Computational and experimental analysis of wound healing processes

Wound healing in epidermis is a complex physiological process in which new cells are created to repair the damaged tissue. The timing of cell division and growth mechanisms in wound healing are influenced by biological, mechanical and medical factors. In this work we aim to provide a numerical model based on the observations realised in in-vitro experiments for the understanding of wound healing. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the construction of analytic solutions for a diffusion-reaction equation with a discontinuous switch mechanism

The existence of waiting times, before boundary motion sets in, for a diffusion-diffusion reaction equation with a discontinuous switch mechanism, is demonstrated. Limit cases of the waiting times are discussed in mathematical rigor. Further, analytic solutions for planar and circular wounds are derived. The waiting times, as predicted using these analytic solutions, are perfectly between the derived bounds. Furthermore, it is demonstrated by both physical reasoning and mathematical rigor that the movement of the boundary can be delayed once it starts moving. The proof of this assertion resides on continuity and monotonicity arguments. The theory sustains the construction of analytic solutions. The model is applied to simulation of biological processes with a threshold behavior, such as wound healing or tumor growth.     

Nonsmooth nonconvex optimization approach to clusterwise linear regression problems

Clusterwise regression consists of finding a number of regression functions each approximating a subset of the data. In this paper, a new approach for solving the clusterwise linear regression problems is proposed based on a nonsmooth nonconvex formulation. We present an algorithm for minimizing this nonsmooth nonconvex function. This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate a good starting point for solving global optimization problems at each iteration of the incremental algorithm. Such an approach allows one to find global or near global solution to the problem when the data sets are sufficiently dense. The algorithm is compared with the multistart Späth algorithm on several publicly available data sets for regression analysis.     

A new weighted Gompertz distribution with applications to reliability data

A new weighted version of the Gompertz distribution is introduced. It is noted that the model represents a mixture of classical Gompertz and second upper record value of Gompertz densities, and using a certain transformation it gives a new version of the two-parameter Lindley distribution. The model can be also regarded as a dual member of the log-Lindley-X family. Various properties of the model are obtained, including hazard rate function, moments, moment generating function, quantile function, skewness, kurtosis, conditional moments, mean deviations, some types of entropy, mean residual lifetime and stochastic orderings. Estimation of the model parameters is justified by the method of maximum likelihood. Two real data sets are used to assess the performance of the model among some classical and recent distributions based on some evaluation goodness-of-fit statistics. As a result, the variance-covariance matrix and the confidence interval of the parameters, and some theoretical measures have been calculated for such data for the proposed model with discussions.     

Modeling loss data using composite models

We develop several new composite models based on the Weibull distribution for heavy tailed insurance loss data. The composite model assumes different weighted distributions for the head and tail of the distribution and several such models have been introduced in the literature for modeling insurance loss data. For each model proposed in this paper, we specify two parameters as a function of the remaining parameters. These models are fitted to two real insurance loss data sets and their goodness-of-fit is tested. We also present an application to risk measurements and compare the suitability of the models to empirical results.     

Numerical techniques for simulating a fractional mathematical model of epidermal wound healing

A number of mathematical models investigating certain aspects of the complicated process of wound healing are reported in the literature in recent years. However, effective numerical methods and supporting error analysis for the fractional equations which describe the process of wound healing are still limited. In this paper, we consider the numerical simulation of a fractional mathematical model of epidermal wound healing (FMM-EWH), which is based on the coupled advection-diffusion equations for cell and chemical concentration in a polar coordinate system. The space fractional derivatives are defined in the Left and Right Riemann-Liouville sense. Fractional orders in the advection and diffusion terms belong to the intervals (0,1) or (1,2], respectively. Some numerical techniques will be used. Firstly, the coupled advection-diffusion equations are decoupled to a single space fractional advection-diffusion equation in a polar coordinate system. Secondly, we propose a new implicit difference method for simulating this equation by using the equivalent of Riemann-Liouville and Grünwald-Letnikov fractional derivative definitions. Thirdly, its stability and convergence are discussed, respectively. Finally, some numerical results are given to demonstrate the theoretical analysis.     

