A production policy considering reworking of imperfect items and trade credit

Making production decisions that will reduce total cost is a goal that most manufacturers pursue actively. However, the traditional production model development assumed that all products are perfect quality, which is far from reality. Since trade credit is a popular payment method in today??s business environment, this paper analyzes the production problem under trade credit and imperfect product reworking conditions. This work extends the traditional production model by considering reworking imperfect items and trade credit to cope with realistic situations. The objective of this study is to determine the optimal production lot size while minimizing the total cost. This paper develops an easy-to-use algorithm to solve the problem described, provides numerical examples to illustrate the proposed solution procedure, and discusses the impact of various system parameters.     

Spike Inference from Calcium Imaging Using Sequential Monte Carlo Methods

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest—spike trains and/or time varying intracellular calcium concentrations—are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., ∼5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.     

Iterative algorithms for processing experimental data

The need to solve linear and nonlinear integral equations arise, e.g., in recovering plasma parameters from the data of multichannel diagnostics. The paper presents an iterative method for solving integral equations with a singularity at the upper limit of integration. The method consists in constructing successive approximations and calculating the integral by quadrature formulas in each integration interval. An example of application of the iterative algorithm to numerically solve an integral equation similar to those arising in recovering the plasma density profile from reflectometry data is presented.     

Cell-Specific Cardiac Electrophysiology Models

The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.     

Criteria for excitation and inhibition in peristimulus spike train activity

1) Five possibilities of defining a coefficient of facilitation and inhibition are described. 2) It is shown that the application of these definition to the same spike train activity eventually leads to considerably different results, e.g., a response which is inhibitory according to one definition sometimes is facilitatory according to another definition. 3) To find the most reliable definition the theoretical differences between the five alternatives are examined, whereby a coefficient is considered reliable if it is reproducible and independent of external experimental parameters, such as the record length. 4) As an experimental example spike trains were recorded from mitral cells in the olfactory bulb of the goldfish. We divide every response into two sections: an initial reaction and a steady state reaction. In this way each response can be uniquely classified. 5) The most reliable definition of a coefficient of facilitation turns out to be based on the steady state levels of an averaged peristimulus time histogramme. Under certain conditions this corresponds to considering the means of the sample mean rates of a stochastic point process before and after the stimulus application, whereby the initial reactions are neglected. These should be classified by other methods and notions.     

Analysis of the Precision,Robustness, and Speed of Elastic Resonator Interference Stress Microscopy

Localization-microscopy-based methods are widely used to map the forces that cells apply to their substrates and to study important questions of cellular biomechanics. By contrast, elastic resonator interference stress microscopy (ERISM) uses an interference-based approach, which requires low light intensity and facilitates imaging of cellular forces with extreme precision (down to pN forces) and robustness (e.g., for continuous force monitoring over weeks). Here, the measurement trade-offs and numerical considerations required to optimize the performance of ERISM are described. The crucial parts of the fitting algorithm and the computational tools used to evaluate the data are explained in detail, and the precision and accuracy achievable with ERISM are analyzed. Additional features that can improve the robustness of ERISM further are discussed. The implementation of the analysis algorithm is verified with simulated test data and with experimental data. In addition, an approach to increase the acquisition speed of ERISM by a factor of four compared to the original implementation is described. In combination, these strategies allow us to measure the forces generated by a neural growth cone with high temporal resolution and continuously over several hours.     

Global analysis of three-state protein unfolding data

A new method for analyzing three-state protein unfolding equilibria is described that overcomes the difficulties created by direct effects of denaturants on circular dichroism (CD) and fluorescence spectra of the intermediate state. The procedure begins with a singular value analysis of the data matrix to determine the number of contributing species and perturbations. This result is used to choose a fitting model and remove all spectra from the fitting equation. Because the fitting model is a product of a matrix function which is nonlinear in the thermodynamic parameters and a matrix that is linear in the parameters that specify component spectra, the problem is solved with a variable projection algorithm. Advantages of this procedure are perturbation spectra do not have to be estimated before fitting, arbitrary assumptions about magnitudes of parameters that describe the intermediate state are not required, and multiple experiments involving different spectroscopic techniques can be simultaneously analyzed. Two tests of this method were performed: First, simulated three-state data were analyzed, and the original and recovered thermodynamic parameters agreed within one standard error, whereas recovered and original component spectra agreed within 0.5%. Second, guanidine-induced unfolding titrations of the human retinoid-X-receptor ligand-binding domain were analyzed according to a three-state model. The standard unfolding free energy changes in the absence of guanidine and the guanidine concentrations at zero free-energy change for both transitions were determined from a joint analysis of fluorescence and CD spectra. Realistic spectra of the three protein states were also obtained.     

