed by the Delaunay tessellation in the vicinity of each point in the set. By computing the mean field of the ensemble we are able to significantly reduce artefacts arising from the Delaunay tessellation i

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Carletti, Margherita, and Malay Banerjee. "A Backward Technique for Demographic Noise in Biological Ordinary Differential Equation Models." Mathematics 7, no. 12 (2019): 1204. http://dx.doi.org/10.3390/math7121204.

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Physical systems described by deterministic differential equations represent idealized situations since they ignore stochastic effects. In the context of biomathematical modeling, we distinguish between environmental or extrinsic noise and demographic or intrinsic noise, for which it is assumed that the variation over time is due to demographic variation of two or more interacting populations (birth, death, immigration, and emigration). The modeling and simulation of demographic noise as a stochastic process affecting units of populations involved in the model is well known in the literature,

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Wilson, Spencer, Abdullah Alabdulkarim, and David Goldsman. "Green Simulation of Pandemic Disease Propagation." Symmetry 11, no. 4 (2019): 580. http://dx.doi.org/10.3390/sym11040580.

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This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previou

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CHAN, M. S., F. MUTAPI, M. E. J. WOOLHOUSE, and V. S. ISHAM. "Stochastic simulation and the detection of immunity to schistosome infections." Parasitology 120, no. 2 (2000): 161–69. http://dx.doi.org/10.1017/s003118209900534x.

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In this paper we address the question of detecting immunity to helminth infections from patterns of infection in endemic communities. We use stochastic simulations to investigate whether it would be possible to detect patterns predicted by theoretical models, using typical field data. Thus, our technique is to simulate a theoretical model, to generate the data that would be obtained in field surveys and then to analyse these data using methods usually employed for field data. The general behaviour of the model, and in particular the levels of variability of egg counts predicted, show that the

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Yang, Hong-an, Yangyang Lv, Changkai Xia, Shudong Sun, and Honghao Wang. "Optimal Computing Budget Allocation for Ordinal Optimization in Solving Stochastic Job Shop Scheduling Problems." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/619254.

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We focus on solving Stochastic Job Shop Scheduling Problem (SJSSP) with random processing time to minimize the expected sum of earliness and tardiness costs of all jobs. To further enhance the efficiency of the simulation optimization technique of embedding Evolutionary Strategy in Ordinal Optimization (ESOO) which is based on Monte Carlo simulation, we embed Optimal Computing Budget Allocation (OCBA) technique into the exploration stage of ESOO to optimize the performance evaluation process by controlling the allocation of simulation times. However, while pursuing a good set of schedules, “su

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Cahen, Ewan Jacov, Michel Mandjes, and Bert Zwart. "RARE EVENT ANALYSIS AND EFFICIENT SIMULATION FOR A MULTI-DIMENSIONAL RUIN PROBLEM." Probability in the Engineering and Informational Sciences 31, no. 3 (2017): 265–83. http://dx.doi.org/10.1017/s0269964816000553.

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This paper focuses on the evaluation of the probability that both components of a bivariate stochastic process ever simultaneously exceed some large level; a leading example is that of two Markov fluid queues driven by the same background process ever reaching the set (u, ∞)×(u, ∞), for u>0. Exact analysis being prohibitive, we resort to asymptotic techniques and efficient simulation, focusing on large values of u. The first contribution concerns various expressions for the decay rate of the probability of interest, which are valid under Gärtner–Ellis-type conditions. The second contributio

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Ren, Y. J., I. Elishakoff, and M. Shinozuka. "Simulation of Multivariate Gaussian Fields Conditioned by Realizations of the Fields and Their Derivatives." Journal of Applied Mechanics 63, no. 3 (1996): 758–65. http://dx.doi.org/10.1115/1.2823360.

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This paper investigates conditional simulation technique of multivariate Gaussian random fields by stochastic interpolation technique. For the first time in the literature a situation is studied when the random fields are conditioned not only by a set of realizations of the fields, but also by a set of realizations of their derivatives. The kriging estimate of multivariate Gaussian field is proposed, which takes into account both the random field as well as its derivative. Special conditions are imposed on the kriging estimate to determine the kriging weights. Basic formulation for simulation

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Qi, Ji, and Yanhui Li. "L1 control for Itô stochastic nonlinear networked control systems." Transactions of the Institute of Measurement and Control 42, no. 14 (2020): 2675–85. http://dx.doi.org/10.1177/0142331220923770.

