One-parameter Model Research Articles

Hydrological model parameterization using NDVI values to account for the effects of land cover change on the rainfall–runoff response

Abstract Classic rainfall–runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, model parameters change temporally. To overcome this problem, normalized difference vegetation index (NDVI) derived from remotely sensed data was used in this study to investigate the effect of land cover variations on hydrological response of watersheds using a conceptual rainfall–runoff model. The study area consists of two sub-watersheds (Hervi and Lighvan) with varied land cover conditions. Obtained results show that the one-parameter model generates runoff forecasts with acceptable level of the considered criteria. Remote sensing data were employed to relate land cover properties of the watershed to the model parameter. While a power form of the regression equation could be best fitted to the parameter values using available images of Hervi sub-watershed, for the Lighvan sub-watershed the fitted equation shows somewhat lower correlation due to higher fluctuations of the model parameter. The average values of the Nash–Sutcliffe efficiency criterion of the model were obtained as 0.87 and 0.55, respectively, for Hervi and Lighvan sub-watersheds. Applying this methodology, the model's parameters might be determined using temporal NDVI values.

195 Pt NMR parameters as strong descriptors in one-parameter QSAR models for platinum-based antitumor compounds.

Highly predictive one-parameter quantitative structure-activity relationship models have been developed for platinum-based anticancer drugs using the 195 Pt NMR parameters as strong descriptors. The developed quantitative structure-activity relationship models were applied in diverse homogeneous sets of antiproliferative Pt(II) and Pt(IV) compounds. These observations form the basis for making predictions of cytotoxicity for a broad range of platinum-based antitumor compounds just from inspection of calculated or experimentally determined 195 Pt NMR parameters. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey

BackgroundMany continuous item responses (CIRs) are encountered in healthcare settings, but no one uses item response theory’s (IRT) probabilistic modeling to present graphical presentations for interpreting CIR results. A computer module that is programmed to deal with CIRs is required.To present a computer module, validate it, and verify its usefulness in dealing with CIR data, and then to apply the model to real healthcare data in order to show how the CIR that can be applied to healthcare settings with an example regarding a safety attitude survey.MethodsUsing Microsoft Excel VBA (Visual Basic for Applications), we designed a computer module that minimizes the residuals and calculates model’s expected scores according to person responses across items. Rasch models based on a Wright map and on KIDMAP were demonstrated to interpret results of the safety attitude survey.ResultsThe author-made CIR module yielded OUTFIT mean square (MNSQ) and person measures equivalent to those yielded by professional Rasch Winsteps software. The probabilistic modeling of the CIR module provides messages that are much more valuable to users and show the CIR advantage over classic test theory.ConclusionsBecause of advances in computer technology, healthcare users who are familiar to MS Excel can easily apply the study CIR module to deal with continuous variables to benefit comparisons of data with a logistic distribution and model fit statistics.

Foundation plate on the elastic half-space, deterministic and probabilistic approach

Interaction between the foundation plate and subgrade can be described by different mathematical - physical model. Elastic foundation can be modelled by different types of models, e.g. one-parametric model, two-parametric model and a comprehensive model - Boussinesque (elastic half-space) had been used. The article deals with deterministic and probabilistic analysis of deflection of the foundation plate on the elastic half-space. Contact between the foundation plate and subsoil was modelled using contact elements node-node. At the end the obtained results are presented.

Improving the efficiency of water purification from dissolved gases at TPP

The method for increasing the efficiency of thermal deaerators and calciners of a TPP is considered; it consists of the use of a turbulent mass transfer device with random small packing. Before entering the packed bed in water, air (decarbonization) or water vapor (deaeration) is supplied. Chaotic nozzle creates intense turbulent interaction mode of air (vapor) with water and splitting it into small bubbles; thus the specific surface area of the contact of phases significantly increases, and high efficiency of mass transfer (extraction of dissolved gases) is ensured. A turbulent mass transfer device is a circular channel with connections for connecting of the source water to a pipeline. Inzhekhim chaotic nozzle is used with large free volume (95%) and the specific surface area of 150–300 m2/m3. The nozzle is made of a thin metal strip that may have a rough surface and is retained in the channel by means of two grids. For the calculation of turbulent mixer, mathematical model of the flow structure is presented, which is built with the use of a one-parameter diffusion model and a semiempirical reverse mixing ratio. Accounting of interphase transfer of dissolved gases is carried out via volume source of weight. The equation to determine the weight source and calculation of its parameters is presented. In the particular case, transition to the cell model is made and an expression for calculating the profile of concentrations of dissolved gas is obtained along the channel with a nozzle. An example of calculating the efficiency of turbulent mixer upon removing dissolved carbon dioxide from water at a TPP is shown. Recommendations on the use of the considered technical device are given.

Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction.

Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is “distance penalization.” But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices–common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data (P > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance which was first applied in models of growth networks. Our results suggest that PA is not suitable for predicting connectivity in a small-world network. Furthermore, in order to evaluate the predictions rapidly, prediction power was proposed as an evaluation metric. The current study compares the predictions of functional connectivity with seven local information indices and provides a reference of method selection for construction of prediction models.

An entropy-like parameter of particle size distributions as packing density index in complex granular media

Information entropy dimension (ED) has been used earlier in the characterization of the particle size distribution (PSD) in complex multi-particle granular media. In this work the ED is first proposed as an indicator of the packing density on the basis of the theoretical based interpretation of the ED in the granular media PSD context. A one-parametric exact self-similar PSD model, where the information ED is known, together with a 2-D computational random packing algorithm, are used to test the ability of the ED as an indicator of packing density. It is found that the packing density increases when the ED does. Moreover, results show a strong linear dependence between packing density and the ED. Empirical results from a large soil data base also reinforce the computational results. As ED may be estimated from field or laboratory data, the above mentioned results suggest its use as an indicator of packing density in complex granular media and material science in order to predict their properties.

Defocus Map Estimation From a Single Image Based on Two-Parameter Defocus Model

Defocus map estimation (DME) is highly important in many computer vision applications. Nearly, all existing approaches for DME from a single image are based on a one-parameter defocus model, which does not allow for the variation of depth over edges. In this paper, a novel two-parameter model of defocused edges is proposed for DME from a single image. We can estimate the defocus amounts for each side of the edges through this proposed model, and the confidence that the edge is a pattern edge, where the depth remains the same over the edge, can be generated. Then, we modify the TV-L1 algorithm for structure-texture decomposition by taking advantage of this confidence to eliminate pattern edges while preserving structural ones. Finally, the defocus amounts estimated at the edge positions are used as initial values, and the structure component is employed as a guidance in the following Laplacian matting procedure to avoid the influence of pattern edges on the final defocus map. Experiment results show that the proposed method can effectively eliminate the influence of pattern edges compared with the state-of-art method. Furthermore, the estimated defocus map is feasible in applications of depth estimation and foreground/background segmentation.

Simple model of surface roughness for binary collision sputtering simulations

It has been shown that surface roughness can strongly influence the sputtering yield – especially at glancing incidence angles where the inclusion of surface roughness leads to an increase in sputtering yields. In this work, we propose a simple one-parameter model (the “density gradient model”) which imitates surface roughness effects. In the model, the target’s atomic density is assumed to vary linearly between the actual material density and zero. The layer width is the sole model parameter. The model has been implemented in the binary collision simulator IMSIL and has been evaluated against various geometric surface models for 5keV Ga ions impinging an amorphous Si target. To aid the construction of a realistic rough surface topography, we have performed MD simulations of sequential 5keV Ga impacts on an initially crystalline Si target. We show that our new model effectively reproduces the sputtering yield, with only minor variations in the energy and angular distributions of sputtered particles. The success of the density gradient model is attributed to a reduction of the reflection coefficient – leading to increased sputtering yields, similar in effect to surface roughness.

Modeling Test Context Effects in Longitudinal Achievement Data: Examining Position Effects in the Longitudinal German PISA 2012 Assessment

Position effects (PE) in school achievement tests are a specific kind of test context effects (TCEs) that refer to the phenomenon of items becoming more difficult, the later they are positioned in a test. Up until today, PEs have been investigated mainly in cross-sectional settings; this means that little is known about how the size of PEs changes when retesting students. In the present article, we investigate TCEs in the longitudinal extension of the PISA 2012 assessment in Germany. To this end, we propose an extension of the two-dimensional one-parameter item response model, with one dimension per measurement occasion, that includes the effects of booklets (i.e., test forms) on item clusters (i.e., item bundles) that are allowed to vary between assessment occasions and groups (school types). Results indicate that the TCEs uncovered in all domains tested (mathematics, science, and reading) are closely in line with PEs, with reading being most strongly affected, and mathematics being least affected. The size of PEs increased in the second assessment, although the domains were differently affected. This pattern of effects was more pronounced in nonacademic school types. Finally, estimates of average achievement gains appeared to be underestimated by IRT models that neglected TCEs, with differences being largest in domains most strongly affected by PEs (i.e., science and reading).

