Wide Range Of Parameter Combinations Research Articles

Quantitative evaluation of the roles of ocean chemistry and climate on ooid size across the Phanerozoic: Global versus local controls

AbstractOoid size in natural systems can be predicted based on the expected equilibrium between precipitation and abrasion rates. Therefore, ooid size distributions may enable quantitative inferences about past ocean and climate conditions from sedimentology that complement insights from geochemistry. A few previous attempts have been made to compile information on ooid sizes across the Phanerozoic, but no systematic ooid size dataset is available and equilibrium models of ooid growth have not been explored comprehensively. In this study, convolutional neural network‐based segmentation was used to automate ooid size measurement and provide a systematic sampling of ooid sizes spanning the Phanerozoic. This data set is coupled with Monte Carlo simulations to quantitatively explore the interplay between three testable physicochemical parameters (long‐term average carbonate saturation state, bed shear velocity and intermittency of transport) in determining equilibrium ooid size. The numerical model shows that a typical‐sized ooid (≤1000 µm) can grow under a wide range of parameter combinations whereas giant ooids (>2000 µm) can only form under a more restricted set of circumstances. Furthermore, the model predicts that the formation of giant ooids is common when conditions favourable for rapid CaCO3 precipitation are achieved. This finding is consistent with the naturally rare occurrences of giant ooids, which developed under exceptional environmental conditions and are primarily associated with intervals of bimineralic seas and elevated sea‐surface temperature (greenhouse or transitional climate). Whereas global controls could help explain secular trends in ooid size, significant spatial variability of ooid size in several time intervals further demonstrates the importance of local environmental parameters in the growth of ooids. Together, spatial and temporal variation in ooid size distributions can complement geochemical proxies in quantitatively reconstructing ancient tropical marine environments.

Genetic load and extinction in peripheral populations: the roles of migration, drift and demographic stochasticity

We analyse how migration from a large mainland influences genetic load and population numbers on an island, in a scenario where fitness-affecting variants are unconditionally deleterious, and where numbers decline with increasing load. Our analysis shows that migration can have qualitatively different effects, depending on the total mutation target and fitness effects of deleterious variants. In particular, we find that populations exhibit a genetic Allee effect across a wide range of parameter combinations, when variants are partially recessive, cycling between low-load (large-population) and high-load (sink) states. Increased migration reduces load in the sink state (by increasing heterozygosity) but further inflates load in the large-population state (by hindering purging). We identify various critical parameter thresholds at which one or other stable state collapses, and discuss how these thresholds are influenced by the genetic versus demographic effects of migration. Our analysis is based on a ‘semi-deterministic’ analysis, which accounts for genetic drift but neglects demographic stochasticity. We also compare against simulations which account for both demographic stochasticity and drift. Our results clarify the importance of gene flow as a key determinant of extinction risk in peripheral populations, even in the absence of ecological gradients.This article is part of the theme issue ‘Species’ ranges in the face of changing environments (part I)’.

Depth-sensing spherical indentation of an elastic sphere on an elastic substrate

The axisymmetric three-body, double-unilateral contact problem for an elastic sphere is treated by analytical techniques. It is assumed that the elastic sphere is put on the surface of an elastic layer of finite thickness and, afterwards, is indented at the upper pole of the sphere with a rigid spherical punch. Friction is neglected at both contact interfaces. A first-order asymptotic solution for the displacement–force relation, which generalizes the corresponding Hertzian formula, is derived in explicit form in terms of dimensionless asymptotic constants that account for the finite sizes of the elastic sphere and layer. Under the assumption that the sphere/substrate contact diameter is less than the substrate thickness, the presented results can cover a wide range of parameter combinations, which may be used for the purpose of benchmark assessments in the finite element analysis. The constructed asymptotic model is remarkably simple and elegant, and yet can be applied to various contact problems of practical importance (including a very timely problem of indentation of spherical viruses). The underlying theoretical framework is versatile and can be further extended for analysis of multiple contacts involving elastic spheres and elastic layers.