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This paper investigates L1 control problem for a class of nonlinear stochastic networked control systems (NCSs) described by Takagi-Sugeno (T-S) fuzzy model. By exploiting a delay-dependent and basis-dependent Lyapunov-Krasovskii function and by means of the Itô stochastic differential equation technique, results on stability and L1 performance are proposed for the T-S fuzzy stochastic NCS. Specially, attention is focused on the fuzzy controller design that guarantees the closed-loop T-S fuzzy stochastic NCS is mean-square asymptotically stable and satisfies a prescribed L1 noise attenuation l

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Mohamed, Linah, Mike Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." SPE Journal 15, no. 01 (2009): 31–38. http://dx.doi.org/10.2118/119139-pa.

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Summary History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir (e.g., from analog outcrop data) and update our reservoir models with observations (e.g., from production data or time-lapse seismic). The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history-matched models is the sampling algorithm us

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Ahmed, N. U., and X. H. Ouyang. "Suboptimal RED Feedback Control for Buffered TCP Flow Dynamics in Computer Network." Mathematical Problems in Engineering 2007 (2007): 1–17. http://dx.doi.org/10.1155/2007/54683.

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We present an improved dynamic system that simulates the behavior of TCP flows and active queue management (AQM) system. This system can be modeled by a set of stochastic differential equations driven by a doubly stochastic point process with intensities being the controls. The feedback laws proposed monitor the status of buffers and multiplexor of the router, detect incipient congestion by sending warning signals to the sources. The simulation results show that the optimal feedback control law from the class of linear as well as quadratic polynomials can improve the system performance signifi

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Rostampour, Vahab, and Tamás Keviczky. "Distributed Computational Framework for Large-Scale Stochastic Convex Optimization." Energies 14, no. 1 (2020): 23. http://dx.doi.org/10.3390/en14010023.

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This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of required scenarios to approximate the stochasticity of these problems, the stochastic optimization involves formulating a large-scale scenario program, which is in general computationally demanding. We present two novel ideas in this paper to address this issue. We first develop a technique to decomp

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AVELLANEDA, MARCO, ROBERT BUFF, CRAIG FRIEDMAN, NICOLAS GRANDECHAMP, LUKASZ KRUK, and JOSHUA NEWMAN. "WEIGHTED MONTE CARLO: A NEW TECHNIQUE FOR CALIBRATING ASSET-PRICING MODELS." International Journal of Theoretical and Applied Finance 04, no. 01 (2001): 91–119. http://dx.doi.org/10.1142/s0219024901000882.

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A general approach for calibrating Monte Carlo models to the market prices of benchmark securities is presented. Starting from a given model for market dynamics (price diffusion, rate diffusion, etc.), the algorithm corrects price-misspecifications and finite-sample effects in the simulation by assigning "probability weights" to the simulated paths. The choice of weights is done by minimizing the Kullback–Leibler relative entropy distance of the posterior measure to the empirical measure. The resulting ensemble prices the given set of benchmark instruments exactly or in the sense of least-squa

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Федорович, Олег Євгенович, Олег Семенович Уруський, Людмила Миколаївна Лутай та Ксенія Олегівна Западня. "ОПТИМІЗАЦІЯ ЖИТТЄВОГО ЦИКЛУ СТВОРЕННЯ НОВОЇ ТЕХНІКИ В УМОВАХ КОНКУРЕНЦІЇ ТА СТОХАСТИЧНОЇ ПОВЕДІНКИ РИНКУ ЗБУТУ ВИСОКОТЕХНОЛОГІЧНОЇ ПРОДУКЦІЇ". Aerospace technic and technology, № 6 (27 листопада 2020): 80–85. http://dx.doi.org/10.32620/aktt.2020.6.09.

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The task to optimize the life cycle of new technique (aerospace, engineering products, etc.) in difficult economic conditions is stated and solved. The aim of the study is to develop a method to reduce the life cycle of complex technique creation. The subject of the research is the planning and management of the life cycle of complex technique in a highly competitive environment and stochastic behavior of high-tech products market. The paper shows the contradictions between the planned nature of modern production, which operates in conditions of Industry 4.0 and the stochastic behavior of the

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Luo, Junzhi, and Wanly Yang. "H∞Control of Supply Chain Based on Switched Model of Stock Level." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/464256.

