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An algorithm is presented to relabel automatically the nodes of an arbitrary finite-element mesh. The purpose of such relabeling is to reduce the bandwidth of the master stiffness matrix produced by the finite-element method. The algorithm uses a random process for the relabeling. Computing time is reduced substantially, compared to systematic methods.
The robustness of data load of metal–oxide–semiconductor/magnetic tunnel junction (MOS/MTJ) hybrid latches at power-on is examined by using Monte Carlo simulation with the variations in magnetoresistances for MTJs and in threshold voltages for MOSFETs involved in 90 nm technology node. Three differential pair type spin-transfer-torque-magnetic random access memory cells (4T2MTJ, 6T2MTJ, and 8T2MTJ) are compared for their successful data load at power-on. It is found that the 4T2MTJ cell has the largest pass area in the shmoo plot in TMR ratio (tunnel magnetoresistance ratio) and V{sub dd} in which a whole 256 kb cell array can be powered-on successfully. The minimum TMR ratio for the 4T2MTJ in 0.9 V < V{sub dd} < 1.9 V is 140%, while the 6T2MTJ and the 8T2MTJ cells require TMR ratio larger than 170%.
The Pahute Mesa Corrective Action Unit on the Nevada National Security Site (NNSS) contains several fractured aquifers that can potentially provide high-permeability pathways for radionuclides to migrate away from underground nuclear testing locations. Although the geometric properties of fractures, such as length and orientation, can be found using geophysical methods and borehole image analyses, the hydraulic property of fractures (which are primarily influenced by apertures) are often unknown or have a high degree of uncertainty attached to their values. The aperture values have a profound impact on groundwater velocity and are critical to understanding transport scales at Pahute Mesa. Fracture aperture values and their distributions also have dominant control over radionuclide transport with potential for long-range correlations or scale-invariant dynamics. Understanding of the small-scale transport variability in fractures could lead to identification of distinct characteristics on larger scales. Furthermore, upscaling techniques need to be developed to adequately predict transport behavior at field scales by capturing the transport trends of smaller scale networks. This report explores these vital modeling needs to understand the role of fractures in Pahute Mesa. Discrete fracture network (DFN) models are the basis for constructing a well-calibrated model to ascertain aperture values of fractures in lava-flow aquifers. These models are also used to develop and compare the usefulness of several random-walk-based models to perform transport upscaling.
We have studied the intercage interactions between the adjacent C{sub 60} cages and expansion of lattice due to the intercalation of alkali atoms based on the spring model to estimate phonon frequencies from the dynamical matrix for the intermolecular alkali-C{sub 60} phonons. We considered a two-peak model for the phonon density of states to investigate the nature of electron pairing mechanism for superconducting state in fullerides. Coulomb repulsive parameter and the electron phonon coupling strength are obtained within the random phase approximation. Transition temperature, T{sub c}, is obtained in a situation when the free electrons in lowest molecular orbital are coupled with alkali-C{sub 60} phonons as 5 K, which is much lower as compared to reported T{sub c} (20 K). The superconducting pairing is mainly driven by the high frequency intramolecular phonons and their effects enhance it to 22 K. The importance of the present study, the pressure effect and normal state transport properties are calculated within the same model leading superconductivity.
A model that describes bilayered bipolar resistive random access memory (BL-ReRAM) switching in oxide with a large band gap is presented. It is shown that, owing to the large energy barrier between the electrode and thin oxide layer, the electronic conduction is dominated by trap-assisted tunneling. The model is composed of an atomic oxygen vacancy migration model and an electronic tunneling conduction model. We also show experimentally observed three-resistance-level switching in Ru/ZrO{sub 2}/TaO{sub x} BL-ReRAM that can be explained by the two types of traps, i.e., shallow and deep traps in ZrO{sub 2}.
Simplified methods are described to estimate the test criteria of primary structures at component attachment points subjected to broadband random acoustic excitations. The current method utilizes a constant smeared component mass attenuation factor across the frequency range of interest. The developed method indicates that the attenuation factor is based on a frequency dependent ratio of the mechanical impedances of both the component and primary structures. The procedures used to predict the structural responses are considered as the present state-of-the-art and provide satisfactory prediction results. Example problems are used to illustrate the application procedures of the two methods and to compare the significant difference. It was found that the lower test criteria obtained by the impedance ratio method is due to the results of considering the effects of component/primary structure interaction.
The correlation and spectral analysis methods for uniformly sampled stationary random signals, estimation of their spectral moments, and problems arising due to nonstationary are reviewed. Some of these methods are already in routine use in atmospheric radar experiments. Other methods based on the maximum entropy principle and time series models have been used in analyzing data, but are just beginning to receive attention in the analysis of radar signals. These methods are also briefly discussed.