Prediction of 195 Pt NMR of photoactivable diazido- and azine-Pt(IV) anticancer agents by DFT computational protocols.

195 Pt NMR chemical shifts for a series of large-sized photoactivable anticancer diazido-Pt(IV), homopiperizine-Pt(IV) and multifunctional azine-Pt(IV) complexes hardly to be probed experimentally and by sophisticated four-component and two-component relativistic calculations are predicted with high accuracy by density functional theory computational protocols. The calculated 195 Pt NMR chemical shifts constitute a crucial descriptor for making highly predictive one-parameter quantitative structure activity relationships models that help in designing photoactivable Pt(IV)-based antitumor agents with high cytotoxicity and selectivity. Copyright © 2016 John Wiley & Sons, Ltd.

Quantifying tensions between CMB and distance data sets in models with free curvature or lensing amplitude

Recent measurements of the Cosmic Microwave Background (CMB) by the Planck Collaboration have produced arguably the most powerful observational evidence in support of the standard model of cosmology, i.e. the spatially flat $\Lambda$CDM paradigm. In this work, we perform model selection tests to examine whether the base CMB temperature and large scale polarization anisotropy data from Planck 2015 (P15) prefer any of eight commonly used one-parameter model extensions with respect to flat $\Lambda$CDM. We find a clear preference for models with free curvature, $\Omega_\mathrm{K}$, or free amplitude of the CMB lensing potential, $A_\mathrm{L}$. We also further develop statistical tools to measure tension between datasets. We use a Gaussianization scheme to compute tensions directly from the posterior samples using an entropy-based method, the surprise, as well as a calibrated evidence ratio presented here for the first time. We then proceed to investigate the consistency between the base P15~CMB data and six other CMB and distance datasets. In flat $\Lambda$CDM we find a $4.8\sigma$ tension between the base P15~CMB data and a distance ladder measurement, whereas the former are consistent with the other datasets. In the curved $\Lambda$CDM model we find significant tensions in most of the cases, arising from the well-known low power of the low-$\ell$ multipoles of the CMB data. In the flat $\Lambda$CDM $+A_\mathrm{L}$ model, however, all datasets are consistent with the base P15~CMB observations except for the CMB lensing measurement, which remains in significant tension. This tension is driven by the increased power of the CMB lensing potential derived from the base P15~CMB constraints in both models, pointing at either potentially unresolved systematic effects or the need for new physics beyond the standard flat $\Lambda$CDM model.

Assortative mating can impede or facilitate fixation of underdominant alleles

Underdominant mutations have fixed between divergent species, yet classical models suggest that rare underdominant alleles are purged quickly except in small or subdivided populations. We predict that underdominant alleles that also influence mate choice, such as those affecting coloration patterns visible to mates and predators alike, can fix more readily. We analyze a mechanistic model of positive assortative mating in which individuals have n chances to sample compatible mates. This one-parameter model naturally spans random mating (n=1) and complete assortment (n→∞), yet it produces sexual selection whose strength depends non-monotonically on n. This sexual selection interacts with viability selection to either inhibit or facilitate fixation. As mating opportunities increase, underdominant alleles fix as frequently as neutral mutations, even though sexual selection and underdominance independently each suppress rare alleles. This mechanism allows underdominant alleles to fix in large populations and illustrates how life history can affect evolutionary change.

Some improvements on Sun–Chen’s one-parameter plasticity model for fibrous composites – Part I: Constitutive modelling for tension–compression asymmetry response

