Set Of Best-fit Parameters Research Articles

Parameterization of a mesoscopic model for the self-assembly of linear sodium alkyl sulfates.

A systematic approach to develop mesoscopic models for a series of linear anionic surfactants (CH3(CH2)n - 1OSO3Na, n = 6, 9, 12, 15) by dissipative particle dynamics (DPD) simulations is presented in this work. The four surfactants are represented by coarse-grained models composed of the same head group and different numbers of identical tail beads. The transferability of the DPD model over different surfactant systems is carefully checked by adjusting the repulsive interaction parameters and the rigidity of surfactant molecules, in order to reproduce key equilibrium properties of the aqueous micellar solutions observed experimentally, including critical micelle concentration (CMC) and average micelle aggregation number (Nag). We find that the chain length is a good index to optimize the parameters and evaluate the transferability of the DPD model. Our models qualitatively reproduce the essential properties of these surfactant analogues with a set of best-fit parameters. It is observed that the logarithm of the CMC value decreases linearly with the surfactant chain length, in agreement with Klevens' rule. With the best-fit and transferable set of parameters, we have been able to calculate the free energy contribution to micelle formation per methylene unit of -1.7 kJ/mol, very close to the experimentally reported value.

Electron paramagnetic resonance of Fe3+in gallium borate: Superposition model analysis

A series of high quality FexGa1−xBO3 single crystals with 0 ≤ x ≤ 1 was prepared and studied by electron magnetic resonance in the temperature range from 4 to 290 K. At low x, only the electron paramagnetic resonance (EPR) of diluted Fe3+ ions is present. The EPR spectra for different orientations of the magnetizing field have been computer simulated. With a conventional spin Hamiltonian including the Zeeman and fine-structure terms, two different sets of best-fit parameters have been found. This ambiguity has been resolved on the basis of the general spin Hamiltonian with parameters determined from the crystallographic data using the Newman superposition model. A detailed fitting to the experimental EPR spectra assuming statistical distributions of Fe3+ ligand coordinates has revealed the existence of a certain degree of local disorder in FexGa1−xBO3 single crystals.

Validation and Calibration of a Computer Simulation Model of Pediatric HIV Infection

BackgroundComputer simulation models can project long-term patient outcomes and inform health policy. We internally validated and then calibrated a model of HIV disease in children before initiation of antiretroviral therapy to provide a framework against which to compare the impact of pediatric HIV treatment strategies.MethodsWe developed a patient-level (Monte Carlo) model of HIV progression among untreated children <5 years of age, using the Cost-Effectiveness of Preventing AIDS Complications model framework: the CEPAC-Pediatric model. We populated the model with data on opportunistic infection and mortality risks from the International Epidemiologic Database to Evaluate AIDS (IeDEA), with mean CD4% at birth (42%) and mean CD4% decline (1.4%/month) from the Women and Infants’ Transmission Study (WITS). We internally validated the model by varying WITS-derived CD4% data, comparing the corresponding model-generated survival curves to empirical survival curves from IeDEA, and identifying best-fitting parameter sets as those with a root-mean square error (RMSE) <0.01. We then calibrated the model to other African settings by systematically varying immunologic and HIV mortality-related input parameters. Model-generated survival curves for children aged 0-60 months were compared, again using RMSE, to UNAIDS data from >1,300 untreated, HIV-infected African children. ResultsIn internal validation analyses, model-generated survival curves fit IeDEA data well; modeled and observed survival at 16 months of age were 91.2% and 91.1%, respectively. RMSE varied widely with variations in CD4% parameters; the best fitting parameter set (RMSE = 0.00423) resulted when CD4% was 45% at birth and declined by 6%/month (ages 0-3 months) and 0.3%/month (ages >3 months). In calibration analyses, increases in IeDEA-derived mortality risks were necessary to fit UNAIDS survival data. ConclusionsThe CEPAC-Pediatric model performed well in internal validation analyses. Increases in modeled mortality risks required to match UNAIDS data highlight the importance of pre-enrollment mortality in many pediatric cohort studies.

