Combinations Of Parameter Values Research Articles

Hierarchical multivariate regression-based sensitivity analysis reveals complex parameter interaction patterns in dynamic models

Dynamic models of biological systems often possess complex and multivariate mappings between input parameters and output state variables, posing challenges for comprehensive sensitivity analysis across the biologically relevant parameter space. In particular, more efficient and robust ways to obtain a solid understanding of how the sensitivity to each parameter depends on the values of the other parameters are sorely needed.We report a new methodology for global sensitivity analysis based on Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR)-based approximations (metamodelling) of the input–output mappings of dynamic models, which we expect to be generic, efficient and robust, even for systems with highly nonlinear input–output relationships. The two-step HC-PLSR metamodelling automatically separates the observations (here corresponding to different combinations of input parameter values) into groups based on the dynamic model behaviour, then analyses each group separately with Partial Least Squares Regression (PLSR). This produces one global regression model comprising all observations, as well as regional regression models within each group, where the regression coefficients can be used as sensitivity measures. Thereby a more accurate description of complex interactions between inputs to the dynamic model can be revealed through analysis of how a certain level of one input parameter affects the model sensitivity to other inputs. We illustrate the usefulness of the HC-PLSR approach on a dynamic model of a mouse heart muscle cell, and demonstrate how it reveals interaction patterns of probable biological significance not easily identifiable by a global regression-based sensitivity analysis alone.Applied for sensitivity analysis of a complex, high-dimensional dynamic model of the mouse heart muscle cell, several interactions between input parameters were identified by the two-step HC-PLSR analysis that could not be detected in the single-step global analysis. Hence, our approach has the potential to reveal new biological insight through the identification of complex parameter interaction patterns. The HC-PLSR metamodel complexity can be adjusted according to the nonlinear complexity of the input–output mapping of the analysed dynamic model through adjustment of the number of regional regression models included. This facilitates sensitivity analysis of dynamic models of varying complexities.

RETRIEVING FOREST STRUCTURE VARIABLES FROM VERY HIGH RESOLUTION SATELLITE IMAGES USING AN AUTOMATIC METHOD

Abstract. The main goal of this study is to define a method to describe the forest structure of maritime pine stands from Very High Resolution satellite imagery. The emphasis is placed on the automatisation of the process to identify the most relevant image features, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick’s texture features (derived from Grey Level Co-occurrence Matrix). The main drawback of this well- known texture representation is the underlying parameters (window size, displacement length, orientation and quantification level) which are extremely difficult to set due to the spatial complexity of forest structure. To tackle this major issue, probably the main cause of poor texture analysis in practice, we propose an automatic feature selection process whose originality lies on the use of image test frames of adequate forest samples whose forest structure variables were measured at ground. This method, inspired by camera calibration protocols, selects the best image features via statistical modelling, exploring a wide range of parameter values. Hence, just a few samples are required to build up the test frames but allow a fast assessment of thousands of descriptors, given the large number of tested combinations of parameters values. This method was developed and tested on Quickbird panchromatic and multispectral images. It has been successfully applied to the modelling of 7 typical forest structure variables (age, tree height, crown diameter, diameter at breast height, basal area, density and tree spacing). The coefficient of correlation, R2, of the best single models for 6 of the forest variables of interest, estimated from the test frames, ranges from 0.89 to 0.97. Only the basal area was weakly correlated to the considered image features (0.64). To improve the results, combinations of panchromatic and or multi-spectral features were tested using multiple linear regressions. As collinearity is a very perturbing problem in multi-linear regression, this issue is carefully addressed. Different variables subset selection methods are tested. A new stepwise method, derived from LARS (Least Angular Regression), turned out the most convincing, significantly improving the quality of estimation for all the forest structure variables (R2 > 0:98). Validation is done through stand ages retrieval along the whole site. The best estimation results are obtained from subsets combining multi-spectral and panchromatic features, with various values of window size, highlighting the potential of a multi-scale approach for retrieving forest structure variables from VHR satellite images.