Healing times for circular wounds on plane and spherical bone surfaces

A mathematical model is developed for the rate of healing of a circular wound in a spherical “skull”. The motivation for this model is based on experimental studies of the “critical size defect” (CSD) in animal models; this has been defined as the smallest intraosseous wound that does not heal by bone formation during the lifetime of the animal [1]. For practical purposes, this timescale can usually be taken as one year. In [2], the definition was further extended to a defect which has less than ten percent bony regeneration during the lifetime of the animal. CSDs can “heal” by fibrous connective tissue formation, but since this is not bone, it does not have the properties (strength, etc.) that a completely healed defect would. Earlier models of bone wound healing [3,4] have focused on the existence (or not) of a CSD based on a steady-state analysis, so the time development of the wound was not addressed. In this paper, the time development of a circular cylindrical wound is discussed from a general point of view. An integro-differential equation is derived for the radial contraction rate of the wound in terms of the wound radius and parameters related to the postulated healing mechanisms. This equation includes the effect of the curvature of the (spherical) skull, since it is clear from the experimental evidence that the size of the CSD increases monotonically with the size of the calvaria. Certain special cases for a planar wound are highlighted to illustrate the qualitative features of the model, in particular, the dependence of the wound healing time on the initial wound size and the thickness of the healing rim.     

Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process

This paper introduces an artificial neural network (ANN) application to a hot strip mill to improve the model’s prediction ability for rolling force and rolling torque, as a function of various process parameters. To obtain a data basis for training and validation of the neural network, numerous three dimensional finite element simulations were carried out for different sets of process variables. Experimental data were compared with the finite element predictions to verify the model accuracy. The input variables are selected to be rolling speed, percentage of thickness reduction, initial temperature of the strip and friction coefficient in the contact area. A comprehensive analysis of the prediction errors of roll force and roll torque made by the ANN is presented. Model responses analysis is also conducted to enhance the understanding of the behavior of the NN model. The resulted ANN model is feasible for on-line control and rolling schedule optimization, and can be easily extended to cover different aluminum grades and strip sizes in a straight-forward way by generating the corresponding training data from a FE model.     

Preference-neutral attribute weights in the journal-ranking problem

A linear programming model is proposed for assigning linear attribute weights in the journal-ranking problem. The constraints in the model are derived solely from any quasi-dominance relations that can be established between the journals. The objective function of the model minimizes the maximum difference between the implied valuations for the pair of journals that define a constraint. In the sense that personal inputs are not introduced, the derived weights are preference neutral. The feasibility of the procedure is demonstrated for two sets of data. By considering various random samples of journals from the larger data set, it is shown that large differences can emerge in the attribute weights and in the journal rankings from different samples of journals, even when the sample sizes are large relative to the population size.     

Exploration of the variability of variable selection based on distances between bootstrap sample results

It is well known that variable selection in multiple regression can be unstable and that the model uncertainty can be considerable. The model uncertainty can be quantified and explored by bootstrap resampling, see Sauerbrei et al. (Biom J 57:531–555, 2015). Here approaches are introduced that use the results of bootstrap replications of the variable selection process to obtain more detailed information about the data. Analyses will be based on dissimilarities between the results of the analyses of different bootstrap samples. Dissimilarities are computed between the vector of predictions, and between the sets of selected variables. The dissimilarities are used to map the models by multidimensional scaling, to cluster them, and to construct heatplots. Clusters can point to different interpretations of the data that could arise from different selections of variables supported by different bootstrap samples. A new measure of variable selection instability is also defined. The methodology can be applied to various regression models, estimators, and variable selection methods. It will be illustrated by three real data examples, using linear regression and a Cox proportional hazards model, and model selection by AIC and BIC.

     

基于犹豫模糊集的CMMI过程评估方法研究

CMMI过程评估是一个典型的多属性决策问题,评估环节采用主任评估师负责制,已有的评估方法得到的结果受到企业的争议.论文从CMMI评估中多个专家参与,专家意见不一致等固有特征出发,在评估中引入犹豫模糊集的思想,将专家打分转换为犹豫模糊值进行计算.基于指标提取、专家打分、数据处理、数据补齐、数据集结等几个环节设计了CMMI过程评估,通过案例计算,得到了客观科学的评估结果.将犹豫模糊集引入到CMMI过程评估中,一方面为CMMI过程评估提供了新的思路和方法,另一方面也拓展了犹豫模糊集的应用领域.     

Analysis of Data from a Series of Events by a Geometric Process Model

Geometric process was first introduced by Lam.A stochastic process {X_i,i=1,2,...} iscalled a geometric process (GP) if,for some a>0,{a~(i-1)X_i,i=1,2,...} forms a renewal process.In thispaper,the GP is used to analyze the data from a series of events.A nonparametric method is introduced forthe estimation of the three parameters in the GP.The limiting distributions of the three estimators are studied.Through the analysis of some real data sets,the GP model is compared with other three homogeneous andnonhomogeneous Poisson models.It seems that on average the GP model is the best model among these fourmodels in analyzing the data from a series of events.     

Flexible bivariate beta distributions

Bivariate beta distributions which can be used to model data sets exhibiting positive or negative correlation are introduced. Properties of these bivariate beta distributions and their applications in Bayesian analysis are discussed. Three methods for parameter estimation are presented. The performance of these estimators is evaluated based on Monte Carlo simulations. Examples are provided to illustrate how additional parameters can be introduced to gain even more modeling flexibility. A possible extension of the proposed bivariate beta model and a multivariate generalization are also discussed.     