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This paper is concerned with the problem ofH∞control for a class of discrete supply chain systems. A new method based on network control technique is presented to address this issue. Supply chain systems are modeled as networked systems with stochastic time delay. Sufficient conditions forH∞controller design are given in terms of a set of linear matrix inequalities, based on which the mean-square asymptotic stability as well asH∞performance is satisfied for such systems. Simulation results are provided to demonstrate the effectiveness of the proposed method.

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Hou, Zenghao, and Joyoung Lee. "Multi-Thread Optimization for the Calibration of Microscopic Traffic Simulation Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (2018): 98–109. http://dx.doi.org/10.1177/0361198118796395.

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This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Particle Swarm Optimization (PSO) algorithm, the proposed approach, namely the Quasi-Monte Carlo Particle Swarm (QPS) calibration method, is designed to boost the searching process without prejudice to the calibration accuracy. Given the search space constructed by the combinations of simulation parameters, the QMC sampling technique filters the searching space, followed by the multi-thread optimization

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Prato, Carlo Giacomo. "META-ANALYSIS OF CHOICE SET GENERATION EFFECTS ON ROUTE CHOICE MODEL ESTIMATES AND PREDICTIONS." TRANSPORT 27, no. 3 (2012): 286–98. http://dx.doi.org/10.3846/16484142.2012.719840.

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Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation.

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Chen, Wei, Yuanyuan Zou, Nan Xiao, and Yugang Niu. "Quantized H∞ filtering for discrete-time systems over fading channels." Transactions of the Institute of Measurement and Control 40, no. 10 (2017): 3115–24. http://dx.doi.org/10.1177/0142331217714862.

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This paper addresses the problem of quantized [Formula: see text] filtering for multi-output discrete-time systems over independent identically distributed (i.i.d.) fading channels and Markov fading channels, respectively. The measurement outputs are quantized by a logarithmic quantizer, and then transmitted to the filter over fading channels. For the i.i.d. fading channels, the stochastic multiplicative noise form is used to model the unreliable communication environment. For the Markov fading channels, a set of Markov channel state processes is introduced to model time-varying fading channel

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Montesi, Giuseppe, Giovanni Papiro, Massimiliano Fazzini, and Alessandro Ronga. "Stochastic Optimization System for Bank Reverse Stress Testing." Journal of Risk and Financial Management 13, no. 8 (2020): 174. http://dx.doi.org/10.3390/jrfm13080174.

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The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a stochastic simulation optimization system. This methodology enables users to derive the critical combination of risk factors that, by triggering a preset key capital indicator threshold, causes the bank’s default, thus detecting the set of assumptions that defines the reverse stress test scenario. This article

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Kalidass, Mathiyalagan, Hongye Su, and Sakthivel Rathinasamy. "Robust Stochastic Stability of Discrete-Time Markovian Jump Neural Networks with Leakage Delay." Zeitschrift für Naturforschung A 69, no. 1-2 (2014): 70–80. http://dx.doi.org/10.5560/zna.2013-0078.

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This paper presents a robust analysis approach to stochastic stability of the uncertain Markovian jumping discrete-time neural networks (MJDNNs) with time delay in the leakage term. By choosing an appropriate Lyapunov functional and using free weighting matrix technique, a set of delay dependent stability criteria are derived. The stability results are delay dependent, which depend on not only the upper bounds of time delays but also their lower bounds. The obtained stability criteria are established in terms of linear matrix inequalities (LMIs) which can be effectively solved by some standard

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Gordon, T. J., C. Marsh, and Q. H. Wu. "Stochastic Optimal Control of Active Vehicle Suspensions Using Learning Automata." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 3 (1993): 143–52. http://dx.doi.org/10.1243/pime_proc_1993_207_333_02.

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This paper is concerned with the application of reinforcement learning to the stochastic optimal control of an idealized active vehicle suspension system. The use of learning automata in optimal control is a new application of this machine learning technique, and the principal aim of this work is to define and demonstrate the method in a relatively simple context, as well as to compare performance against results obtained from standard linear optimal control theory. The most distinctive feature of the approach is that no formal modelling is involved in the control system design; once implement

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Nussbaumer, Raphaël, Grégoire Mariethoz, Erwan Gloaguen, and Klaus Holliger. "Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling." Geophysical Journal International 221, no. 3 (2020): 2184–200. http://dx.doi.org/10.1093/gji/ggaa072.

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SUMMARY Bayesian sequential simulation (BSS) is a geostastistical technique, which uses a secondary variable to guide the stochastic simulation of a primary variable. As such, BSS has proven significant promise for the integration of disparate hydrogeophysical data sets characterized by vastly differing spatial coverage and resolution of the primary and secondary variables. An inherent limitation of BSS is its tendency to underestimate the variance of the simulated fields due to the smooth nature of the secondary variable. Indeed, in its classical form, the method is unable to account for this

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Deng, Qiqi, and Tianshou Zhou. "Memory-Induced Bifurcation and Oscillations in the Chemical Brusselator Model." International Journal of Bifurcation and Chaos 30, no. 10 (2020): 2050151. http://dx.doi.org/10.1142/s0218127420501515.

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Previous studies assumed that the reaction processes in the chemical Brusselator model are memoryless or Markovian. However, as long as a reactant interacts with its environment, the reaction kinetics cannot be described as a memoryless process. This raises a question: how do we predict the behavior of the chemical Brusselator system with molecular memory characterized by nonexponential waiting-time distributions? Here, a novel technique is developed to address this question. This technique converts a non-Markovian question to a Markovian one by introducing effective transition rates that expl

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Wang, L., P. M. Wong, and S. A. R. Shibli. "Modeling Porosity Distribution in the A'nan Oilfield: Use of Geological Quantification, Neural Networks, and Geostatistics." SPE Reservoir Evaluation & Engineering 2, no. 06 (1999): 527–32. http://dx.doi.org/10.2118/59090-pa.

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Summary A'nan Oilfield is located in the northeast of the Erlian Basin in North China. The porosity distribution of the oil-bearing stratum is primarily controlled by complex distribution patterns of sedimentary lithofacies and diagenetic facies. This paper describes a methodology to provide a porosity model for the A'nan Oilfield using limited well porosity data, with the incorporation of the conceptual reservoir architecture. Neural network residual kriging or simulation is employed to tackle the problem. The integrated technique is developed based on a combined use of radial basis function

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Elloumi, Mourad, and Samira Kamoun. "Improved Discrete Techniques of Time-Delay and Order Estimation for Large-Scale Interconnected Nonlinear Systems." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1919823.

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The selection of a suitable model structure is an essential step in system modeling. Model structure is defined by determining the class, the time delay, and the model order. This paper proposes improved structural estimation procedures for large-scale interconnected nonlinear systems which are composed of a set of interconnected Single-Input Single-Output (SISO) Hammerstein structures and described by discrete-time stochastic models with unknown time-varying parameters. An extensive Determinant Report (DR) algorithm is developed to determine the order of the process. An improved discrete-time

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Albatran, Saher, Muwaffaq I. Alomoush, and Ahmed M. Koran. "Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (2018): 780. http://dx.doi.org/10.11591/ijece.v8i2.pp780-792.

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Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set

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Abu Dabous, Saleh, and Ghadeer Al-Khayyat. "A Flexible Bridge Rating Method Based on Analytical Evidential Reasoning and Monte Carlo Simulation." Advances in Civil Engineering 2018 (June 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/1290632.

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Several bridge inspection standards and condition assessment practices have been developed around the globe. Some practices employ four linguistic expressions to rate bridge elements while other practices use five or six, or adopt numerical ratings such as 1 to 9. This research introduces a condition rating method that can operate under different condition assessment practices and account for uncertainties in condition assessment by means of the Evidential Reasoning (ER) theory. The method offers flexibility in terms of using default elements and their weights or selecting alternative set of e

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Hossain, F., E. N. Anagnostou, and K. H. Lee. "A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model." Nonlinear Processes in Geophysics 11, no. 4 (2004): 427–40. http://dx.doi.org/10.5194/npg-11-427-2004.

