Hypothetical Set Research Articles

Ratio-dependent predator–prey model: effect of environmental fluctuation and stability

This paper deals with the problem of a ratio-dependent predator–prey model. The deterministic and stochastic behaviour of the model system around biologically feasible equilibria are studied. Conditions for which the deterministic model enter into Hopf-bifurcation are worked out. Stochastic stability of the system around positive interior equilibrium is studied. To substantiate our analytical findings numerical simulations are carried out for a hypothetical set of parameter values.

Inference of Codon Preferences inArabidopsis thaliana

In many organisms, variation among genes in the relative frequencies of synonymous codons reflects variation in local compositional biases and the intensity of natural selection on codon usage. Identification of preferred codons has been straightforward in most important model organisms, but this has not been the case with Arabidopsis thaliana. A data set of 13,812 genes suitable for our analyses was constructed from the most recent annotation of the complete A. thaliana genome. Factor analysis was performed to identify the primary trend through overall codon usage. Although the primary trend in codon usage in A. thaliana reflects the relative usage of G/C‐ending compared with A/U‐ending codons, there is no correlation with noncoding $$\mathrm{G}\,+\mathrm{C}\,$$ content. The usage of G‐ and C‐ending codons is actually negatively correlated with noncoding $$\mathrm{G}\,+\mathrm{C}\,$$ content. However, the usages of several codons, mainly C‐ and U‐ending, are correlated with recently published data on gene expression in roots. These codons tend to correspond to the more commonly represented tRNA anticodons. Thus, a hypothetical set of translationally optimal codons has been identified in this important model system.

Optimization of Subtractive Three Color Dyes for Dye-Based Color Ink Jet Printer

Focusing on subtractive color dyes currently used in dye-based color ink jet printers (IJs), we consider that it is important to increase the size of color gamut on high quality coated paper in particular. Development of the optimum subtractive color dyes will rely on full understanding of the relationship between dye amounts placed on the paper and resultant colors. In a previous study, the author verified that models were able to be used to predict the colorimetric gamut of a hypothetical dye set. Among the models, the Kubelka-Munk equation (KM) which relies only on the spectral reflectances of the primary colors achieved reproducible performance for the color gamut on coated paper used in a dye-based color IJ. Based on this result, the present study has used KM for prediction of the color gamut of a hypothetical dye set. The color gamut has been calculated by means of computer simulation, and the optimum combination of three dyes for a dye-based color IJ has been solved by constrained nonlinear programming. Moreover, the color gamut has been compared between the optimum three dyes obtained in this study and typical C, M and Y dyes for a dye-based color IJ. The result showed that the area of the color gamut obtained by the optimum combination of three dyes was only 1.1 times larger than that by the typical C, M and Y dyes, and the optimum peak wavelengths and absorption bandwidths depended on lightness.

Analysis of the Kamionkowski-Loeb method of reducing cosmic variance with CMB polarization

Part of the CMB polarization signal in the direction of galaxy clusters is produced by Thomson scattering of the CMB temperature quadrupole. In principle this allows measurement of the CMB power spectrum harmonic C{sub 2}(z) with higher accuracy (at z>0) than the cosmic variance limit imposed by sample variance on one CMB sky. However the observed signals are statistically correlated if the comoving separation between the clusters is small enough. Thus one cannot reduce the sample variance by more than roughly the number of separate regions available which produce uncorrelated signals, as first pointed out by Kamionkowski and Loeb. In this paper we analyze in detail the procedure outlined by Kamionkowski and Loeb, computing the correlation of the polarization signals by considering the variation of the spherical harmonic expansion coefficients of the temperature anisotropy on our past light cone. Given a hypothetical set of Stokes parameter measurements of the CMB polarization in the directions of galaxy clusters, distributed at random on a given redshift shell, we show how to construct an estimator of the angular power spectrum harmonic C{sub 2} at that redshift. We then compare the variance of this estimator with the cosmic variance of the CMB multipole onmore » our sky which probes the same scale. We find that in fact the cosmic variance is not reducible below the single sky CMB value using the cluster method. Thus this method is not likely to be of use for reconstruction of the primordial power spectrum. However the method does yield a measurement of C{sub 2} as a function of redshift with increasing accuracy at higher redshift, and thus potentially a probe of the mechanism which may have suppressed the quadrupole. We also examine to what extent the redshift dependence of C{sub 2} can be used to probe the time changing potential anisotropy as the universe evolves into the vacuum dominated phase (the late-time integrated Sachs-Wolfe effect). We find that this effect is not observable in the time dependence of C{sub 2} since it is swamped by cosmic variance, but there is an observable signature in the correlation functions of the Stokes parameters.« less

A Measure of Agreement for Interval or Nominal Multivariate Observations by Different Sets of Judges

This article addresses the problem of accounting overall multivariate chance-corrected interobserver agreement when targets have been rated by different sets of judges (not necessarily equal in number). The proposed approach builds on Janson and Olsson’s multivariate generalization of Cohen’s kappa but incorporates weighting for number of judges and applies an expression for expected disagreement suitable for the case with different judges. The authors suggest that the attractiveness of this approach to multivariate agreement measurement lies in the interpretability of the terms of expected and observed disagreement as average distances between observations, and that addressing agreement without regard to the covariance structure among variables has advantages in simplicity and interpretability. Correspondences to earlier approaches are noted, and application of the proposed measure is exemplified using hypothetical data sets.