Herein, a one-parameter plasticity model proposed by Sun and Chen [Sun CT and Chen JL. A simple flow rule for characterizing nonlinear behavior of fiber composites. J Compos Mater 1989; 23: 1009–1020] demonstrates features that make it highly attractive for characterizing non-linear responses of fibrous composites. However, a detailed exploitation of the model’s potential has been halted by unresolved problems that include tension–compression asymmetry in stress–strain curves, FEM implementation as well as optimal parameters determination, which are addressed in this investigation as well as proposed solutions are presented. The major focus in Part I of this three-part study was devoted to developing a simple model for predicting the tension–compression asymmetry in stress–strain curves for fibrous composites, which was based on Sun and Chen’s one-parameter plasticity model. A generalized Hill yield criterion was proposed from combinations of the Drucker–Prager yield criterion that considers the effect of hydrostatic pressure for isotropic materials and the Hill yield criterion suitable for anisotropic materials. By incorporating the yield strength-differential effect on the plastic flow rule in composite laminates, the one-parameter plasticity model was extended to a strength-differential effect-incorporated model. The improved model has been calibrated and validated by off-axis tension and compression tests on unidirectional carbon/epoxy (IM600/Q133) composite laminates. Results verified that the proposed model captured the complex tension–compression asymmetry in observed non-linear responses of stress–strain curves.

Some improvements on Sun-Chen's one-parameter plasticity model for fibrous composites (Part II: Finite element method implementation and applications)

A SD-effect-incorporated nonlinear model for investigating the tension-compression asymmetry in stress–strain curves of fibrous composites was developed in Part I as a new application of the one-parameter plasticity model. In Part II of this investigation, the model is further developed to three-dimensional finite element models and is implemented in the finite element code ABAQUS; in addition, the model is validated against experimental results of stress–strain curves for unidirectional and angle-ply laminates under off-axis tension and compression. Last, parametric studies for a composite cantilever beam with and without SD effect are discussed to illustrate the features of the implemented model that is proposed herein.

Initial strain energy-based thermo-elastoviscoplastic two-parameter damage self-healing model for bituminous composites—Part II: Computational aspects

This paper describes the implementation of the initial strain energy-based thermo-elastoviscoplastic two-parameter damage self-healing models for bituminous materials to compare the model predictions with experimental measurements. Computational algorithms based on the two-step operator splitting methodology are proposed and employed for numerical results. The elastic two-parameter damage self-healing predictor and the viscoplastic corrector are coupled with the Arrhenius-type temperature term via the net stress concept in conjunction with the hypothesis of strain equivalence and then systematically used for numerical implementation of the models. Four numerical examples of three-dimensional problems are first demonstrated for the effect of temperature and rest period for healing phenomenon. Second, experimental validation of the proposed model against monotonic constant-strain test at different temperatures and controlled-strain cyclic tension test is presented. It is observed that numerical simulations by the two-parameter model agree well with experimental results. In particular, the softening responses of the bituminous composites are well predicted for monotonic constant-strain rate test and the viscous damage behavior can be reasonably predicted by the proposed two-parameter damage models. It is noted that the two-parameter model can better predict the thermo-mechanical behavior of asphalt and is more versatile than the one-parameter isotropic damage self-healing model.

Isogeometric analysis for sixth-order boundary value problems of gradient-elastic Kirchhoff plates

Sixth-order boundary value problems of a one-parameter gradient-elastic Kirchhoff plate model are formulated in a weak form within an H3 Sobolev space setting with the corresponding equilibrium equations and general boundary conditions. The corresponding conforming Galerkin method is proposed with error estimates for discretizations satisfying C2 continuity requirements. Continuity, coercivity and consistency of the corresponding bilinear form are utilized for proving the theoretical results. Numerical computations with conforming isogeometric discretizations of Cp−1-continuous NURBS basis functions of order p≥3 confirm the theoretical results and illustrate the features of the problem for both statics and free vibrations. In particular, the effects of the additional boundary conditions and parameter-dependent boundary layers corresponding to the gradient elasticity theory are addressed by the numerical examples.

Large random simplicial complexes, I

In this paper we introduce and develop the multi-parameter model of random simplicial complexes with randomness present in all dimensions. Various geometric and topological properties of such random simplicial complexes are characterised by convex domains in the high-dimensional parameter space (rather than by intervals, as in the usual one-parameter models). We find conditions under which a multi-parameter random simplicial complex is connected and simply connected. Besides, we give an intrinsic characterisation of the multi-parameter probability measure. We analyse links of simplexes and intersections of multi-parameter random simplicial complexes and show that they are also multi-parameter random simplicial complexes.

The positive effects of population-based preferential sampling in environmental epidemiology.

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.

Optimal designs for regression models with autoregressive errors

In the one-parameter regression model with AR(1) and AR(2) errors we find explicit expressions and a continuous approximation of the optimal discrete design for the signed least square estimator. The results are used to derive the optimal variance of the best linear estimator in the continuous time model and to construct efficient estimators and corresponding optimal designs for finite samples.