Developmental Dynamics and Early Growth Vigour in Rice 2. Modelling Genetic Diversity Using Ecomeristem

AbstractEarly vigour is an important objective in rice breeding. A previous study reported strong positive effects of development rate (DR, 1/phyllochron) on early growth vigour in two rice panels. This study provided a model‐based analysis of DR effects on rice early vigour and underlying source–sink processes during exponential growth, using Ecomeristem model. Relevant model parameters were fitted to panel observations, and their effect on early vigour was quantified. A sensitivity analysis was performed to quantify the impact of model parameters on simulation outputs. The simulated behaviour of a population of virtual genotypes defined by the combination of model parameter values was compared with that of diversity panel. Finally, a simulation experiment was conducted to analyse source–sink adjustments constituting early vigour across a range of DR. Parameters governing structural development, particularly DR, had greater impact on vigour than parameters for resource acquisition. High DR was associated with rapid dry weight accumulation and low transitory carbohydrate reserves in both real and virtual populations. Genotypic DR is thus a major driver of early vigour in rice under stress‐free conditions. To evaluate traits contributing to vigour, the capacity of crop models to simulate interactions between structural development and resource acquisition must be improved.

FRACTAL SELF-ORGANIZATION OF BACTERIA-INSPIRED AGENTS

We develop an agent-based model as a preliminary theoretical basis to guide the synthesis of a new class of materials with dynamic properties similar to bacterial colonies. Each agent in the model is representative of an individual bacterium capable of: the uptake of chemicals (nutrients), which are metabolized; active movement (part viscous, part diffusive), consuming metabolic energy; and cellular division, when agents have doubled in size. The agents grow in number and self-organize into fractal structures, depending on the rules that define the actions of the agents and the parameter values. The environment of the agents includes chemicals responsible for their growth and is described by a diffusion-reaction equation with Michaelis-Menten kinetics. These rules are modeled mathematically by a set of equations with five dimensionless groups that are functions of physical parameters. Simulations are performed for different parameter values. The resulting structures are characterized by their fractal scaling regime, box-counting and mass-radius dimensions, and lacunarity. Each parameter influences the overall structure in a unique way, generating a wide spectrum of structures. For certain combinations of parameter values, the model converges to a steady state, with a finite population of agents that no longer divide.

Parameter control system of evolutionary algorithm that is aided by the entire search history

In solving problems with evolutionary algorithms (EAs), the performance of the EA will be affected by its properties. As the properties of EA depend on the parameter setting, users need to tune the parameters to optimize the performance on different problems. In the case that the user does not have any prior knowledge of the problem, parameter tuning is very difficult and time consuming. One needs to try different combinations of parameter values to find the best setting. To solve this problem, one way is to control the parameters during the EA run. This paper proposes a new adaptive parameter control system, called Parameter Control system using entire Search History (PCSH). It is a general add-on system which is not restricted to a specific class of EA. Users are only required to know the range of the parameters. It automatically adjusts the parameters of an EA according to the entire search history, in a parameter-less manner. To illustrate the performance of PCSH, it is applied to control the parameters of three common classes of EAs: (1) canonical Genetic Algorithm (GA), (2) Particle Swarm Optimization (PSO) and (3) Differential Evolution (DE). For GA, we show that PCSH can automatically control the crossover operator, crossover values (uniformly sampled from the range) and mutation operator. For DE, we show that PCSH can automatically control the crossover operator, crossover values and the differential amplification factor (uniformly sampled from the ranges). For PSO, we show that PCSH can automatically control the two learning factors and the inertia weight (uniformly sampled from the range). Moreover, no special provision is needed at the initialization. 34 benchmark functions are used to evaluate the performance comprehensively. The test results show that, in most of the benchmark functions, the performance of the test EAs are improved or similar after adopting PCSH. It shows that PCSH keeps or improves the performance of the test EAs while relieving the heavy burden of the algorithm designer on the setting of some parameters.

Identifying Place Histories from Activity Traces with an Eye to Parameter Impact

Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Important and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile phone operators or photos on photo sharing web sites. We developed a suite of visual analytics methods for reconstructing past events from these activity traces. Our tools combine geocomputations, interactive geovisualizations, and statistical methods to enable integrated analysis of the spatial, temporal, and thematic components of the data, including numeric attributes and texts.We also support interactive investigation of the sensitivity of the analysis results to the parameters used in the computations. For this purpose, statistical summaries of computation results obtained with different combinations of parameter values are visualized in a way facilitating comparisons. We demonstrate the utility of our approach on two large real data sets, mobile phone calls in Milano during 9 days and flickr photos made on British Isles during 5 years.