单纯形加速法拟合生态学中的非线性模型

本文以Logistic模型,Taylor幂法则模型,Holling功能反应模型,以及种群内禀增长力Rm等模型的拟合和参数估计为例,探讨单纯形加速法在生态模型优化拟合和参数估计中的应用.结果表明,单纯形加速法拟合生态学中的非线性模型不仅适用广泛,而且拟合过程是直接求原来非线性模型的最优拟合,因而优于生态学中通常使用的将原模型“线性化后再拟合”的方法,而与其它一些最优化方法,如:麦夸方法、枚举选优法等比较,由于单纯形法不需计算目标函数的偏导数,因而计算不受目标函数及其偏导函数复杂程度的限制,而且对于各种模型其求优计算过程十分相似,可以编制统一的计算程序.本研究所编制的计算机程序对于本文未提到的其它一些模型也是完全适用的,在应用时仅需修改定义目标函数的自定义函数语句即可.研究也发现,在求优过程中,只要搜索系数选择适当和实际数据合理,是可以保证寻优成功的.     

A novel approach to evaluate the permeability of cake layer during cross-flow filtration in the flocculants added membrane bioreactors

In order to obtain a better understanding of the cake layer formation mechanism in the flocculants added MBRs, a model was developed on the basis of particle packing model considering cake collapse effect and a frictional force balance equation to predict the porosity and permeability of the cake layers. The important characteristic parameters of the flocs (e.g., floc size, fractal dimensions) and operating parameters of MBRs (e.g., transmembrane pressure, cross-flow velocity) are considered in this model. With this new model, the calculated results of porosities and specific cake resistances under different MBR operational conditions agree fairly well with the experimental data.     

An investigation of the model of aerobic waste water treatment processes

The aim of the paper is to derive and investigate the sensitivity model of the aerobic batch waste water treatment processes, described by Monod equations, in order to correct the a-priory information about the model parameters to match the current experimental data. The sensitivity analysis enables to determine the effective parameters and their initial values in an estimation procedure by considering their influence on the substrate and microorganisms concentration. This contributes to the solution of some problems concerning the uniqueness and convergence of the parameter estimation.     

Dynamics of blood flow: modeling of the Fåhræus–Lindqvist effect

To model the Fåhræus–Lindqvist effect, Haynes’ marginal zone theory is used, following previous works, i.e., a core layer of uniform red blood cells (RBCs) is assumed to be surrounded by an annular plasma layer in which no RBCs are present. A simplified trial-and-error solution procedure is provided to determine the size of the core region and the hematocrit level in that zone in addition to the apparent viscosity, given the (upstream) large vessel hematocrit level and the average hematocrit level in the (downstream) small vessel. To test the model, a set of experimental data is selected to provide not only apparent viscosity data but also the average hematocrit levels in small tubes of different diameters. The results are found to support Haynes’ marginal theory, with no fitting parameters used in the computations. Viscous dissipation is determined. The use of the mechanical energy balance is found to lead to results that are consistent with those based on the momentum balance, while leaving the average hematocrit level undetermined and required by either experimental data or an additional equation based on further theoretical work. The present analysis is used to model bifurcation using published empirical correlations quantifying the Fåhræus effect and phase separation. The model equations are extended to microvascular networks with repeated bifurcations.     

