Direct numerical simulation of homogeneous nucleation and growth in a phase-field model using cell dynamics method

Homogeneous nucleation and growth in a simplest two-dimensional phase field model is numerically studied using the cell dynamics method. Whole process from nucleation to growth is simulated and is shown to follow closely the Kolmogorov-Johnson-Mehl-Avrami (KJMA) scenario of phase transformation. Specifically the time evolution of the volume fraction of new stable phase is found to follow closely the KJMA formula. By fitting the KJMA formula directly to the simulation data, not only the Avrami exponent but the magnitude of nucleation rate and, in particular, of incubation time are quantitatively studied. The modified Avrami plot is also used to verify the derived KJMA parameters. It is found that the Avrami exponent is close to the ideal theoretical value m=3. The temperature dependence of nucleation rate follows the activation-type behavior expected from the classical nucleation theory. On the other hand, the temperature dependence of incubation time does not follow the exponential activation-type behavior. Rather the incubation time is inversely proportional to the temperature predicted from the theory of Shneidman and Weinberg [J. Non-Cryst. Solids {\bf 160}, 89 (1993)]. A need to restrict thermal noise in simulation to deduce correct Avrami exponent is also discussed.Comment: 9 pages, 8 figures, Journal of Chemical Physics to be publishe

Erythropoietin production by fetal mouse liver cells in response to hypoxia and adenylate cyclase stimulation

This study was done to investigate aspects of control of extrarenal erythropoietin (Ep) production. To this end we studied the effects of three stimuli of renal Ep production in the adult, i.e. hypoxia, cobalt, and activation of adenylate cyclase on Ep generation by cultured fetal mouse liver cells. The fetal liver was taken as a model for extrarenal Ep production because this organ is considered the predominant site of extrarenal Ep production. We found that Ep production by the cells increased as the oxygen concentration was decreased in the incubation atmosphere from 20% to 1%. Cobalt (10(-4)-10(-5) M) had no effect on Ep production. Activation of adenylate cyclase by forskolin (10(-5) M) or isoproterenol (10(-5) M) greatly enhanced Ep production. These findings indicate that the Ep-stimulating effect of cobalt is specific for the kidney. However, oxygen depletion and activation of adenylate cyclase seem to be more general stimuli in Ep-producing cells. Furthermore we found that Ep production in hypoxia correlated with lactate formation in the cultured liver cells. This finding suggests that Ep production in fetal livers under hypoxic conditions parallels the shift from aerobic to anaerobic cellular energy metabolism

A Laboratory Method for Assessing Audibility and Localization of Rotorcraft Fly-In Noise

The low frequency content of rotorcraft noise allows it to be heard over great distances. This factor contributes to the disruption of natural quiet in national parks and wilderness areas, and can lead to annoyance in populated areas. Further, it can result in the sound being heard at greater distances compared to higher altitude fixed wing aircraft operations. Human response studies conducted in the field are challenging since test conditions are difficult to control. This paper presents a means of quantitatively determining the audibility and localization of rotorcraft fly-in noise, under a specified ambient noise condition, within a controlled laboratory environment. It is demonstrated using synthetic fly-in noise of a light utility helicopter. The method is shown to resolve differences in audibility distances due to different ground impedances, propagation modeling methods, and directivity angles. Further, it demonstrates the efficacy of an accelerated test method

Unfolding Quantum Computer Readout Noise

In the current era of noisy intermediate-scale quantum (NISQ) computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics, and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to high energy physics, where prior-independent regularized matrix inversion techniques (`unfolding') have been developed for years to correct for acceptance and detector effects when performing differential cross section measurements. We study various unfolding methods in the context of universal gate-based quantum computers with the goal of connecting the fields of quantum information science and high energy physics and providing a reference for future work. The method known as iterative Bayesian unfolding is shown to avoid pathologies from commonly used matrix inversion and least squares methods.Comment: 13 pages, 16 figures; v2 has a typo fixed in Eq. 3 and a series of minor modification

Variational Sequential Monte Carlo

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits