Advanced Multivariate Catastrophe Model for Quantitative Analysis of Complex Systems With Case Studies and Validation.

Analysis of crowd jam in public buildings based on cusp-catastrophe theory

Direct measurements of soil water retention curve (SWRC) are costly and time consuming. So far, less investigation has been carried out on the prediction capability of different models using artificial neural networks (ANNs). In this study in total 75 soil samples were collected from Guilan province, north of Iran. The basic soil properties namely sand, clay and bulk density were used as predictors and the parameters of ten SWRC models were forecasted by ANNs. The prediction capability of each model was examined based on three criteria in nine groups of samples: total, fine (clay and silty clay) and medium (clay loam, silt loam, silty clay loam and loam) textural groups and six soil texture classes. Overall, the Boltzman, Tani, Gardner, Campbell and van Genuchten models produced the best results. However, bimodal models (Durner, Seki and Dexter) established on non-uniform pore size distribution with two modes (peaks) in soils showed low prediction capability in this study. Therefore, further research is needed. Sensitivity analysis indicated that the residual and saturated water contents were largely dependent on clay content.

Chapter 6: Insurance industry

Neural network prediction analysis: The bankruptcy case

Materials Science is highly interdisciplinary; greater insight into structure–properties relationships requires the development of multiscale/multiphysics models and comparably advanced experimental design, instruments, and analysis. Such challenges and expanding research scope motivate synergies between experimental and the computational communities. Outcomes of this emerging field within the Materials Science community open up new frontiers and research directions at the crossroads of traditional computational Materials Science, experimental Materials Science, and Integrated Computational Mechanics. Such cooperative interactions find many applications in the development, the characterization, and the design of complex material systems. The manuscripts for this special section included in this issue of the Journal of Materials Science highlight examples of recent advances in coupled computational/experimental approaches in predicting various physical phenomena and mechanisms in materials. The manuscripts can be grouped into several topical areas that include: (i) Materials Science at the atomistic scale; (ii) The development of approaches for heterogeneous microstructures; and (iii) 3-D microstructure analysis, including microstructure evolution and mechanical behavior at the microscale. In ‘‘Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales,’’ we provide a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization. This review article highlights recent synergies at various scales both from an experimental perspective and from a modeling perspective, discussing the roles of experiments in multiscale models and vice versa. This review article ends with a discussion on some problems and gaps that have to be addressed in order for this coupled approach to impact research and development in the broad scope of structure– property relations successfully in the future. Yamakov et al.’s ‘‘Multiscale Modeling of Sensory Properties of Co–Ni–Al Shape Memory Particles Embedded in an Al Metal Matrix’’ features an atomistic-to-continuum multiscale model to study the efficacy and variability in the sensory particle transformation to detect damage processes in novel ferromagnetic shape memory alloys. This manuscript exemplifies recent efforts within the modeling community to develop new algorithms and methodologies to not only bridge length scales within heterogeneous microstructures but also to account for the multiphysics dimension associated with such complex materials systems. In ‘‘Experimental and computational studies on the role of surface functional groups in the mechanical behavior of interfaces between single-walled carbon nanotubes (CNTs) and metals’’, Hartmann et al. investigate the structure– property correlation of single-walled CNTs embedded in a noble metal (Pd or Au) through the combination of an experimental and computational approach. The experimental component consists of nanoscale pull-out tests with in situ scanning electron microscope experiments, while & Remi Dingreville rdingre@sandia.gov

Comparative assessment of transitional turbulence models for airfoil aerodynamics in the low Reynolds number range

Experimental and computational investigation of local scour around bridge piers

A better understanding of the fate of dietary P use by growing pigs will allow an optimization of P use and enhance sustainable practices. The optimization of P utilization is complicated by the multiple criteria, such as growth performance, bone mineralization, and manure P used for assessment of needs. Mathematical modeling is a useful tool to describe relevant biological mechanisms and predict relationships that describe the whole system behavior. Modeling allows development of robust multicriteria approaches to optimize P utilization, feeding cost, and manure application cost. This paper describes and evaluates a model developed to simulate the fate of dietary P, that is, to simulate its digestive and metabolic utilization through digestion, soft tissue, and ash modules. The digestion module takes into account the varied sources of dietary minerals including responses to microbial and plant phytase and Ca and P interactions and predicts absorption and fecal excretion. The soft tissue module simulates the growth of the protein and is based on InraPorc model principles. The ash module simulates the partitioning of absorbed Ca and P into the bone, protein, and lipid compartments as well as urinary excretion. Model behavior showed that the model was able to accurately represent the impact of Lys deficiency on P retention, of Ca and P imbalances, and of Ca and P depletion and repletion sequences. The model's prediction capabilities in simulating whole-body protein, Ca, P, and ash based on published data showed high accuracy, with a slope and intercept that did not differ from 1 and 0, respectively, and an error due to disturbance (ED; variance not accounted for by regression of observed on predicted values). The model's prediction capabilities in simulating balance trial data showed good accuracy for apparent total tract digestibility (ATTD) of P (observed = -0.77 + 1.06 predicted) and P retention coefficient (observed = -4.5 + 1.15 predicted) with an ED of 89% for both criteria. The model's prediction capabilities in simulating Ca ATTD and Ca retention coefficient are lower (ED of 88 and 28%, respectively). This model simulates body ash independently of body protein and accounts for the impact of past and current dietary Ca and P supply. That ability is essential for the real-time adaptation of mineral supplies to suit individual production objectives, which would contribute to the overall success of pig production.