The switching parameter variation of resistive switching memory is one of the most important challenges in its application. In this letter, we have studied the set statistics of conductive bridge random access memory with a Cu/HfO{sub 2}/Pt structure. The experimental distributions of the set parameters in several off resistance ranges are shown to nicely fit a Weibull model. The Weibull slopes of the set voltage and current increase and decrease logarithmically with off resistance, respectively. This experimental behavior is perfectly captured by a Monte Carlo simulator based on the cell-based set voltage statistics model and the Quantum Point Contact electron transport model. Our work provides indications for the improvement of the switching uniformity.
This theoretical treatment of low-energy compound nucleus reactions begins with the Bohr hypothesis, with corrections, and various statistical theories. The author investigates the statistical properties of the scattering matrix containing a Gaussian Orthogonal Ensemble (GOE) Hamiltonian in the propagator. The following conclusions are reached: For all parameter values studied, the numerical average of MC-generated cross sections coincides with the result of the Verbaarschot, Weidenmueller, Zirnbauer triple-integral formula. Energy average and ensemble average agree reasonably well when the width I is one or two orders of magnitude larger than the average resonance spacing d. In the strong-absorption limit, the channel degree-of-freedom ν a is 2. The direct reaction increases the inelastic cross sections while the elastic cross section is reduced.
Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.
The standard technique for measurement of random uncertainties of star formation histories (SFHs) is the bootstrap Monte Carlo, in which the color-magnitude diagram (CMD) is repeatedly resampled. The variation in SFHs measured from the resampled CMDs is assumed to represent the random uncertainty in the SFH measured from the original data. However, this technique systematically and significantly underestimates the uncertainties for times in which the measured star formation rate is low or zero, leading to overly (and incorrectly) high confidence in that measurement. This study proposes an alternative technique, the Markov Chain Monte Carlo (MCMC), which samples the probability distribution of the parameters used in the original solution to directly estimate confidence intervals. While the most commonly used MCMC algorithms are incapable of adequately sampling a probability distribution that can involve thousands of highly correlated dimensions, the Hybrid Monte Carlo algorithm is shown to be extremely effective and efficient for this particular task. Several implementation details, such as the handling of implicit priors created by parameterization of the SFH, are discussed in detail.
The multicomponent approach has been successfully expanded to the Ruddlesden–Popper structure with the synthesis of two different high-entropy cuprate compositions: (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 and (La 0.2 Pr 0.2 Nd 0.2 Sm 0.2 Eu 0.2 ) 2 CuO 4 . Additionally, the effect of synthesis method is explored using both solid-state reaction and polymeric steric entrapment (PSE) methods. It is found that PSE leads to more randomly distributed cation species, providing an advantageous method of synthesis for the growing field of high entropy oxides. In situ high-temperature x-ray diffraction tracks the amorphous to crystalline phase transformation in (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 powder, synthesized using the PSE method. Using the High-Temperature XRD data, a method for gaining information on the kinetic behavior is also applied. Magnetometry of both compositions indicates ferrimagnetic behavior at low temperatures.
The response functions, S(E), and centroid energies, E CEN , of the isoscalar and isovector giant resonances of multipolarity L = 0 - 3 in 92,94,96,98,100 Mo and 90,92,94 Zr have been calculated employing the spherical Hartree-Fock (HF) based random phase approximation (RPA) method, using the Skyrme-type effective nucleon-nucleon interaction. In particular, we investigate the recent experimental results of the uncharacteristic behavior of E CEN for the isoscalar giant resonances, by extending our HF-based RPA calculations with only the KDE0v1 interaction to 32 additional Skyrme interactions. The main result of our investigation is that we find significant disagreements between the theoretical and the experimental values of E CEN for isoscalar giant monopole and dipole resonance of some nuclei. For the isoscalar giant octupole resonance we find the theoretical values of the E CEN to be well above the experimental results. We also study the sensitivity of E CEN to nuclear matter (NM) properties, including the effective mass, the incompressibility coefficient and the symmetry energy at saturation density, by determining the Pearson linear correlation coefficient between the calculated values of E CEN and the various nuclear matter properties of each Skyrme parametrization. So, for the isovector giant dipole resonance we find good agreement between the experimental and theoretical values of E CEN for Skyrme interactions with values of the enhancement coefficient of the energy weighted sum rule, κ, in the range between 0.25 – 0.70
We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M {sub ⊙} formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.
