Determination of float time for individual construction tasks using constraint-based discrete-event simulation

Die cavity design of near flashless forging process using FEM-based backward simulation

BackgroundThere are several situations in population biology research where simulating DNA sequences is useful. Simulation of biological populations under different evolutionary genetic models can be undertaken using backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally efficient. The reason is that they are based on the history of lineages with surviving offspring in the current population. On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. Hence, there is an increasing interest in forward population genetic simulation and efficient new tools have been developed recently. Software tools that allow efficient simulation of large DNA fragments under complex evolutionary models will be very helpful when trying to better understand the trace left on the DNA by the different interacting evolutionary forces. Here I will introduce GenomePop, a forward simulation program that fulfills the above requirements. The use of the program is demonstrated by studying the impact of intracodon recombination on global and site-specific dN/dS estimation.ResultsI have developed algorithms and written software to efficiently simulate, forward in time, different Markovian nucleotide or codon models of DNA mutation. Such models can be combined with recombination, at inter and intra codon levels, fitness-based selection and complex demographic scenarios.ConclusionGenomePop has many interesting characteristics for simulating SNPs or DNA sequences under complex evolutionary and demographic models. These features make it unique with respect to other simulation tools. Namely, the possibility of forward simulation under General Time Reversible (GTR) mutation or GTR×MG94 codon models with intra-codon recombination, arbitrary, user-defined, migration patterns, diploid or haploid models, constant or variable population sizes, etc. It also allows simulation of fitness-based selection under different distributions of mutational effects. Under the 2-allele model it allows the simulation of recombination hot-spots, the definition of different frequencies in different populations, etc. GenomePop can also manage large DNA fragments. In addition, it has a scaling option to save computation time when simulating large sequences and population sizes under complex demographic and evolutionary situations. These and many other features are detailed in its web page [1].

An upper bound elemental technique approach to the process design of axisymmetric forging by forward and backward simulation

Backward tracing simulation of precision forging process for blade based on 3D FEM

The paper illustrates the use of a forward and backward linkage agricultural production simulation model. CHICKSIM III is a comprehensive, farm-level, and stochastic capital budgeting simulation model which provides an analytical framework for simulating probable consequences of production, marketing, financial, and policy variables on the profitability of a farm or agribusiness firm. The model is applied to evaluate the profitability of a representative farm producing broiler, beef, corn, and kenaf.

A general automaton model for timing-based systems is presented and is used as the context for developing a variety of simulation proof techniques for such systems. As a first step, a comprehensive overview of simulation techniques for simple untimed automata is given. In particular, soundness and completeness results for (1) refinements, (2) forward and backward simulations, (3) forward-backward and backward-forward simulations, and (4) history and prophecy relations are given. History and prophecy relations are new and are abstractions of the history variables of Owicki and Gries and the prophecy variables of Abadi and Lamport, respectively. As a subsequent step, it is shown how most of the results for untimed automata can be carried over to the setting of timed automata. In fact many of the results for the timed case are obtained as consequences of the analogous results for the untimed case.

Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of relevant events and conditions naturally provides a more accurate model, but also increases the computational workload associated with the simulation. To manage these processing requirements in a scalable manner, a discrete event simulation can be distributed across a number of computing resources. However, individual tasks in the simulation are stateful, and therefore require inter-task communication and synchronization to produce an accurate model. This property not only complicates the orchestration of the discrete event simulation in a distributed setting, but also makes providing reliable, fault-tolerant execution a challenge, especially when compared to conventional distributed fault tolerance schemes.In this paper, we propose an autonomous agent that provides fault tolerance functionality for discrete event simulations by predicting state changes in the simulation and adjusting its fault tolerance policy accordingly. This allows the system to avoid negatively impacting overall execution times while preserving reliability guarantees. To underscore the viability of our solution, we provide benchmarks of a production discrete event simulation that can sustain failures while running under the supervision of our fault tolerance framework.

<p class="Abstract">We are developing a backward simulator, which determines the unknown system input from the system output by using a system model. However, its processing time would increase enormously if the simulation model requires the multiple case branching, which is typical for backward simulations. In some target applications, we can use forward simulation processing in the backward data flow with significant reduction of processing time. This paper shows an example of such application to determine system input of heater operation from measured data of room temperature. Although the resolution of measurement restricts the performance of the simulation result, we also used the model to improve the resolution of measured data and show its effect to simulation. Furthermore, we show the result of reduction of noise caused by quantizing LSB jitters.</p>