Data envelopment analysis for evaluating the efficiency of genetic algorithms on solving the vehicle routing problem with soft time windows

This study proposes an alternative to the conventional empirical analysis approach for evaluating the relative efficiency of distinct combinations of algorithmic operators and/or parameter values of genetic algorithms (GAs) on solving the pickup and delivery vehicle routing problem with soft time windows (PDVRPSTW). Our approach considers each combination as a decision-making unit (DMU) and adopts data envelopment analysis (DEA) to determine the relative and cross efficiencies of each combination of GA operators and parameter values on solving the PDVRPSTW. To demonstrate the applicability and advantage of this approach, we implemented a number of combinations of GA’s three main algorithmic operators, namely selection, crossover and mutation, and employed DEA to evaluate and rank the relative efficiencies of these combinations. The numerical results show that DEA is well suited for determining the efficient combinations of GA operators. Among the combinations under consideration, the combinations using tournament selection and simple crossover are generally more efficient. The proposed approach can be adopted to evaluate the relative efficiency of other meta-heuristics, so it also contributes to the algorithm development and evaluation for solving combinatorial optimization problems from the operational research perspective.

A model of axonal transport drug delivery

Abstract In this paper a model of targeted drug delivery by means of active (motor-driven) axonal transport is developed. The model is motivated by recent experimental research by Filler et al. (A.G. Filler, G.T. Whiteside, M. Bacon, M. Frederickson, F.A. Howe, M.D. Rabinowitz, A.J. Sokoloff, T.W. Deacon, C. Abell, R. Munglani, J.R. Griffiths, B.A. Bell, A.M.L. Lever, Tri-partite complex for axonal transport drug delivery achieves pharmacological effect, Bmc Neuroscience 11 (2010) 8) that reported synthesis and pharmacological efficiency tests of a tri-partite complex designed for axonal transport drug delivery. The developed model accounts for two populations of pharmaceutical agent complexes (PACs): PACs that are transported retrogradely by dynein motors and PACs that are accumulated in the axon at the Nodes of Ranvier. The transitions between these two populations of PACs are described by first-order reactions. An analytical solution of the coupled system of transient equations describing conservations of these two populations of PACs is obtained by using Laplace transform. Numerical results for various combinations of parameter values are presented and their physical significance is discussed.

Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis

Most object-based image analysis use parameters to control the size, shape, and homogeneity of segments. Because each parameter may take a range of possible values, different combinations of value between parameters may produce different segmentation results. Assessment of segmentation quality, such as the discrepancy between reference polygons and corresponding image segments, can be used to identify the optimal combination of parameter values. In this research, we (1) evaluate four existing indices that describe the discrepancy between reference polygons and corresponding segments, (2) propose three new indices to evaluate both geometric and arithmetic discrepancies, and (3) compare the effectiveness of the existing and proposed indices in identifying optimal combinations of parameter values for image segmentation through a case study. A Landsat 5 Thematic Mapper (TM) image and an ALOS image of arid Northwestern China were used in the case study. The four existing indices include Quality Rate (QR), Over-segmentation Rate (OR), Under-segmentation Rate (UR), and Euclidean Distance 1 (ED1). The three proposed discrepancy indices include Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2). These indices measure overlap, over-segmentation, and under-segmentation between reference polygons and corresponding image segments. Results show that the three proposed indices PSE, NSR, and ED2 are more effective than the four existing indices QR, OR, UR, and ED1 in their ability to identify optimal combinations of parameter values. ED2 that represents both geometric (PSE) and arithmetic (NSR) discrepancies is most effective.

Improvements and Corrections to AT123D Code

Although based on exact analytical solutions, semi-analytical solute transport models can have significant numerical error in applications with high frequency oscillatory source terms and when parameter value combinations cause series solution approximations to converge slowly. Methods for correcting these numerical errors are presented and implemented in the AT123D code, which employs Green's functions to represent point, linear, and rectangular prismatic source zones. In order to increase its computational accuracy, a Romberg numerical integration scheme was added to AT123D with prespecified error criteria, variable time stepping, and partitioning of the integral to handle rapidly changing source terms. More rapidly converging series solution approximations for the Green's functions were also incorporated to improve both accuracy and computational efficiency for finite-depth aquifers. AT123D also has been modified to eliminate redundant calculations at points where approximate steady-state conditions have been reached to improve computational efficiency during numerical integration. These modifications help to decrease computer run times that can be excessive for three-dimensional problems with large numbers of computational points, small time steps, and/or long simulation time periods. Errors in the original AT123D code also were corrected in this modified version, AT123D-AT, in order to accurately simulate finite-duration (pulse) source releases.