The evolution of a coronal loop is studied by means of numerical simulations of the fully compressible three-dimensional magnetohydrodynamic equations using the HYPERION code. The footpoints of the loop magnetic field are advected by random motions. As a consequence, the magnetic field in the loop is energized and develops turbulent nonlinear dynamics characterized by the continuous formation and dissipation of field-aligned current sheets: energy is deposited at small scales where heating occurs. Dissipation is nonuniformly distributed so that only a fraction of the coronal mass and volume gets heated at any time. Temperature and density are highly structured at scales that, in the solar corona, remain observationally unresolved: the plasma of our simulated loop is multithermal, where highly dynamical hotter and cooler plasma strands are scattered throughout the loop at sub-observational scales. Numerical simulations of coronal loops of 50,000 km length and axial magnetic field intensities ranging from 0.01 to 0.04 T are presented. To connect these simulations to observations, we use the computed number densities and temperatures to synthesize the intensities expected in emission lines typically observed with the Extreme Ultraviolet Imaging Spectrometer on Hinode. These intensities are used to compute differential emission measure distributions using the Monte Carlo Markov Chain code, which are very similar to those derived from observations of solar active regions. We conclude that coronal heating is found to be strongly intermittent in space and time, with only small portions of the coronal loop being heated: in fact, at any given time, most of the corona is cooling down.
We investigate sets of random variables that can be arranged sequentially such that a given variable only depends conditionally on its immediate predecessor. For such sets, we show that the full joint probability distribution may be expressed exclusively in terms of uni- and bivariate marginals. Under the assumption that the cosmic microwave background (CMB) power spectrum likelihood only exhibits correlations within a banded multipole range, Δl{sub C}, we apply this expression to two outstanding problems in CMB likelihood analysis. First, we derive a statistically well-defined hybrid likelihood estimator, merging two independent (e.g., low- and high-l) likelihoods into a single expression that properly accounts for correlations between the two. Applying this expression to the Wilkinson Microwave Anisotropy Probe (WMAP) likelihood, we verify that the effect of correlations on cosmological parameters in the transition region is negligible in terms of cosmological parameters for WMAP; the largest relative shift seen for any parameter is 0.06σ. However, because this may not hold for other experimental setups (e.g., for different instrumental noise properties or analysis masks), but must rather be verified on a case-by-case basis, we recommend our new hybridization scheme for future experiments for statistical self-consistency reasons. Second, we use the same expression to improve the convergence rate of the Blackwell-Rao likelihood estimator, reducing the required number of Monte Carlo samples by several orders of magnitude, and thereby extend it to high-l applications.
We consider elliptic stochastic partial differential equations (SPDEs) with random coefficients and solve them by expanding the solution using generalized polynomial chaos (gPC). Under some mild conditions on the coefficients, the solution is “sparse” in the random space, i.e., only a small number of gPC basis makes considerable contribution to the solution. To exploit this sparsity, we employ reweighted l{sub 1} minimization to recover the coefficients of the gPC expansion. We also combine this method with random sampling points based on the Chebyshev probability measure to further increase the accuracy of the recovery of the gPC coefficients. We first present a one-dimensional test to demonstrate the main idea, and then we consider 14 and 40 dimensional elliptic SPDEs to demonstrate the significant improvement of this method over the standard l{sub 1} minimization method. For moderately high dimensional (∼10) problems, the combination of Chebyshev measure with reweighted l{sub 1} minimization performs well while for higher dimensional problems, reweighted l{sub 1} only is sufficient. The proposed approach is especially suitable for problems for which the deterministic solver is very expensive since it reuses the sampling results and exploits all the information available from limited sources.
We accurately determine a new Cepheid distance to M101 (NGC 5457) using archival Hubble Space Telescope (HST)/Advanced Camera for Surveys V and I time series photometry of two fields within the galaxy. We make a slight modification to the ISIS image subtraction package to obtain optimal differential light curves from HST data. We discovered 827 Cepheids with periods between 3 and 80 days, the largest extragalactic sample of Cepheids observed with HST by a factor of two. With this large Cepheid sample, we find that the relative distance of M101 from the Large Magellanic Cloud is {Delta}{mu}{sub LMC} = 10.63 {+-} 0.04 (random) {+-} 0.06 (systematic) mag. If we use the geometrically determined maser distance to NGC 4258 as our distance anchor, the distance modulus of M101 is {mu}{sub 0} = 29.04 {+-} 0.05 (random) {+-} 0.18 (systematic) mag or D = 6.4 {+-} 0.2 (random) {+-} 0.5 (systematic) Mpc. The uncertainty is dominated by the maser distance estimate ({+-}0.15 mag), which should improve over the next few years. We determine a steep metallicity dependence, {gamma}, for our Cepheid sample through two methods, yielding {gamma} = -0.80 {+-} 0.21 (random) {+-} 0.06 (systematic) mag dex{sup -1} and {gamma} = -0.72{sup +0.22}{sub -0.25} (random) {+-} 0.06 (systematic) mag dex{sup -1}. We see marginal evidence for variations in the Wesenheit period-luminosity relation slope as a function of deprojected galactocentric radius. We also use the tip of the red giant branch method to independently determine the distance modulus to M101 of {mu}{sub 0} = 29.05 {+-} 0.06 (random) {+-} 0.12 (systematic) mag.