The eigenmode perspective of NMR spin relaxation in proteins

We developed in recent years the two-body (protein and probe) coupled-rotator slowly relaxing local structure (SRLS) approach for elucidating protein dynamics from NMR spin relaxation. So far we used as descriptors the set of physical parameters that enter the SRLS model. They include the global (protein-related) diffusion tensor, D1, the local (probe-related) diffusion tensor, D2, and the local coupling∕ordering potential, u. As common in analyzes based on mesoscopic dynamic models, these parameters have been determined with data-fitting techniques. In this study, we describe structural dynamics in terms of the eigenmodes comprising the SRLS time correlation functions (TCFs) generated by using the best-fit parameters as input to the Smoluchowski equation. An eigenmode is a weighted exponential with decay constant given by an eigenvalue of the Smoluchowski operator, and weighting factor determined by the corresponding eigenvector. Obviously, both quantities depend on the SRLS parameters as determined by the SRLS model. Unlike the set of best-fit parameters, the eigenmodes represent patterns of motion of the probe-protein system. The following new information is obtained for the typical probe, the (15)N-(1)H bond. Two eigenmodes, associated with the protein and the probe, dominate when the time scale separation is large (i.e., D2 >> D1), the tensorial properties are simple, and the local potential is either very strong or very weak. When the potential exceeds these limits while the remaining conditions are preserved, new eigenmodes arise. The multi-exponentiality of the TCFs is associated in this case with the restricted nature of the local motion. When the time scale separation is no longer large, the rotational degrees of freedom of the protein and the probe become statistically dependent (coupled dynamically). The multi-exponentiality of the TCFs is associated in this case with the restricted nature of both the local and the global motion. The effects of local diffusion axiality, potential strength, and extent of mode-coupling on the eigenmode setup are investigated. We detect largely global motional or largely local motional eigenmodes. In addition, we detect mixed eigenmodes associated with correlated∕prograde or anti-correlated∕retrograde rotations of the global (D1) and local (D2) motional modes. The eigenmode paradigm is applied to N-H bond dynamics in the β-sheet residue K19, and the α-helix residue A34, of the third immunoglobulin-binding domain of streptococcal protein G. The largest contribution to the SRLS TCFs is made by mixed anti-correlated D1 and D2 eigenmodes. The next largest contribution is made by D1-dominated eigenmodes. Eigenmodes dominated by the local motion contribute appreciably to A34 and marginally to K19. Correlated D1 and D2 eigenmodes contribute exclusively to K19 and do not contribute above 1% to A34. The differences between K19 and A34 are delineated and rationalized in terms of the best-fit SRLS parameters and mode-mixing. It may be concluded that eigenmode analysis is complementary and supplementary to data-fitting-based analysis.

The ratio of CO to total gas mass in high-redshift galaxies

Walter et al. (20012) have recently identified the J=6-5, 5-4, and 2-1 CO rotational emission lines, and [C_{II}] fine-structure emission line from the star-forming interstellar medium in the high-redshift submillimeter source HDF 850.1, at z = 5.183. We employ large velocity gradient (LVG) modeling to analyze the spectra of this source assuming the [C_{II}] and CO emissions originate from (i) separate unvirialized regions, (ii) separate virialized regions, (iii) uniformly mixed unvirialized region, and (iv) uniformly mixed virialized regions. We present the best fit set of parameters, including for each case the ratio $\alpha$ between the total hydrogen/helium gas mass and the CO(1-0) line luminosity. We also present computations of the ratio of H_{2} mass to [C_{II}] line-luminosity for optically thin conditions, for a range of gas temperatures and densities, for direct conversion of [C_{II}] line-luminosities to "dark-H_{2}" masses. For HDF 850.1 we find that a model in which the CO and C^{+} are uniformly mixed in gas that is shielded from UV radiation, requires a cosmic-ray or X-ray ionization rate of $\zeta \approx$ 10^{-13} s^{-1}, plausibly consistent with the large star-formation rate ($\sim$ 10^{3} M$_{\odot}$ yr^{-1}) observed in this source. Enforcing the cosmological constraint posed by the abundance of dark matter halos in the standard $\Lambda$CDM cosmology and taking into account other possible contributions to the total gas mass, we find that three of these four models are less likely at the 2$\sigma$ level. We conclude that modeling HDF 850.1's ISM as a collection of unvirialized molecular clouds with distinct CO and C^{+} layers, for which $\alpha$ = 0.6 M$_{\odot}$ (K km s^{-1} pc^{2})^{-1} for the CO to H_{2} mass-to-luminosity ratio, (similar to the standard ULIRG value), is most consistent with the $\Lambda$CDM cosmology.