The effect of end-point adjustments on smoothing splines used for tree-ring standardization

The cubic smoothing spline has been a popular method for detrending tree-ring data since the 1980s. The common implementation of this procedure (e.g., ARSTAN, dplR) uses a unique method for determining the smoothing parameter that is widely known as the %n criterion. However, this smoothing parameter selection method carries the assumption that end point effects are ignorable. In this paper, we complete the mathematical derivation and show how the original method differs from the complete version, both in the interpretation of the smoothing parameter and in the spline fit. Frequency response curves (FRC) demonstrate how the smoothing parameter is affected by the original assumption. For example, the FRC results indicate that a tree core of 250-year length has a 14% difference in the cut-off frequency when looking at the 67%n criterion. The FRC analysis shows that the existing approach produces a more flexible fit than anticipated, i.e., it is removing more variance than previously thought. For example, a 67%n spline under the existing approach corresponds to a 53%n spline fit. By using both simulated tree-core sequences and a dataset from a Midwest forest, we discuss which conditions result in greater differences between the spline fits and which conditions will have small differences. Tree-core sequences that have more curvature, such as a large-amplitude growth release, will lead to greater differences. Finally, we provide approximations to the end-point effect procedure. For example, using an 83%n criterion under the original approach produces a spline fit approximating the 67%n fit under the complete approach. These approximations could be easily implemented within existing programs like ARSTAN.     

A new approach of fitting biomass dynamics models to data

A non-traditional approach of fitting dynamic resource biomass models to data is developed in this paper. A variational adjoint technique is used for dynamic parameter estimation. In the variational formulation, a cost function measuring the distance between the model solution and the observations is minimized. The data assimilation method provides a novel and computationally efficient procedure for combining all available information, i.e., the data and the model in the analysis of a resource system. This technique will be used to analyze data for the North-east Arctic cod stock. Two alternative population growth models: the logistic and the Gompertz model are used for estimating parameters of simple bioeconomic models by the method of constrained least squares. Estimates of the parameters of the models dynamics are reasonable and can be accepted. The main inference from the work is that the average fishing mortality is found to be significantly above the maximum sustainable yield value.     

关于农药消解模型参数拟合优化算法的研究

参数拟合是数学建模的重要方面．本文对农药消解模型参数拟合的算法进行优化,用麦夸方法最优拟合消解方程y=c0e～-kt,给出参数和半衰期的数值解．理论和实例计算均表明：用麦夸方法得到的消解模型的预测值与实测值拟合度优于其他方法．优化的麦夸算法简便实用,且具有普遍适用性．     

Reconstruction of Underlying Nonlinear Deterministic Dynamics Embedded in Noisy Spike Trains

An experimentally recorded time series formed by the exact times of occurrence of the neuronal spikes (spike train) is likely to be affected by observational noise that provokes events mistakenly confused with neuronal discharges, as well as missed detection of genuine neuronal discharges. The points of the spike train may also suffer a slight jitter in time due to stochastic processes in synaptic transmission and to delays in the detecting devices. This study presents a procedure aimed at filtering the embedded noise (denoising the spike trains) the spike trains based on the hypothesis that recurrent temporal patterns of spikes are likely to represent the robust expression of a dynamic process associated with the information carried by the spike train. The rationale of this approach is tested on simulated spike trains generated by several nonlinear deterministic dynamical systems with embedded observational noise. The application of the pattern grouping algorithm (PGA) to the noisy time series allows us to extract a set of points that form the reconstructed time series. Three new indices are defined for assessment of the performance of the denoising procedure. The results show that this procedure may indeed retrieve the most relevant temporal features of the original dynamics. Moreover, we observe that additional spurious events affect the performance to a larger extent than the missing of original points. Thus, a strict criterion for the detection of spikes under experimental conditions, thus reducing the number of spurious spikes, may raise the possibility to apply PGA to detect endogenous deterministic dynamics in the spike train otherwise masked by the observational noise.     

Hierarchic Stochastic Modelling Applied to Intracellular Ca2+ Signals

Important biological processes like cell signalling and gene expression have noisy components and are very complex at the same time. Mathematical analysis of such systems has often been limited to the study of isolated subsystems, or approximations are used that are difficult to justify. Here we extend a recently published method (Thurley and Falcke, PNAS 2011) which is formulated in observable system configurations instead of molecular transitions. This reduces the number of system states by several orders of magnitude and avoids fitting of kinetic parameters. The method is applied to signalling. is a ubiquitous second messenger transmitting information by stochastic sequences of concentration spikes, which arise by coupling of subcellular release events (puffs). We derive analytical expressions for a mechanistic model, based on recent data from live cell imaging, and calculate spike statistics in dependence on cellular parameters like stimulus strength or number of channels. The new approach substantiates a generic model, which is a very convenient way to simulate spike sequences with correct spiking statistics.