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Abstract. This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM). The scheme is assessed within a Monte Carlo (MC) simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linea

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Ramsay, Travis. "Uncertainty Quantification of an Explicitly Coupled Multiphysics Simulation of In-Situ Pyrolysis by Radio Frequency Heating in Oil Shale." SPE Journal 25, no. 03 (2020): 1443–61. http://dx.doi.org/10.2118/200476-pa.

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Summary In-situ pyrolysis provides an enhanced oil recovery (EOR) technique for exploiting oil and gas from oil shale by converting in-place solid kerogen into liquid oil and gas. Radio-frequency (RF) heating of the in-place oil shale has previously been proposed as a method by which the electromagnetic energy gets converted to thermal energy, thereby heating in-situ kerogen so that it converts to oil and gas. In order to numerically model the RF heating of the in-situ oil shale, a novel explicitly coupled thermal, phase field, mechanical, and electromagnetic (TPME) framework is devised using

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Jhwueng, Dwueng-Chwuan, and Chih-Ping Wang. "Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution." Entropy 23, no. 2 (2021): 218. http://dx.doi.org/10.3390/e23020218.

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Regression analysis using line equations has been broadly applied in studying the evolutionary relationship between the response trait and its covariates. However, the characteristics among closely related species in nature present abundant diversities where the nonlinear relationship between traits have been frequently observed. By treating the evolution of quantitative traits along a phylogenetic tree as a set of continuous stochastic variables, statistical models for describing the dynamics of the optimum of the response trait and its covariates are built herein. Analytical representations

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Sokolov, Vladimir Yu, Chin-Hsiung Loh, and Kuo-Liang Wen. "Empirical Models for Site- and Region-Dependent Ground-Motion Parameters in the Taipei Area: A Unified Approach." Earthquake Spectra 17, no. 2 (2001): 313–31. http://dx.doi.org/10.1193/1.1586177.

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We calculated peak ground accelerations and response spectra for the Taipei area using stochastic simulation technique on the basis of recently obtained empirical models. The source, path and site effects were characterized separately on the basis of the analysis of a large collection of ground-motion recordings obtained since 1991 in the Taiwan area. The simple ω-squared Brune's point-source model combined with regional anelastic attenuation ( Q) and duration (τ0.9) models provide a satisfactory estimation of ground-motion parameters for rock sites. Effects of local site response are consider

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Dexter, Nick, Hoang Tran та Clayton Webster. "A mixed ℓ1 regularization approach for sparse simultaneous approximation of parameterized PDEs". ESAIM: Mathematical Modelling and Numerical Analysis 53, № 6 (2019): 2025–45. http://dx.doi.org/10.1051/m2an/2019048.

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We present and analyze a novel sparse polynomial technique for the simultaneous approximation of parameterized partial differential equations (PDEs) with deterministic and stochastic inputs. Our approach treats the numerical solution as a jointly sparse reconstruction problem through the reformulation of the standard basis pursuit denoising, where the set of jointly sparse vectors is infinite. To achieve global reconstruction of sparse solutions to parameterized elliptic PDEs over both physical and parametric domains, we combine the standard measurement scheme developed for compressed sensing

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Rzhetsky, Andrey, Joaquín Dopazo, Eric Snyder, Charles A. Dangler, and Francisco José Ayala. "Assessing Dissimilarity of Genes by Comparing Their RNAse A Mismatch Cleavage Patterns." Genetics 144, no. 4 (1996): 1975–83. http://dx.doi.org/10.1093/genetics/144.4.1975.

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We propose a simple algorithm for estimating the number of nucleotide differences between a pair of RNA or DNA sequences through comparison of their RNAse A mismatch cleavage patterns. In the RNAse A mismatch cleavage technique two or more sample sequences are hybridized to the same RNA probe, the hybrids are partially digested with RNAse A, and the digestion products are compared on an electrophoretic gel. Here we provide an algorithm for converting the numbers of unique and matching electrophoretic bands into an estimate of the number of nucleotide differences between the sequences. Computer

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Nouy, Anthony, and Florent Pled. "A multiscale method for semi-linear elliptic equations with localized uncertainties and non-linearities." ESAIM: Mathematical Modelling and Numerical Analysis 52, no. 5 (2018): 1763–802. http://dx.doi.org/10.1051/m2an/2018025.