Fast and accurate fitting of relaxation dispersion data using the flexible software package GLOVE

Relaxation dispersion spectroscopy is one of the most widely used techniques for the analysis of protein dynamics. To obtain a detailed understanding of the protein function from the view point of dynamics, it is essential to fit relaxation dispersion data accurately. The grid search method is commonly used for relaxation dispersion curve fits, but it does not always find the global minimum that provides the best-fit parameter set. Also, the fitting quality does not always improve with increase of the grid size although the computational time becomes longer. This is because relaxation dispersion curve fitting suffers from a local minimum problem, which is a general problem in non-linear least squares curve fitting. Therefore, in order to fit relaxation dispersion data rapidly and accurately, we developed a new fitting program called GLOVE that minimizes global and local parameters alternately, and incorporates a Monte-Carlo minimization method that enables fitting parameters to pass through local minima with low computational cost. GLOVE also implements a random search method, which sets up initial parameter values randomly within user-defined ranges. We demonstrate here that the combined use of the three methods can find the global minimum more rapidly and more accurately than grid search alone.

Quintessence versus phantom dark energy: the arbitrating power of current and future observations

We analyze the possibility to distinguish between quintessence and phantom scalar field models of dark energy usingobservations of luminosity distance moduli of SNe Ia, CMB anisotropies and polarization, matter density perturbations andbaryon acoustic oscillations. Among the present observations only Planck data on CMB anisotropy and SDSS DR9 data on baryon acoustic oscillations may be able to decide between quintessence or phantom scalar field models, however for each model a set of best-fit parameters exists, which matches all data with similar goodness of fit. We compare the relative differences of best-fit model predictions with observational uncertainties for each type of data and we show that the accuracy of SNe Ia luminosity distance data is far from the one necessary to distinguish these types of dark energy models, while the CMB data (WMAP, ACT, SPT and especially Planck) are close to being able to reliably distinguish them. Also an improvement of the large-scale structure data (future releases of SDSS BOSS and e.g. Euclid or BigBOSS) will enable us to surely decide between quintessence and phantom dark energy.

Consistent treatment of transport properties for five-species air direct simulation Monte Carlo/Navier-Stokes applications

A general approach for achieving consistency in the transport properties between direct simulation Monte Carlo (DSMC) and Navier-Stokes (CFD) solvers is presented for five-species air. Coefficients of species diffusion, viscosity, and thermal conductivities are considered. The transport coefficients that are modeled in CFD solvers are often obtained by expressions involving sets of collision integrals, which are obtained from more realistic intermolecular potentials (i.e., ab initio calculations). In this work, the self-consistent effective binary diffusion and Gupta et al.–Yos tranport models are considered. The DSMC transport coefficients are approximated from Chapman-Enskog theory in which the collision integrals are computed using either the variable hard sphere (VHS) and variable soft sphere (VSS) (phenomenological) collision cross section models. The VHS and VSS parameters are then used to adjust the DSMC transport coefficients in order to achieve a best-fit to the coefficients computed from more realistic intermolecular potentials over a range of temperatures. The best-fit collision model parameters are determined for both collision-averaged and collision-specific pairing approaches using the Nelder-Mead simplex algorithm. A consistent treatment of the diffusion, viscosity, and thermal conductivities is presented, and recommended sets of best-fit VHS and VSS collision model parameters are provided for a five-species air mixture.