Effect of LCRS clogging on leachate recirculation and landfill slope stability.

Vertical wells are commonly used for recirculating leachate into a landfill which can offer significant environmental and economic benefits. However, in some cases, the leachate collection and removal system (LCRS) at the bottom is overloaded and clogged due to biological and chemical processes. This results in a relatively high leachate level which could pose a threat to landfill slope stability. This study develops a three-dimensional landfill slope model with vertical recirculation wells and then investigates the effect of LCRS clogging on leachate recirculation and slope stability in terms of leachate saturation, pore water pressure, and factor of safety (FS) of a landfill slope. The results show that with an increase in clogging level that is characterized by an increased leachate level, the pore water pressure below the well injection screen is significantly increased by leachate recirculation, giving rise to a decreased slope FS value. In such conditions, the landfill slope formed by highly anisotropic waste is more likely to suffer instability. To prevent this kind of slope failure, a safe injection pressure of vertical recirculation wells is proposed for a wide range of parameter combinations involving waste anisotropy, clogging level, and the setback distance from the slope surface. This design guideline can be used to control the injection pressure in leachate recirculation applications and contributes to a better understanding of the slope stability of a bioreactor landfill.

Control of a tandem queue with a startup cost for the second server

Various systems across a broad range of applications contain tandem queues. Strong dependence between the servers has proven to make such networks complicated and difficult to study. Exact analysis is rarely computationally tractable and sometimes not even possible. Nevertheless, as it is most often the case in reality, there are costs associated with running such systems, and therefore, optimizing the control of tandem queues is of main interest from both a theoretical and a practical point of view. Motivated by this, the present paper considers a tandem queueing network with linear holding costs and a startup cost for the second server. In our work, we present a rather intuitive, easy to understand, and at the same time very accurate technique to approximate the optimal decision policy. Extensive numerical experimentation shows that the approximation works extremely well for a wide range of parameter combinations.

Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach.

A general method for parametrizing atomic interaction functions is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple molecules to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against experimental solvation free enthalpies in water and hexane as well as the density and heat of vaporization of the liquid at atmospheric pressure for a set of 10 aromatic-chloro compounds simultaneously. Single-step perturbation is used to efficiently calculate solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.

Vector population growth and condition-dependent movement drive the spread of plant pathogens.

Plant viruses, often spread by arthropod vectors, impact natural and agricultural ecosystems worldwide. Intuitively, the movement behavior and life history of vectors influence pathogen spread, but the relative contribution of each factor has not been examined. Recent research has highlighted the influence of host infection status on vector behavior and life history. Here, we developed a model to explore how vector traits influence the spread of vector-borne plant viruses. We allowed vector life history (growth rate, carrying capacity) and movement behavior (departure and settlement rates) parameters to be conditional on whether the plant host is infected or healthy and whether the vector is viruliferous (carrying the virus) or not. We ran simulations under a wide range of parameter combinations and quantified the fraction of hosts infected over time. We also ran case studies of the model for Barley yellow dwarf virus, a persistently transmitted virus, and for Potato virus Y, a non-persistently transmitted virus. We quantified the relative importance of each parameter on pathogen spread using Latin hypercube sampling with the statistical partial rank correlation coefficient technique. We found two general types of mechanisms in our model that increased the rate of pathogen spread. First, increasing factors such as vector intrinsic growth rate, carrying capacity, and departure rate from hosts (independent of whether these factors were condition-dependent) led to more vectors moving between hosts, which increased pathogen spread. Second, changing condition-dependent factors such as a vector's preference for settling on a host with a different infection status than itself, and vector tendency to leave a host of the same infection status, led to increased contact between hosts and vectors with different infection statuses, which also increased pathogen spread. Overall, our findings suggest that vector population growth rates had the greatest influence on rates of virus spread, but rates of vector dispersal from infected hosts and from hosts of the same infection status were also very important. Our model highlights the importance of simultaneously considering vector life history and behavior to better understand pathogen spread. Although developed for plant viruses, our model could readily be utilized with other vector-borne pathogen systems.