Population biology and epidemiology of plant virus epidemics: from tripartite to tritrophic interactions

Natural enemies have long been used in biological control programs to mitigate the damage caused by herbivory. Many herbivorous insect species also act as plant virus vectors, enabling virus transmission from plant to plant and hence disease development in a plant population. Whilst an intuitive assumption would be to expect a decrease in vector numbers to lead to subsequent reductions in virus transmission, recent evidence suggests that introduction of natural enemies (parasitoids and predators) may in some cases increase plant virus transmission while at the same time reducing vector numbers. In this paper we review the evidence for plant-virus-vector-natural enemy interactions, the signalling mechanisms involved and their implications for virus transmission, and show how a modelling approach can assist in identifying the key parameters and relationships involved in determining the disease outcome. A mathematical model linking the population dynamics of a vector-parasitoid system with virus transmission was used to investigate the effects of virus inoculation and acquisition rates, parasitoid attack rate and vector aggregation on disease dynamics across a wide range of parameter value combinations. Virus spread was found to increase with enhanced inoculation, acquisition and parasitoid attack rate but decrease with high levels of vector aggregation.

Bias in Cross-Sectional Analyses of Longitudinal Mediation: Partial and Complete Mediation Under an Autoregressive Model

Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can also occur with partial mediation. In particular, cross-sectional analyses can imply the existence of a substantial indirect effect even when the true longitudinal indirect effect is zero. Thus, a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis. In addition, we show that very different combinations of longitudinal parameter values can lead to essentially identical cross-sectional correlations, raising serious questions about the interpretability of cross-sectional mediation data. More generally, researchers are encouraged to consider a wide variety of possible mediation models beyond simple cross-sectional models, including but not restricted to autoregressive models of change.

SWAT-CUP을 이용한 대청호 유역 장기 유출 유량 보정 및 검증

SWAT (Soil and Water Assessment Tool) model was calibrated for the flow rate of the Deachung lake with a large area of 3108.29 . Application of SWAT model requires significant number of input data and is prone to result in uncertainties due to errors in input data, model structure and model parameters. The SUFI-2 (Sequential Uncertainty Fitting Ver. 2) program and GLUE (Generalized Likelihood Uncertainty Estimation) program in SWAT-CUP (SWAT-Calibration and Uncertainty Program) are used to select the best parameters for SWAT model. Optimal combination of parameter values was determined through 2,000 iterative SWAT model runs. The Nash-Sutcliffe values and values were 0.87 and 0.89 respectively indicating both methods show good agreements with observed data successfully. RMSE and MSE values also showed similar results for both programs. It seems the SWAT-CUP has a great practical appeal for parameter optimization especially for large basin area and it also can be used for less experienced SWAT model users.

Ensemble prediction and parameter estimation system: the concept

AbstractThis paper discusses algorithmic estimation of numerical weather prediction (NWP) model closure parameters which appear in physical parametrizations of sub‐grid scale processes. Currently, these parameters are specified manually. Our hypothesis is that the NWP model forecast skill is sensitive to the specified parameter values. Therefore some parameter value combinations score better than others, but it is very demanding to manually specify the optimal combination. First, we approximate the number of test forecasts that would be needed to estimate the posterior probability densities of a limited number of closure parameters, in a spirit of the Bayesian parameter estimation approach. Second, we discuss various options to accommodate the related huge computational task; we end up proposing that operational ensemble prediction might be a potential host. Third, we analyse the ensemble prediction system characteristics from the parameter estimation point of view. We rule out various standard parameter estimation methods; instead we suggest an estimation concept that can be on‐line coupled with existing ensemble prediction systems to utilize the operational ensemble simulations. Thus practically no additional computations are introduced. Such a method, the ensemble prediction and parameter estimation system (EPPES), is developed here and demonstrated in low‐order numerical tests in the companion paper (Laine M, Solonen A, Haario H, Järvinen H. 2011. Q. J. R. Meteorol. Soc. DOI: 10.1002/qj.922). Copyright © 2011 Royal Meteorological Society

Propagation of uncertainties in water distribution systems modeling

Optimal design and systematic uncertainty modeling of a municipal Water Distribution System (WDS) aim to minimize operational & construction costs, while meeting demands with required pressure levels and water quality. Study about the propagation of uncertainty through the hydraulic model provides a basis for general model improvement in an efficient and economic way. The present study introduces a novel technique for uncertainty analysis of a WDS, which synchronizes the hydraulic network and water quality solving capabilities of EPANET with an interface to MATLAB. It treats the pipe friction factors and the nodal demands as input fuzzy variables whereas the output fuzzy parameters include pipe discharges, nodal heads and chlorine concentrations. The methodology involves decomposition of the given uncertainties into a set of all possible combinations of input parameter values, numerical analysis of the hydraulic network, calculation of extremities of each unknown variable at each alpha cut level and the f...