Cusp catastrophe models are unique to advance life sciences, psychology and behavioral studies. Extensive progresses have been made to utilize this modeling technique for continuous outcome and there is no development for binary data. To fill this gap, this chapter is then aimed to develop a cusp catastrophe modelling method for binary outcome. Building upon our previous research on the nonlinear regression cusp (RegCusp) catastrophe model for continuous outcome, we propose a logistic cusp catastrophe regression (LogisticCusp). LogisticCusp is based on the principles of logistic regression for binary outcome variable y (yes/no) being expressed as a latent binary variable Y through a logit link. This latent regression provides a mathematical connection between an observed outcome variable as a binomially distributed random variable and the deterministic cusp catastrophe at its equilibrium. By connecting the two, Y in the LogisticCusp is considered as one of the true roots of the deterministic cusp catastrophe model determined using the Maxwell or Delay conventions. We validate the method using a 5-step Monte-Carlo simulation with two predictors and three parameters for both bifurcation and asymmetry control variables. We further tested the method with binge drinking behavior in youth with data from the Monitoring the Future Study. Results from 5000 Monte-Carlo simulations indicate that the parameter estimates obtained through LogisticCusp are unbiased and efficient using maximum likelihood estimation with quasi-Newton numerical search algorithm. Results from empirical testing with real data are consistent with those estimated using other methods. LogisticCusp adds a new tool for researchers to examine many issues in psychology, life sciences, and behavioral studies, particularly, issues in medicine and public health with the powerful cusp catastrophe modeling for binary outcome.

Catastrophe theory is widely used in almost every aspect since it was founded, and usually the research target is the nonlinear system. However, how to build the catastrophe models of a nonlinear system is a hard problem. Catastrophe models are the key points to analyze the catastrophe characteristics of a system. This paper discussed the universal transformation methods of four elementary catastrophe modeling, which are fold catastrophe modeling, cusp catastrophe modeling, swallowtail catastrophe modeling and butterfly catastrophe modeling. Once the catastrophe model of the system is deduced, the catastrophe analysis of the system then can be applied.

Chapter 7 - Quantum Principles

Model validation and predictive capability for the thermal challenge problem

Convenience stores in Taiwan have made remarkable successes with retail delivery services by integrating E-commerce and logistics systems to form a new retail delivery model: “On-line shopping with pick-ups at convenience stores.” Although choice behavior has been discussed in marketing, few studies describe the non-linear characteristic of choice behavior. The catastrophe model was used to analyze the linkages between customer satisfaction and switching cost on pick-up point service loyalty. The results indicated that the switching cost plays the splitting factor in the catastrophe model, and a high switching cost makes the discontinuous choice behavior. In the cusp catastrophe mode, “Reselection electronic map” is the main index of the splitting factor. Nevertheless, “The quality of relative service concerning the electronic map” is the main index of the normal factor. It has been expected that a catastrophe approach to discontinuous behavior has made clearly abundant implications. Based on the findings of loyalty in the application of cusp catastrophe theory, the cusp catastrophe model is an appropriate model to know the process of loyalty. It suggests that other researchers could consider the cusp catastrophe theory and other nonlinear techniques, especially for standard approaches not adequately to capture the underlying dynamic.

IntroductionBlunt chest wall trauma accounts for over 15% of all trauma admissions to Emergency Departments worldwide. Reported mortality rates vary between 4 and 60%. Management of this patient group is challenging as a result of the delayed on-set of complications. The aim of this study was to develop and validate a prognostic model that can be used to assist in the management of blunt chest wall trauma.MethodsThere were two distinct phases to the overall study; the development and the validation phases. In the first study phase, the prognostic model was developed through the retrospective analysis of all blunt chest wall trauma patients (n = 274) presenting to the Emergency Department of a regional trauma centre in Wales (2009 to 2011). Multivariable logistic regression was used to develop the model and identify the significant predictors for the development of complications. The model’s accuracy and predictive capabilities were assessed. In the second study phase, external validation of the model was completed in a multi-centre prospective study (n = 237) in 2012. The model’s accuracy and predictive capabilities were re-assessed for the validation sample. A risk score was developed for use in the clinical setting.ResultsSignificant predictors of the development of complications were age, number of rib fractures, chronic lung disease, use of pre-injury anticoagulants and oxygen saturation levels. The final model demonstrated an excellent c-index of 0.96 (95% confidence intervals: 0.93 to 0.98).ConclusionsIn our two phase study, we have developed and validated a prognostic model that can be used to assist in the management of blunt chest wall trauma patients. The final risk score provides the clinician with the probability of the development of complications for each individual patient.

The relationship between right ventricle afterloading pressure (P) and outflow (Q) was studied in three isolated canine right ventricle (RV) preparations. Right atrial pressure was held constant while graded elevations in P were induced with stepwise occlusions of the right and left branches of the pulmonary artery. P and Q signals were collected and analyzed using a digital computer system. Data were analyzed by assuming a model structure for the RV and comparing resultant model predictions of Q with actual observations. The model structure was modified in accordance with the discrepancy between prediction and observation to improve the model's predictive capability. The initial model tested was the time-varying linear relationship between ventricular volume and pressure. Utilizing this model, accurate predictions of RV outflow in the face of varying pressure afterloads could not be made. The addition of a series resistance to this elementary model resulted in marked improvement in predictive performance. The addition of greater complexity to the model gave only marginal improvement to the model's predictive capability. It was concluded that a time-varying capacitance and series resistance adequately model internal properties of the RV that relate outflow to afterloading pressure.