What Level of Risk Compensation Would Offset the Preventive Effect of Early Antiretroviral Therapy? Simulations From the TEMPRANO Trial.

Whether risk compensation could offset the preventive effect of early initiation of antiretroviral therapy (ART) on human immunodeficiency virus (HIV) transmission remains unknown. Using virological and behavioral data collected 12 months after inclusion in the TEMPRANO randomized trial of early ART (Abidjan, Côte d'Ivoire, 2009-2012), we estimated the risk of HIV transmission and compared it between the intervention (early ART; n=490) and control (deferred ART; n=467) groups. We then simulated increases in various sexual risk behaviors in the intervention group and estimated the resulting preventive effect. On the basis of reported values of sexual behaviors, we estimated that early ART had an 89% (95% confidence interval: 81, 95) preventive effect on the cumulative risk of HIV transmission over a 1-month period. This preventive effect remained significant for a wide range of parameter combinations and was offset (i.e., nonsignificant) only for dramatic increases in different sexual behaviors simulated simultaneously. For example, when considering a 2-fold increase in serodiscordance and the frequency of sexual intercourse together with a 33% decrease in condom use, the resulting preventive effect was 47% (95% confidence interval: -3, 74). An important reduction of HIV transmission may thus be expected from the scale-up of early ART, even in the context of behavioral change.

Predicted impact of mass drug administration on the development of protective immunity against Schistosoma haematobium.

Previous studies suggest that protective immunity against Schistosoma haematobium is primarily stimulated by antigens from dying worms. Praziquantel treatment kills adult worms, boosting antigen exposure and protective antibody levels. Current schistosomiasis control efforts use repeated mass drug administration (MDA) of praziquantel to reduce morbidity, and may also reduce transmission. The long-term impact of MDA upon protective immunity, and subsequent effects on infection dynamics, are not known. A stochastic individual-based model describing levels of S. haematobium worm burden, egg output and protective parasite-specific antibody, which has previously been fitted to cross-sectional and short-term post-treatment egg count and antibody patterns, was used to predict dynamics of measured egg output and antibody during and after a 5-year MDA campaign. Different treatment schedules based on current World Health Organisation recommendations as well as different assumptions about reductions in transmission were investigated. We found that antibody levels were initially boosted by MDA, but declined below pre-intervention levels during or after MDA if protective immunity was short-lived. Following cessation of MDA, our models predicted that measured egg counts could sometimes overshoot pre-intervention levels, even if MDA had had no effect on transmission. With no reduction in transmission, this overshoot occurred if protective immunity was short-lived. This implies that disease burden may temporarily increase following discontinuation of treatment, even in the absence of any reduction in the overall transmission rate. If MDA was additionally assumed to reduce transmission, a larger overshoot was seen across a wide range of parameter combinations, including those with longer-lived protective immunity. MDA may reduce population levels of immunity to urogenital schistosomiasis in the long-term (3–10 years), particularly if transmission is reduced. If MDA is stopped while S. haematobium is still being transmitted, large rebounds (up to a doubling) in egg counts could occur.

The Application of Specific Point Energy Analysis to Laser Cutting with 1μm Laser Radiation

Specific point energy (SPE) is a concept that has been successfully used in laser welding where SPE and power density determine penetration depth. This type of analysis allows the welding characteristics of different laser systems to be directly compared. This paper investigates if the SPE concept can usefully be applied to laser cutting. In order to provide data for the analysis laser cutting of various thicknesses of mild steel with a 2kW fibre laser was carried out over a wide range of parameter combinations. It was found that the SPE concept is applicable to laser cutting within the range of parameters investigated here.