A correction for the vertical gradient of air density has been incorporated into a skewed probability density function formulation for turbulence in the convective boundary layer. The related drift term for Lagrangian stochastic dispersion modelling has been derived based on the well-mixed condition. Furthermore, the formulation has been extended to include unsteady turbulence statistics and the related additional component of the drift term obtained. These formulations are an extension of the drift formulation reported by Luhar et al. (Atmos Environ 30:1407–1418, 1996) following the well-mixed condition proposed by Thomson (J Fluid Mech 180:529–556, 1987). Comprehensive tests were carried out to validate the formulations including consistency between forward and backward simulations and preservation of a well-mixed state with unsteady conditions. The stationary state CBL drift term with density correction was incorporated into the FLEXPART and FLEXPART-WRF Lagrangian models, and included the use of an ad hoc transition function that modulates the third moment of the vertical velocity based on stability parameters. Due to the current implementation of the FLEXPART models, only a steady-state horizontally homogeneous drift term could be included. To avoid numerical instability, in the presence of non-stationary and horizontally inhomogeneous conditions, a re-initialization procedure for particle velocity was used. The criteria for re-initialization and resulting errors were assessed for the case of non-stationary conditions by comparing a reference numerical solution in simplified unsteady conditions, obtained using the non-stationary drift term, and a solution based on the steady drift with re-initialization. Two examples of “real-world” numerical simulations were performed under different convective conditions to demonstrate the effect of the vertical gradient in density on the particle dispersion in the CBL.

Calculating the kinetic rates for rare transitions is an important objective for molecular simulations. Here I prove equalities that relate the transition rates to the equilibrium free energy and the statistics of the transition paths. In particular, the durations of the transition paths within given intervals of the reaction coordinate provide the kinetic pre-factor in the rate formula. Based on the available free energy, the transition rates can further be rigorously calculated by initiating forward and backward simulations and evaluating the duration of each transition path. Validation on a model system confirms that the approach correctly predicts the transition rates from the simulations and demonstrates that whereas the relations here are general and valid for any chosen reaction coordinate, a good reaction coordinate will enable a more efficient sampling of the transition paths and thus a more reliable rate calculation.

Simulations of weighted tree automata (wta) are considered. It is shown how such simulations can be decomposed into simpler functional and dual functional simulations also called forward and backward simulations. In addition, it is shown in several cases (fields, commutative rings, NOETHERIAN semirings, semiring of natural numbers) that all equivalent wta M and N can be joined by a finite chain of simulations. More precisely, in all mentioned cases there is a single wta that simulates both M and N. Those results immediately yield decidability of equivalence provided that the semiring is finitely (and effectively) presented.

Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of events and conditions provides a more nuanced model, but also increases its computational footprint. To manage these processing requirements in a scalable manner, discrete event simulations can be distributed across multiple computing resources. Orchestrating the simulations in a distributed setting involves coping with resource uncertainty. We consider three key aspects of resource uncertainty: resource failures, heterogeneity, and slowdowns. Each of these aspects is managed autonomously, which involves making accurate predictions of future execution times and latencies while also accounting for differences in hardware capabilities and dynamic resource consumption profiles. Further complicating matters, individual tasks within the simulation are stateful and stochastic, requiring inter-task communication and synchronization to produce accurate outcomes. We deal with these challenges through intelligent state collection and migration, active resource monitoring, and empirical evaluation of resource capabilities under changing conditions. To underscore the viability of our solution, we provide benchmarks using a production discrete event simulation that can simultaneously sustain failures, manage resource heterogeneity, and handle slowdowns while being orchestrated by our framework.

SummaryTesting for small‐delay defects (SDDs) has become an important component of integrated circuit testing. In this paper, an efficient small‐delay fault simulator, a hybrid method combining forward serial simulation and backward critical path tracing simulation for SDDs is proposed, which aims to determine the coverage of small‐delay defects for a given test set fast and accurately. In our proposed method, a unit delay model is employed, and reconvergent sensitization as well as hazard‐based detection is considered. Signal waveforms are expressed by bitmap data forms. In addition to providing an accurate result for fault simulation, the proposed simulator can well assist test generation. Experimental results demonstrate that the proposed simulator can further accelerate the simulation by one or two orders of magnitude compared with previous works. Copyright © 2014 John Wiley & Sons, Ltd.

A novel hybrid scattering order-dependent variance reduction method for Monte Carlo simulations of radiative transfer in cloudy atmosphere

The simulation of correlated multivariate Poisson processes with negative correlation between their components has many important applications in Finance, Insurance, Geophysics, and many other areas of applied probability. Introduced in our earlier work, the Backward Simulation (BS) approach to the simulation of correlated multivariate Poisson processes is able to capture a wide range of correlation, including extreme positive and extreme negative correlation, that is not possible with other approaches such as the forward simulation approach. Moreover, the BS approach enables simple and efficient generation of sample paths of correlated multivariate Poisson processes. In this work, we extend the BS approach to multivariate mixed Poisson processes.