Determination of best-fit potential parameters for a reactive force field using a genetic algorithm

The ReaxFF interatomic potential, used for organic materials, involves more than 600 adjustable parameters, the best-fit values of which must be determined for different materials. A new method of determining the set of best-fit parameters for specific molecules containing carbon, hydrogen, nitrogen and oxygen is presented, based on a parameter reduction technique followed by genetic algorithm (GA) minimization. This work has two novel features. The first is the use of a parameter reduction technique to determine which subset of parameters plays a significant role for the species of interest; this is necessary to reduce the optimization space to manageable levels. The second is the application of the GA technique to a complex potential (ReaxFF) with a very large number of adjustable parameters, which implies a large parameter space for optimization. In this work, GA has been used to optimize the parameter set to determine best-fit parameters that can reproduce molecular properties to within a given accuracy. As a test problem, the use of the algorithm has been demonstrated for nitromethane and its decomposition products.

Operational parameter optimisation of MZI-SOA using multi-objective genetic algorithms

A methodology for achieving the best-fit set of parameters for a Mach-Zehnder interferometer with a semiconductor optical amplifier (MZI-SOA) static model is proposed. A multi-objective genetic algorithm is exploited and the quality of the approach is validated by applying it in an existing sample. Optimisation of performance and determination of operational limits are enabled by the proposed methodology and good agreement was obtained between simulated and practical results.

The Probabilistic Niche Model Reveals the Niche Structure and Role of Body Size in a Complex Food Web

The niche model has been widely used to model the structure of complex food webs, and yet the ecological meaning of the single niche dimension has not been explored. In the niche model, each species has three traits, niche position, diet position and feeding range. Here, a new probabilistic niche model, which allows the maximum likelihood set of trait values to be estimated for each species, is applied to the food web of the Benguela fishery. We also developed the allometric niche model, in which body size is used as the niche dimension. About 80% of the links in the empirical data are predicted by the probabilistic niche model, a significant improvement over recent models. As in the niche model, species are uniformly distributed on the niche axis. Feeding ranges are exponentially distributed, but diet positions are not uniformly distributed below the predator. Species traits are strongly correlated with body size, but the allometric niche model performs significantly worse than the probabilistic niche model. The best-fit parameter set provides a significantly better model of the structure of the Benguela food web than was previously available. The methodology allows the identification of a number of taxa that stand out as outliers either in the model's poor performance at predicting their predators or prey or in their parameter values. While important, body size alone does not explain the structure of the one-dimensional niche.

A Design Strength Equation for Collapse of Expanded OCTG

Summary Expandable tubulars provide exciting new opportunities for well design and construction. This technology has permitted access to hydrocarbons that could not be reached by conventional drilling techniques. Design tools similar to those available for conventional oil-country tubular goods (OCTG) are required to facilitate further dissemination and application of expandable tubulars. In particular, equations for performance properties of expanded pipe are needed. This paper describes a method for developing an equation for the design collapse strength of any particular expandable-pipe product. It is based on the combination of collapse-test data and theoretical modeling, and its statistical approach is fully consistent with that used for conventional OCTG described in ISO/TR 10400:2007 (2007) or API TR 5C3 (2008). On the basis of an example set of expanded L80 pipes, results are presented fitting the collapse data with an anisotropic version of the Klever and Tamano (KT) collapse equation (Klever and Tamano 2006) that has been calibrated recently to conventional OCTG. By adapting three parameters, the anisotropic KT model can also model the collapse strength of the expanded-pipe data over a range of pipe diameters, wall thicknesses, and expansion ratios. The historical API BULL 5C3 (1994) collapse formulas, even with adapted plastic-collapse parameters and reduced yield strength (YS), were found not suitable for matching this set of expanded-pipe collapse data. Manufacturers may use the procedure described in this paper to develop design strength equations for specific expandable-pipe products, leading to product-specific sets of best-fit KT model parameters. The new equation takes account of the theoretical effect of internal pressure and axial load on collapse. The API SC5 RG ET Resource Group on expandable tubulars is incorporating the approach presented in this paper into the draft API RP 5-EX Recommended Practice.