Hybrid reactor system for wastewater treatment ? application and approach of modelling

Wastewater treatment by the hybrid reactor system has become wide-spread as it provides advantages of both the suspended and attached growth phase at the same time. It may be used to treat some rate-limiting substrate, priority pollutants, volatile organic compounds etc. as well as for nitrification. This versatile nature of hybrid reactor demands for a detailed investigation on the mechanism, mode of operation, different applications and major configurations available. The present article is devoted to explore these issues with respect to previous background and successive development in this area. Apart from the laboratory and pilot-scale study, some industrial applications have been overviewed to understand the performance of hybrid reactor in the concerned field. The approach of modelling for the hybrid reactor system is also demonstrated with the hypothetical data set. A comprehensive details about major hybrid processes is presented along with their schematic diagrams. The review on hybrid process revealed that it would be economic for upgradation of existing activated sludge system, ensuring carbonaceous oxidation and nitrification in a single reactor and treatment of slowly bio-degradable substances also.

The Effects of Industry Specialization on Audit Risk Assessments and Audit-Planning Decisions

This study investigates the effects of industry specialization on auditors' risk assessments and audit-planning decisions. In an experiment, auditors from different industry specializations complete a hypothetical audit case set in a specific (bank) industry, which creates either a match or a mismatch between the auditors' industry specialization and the hypothetical client's industry. Furthermore, I manipulate the industry-specific case information to achieve differential audit risk levels. I also provide the auditors with a set of preliminary audit procedures and a constrained time budget. I find that the auditors' knowledge of the client's industry improves their audit risk assessments and directly influences the nature and the perceived quality of their audit-planning decisions. In addition, the auditors' knowledge of the client's industry moderates the sensitivity of the auditors' planning decisions to their audit risk assessments.

Classical predator–prey system with infection of prey population—a mathematical model

AbstractThe present paper deals with the problem of a classical predator–prey system with infection of prey population. A classical predator–prey system is split into three groups, namely susceptible prey, infected prey and predator. The relative removal rate of the susceptible prey due to infection is worked out. We observe the dynamical behaviour of this system around each of the equilibria and point out the exchange of stability. It is shown that local asymptotic stability of the system around the positive interior equilibrium ensures its global asymptotic stability. We prove that there is always a Hopf bifurcation for increasing transmission rate. To substantiate the analytical findings, numerical experiments have been carried out for hypothetical set of parameter values. Our analysis shows that there is a threshold level of infection below which all the three species will persist and above which the disease will be epidemic. Copyright © 2003 John Wiley & Sons, Ltd.

A confidence interval approach to data analysis

The objective of ecological experiments is often to determine whether observed effects are large enough to be ecologically important. Despite this, effect size measures and associated measures of precision are frequently missing in published ecological research. In many cases, P-values are the only information available with which to assess the ecological importance of observed effects, but they provide a poor means of assessment. It is argued that specifying an important effect size a priori and then presenting observed effects with their associated confidence intervals is often a more informative way of presenting ecological data. A hypothetical data set is analysed and interpreted using both P-values and confidence intervals and the results from these two approaches compared. Effects interpreted using P-values were either statistically significant or not, while confidence intervals provided information about statistical significance, the precision of the estimates, and produced a range within which values for the true effects might plausibly lie. The results show that both statistically significant (<0.001) and non-significant (0.100) P-values did not provide useful information about the importance of their associated effects. The capacity to use confidence intervals for analysing complex ANOVA designs is discussed and the implications of different data analysis and presentation techniques for forest management are considered.