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A multiscale numerical method is proposed for the solution of semi-linear elliptic stochastic partial differential equations with localized uncertainties and non-linearities, the uncertainties being modeled by a set of random parameters. It relies on a domain decomposition method which introduces several subdomains of interest (called patches) containing the different sources of uncertainties and non-linearities. An iterative algorithm is then introduced, which requires the solution of a sequence of linear global problems (with deterministic operators and uncertain right-hand sides), and non-l

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Azevedo, Leonardo, Ruben Nunes, Pedro Correia, Amílcar Soares, Luis Guerreiro, and Guenther Schwedersky Neto. "Multidimensional scaling for the evaluation of a geostatistical seismic elastic inversion methodology." GEOPHYSICS 79, no. 1 (2014): M1—M10. http://dx.doi.org/10.1190/geo2013-0037.1.

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Due to the nature of seismic inversion problems, there are multiple possible solutions that can equally fit the observed seismic data while diverging from the real subsurface model. Consequently, it is important to assess how inverse-impedance models are converging toward the real subsurface model. For this purpose, we evaluated a new methodology to combine the multidimensional scaling (MDS) technique with an iterative geostatistical elastic seismic inversion algorithm. The geostatistical inversion algorithm inverted partial angle stacks directly for acoustic and elastic impedance (AI and EI)

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Olaitan, Oladipupo A., and John Geraghty. "Evaluation of production control strategies for negligible‐setup, multi‐product, serial lines with consideration for robustness." Journal of Manufacturing Technology Management 24, no. 3 (2013): 331–57. http://dx.doi.org/10.1108/17410381311318864.

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PurposeThe aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.Design/methodology/approachDiscrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban c

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Navarra, A., M. N. Ward, and N. A. Rayner. "A stochastic model of SST for climate simulation experiments." Climate Dynamics 14, no. 7-8 (1998): 473–87. http://dx.doi.org/10.1007/s003820050235.

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Hetmanski, Joseph H. R., Matthew C. Jones, Fatima Chunara, Jean-Marc Schwartz, and Patrick T. Caswell. "Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration." PLOS Computational Biology 17, no. 3 (2021): e1008213. http://dx.doi.org/10.1371/journal.pcbi.1008213.

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Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and s

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Ouala, Said, Ronan Fablet, Cédric Herzet, et al. "Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature." Remote Sensing 10, no. 12 (2018): 1864. http://dx.doi.org/10.3390/rs10121864.

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The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computat

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Lamont, Byron B., Neal J. Enright, E. T. F. Witkowski, and J. Groeneveld. "Conservation biology of banksias: insights from natural history to simulation modelling." Australian Journal of Botany 55, no. 3 (2007): 280. http://dx.doi.org/10.1071/bt06024.

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We have studied the ecology and conservation requirements of Banksia species in the species-rich sandplains of south-western Australia for 25 years. Loss of habitat through land-clearing has had the greatest impact on their conservation status over the last 50 years. Ascertaining optimal conditions for conservation management in bushland requires detailed knowledge of the species under consideration, including demographic attributes, fire regime, growing conditions and interactions with other species. Where populations have been fragmented, seed production per plant has also fallen. The group

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Wang, S. S., and H. P. Hong. "Partial safety factors for designing and assessing flexible pavement performance." Canadian Journal of Civil Engineering 31, no. 3 (2004): 397–406. http://dx.doi.org/10.1139/l03-109.

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In designing and assessing pavement performance, the uncertainty in material properties and geometrical variables of pavement and in traffic and environmental actions should be considered. A single factor is employed to deal with these uncertainties in the current American Association of State Highway and Transportation Officials (AASHTO) guide for design of pavements. However, use of this single factor may not ensure reliability-consistent pavement design and assessment because different random variables that may have different degrees of uncertainty affect the safety and performance of pavem

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SIÓDMIAK, J., and A. GADOMSKI. "COMPUTER MODEL OF BIOPOLYMER CRYSTAL GROWTH AND AGGREGATION BY ADDITION OF MACROMOLECULAR UNITS — A COMPARATIVE STUDY." International Journal of Modern Physics C 17, no. 07 (2006): 1037–53. http://dx.doi.org/10.1142/s0129183106009643.