The Minimal Failure-Causing Schema of Combinatorial Testing

Combinatorial Testing (CT) involves the design of a small test suite to cover the parameter value combinations so as to detect failures triggered by the interactions among these parameters. To make full use of CT and to extend its advantages, this article first gives a model of CT and then presents a theory of the Minimal Failure-causing Schema (MFS), including the concept of the MFS, proof of its existence, some of its properties, and a method of finding the MFS. Then we propose a methodology for CT based on this MFS theory and the existing research. Our MFS-based methodology emphasizes that CT should work on accurate testing requirements, and has the following advantages: 1) Detect failure to the greatest degree with the least cost. 2) Effectiveness is improved by emphasizing mining of the information in software and making full use of the information gained from test design and execution. 3) Determine the root causes of failures and reveal related faults near the exposed ones. 4) Provide a foundation and model for regression testing and software quality evaluation of CT. A case study is presented to illustrate the MFS-based CT methodology, and an empirical study on a real software developed by us shows that the MFS really exists and the methodology based on MFS can considerably improve CT.

Modelling the ecological–functional diversification of marine Metazoa on geological time scales

The ecological traits and functional capabilities of marine animals have changed significantly since their origin in the late Precambrian. These changes can be analysed quantitatively using multi-dimensional parameter spaces in which the ecological lifestyles of species are represented by particular combinations of parameter values. Here, we present models that describe the filling of this multi-dimensional 'ecospace' by ecological lifestyles during metazoan diversification. These models reflect varying assumptions about the processes that drove ecological diversification; they contrast diffusive expansion with driven expansion and niche conservatism with niche partitioning. Some models highlight the importance of interactions among organisms (ecosystem engineering and predator-prey escalation) in promoting new lifestyles or eliminating existing ones. These models reflect processes that were not mutually exclusive; rigorous analyses will continue to reveal their applicability to episodes in metazoan history.

Hardness results for covering arrays avoiding forbidden edges and error-locating arrays

Covering arrays avoiding forbidden edges (CAFEs) are used in testing applications (software, networks, circuits, drug interaction, material mixtures, etc.) where certain combinations of parameter values are forbidden. Danziger et al. (2009) [8] have studied this problem and shown some computational complexity results. Around the same time, Martinez et al. (2009) [19] defined and studied error-locating arrays (ELAs), which are closely related to CAFEs. Both papers left some computational complexity questions. In particular, these papers showed polynomial-time solvability of the existence of CAFEs and ELAs for binary alphabets (g=2), and the NP-hardness of these problems for g≥5. In this paper, we prove that optimizing CAFEs and ELAs is indeed NP-hard even when restricted to the case of binary alphabets, using a reduction from edge clique covers of graphs (ECCs). We also provide a hardness of approximation result. We explore important relationships between ECCs and CAFEs and give some new bounds for uniform ECCs and CAFEs.

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.

Autocalibration of dynamic bacterial growth model for water distribution system using GA

The presence of residual chlorine and organic matter govern the bacterial regrowth within a water distribution system. The bacterial growth model is essential to predict the spatial and temporal variation of all these substances throughout the system. The parameters governing the bacterial growth and biodegradable dissolved organic carbon (BDOC) utilization are difficult to determine by experimentation. In the present study, the estimation of these parameters is addressed by using simulation-optimization procedure. The optimal solution by genetic algorithm (GA) has indicated that the proper combination of parameter values are significant rather than correct individual values. The applicability of the model is illustrated using synthetic data generated by introducing noise in to the error-free measurements. The GA was found to be a potential tool in estimating the parameters controlling the bacterial growth and BDOC utilization. Further, the GA was also used for evaluating the sensitivity issues relating parameter values and objective function. It was observed that mu and k(cl) are more significant and dominating compared to the other parameters. But the magnitude of the parameters is also an important issue in deciding the dominance of a particular parameter. GA is found to be a useful tool in autocalibration of bacterial growth model and a sensitivity study of parameters.