Structure and kinematics of the nearby dwarf galaxy UGCA 105

Owing to their shallow stellar potential, dwarf galaxies possess thick gas disks, which makes them good candidates for studies of the galactic vertical kinematical structure. We present 21 cm line observations of the isolated nearby dwarf irregular galaxy UGCA 105, taken with the Westerbork Synthesis Radio Telescope (WSRT), and analyse the geometry of its neutral hydrogen (HI) disk and its kinematics. The galaxy shows a fragmented HI distribution. It is more extended than the optical disk, and hence allows one to determine its kinematics out to very large galacto-centric distances. The HI kinematics and morphology are well-ordered and symmetric for an irregular galaxy. The HI is sufficiently extended to observe a substantial amount of differential rotation. Moreover, UGCA 105 shows strong signatures for the presence of a kinematically anomalous gas component. Performing tilted-ring modelling by use of the least-squares fitting routine TiRiFiC, we found that the HI disk of UGCA 105 has a moderately warped and diffuse outermost part. Probing a wide range of parameter combinations, we succeeded in modelling the data cube as a disk with a strong vertical gradient in rotation velocity ($\approx -60\,\rm km\,s^{-1}\,kpc^{-1}$), as well as vertically increasing inwards motion ($\approx -70\,\rm km\,s^{-1}\,kpc^{-1}$) within the radius of the stellar disk. The inferred radial gas inflow amounts to $0.06\,\rm M_\odot \rm yr^{-1}$, which is similar to the star formation rate of the galaxy. The observed kinematics are hence compatible with direct or indirect accretion from the intergalactic medium, an extreme backflow of material that has formerly been expelled from the disk, or a combination of both.

Profile likelihood‐based confidence interval of the intraclass correlation for binary outcome data sampled from clusters

The intraclass correlation in binary outcome data sampled from clusters is an important and versatile measure in many biological and biomedical investigations. Properties of the different estimators of the intraclass correlation based on the parametric, semi-parametric, and nonparametric approaches have been studied extensively, mainly in terms of bias and efficiency [see, for example, Ridout et al., Biometrics 1999, 55:137-148; Paul et al., Journal of Statistical Computation and Simulation 2003, 73:507-523; and Lee, Statistical Modelling 2004, 4: 113-126], but little attention has been paid to extending these results to the problem of the confidence intervals. In this article, we generalize the results of the four point estimators by constructing asymptotic confidence intervals obtaining closed-form asymptotic and sandwich variance expressions of those four point estimators. It appears from simulation results that the asymptotic confidence intervals based on these four estimators have serious under-coverage. To remedy this, we introduce the Fisher's z-transformation approach on the intraclass correlation coefficient, the profile likelihood approach based on the beta-binomial model, and the hybrid profile variance approach based on the quadratic estimating equation for constructing the confidence intervals of the intraclass correlation for binary outcome data. As assessed by Monte Carlo simulations, these confidence interval approaches show significant improvement in the coverage probabilities. Moreover, the profile likelihood approach performs quite well by providing coverage levels close to nominal over a wide range of parameter combinations. We provide applications to biological data to illustrate the methods.

Optimal Job Splitting in Parallel Processor Sharing Queues

The main barrier to the sustained growth of wireless communications is the Shannon limit that applies to the channel capacity. A promising means to realize high-capacity enhancements is the use of multi-path communication solutions to improve reliability and network performance in areas that are covered by a multitude of overlapping wireless access networks. Despite the enormous potential for capacity enhancements offered by multi-path communication techniques, little is known about how to effectively exploit this. Motivated by this, we study a model where jobs are split and downloaded over N multiple parallel networks, each of which is modeled as a processor sharing (PS) queue. Each job is fragmented, according to a fixed splitting rule and re-assembled at the receiving end. The complex correlation structure between the sojourn times at the PS nodes makes an exact detailed mathematical analysis of the model impossible. Therefore, in this article we propose a simple and fast approximation for the splitting rule that minimizes the expected job-download time. Our approximation is validated extensively by simulations. The results show that the outcomes are extremely accurate over a wide range of parameter combinations.