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We discuss the results of a computer simulation of the biopolymer crystal growth and aggregation based on the 2D lattice Monte Carlo technique and the HP approximation of the biopolymers. As a modeled molecule (growth unit) we comparatively consider the previously studied non-mutant lysozyme protein, Protein Data Bank (PDB) ID: 193L, which forms, under a certain set of thermodynamic-kinetic conditions, the tetragonal crystals, and an amyloidogenic variant of the lysozyme, PDB ID: 1LYY, which is known as fibril-yielding and prone-to-aggregation agent. In our model, the site-dependent attachment

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Huang, Chuangxia, Xinsong Yang, and Yigang He. "Stability Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Time-Varying Delays." Discrete Dynamics in Nature and Society 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/439173.

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This paper is concerned withpth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions onpth moment exponential stability for the considered system is presented. The proposed results generalized and improved some earlier publications.

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El-Beltagy, Mohamed A., and Amnah S. Al-Johani. "Numerical Approximation of Higher-Order Solutions of the Quadratic Nonlinear Stochastic Oscillatory Equation Using WHEP Technique." Journal of Applied Mathematics 2013 (2013): 1–21. http://dx.doi.org/10.1155/2013/685137.

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This paper introduces higher-order solutions of the stochastic nonlinear differential equations with the Wiener-Hermite expansion and perturbation (WHEP) technique. The technique is used to study the quadratic nonlinear stochastic oscillatory equation with different orders, different number of corrections, and different strengths of the nonlinear term. The equivalent deterministic equations are derived up to third order and fourth correction. A model numerical integral solver is developed to solve the resulting set of equations. The numerical solver is tested and validated and then used in sim

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Short, Michael. "Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks." Journal of Computer Networks and Communications 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5196092.

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Industrial communication networks like the Controller Area Network (CAN) are often required to operate reliably in harsh environments which expose the communication network to random errors. Probabilistic schedulability analysis can employ rich stochastic error models to capture random error behaviors, but this is most often at the expense of increased analysis complexity. In this paper, an efficient method (of time complexityO(n log n)) to bound the message deadline failure probabilities for an industrial CAN network consisting ofnperiodic/sporadic message transmissions is proposed. The paper

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Basile, Angelo, Antonello Bonfante, Antonio Coppola, et al. "How does PTF Interpret Soil Heterogeneity? A Stochastic Approach Applied to a Case Study on Maize in Northern Italy." Water 11, no. 2 (2019): 275. http://dx.doi.org/10.3390/w11020275.

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Soil water balance on a local scale is generally achieved by applying the classical nonlinear Richards equation that requires hydraulic properties, namely, water retention and hydraulic conductivity functions, to be known. Its application in agricultural systems on field or larger scales involves three major problems being solved, related to (i) the assessment of spatial variability of soil hydraulic properties, (ii) accounting for this spatial variability in modelling large-scale soil water flow, and (iii) measuring the effects of such variability on real field variables (e.g., soil water sto

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Chang, R. J. "Maximum Entropy Approach for Stationary Response of Nonlinear Stochastic Oscillators." Journal of Applied Mechanics 58, no. 1 (1991): 266–71. http://dx.doi.org/10.1115/1.2897162.

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A new approach based on the maximum entropy method is developed for deriving the stationary probability density function of a stable nonlinear stochastic system. The technique is implemented by employing the density function with undetermined parameters from the entropy method and solving a set of algebraic moment equations from a nonlinear stochastic system for the unknown parameters. For a wide class of stochastic systems with given density functions, an explicit density function of the stochastic system perturbed by a nonlinear function of states and noises can be obtained. Three nonlinear

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Menh, Nguyen Cao, and Tran Duong Tri. "On the simulation technique of stochastic processes and nonlinear vibrations." Vietnam Journal of Mechanics 16, no. 3 (1994): 23–31. http://dx.doi.org/10.15625/0866-7136/10171.

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In this paper the procedure and program for simulation of stochastic processes are represented. The program is applied to nonlinear mechanical systems subjected to stochastic stationary excitation. The results obtained are compared with the ones from other methods which are used for estimating the exactitude of simulation technique.

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Adlakha, V. G., and H. Arsham. "A simulation technique for estimation in perturbed stochastic activity networks." SIMULATION 58, no. 4 (1992): 258–67. http://dx.doi.org/10.1177/003754979205800406.

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