THE STELLAR POPULATIONS OF LYMAN BREAK GALAXIES ATz∼ 5

We present the results of Spectral Energy Distribution(SED) fitting analysis for Lyman Break Galaxies(LBGs) at z~5 in the GOODS-N and its flanking fields (the GOODS-FF). With the publicly available IRAC images in the GOODS-N and IRAC data in the GOODS-FF, we constructed the rest-frame UV to optical SEDs for a large sample (~100) of UV-selected galaxies at z~5. Comparing the observed SEDs with model SEDs generated with a population synthesis code, we derived a best-fit set of parameters (stellar mass, age, color excess, and star formation rate) for each of sample LBGs. The derived stellar masses range from 10^8 to 10^11M_sun with a median value of 4.1x10^9M_sun. The comparison with z=2-3 LBGs shows that the stellar masses of z~5 LBGs are systematically smaller by a factor of 3-4 than those of z=2-3 LBGs in a similar rest-frame UV luminosity range. The star formation ages are relatively younger than those of the z=2-3 LBGs. We also compared the results for our sample with other studies for the z=5-6 galaxies. Although there seem to be similarities and differences in the properties, we could not conclude its significance. We also derived a stellar mass function of our sample by correcting for incompletenesses. Although the number densities in the massive end are comparable to the theoretical predictions from semi-analytic models, the number densities in the low-mass part are smaller than the model predictions. By integrating the stellar mass function down to 10^8 M_sun, the stellar mass density at z~5 is calculated to be (0.7-2.4)x10^7M_sun Mpc^-3. The stellar mass density at z~5 is dominated by massive part of the stellar mass function. Compared with other observational studies and the model predictions, the mass density of our sample is consistent with general trend of the increase of the stellar mass density with time.

One-Dimensional CoII and CuII Coordination Polymers and a Discrete CuII4 Complex of Carboxylate-Appended (2-Pyridyl)alkylamine Ligands: Spin-Canting and Anti-/Ferromagnetic Coupling

Structural characterization of the newly synthesized complexes [M(I)I(L(1)OO)(xH2O)][ClO4] x 2 H2O [M = Co, x = 1 (1); M = Cu, x = 0 (2); L(1)OO- = 3-[(2-(pyridin-2-yl)ethyl){2-(pyridin-2-yl)methyl}amino]propionate] reveals that 1 and 2 are 1D chainlike coordination polymers. A tridentate variety of this ligand afforded a discrete tetranuclear complex {[Cu(I)I(L(2)OO)(OClO3)]}4 x MeCN (3) [L(2)OO- = 3-[N-methyl-{2-(pyridine-2-yl)ethyl}amino]propionate]. Analysis of the crystal packing diagrams reveals extensive pi-pi stacking in 1 and C-H...O hydrogen bonding interactions in 3, leading to the formation of network structures. For these complexes, absorption spectral properties have been investigated. All three complexes exhibit exchange interaction between the M(II) ions through a syn-anti bridging carboxylate pathway. Magnetic studies on 1 show spontaneous magnetization below 5 K, which corresponds to the presence of spin-canted antiferromagnetism. At T = 2 K, the values of coercive field (H(c)) and remnant magnetization (M(r)) are 200 G and 0.019 muB, respectively. Analysis of the magnetic data through spin Hamiltonians in the form HH = sigma(i<j)(n) -J(ij)S(i)S(j) (J is positive for a ferromagnetic interaction and negative for an antiferromagnetic interaction) leads to the following set of best-fit parameters: J = -2.65(2), -0.66(1), and +12.2(2) cm(-1) for 1, 2, and 3, respectively. An attempt has been made to rationalize the observed magnetic behavior.