Interval estimation of the over‐dispersion parameter in the analysis of one‐way layout of count data

The over-dispersion parameter is an important and versatile measure in the analysis of one-way layout of count data in biological studies. For example, it is commonly used as an inverse measure of aggregation in biological count data. Its estimation from finite data sets is a recognized challenge. Many simulation studies have examined the bias and efficiency of different estimators of the over-dispersion parameter for finite data sets (see, for example, Clark and Perry, Biometrics 1989; 45:309-316 and Piegorsch, Biometrics 1990; 46:863-867), but little attention has been paid to the accuracy of the confidence intervals (CIs) of it. In this paper, we first derive asymptotic procedures for the construction of confidence limits for the over-dispersion parameter using four estimators that are specified by only the first two moments of the counts. We also obtain closed-form asymptotic variance formulae for these four estimators. In addition, we consider the asymptotic CI based on the maximum likelihood (ML) estimator using the negative binomial model. It appears from the simulation results that the asymptotic CIs based on these five estimators have coverage below the nominal coverage probability. To remedy this, we also study the properties of the asymptotic CIs based on the restricted estimates of ML, extended quasi-likelihood, and double extended quasi-likelihood by eliminating the nuisance parameter effect using their adjusted profile likelihood and quasi-likelihoods. It is shown that these CIs outperform the competitors by providing coverage levels close to nominal over a wide range of parameter combinations. Two examples to biological count data are presented.

Reducing over-reporting of deterministic co-occurrence patterns in biotic communities

Null models of species co-occurrence are widely used to infer the existence of various ecological processes. Here we investigate the susceptibility of the most commonly used of these models (the C-score in conjunction with the sequential swap algorithm) to type 1 and type 2 errors. To do this we use simulated datasets with a range of numbers of sites, species and coefficients of variation (CV) in species abundance. We find that this model is particularly susceptible to type 1 errors when applied to large matrices and those with low CV in species abundance. As expected, type 2 error rates decrease with increasing numbers of sites and species, although they increase with increasing CV in species abundance. Despite this, power remains acceptable over a wide range of parameter combinations. The susceptibility of this analytical method to type 1 errors indicates that many previous studies may have incorrectly reported the existence of deterministic patterns of species co-occurrence. We demonstrate that in order to overcome the problem of high type 1 error rates, the number of swaps used to generate null distributions for smaller matrices needs to be increased to over 50,000 swaps (well beyond the 5000 commonly used in published analyses and the 30,000 suggested by Lehsten and Harmand, 2006). We also show that this approach reduces type 1 error rates in real datasets. However, even using this solution, larger datasets still suffer from high type 1 error rates. Such datasets therefore require the use of very large numbers of swaps, which calls for improvements in the most commonly used software. In general, users of this powerful analytical method must be aware that they need surprisingly large numbers of swaps to obtain unbiased estimates of structuring in biotic communities.

Effective load for flow-level performance modelling of file transfers in wireless LANs

Today, a wide range of 802.11-based Wireless LANs (WLANs) have become dominant to provide wireless Internet access for file transfers. For engineering purposes, there is a need for very simple, explicit, yet accurate, models that predict the performance of WLANs under anticipated load conditions. In this context, several detailed packet-level models have been proposed, based on fixed-point equations. Despite the fact that these models generally lead to accurate performance predictions, they do not lead to simple explicit expressions for the performance of WLANs. Motivated by this, we propose a new analytic model that captures the highly complex combined dynamics and protocol overhead of the 802.11 MAC, IP, TCP and application-layer into an explicit expression for a single parameter which will be called the effective service time. Based on the effective service time, we define the effective load to describe the flow-level performance of file transfers over WLANs with an M/ G/1 Processor Sharing (PS) model. Using the M/ G/1 PS model properties we propose a simple analytic model to obtain WLAN AP buffer content distribution. Despite the fact that PS models are heavily used in modelling flow-level performance in communication networks, an extensive validation of such models has not been published in the field, or context, of WLAN. To this end, our model is validated extensively by comparing the model-based average response times against simulations. The results show that the model leads to highly accurate predictions over a wide range of parameter combinations, including light- and heavy-tailed file-size distributions and light- and heavy-load scenarios. The simplicity and accuracy of the model make the results of high practical relevance and useful for performance engineering purposes.