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA.     

Models of shrinking clusters with applications to epidermal wound healing

Models of growing clusters, such as the Eden model and Diffusion Limited Aggregation (DLA), have been widely used to describe a variety of natural growth processes. In this paper, we develop models of shrinking clusters which we use to model epidermal wound healing. We present two approaches to modeling shrinking clusters. In the first approach, which is motivated by the Eden model, every point on the cluster periphery has equal chance of being healed. Noisy and noisefree versions of this model are investigated. In the second approach, DLA is employed in a unique way so that random walkers launched from infinity eventually reach the cluster and contribute to its reduction. Simulation results are presented which illustrate the evolution of the wound healing process for various wound shapes.     

A very fast algorithm for matrix factorization

We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature.     

A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

Nowadays, with the volume of data growing at an unprecedented rate, large-scale data mining and knowledge discovery have become a new challenge. Rough set theory for knowledge acquisition has been successfully applied in data mining. The recently introduced MapReduce technique has received much attention from both scientific community and industry for its applicability in big data analysis. To mine knowledge from big data, we present parallel large-scale rough set based methods for knowledge acquisition using MapReduce in this paper. We implemented them on several representative MapReduce runtime systems: Hadoop, Phoenix and Twister. Performance comparisons on these runtime systems are reported in this paper. The experimental results show that (1) The computational time is mostly minimum on Twister while employing the same cores; (2) Hadoop has the best speedup for larger data sets; (3) Phoenix has the best speedup for smaller data sets. The excellent speedups also demonstrate that the proposed parallel methods can effectively process very large data on different runtime systems. Pitfalls and advantages of these runtime systems are also illustrated through our experiments, which are helpful for users to decide which runtime system should be used in their applications.     

Valuing risky debt: A new model combining structural information with the reduced-form approach

A new model of credit risk is proposed in which the intensity of default is described by an additional stochastic differential equation coupled with the process of the obligor’s asset value. Such an approach allows us to incorporate structural information as well as to capture the effect of external factors (e.g. macroeconomic factors) in a both parsimonious and economically consistent way. From the practical standpoint, the proposed model offers great flexibility and allows us to obtain credit spread curves of many different shapes, including double humped term structures. Furthermore, an approximate closed-form solution is derived, which is accurate, easy to implement, and allows for an efficient calibration to realized credit spreads. Numerical experiments are presented showing that the novel approach provides a very satisfactory fitting to market data and outperforms the model developed by Madan and Unal (2000).     

Rao’s statistic for constant and proportional hazard models

In this paper we introduce a new family of measures of divergence for the analysis of the degree of departure from a model with a constant hazard function and also for comparing if two models have proportional hazard rates. Our family of measures is based on the family of divergences introduced by Burbea and Rao (see [J. Burbea, C.R. Rao, On the convexity of higher order Jensen differences based on entropy functions, IEEE Transactions on Information Theory 28 (1982) 961–963]). Some well-known sets of data are reanalyzed using the new families of test statistics and confidence intervals introduced in this paper.     

The Elastodynamics of Embryonic Epidermal Wound Closure

This paper is concerned with the elastodynamics of embryonic epidermal wound closing. Underlying the recovery process for this type of wounds is a mechanism of wound recognition through directed cell-to-cell signaling. The observed actin filament realignment induced by the biological signals leads to a purse-string effect and the resulting (unknown) "active stresses." The circumferential contraction of the epidermis surrounding the wound is then determined by the laws of mechanics and propagation properties of the relevant cell–cell signaling that decays with distance. With the wound known to retract for a short period immediately after infliction, the quasi-equilibrium configuration reached during this initial phase serves as the initial condition for the dynamic wound closing phase. A small strain variation of the Murray–Sherratt model of the quasi-equilibrium problem will be formulated for speedy computation of this initial state at the inception of the wound closure phase, with the latter problem being the main concern of this paper. Some theoretical developments are found to be instrumental to an efficient algorithm for the otherwise time-consuming task of calculating the effect of the biological signals generated by the presence of a wound. Application of our elastodynamic model to the case of a circular wound suggests that the propagation range of our choice of cell–cell signaling mechanism must be above a certain minimum fraction of the wound radius for wound closure. As expected, stress concentration occurs adjacent to the edge of the remaining small wound near the end of the wound closing process. At that point, the present model is not expected to be adequate and more appropriate expressions of elastic strain and active stress induced by actin filaments may be in order. Other biological processes such as cell proliferation and differentiation may be involved.