THERMUS—A thermal model package for ROOT

THERMUS is a package of C++ classes and functions allowing statistical-thermal model analyses of particle production in relativistic heavy-ion collisions to be performed within the ROOT framework of analysis. Calculations are possible within three statistical ensembles; a grand-canonical treatment of the conserved charges B, S and Q, a fully canonical treatment of the conserved charges, and a mixed-canonical ensemble combining a canonical treatment of strangeness with a grand-canonical treatment of baryon number and electric charge. THERMUS allows for the assignment of decay chains and detector efficiencies specific to each particle yield, which enables sensible fitting of model parameters to experimental data. Program summary Program title: THERMUS, version 2.1 Catalogue identifier: AEBW_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEBW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 17 152 No. of bytes in distributed program, including test data, etc.: 93 581 Distribution format: tar.gz Programming language: C++ Computer: PC, Pentium 4, 1 GB RAM (not hardware dependent) Operating system: Linux: FEDORA, RedHat, etc. Classification: 17.7 External routines: Numerical Recipes in C [1], ROOT [2] Nature of problem: Statistical-thermal model analyses of heavy-ion collision data require the calculation of both primordial particle densities and contributions from resonance decay. A set of thermal parameters (the number depending on the particular model imposed) and a set of thermalized particles, with their decays specified, is required as input to these models. The output is then a complete set of primordial thermal quantities for each particle, together with the contributions to the final particle yields from resonance decay. In many applications of statistical-thermal models it is required to fit experimental particle multiplicities or particle ratios. In such analyses, the input is a set of experimental yields and ratios, a set of particles comprising the assumed hadron resonance gas formed in the collision and the constraints to be placed on the system. The thermal model parameters consistent with the specified constraints leading to the best-fit to the experimental data are then output. Solution method: THERMUS is a package designed for incorporation into the ROOT [2] framework, used extensively by the heavy-ion community. As such, it utilizes a great deal of ROOT's functionality in its operation. ROOT features used in THERMUS include its containers, the wrapper TMinuit implementing the MINUIT fitting package, and the TMath class of mathematical functions and routines. Arguably the most useful feature is the utilization of CINT as the control language, which allows interactive access to the THERMUS objects. Three distinct statistical ensembles are included in THERMUS, while additional options to include quantum statistics, resonance width and excluded volume corrections are also available. THERMUS provides a default particle list including all mesons (up to the K 4 ∗ (2045)) and baryons (up to the Ω − ) listed in the July 2002 Particle Physics Booklet [3]. For each typically unstable particle in this list, THERMUS includes a text-file listing its decays. With thermal parameters specified, THERMUS calculates primordial thermal densities either by performing numerical integrations or else, in the case of the Boltzmann approximation without resonance width in the grand-canonical ensemble, by evaluating Bessel functions. Particle decay chains are then used to evaluate experimental observables (i.e. particle yields following resonance decay). Additional detector efficiency factors allow fine-tuning of the model predictions to a specific detector arrangement. When parameters are required to be constrained, use is made of the ‘Numerical Recipes in C’ [1] function which applies the Broyden globally convergent secant method of solving nonlinear systems of equations. Since the NRC software is not freely-available, it has to be purchased by the user. THERMUS provides the means of imposing a large number of constraints on the chosen model (amongst others, THERMUS can fix the baryon-to-charge ratio of the system, the strangeness density of the system and the primordial energy per hadron). Fits to experimental data are accomplished in THERMUS by using the ROOT TMinuit class. In its default operation, the standard χ 2 function is minimized, yielding the set of best-fit thermal parameters. THERMUS allows the assignment of separate decay chains to each experimental input. In this way, the model is able to match the specific feed-down corrections of a particular data set. Running time: Depending on the analysis required, run-times vary from seconds (for the evaluation of particle multiplicities given a set of parameters) to several minutes (for fits to experimental data subject to constraints).