Additive and interactive effects of stimulus degradation: No challenge for CDP+.

S. O'Malley and D. Besner (2008) showed that additive effects of stimulus degradation and word frequency in reading aloud occur in the presence of nonwords but not in pure word lists. They argued that this dissociation presents a major challenge to interactive computational models of reading aloud and claimed that no currently implemented model is able to simulate additive effects in these conditions. In the current article, it is shown that the connectionist dual process model (CDP+) can simulate these effects because its nonlexical route is thresholded. The authors present a series of simulations showing that CDP+ can not only simulate the precise dissociation observed by O'Malley and Besner but more generally can produce additive effects for a wide range of parameter combinations and different sets of items. The nonlexical route of CDP+ was not modified post hoc to deal with the effects of stimulus quality, but it had been thresholded for principled reasons before it was known that these effects existed. Together, the effects of stimulus quality on word frequency do not challenge CDP+ but rather provide unexpected support for its architecture and processing dynamics.

Parameter estimation for a physics‐based distributed hydrologic model using measured outflow fluxes and internal moisture states

We use an inverse simulation strategy to estimate soil hydraulic parameter values for an extensively measured planar hillslope plot in Seattle, Washington, United States. Both the integrated (subsurface outflow) and internal (piezometer water levels, volumetric water contents) hydrologic responses are measured at the plot. Inverse simulation scenarios are configured in the physics‐based variably saturated hydrologic model, HYDRUS‐2D, for a nonhysteretic drainage scenario starting from saturated initial conditions. Multiple inverse simulations calibrate the model either to single‐measurement time series or to combinations of multiple types of measurements. Inverse simulations calibrated to different types of measurements give a wide range of parameter combinations, including over 2 orders of magnitude in predicted saturated hydraulic conductivity (Ks), in part because the calibrations to a single measurement type are poorly constrained and biased. Parameter values are better constrained with multiobjective inverse simulations (Ks from 30 to 55 cm h−1). All parameter combinations from inverse simulations were tested in 2‐month‐long continuous simulations of the plot flow response to natural precipitation and evapotranspiration. The long‐term outflow response was predicted best (Nash‐Sutcliffe E = 0.94) by the parameters from a multiobjective inverse simulation calibrated to both the outflow and the piezometer water levels. Overall results show that for an assumed nonhysteretic soil a physics‐based hydrologic response model can be calibrated using one short‐duration drainage‐from‐saturation event if both integrated (outflow) and internal (saturated water level) measurements are used as calibration objectives.

Maintenance expenditures and indeterminacy under increasing returns to scale

This paper develops a one‐sector real business cycle model in which competitive firms allocate resources for the production of goods, investment in new capital and maintenance of existing capital. Firms also choose the utilization rate of existing capital. A higher utilization rate leads to faster capital depreciation, and an increase in maintenance activity has the opposite effect. We show that as the equilibrium ratio of maintenance expenditures to GDP rises, the required degree of increasing returns for local indeterminacy declines over a wide range of parameter combinations. When the model is calibrated to match empirical evidence on the relative size of maintenance and repair activity, we find that local indeterminacy (and belief‐driven fluctuations) can occur with a mild and empirically‐plausible degree of increasing returns: approximately 1.08.

Maintenance Expenditures and Indeterminacy under Increasing Returns to Scale

This paper develops a one-sector real business cycle model in which competitive firms allocate resources for the production of goods, investment in new capital, and maintenance of existing capital. Firms also choose the utilization rate of existing capital. A higher utilization rate leads to faster capital depreciation, while an increase in maintenance activity has the opposite effect. We show that as the equilibrium ratio of maintenance expenditures to GDP rises, the required degree of increasing returns for local indeterminacy declines over a wide range of parameter combinations. When the model is calibrated to match empirical evidence on the relative size of maintenance and repair activity, we find that local indeterminacy (and belief-driven fluctuations) can occur with a mild and empirically-plausible degree of increasing returns–around 1.08.