Pelicans at risk in Salton Sea—an eco-epidemiological model-II

The Salton Sea which is located in the Southeast desert of California is becoming a dangerous habitat for birds. It is supposed that elevated salinity, accelerated eutrophication, blooms of Avian botulism and dramatic water quality fluctuation are the key factors for massive die-off of Tilapia (prey) and Pelican (predator) in the Salton sea. Chattopadhyay and Bairagi [Ecol. Model. 136 (2001) 103] proposed and analyzed a three-component eco-epidemiological model consisting of susceptible fish population, infected fish population and their predator, the Pelican population. We modify their model from more biologically realistic point of view and then analyze it. The main objective of the work is to find out conditions for which the modified system becomes disease free. Numerical simulations for a hypothetical set of parameter values are presented to illustrate the analytical findings. It is observed that if the initial value of the system is contained in the invariant set which contain the disease-free equilibrium, the solution will approach the disease-free equilibrium under suitable parametric conditions. If the initial value of the system is not in the invariant set, impulsive harvesting strategies can be used to change the initial state to the desired disease-free equilibrium state.

Determining constitutive models from conical indentation: Sensitivity analysis

Several procedures have previously been advanced for extracting constitutive relations from the force–displacement curves obtained from indentation. This work addresses the specific problem of determining the elastic modulus E, yield stress Y, and hardening exponent n, which define the isotropic strain-hardening model from a single force–displacement curve with a sharp conical tip. The sensitivity of the inversion process was tested through a series of finite element calculations using ABAQUS. Different magnitudes of normally distributed noise were superimposed on a calculated force–displacement curve to simulate hypothetical data sets for specific values of E, Y, and n. The sensitivity of the parameter confidence intervals to noise was determined using the χ2-curvature matrix, statistical Monte Carlo simulations, and a conjugate gradient algorithm that explicitly searches the global parameter space. All three approaches demonstrate that 1% noise levels preclude the accurate determination of the strain-hardening parameters based on a single force–displacement curve.

A space-time state-space model of phytoplankton allelopathy

An integro-differential equation system with nonlocal effects of interspecific allelopathic interaction has been studied to investigate the formation of spatio-temporal structures in toxin producing phytoplankton population. The model is inherently more realistic than the usual kind of reaction-diffusion model. Bifurcation from uniform steady-state solution has been examined. Evolution of steady-state spatially periodic structure and periodic standing waves have been studied. The model helps to investigate the blooms, pulses and succession in different patches of phytoplankton population. Numerical simulations for a hypothetical set of parameter values and experimental observations have been presented to substantiate the analytical findings.

Evaluation of the fire simulation processes of the National Fire Management System's Initial Attack Analysis processor.

The fire simulation processes of the National Fire Management System's (NFMAS) Initial Attack Analysis (IAA) processor were evaluated by conducting two types of sensitivity analysis: one based on a hypothetical set of data to assess IAA's outputs under a wide range of fire input values, and the other using an actual Stanislaus National Forest database to test IAA's validity with a real set of data. The results revealed that IAA's outputs (projected annual number of fires and area burned) were most sensitive, in descending order, to the input values of the fire spread rate, the productivity rates of the suppression forces, and the initial attack time, for all fuel models tested. In contrast, IAA's outputs were extremely insensitive to variations in the fire size at discovery. Changes are necessary in the ways IAA incorporates the fire size at discovery to facilitate the comparison among various fire detection options. The program's "escaped fire situation" analysis was found inadequate, because the projected annual frequencies and final sizes of the simulated escaped fire events produced unacceptable results with the Stanislaus National Forest database. Assigning final sizes to simulated escaped fires according to the fire intensity level in which they are historically expected to occur provides a consistent way of calculation of the projected annual area burned and the consequent cost plus net value change (C + NVC).

Estimating the causal effects of treatment.

To provide a relatively non-technical review of recent statistical research on the analysis and interpretation of the results of randomised controlled trials in which there are possibly all three of the following types of protocol violation: non-adherence to allocated treatment, contamination (that is, patients receiving treatments other than the one to which they were allocated) and attrition (missing outcome data). The estimation methods involve the use of potential outcomes (counterfactuals) in the definition of a causal effect of treatment and in drawing valid inferences concerning its size. The methods are explained through the use of simple arithmetical expressions involving the counts from three-way contingency tables (Outcome by Treatment Received by Random Allocation). Illustration is provided through the use of a hypothetical data set. Recent advances in statistical methodology enable one to estimate treatment effects from the results of randomized trials in which the treatment actually received is not necessarily the one to which the patient was allocated. These methods allow one to make adjustments to allow for both non-compliance and loss to follow-up. Even for such a 'broken' randomised trial, inference concerning causal effects is safer than that from data arising from an observational study that never involved random allocation in the first place.

Pelican at Risk in Salton Sea – a Delay-Induced Eco-Epidemiological Model

Elevated salinity, accelerated eutrophication, blooms of Avian botulism and dramatic water quality fluctuation are supposed to be the key factors for massive die-off of Tilapia (prey) and Pelican (predator) in the Salton sea. We modify the model of Chattopadhyay and Bairagi [Ecological Modelling 136 (2001), pp. 103-112] with an assumption that the growth rate of susceptible fish population is very high and study the dynamics of the system by introducing a delay factor in the predator response function. It is observed that the otherwise stable system exhibit a stable limit cycle solution when the lag factor attains its critical value. It is also observed that there is a high possibility of an epidemic out break in the fish as well as in the Pelican population if the predation process is delayed by a considerable amount of time. Numerical simulations for a hypothetical set of parameter values are presented to illustrate the analytical findings.