Deducing ground structure using seismic pulses originating from an outdoor explosive source

Near‐surface layering of ground soil can influence propagation of acoustic and seismic pulses originating from above‐surface sources. Simultaneous recording of acoustic air pressure and seismic radial and vertical particle velocities resulting from a small, above ground explosion is used to obtain information about soil structure near the surface. Assuming nonlinear effects to be small at the ranges of interest here, a numerical model called Fast Field Program for Layered Air Ground Systems (FFLAGS), developed originally for continuous sound sources above a porous elastic ground is used to model a porous elastic layered ground system. Suitable optimisation methods were used to predict a set of best fit parameters for the near‐surface ground structure. It is shown that the model can explain multiple seismic arrivals and give a reasonable prediction of wave speeds and layer depths while the pressure pulse can predict permeability of the surface soil.Near‐surface layering of ground soil can influence propagation of acoustic and seismic pulses originating from above‐surface sources. Simultaneous recording of acoustic air pressure and seismic radial and vertical particle velocities resulting from a small, above ground explosion is used to obtain information about soil structure near the surface. Assuming nonlinear effects to be small at the ranges of interest here, a numerical model called Fast Field Program for Layered Air Ground Systems (FFLAGS), developed originally for continuous sound sources above a porous elastic ground is used to model a porous elastic layered ground system. Suitable optimisation methods were used to predict a set of best fit parameters for the near‐surface ground structure. It is shown that the model can explain multiple seismic arrivals and give a reasonable prediction of wave speeds and layer depths while the pressure pulse can predict permeability of the surface soil.

Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, &amp;gt;0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Ambiguity in Determining the Shape of Alkali Alkyl Sulfate Micelles from Small-Angle Scattering Data

Model fitting to small-angle scattering patterns from a series of dilute sodium- and cesium alkyl sulfate micellar solutions results in two significantly different sets of best-fit parameters for each solution. One of the sets defines nearly monodisperse prolate ellipsoids; the other defines slightly, but significantly, polydisperse oblate ellipsoids. In the prolate and oblate minimum locations, the mean form and structure factors as well as the mean core volumes are equal within the experimental error such that the axial ratios are approximately the reciprocals of each other. The experimental finding is numerically generalized: it is demonstrated that, in a Q range, the upper limit of which depends on the axial ratio, the squared mean and the mean square of the scattering amplitude from homogeneous ellipsoids with equatorial radii and axial ratios, respectively (r,eta) and (reta2/3,1/eta), are indistinguishable in practice. In dilute solutions without added salt, neither the best-fit values of the model parameters nor the available thermodynamic models provide direct evidence for the conformation, although the prolate ellipsoidal shape is indirectly supported by experiment. The elongated conformation of ionic micelles in dense and/or salinated systems seems realistic.

Diffusion of Cosmic Rays in the Expanding Universe. II. Energy Spectra of Ultra–High Energy Cosmic Rays

We consider the astrophysical implications of the diffusion equation solution for ultra-high energy cosmic rays (UHECRs) in the expanding universe, which we obtained in Paper I of this series. The UHECR spectra are calculated in a model with sources located in vertices of a cubic grid with a linear constant (source separation) d. The calculations are performed for various magnetic field configurations (Bc, lc), where lc is the basic scale of the turbulence and Bc is the coherent magnetic field on this scale. The main purpose of these calculations is to demonstrate the validity of the solution obtained in Paper I and to compare this solution with the Syrovatsky solution used in previous works. The Syrovatsky solution must be necessarily embedded in the static cosmological model. The formal comparison of the two solutions with all parameters being fixed identically reveals the appreciable discrepancies between two spectra. These discrepancies are less if different sets of best-fit parameters are used in these models.

Prediction of seismic pulses from an outdoor explosive source

Near-surface layering of ground soil can influence propagation of seismic pulses originating from above-surface sources. Furthermore, in a cold climate snow and frozen ground can add to this layering effect. A numerical model called Fast Field Program for Layered Air Ground Systems (FFLAGS), developed originally for continuous sound sources above a porous elastic ground, is used to predict radial and vertical seismic signals from an above ground explosive charge recorded by a geophone. Simultaneous acoustic air pressure recording of the same charge is used to make a prediction of the source pulse shape. This procedure produces an effective linear source. In other words, nonlinear effects are assumed to be small at the ranges of interest here. Subsequently, suitable optimization methods were used to predict a set of best fit parameters for the near-surface ground structure. [Work supported in part by US Army through its European Research Office.]