A laboratory-based introduction to seroepidemiology and its use in vaccination programme design

The development of vaccination programmes relies on an understanding of the epidemiology and transmission dynamics of the pathogen. Whilst these concepts could be delivered using the traditional lecture format, a practical workshop approach would encourage deeper understanding and appreciation. However, an authentic laboratory-based seroepidemiological study using human and pathogen material would be costly, logistically complicated and raise ethical issues. The workshop presented here uses a cheap and safe model of an infectious disease immuno assay alongside spreadsheet analysis as a tool for introducing aspects of infectious disease epidemiology, and some theoretical concepts used in mass immunisation programme design. It isdesigned to represent a typical directly-transmitted childhood viral infection (e.g. measles) within a community prior to mass immunisation. Results can be discussed through comparison with a hypothetical data set and interms of herd immunity, endemicity and the type of transmission as well as average age at infection, the basic reproduction number (Ro) and other parameters of importance to vaccination programme design.

Individual Zone of Optimal Functioning (IZOF): A Probabilistic Estimation

The Individual Zone of Optimal Functioning (IZOF) model postulates the functional relationship between emotions and optimal performance, and aims to predict the quality of upcoming performance with respect to the pre-performance emotional state of the performer. Several limitations associated with the traditional method of determining the IZOF are outlined and a new probabilistic approach is introduced instead. To reliably determine the boundaries of the IZOF and their associated probabilistic curve thresholds, performance outcomes that vary in quality, as well as the emotional intensity associated with them, are taken into account. Several probabilistic models of varying complexity are presented, along with hypothetical and real data to illustrate the concept. The traditional and the new methods are contrasted in one actual set and two hypothetical sets of data. In all cases the proposed probabilistic method was found to show greater sensitivity and to more accurately represent the data than the traditional method. The development of the method is a first stage toward developing models that take into account the interactive nature and multidimensionality of the emotional construct, as well as the fluctuations in emotional intensity and performance throughout the competition phases (i.e., momentum).

Using hierarchical cluster analysis in nursing research.

This article is a pedagogical piece on hierarchical cluster analysis, a method for investigating the structure underlying data. Such methods are useful for finding similar groups of cases in data sets when it is not known a priori how many groups are present. The article is laid out asfollows: First, a brief history and overview of the methods is presented; second, an illustrative example with a small hypothetical data set is used to clarify fundamental concepts; third, hierarchical cluster analysis is applied to a data set from the author's own program of research to illustrate one way in which the methods may be employed in nursing research; fourth, the limitations of the methods are discussed; and finally, a list of suggested readings, at varying levels of detail. are provided for the interested researcher.

Assessment of positivity in immuno-assays with variability in background measurements: a new approach applied to the antibody response to Plasmodium falciparum MSP2

Measurements of immune responses often exhibit considerable heterogeneity, making it impossible to clearly distinguish responders and nonresponders to particular antigens. Typically, in, for example, enzyme-linked immunosorbent assay (ELISA) procedures, a nonexposed control group is used to assign a cutoff value of positivity, calculated as the mean plus either 2 or 3 standard deviations (S.D.). This can cause extremely biased estimates of response rates when the background is variable, and especially when there is overlap between the distribution of the control levels and that of responders. This problem is compounded when results of assays with different background levels are compared. We illustrate this with hypothetical data sets reflecting frequent patterns seen in laboratory and epidemiological studies. We propose that such data should be analysed by statistical modelling of the ratio of numbers of test samples/control samples as a function of the readout from the assay. Rather than classifying samples dichotomously as negative or positive, this provides estimates of the prevalence of positivity λ, and the probability, for each sample, that the measured activity is above background. Several statistical methods can provide such estimates. Analyses of simulated data sets using our preferred estimation method [a latent class model (LCM)] demonstrate that this gives more reliable results than the traditional assignment using cutoff values. We have applied this approach to the analysis of ELISA assessments of antibodies against distinct regions of the Plasmodium falciparum merozoite surface protein 2 (MSP2) in human sera from Tanzania.

Generating classifier outputs of fixed accuracy and diversity

We offer an algorithm for random generation of classifier outputs with specified individual accuracies and pairwise dependencies. The outputs are binary vectors (correct/incorrect classification) for a hypothetical data set. The generated team output can be used to study the majority vote over multiple dependent classifiers.