Computational simulation of the early stage of bone healing under different configurations of locking compression plates

Flexible fixation or the so-called ‘biological fixation’ has been shown to encourage the formation of fracture callus, leading to better healing outcomes. However, the nature of the relationship between the degree of mechanical stability provided by a flexible fixation and the optimal healing outcomes has not been fully understood. In this study, we have developed a validated quantitative model to predict how cells in fracture callus might respond to change in their mechanical microenvironment due to different configurations of locking compression plate (LCP) in clinical practice, particularly in the early stage of healing. The model predicts that increasing flexibility of the LCP by changing the bone–plate distance (BPD) or the plate working length (WL) could enhance interfragmentary strain in the presence of a relatively large gap size (.3 mm). Furthermore, conventional LCP normally results in asymmetric tissue development during early stage of callus formation, and the increase of BPD or WL is insufficient to alleviate this problem.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

A stochastic neighborhood conditional autoregressive model for spatial data

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.

A comparison of the gas phase acidities of phospholipid headgroups: Experimental and computational studies

Proton-bound dimers consisting of two glycerophospholipids with different headgroups were prepared using negative ion electrospray ionization and dissociated in a triple quadrupole mass spectrometer. Analysis of the tandem mass spectra of the dimers using the kinetic method provides, for the first time, an order of acidity for the phospholipid classes in the gas phase of PE < PA << PG < PS < PI. Hybrid density functional calculations on model phospholipids were used to predict the absolute deprotonation enthalpies of the phospholipid classes from isodesmic proton transfer reactions with phosphoric acid. The computational data largely support the experimental acidity trend, with the exception of the relative acidity ranking of the two most acidic phospholipid species. Possible causes of the discrepancy between experiment and theory are discussed and the experimental trend is recommended. The sequence of gas phase acidities for the phospholipid headgroups is found to (1) have little correlation with the relative ionization efficiencies of the phospholipid classes observed in the negative ion electrospray process, and (2) correlate well with fragmentation trends observed upon collisional activation of phospholipid \[M - H](-) anions. (c) 2005 American Society for Mass Spectrometry.

Impact of passenger group dynamics on an airport evacuation process using an agent-based model

The safety of passengers is a major concern to airports. In the event of crises, having an effective and efficient evacuation process in place can significantly aid in enhancing passenger safety. Hence, it is necessary for airport operators to have an in-depth understanding of the evacuation process of their airport terminal. Although evacuation models have been used in studying pedestrian behaviour for decades, little research has been done in considering the evacuees’ group dynamics and the complexity of the environment. In this paper, an agent-based model is presented to simulate passenger evacuation process. Different exits were allocated to passengers based on their location and security level. The simulation results show that the evacuation time can be influenced by passenger group dynamics. This model also provides a convenient way to design airport evacuation strategy and examine its efficiency. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Two-scale computational modelling of unsaturated flow in soils exhibiting small scale heterogeneities

Unsaturated water flow in soil is commonly modelled using Richards’ equation, which requires the hydraulic properties of the soil (e.g., porosity, hydraulic conductivity, etc.) to be characterised. Naturally occurring soils, however, are heterogeneous in nature, that is, they are composed of a number of interwoven homogeneous soils each with their own set of hydraulic properties. When the length scale of these soil heterogeneities is small, numerical solution of Richards’ equation is computationally impractical due to the immense effort and refinement required to mesh the actual heterogeneous geometry. A classic way forward is to use a macroscopic model, where the heterogeneous medium is replaced with a fictitious homogeneous medium, which attempts to give the average flow behaviour at the macroscopic scale (i.e., at a scale much larger than the scale of the heterogeneities). Using the homogenisation theory, a macroscopic equation can be derived that takes the form of Richards’ equation with effective parameters. A disadvantage of the macroscopic approach, however, is that it fails in cases when the assumption of local equilibrium does not hold. This limitation has seen the introduction of two-scale models that include at each point in the macroscopic domain an additional flow equation at the scale of the heterogeneities (microscopic scale). This report outlines a well-known two-scale model and contributes to the literature a number of important advances in its numerical implementation. These include the use of an unstructured control volume finite element method and image-based meshing techniques, that allow for irregular micro-scale geometries to be treated, and the use of an exponential time integration scheme that permits both scales to be resolved simultaneously in a completely coupled manner. Numerical comparisons against a classical macroscopic model confirm that only the two-scale model correctly captures the important features of the flow for a range of parameter values.

Fluid-structure interaction analysis by coupled FE-SPH model based on a novel searching algorithm

Fluid–Structure Interaction (FSI) problem is significant in science and engineering, which leads to challenges for computational mechanics. The coupled model of Finite Element and Smoothed Particle Hydrodynamics (FE-SPH) is a robust technique for simulation of FSI problems. However, two important steps of neighbor searching and contact searching in the coupled FE-SPH model are extremely time-consuming. Point-In-Box (PIB) searching algorithm has been developed by Swegle to improve the efficiency of searching. However, it has a shortcoming that efficiency of searching can be significantly affected by the distribution of points (nodes in FEM and particles in SPH). In this paper, in order to improve the efficiency of searching, a novel Striped-PIB (S-PIB) searching algorithm is proposed to overcome the shortcoming of PIB algorithm that caused by points distribution, and the two time-consuming steps of neighbor searching and contact searching are integrated into one searching step. The accuracy and efficiency of the newly developed searching algorithm is studied on by efficiency test and FSI problems. It has been found that the newly developed model can significantly improve the computational efficiency and it is believed to be a powerful tool for the FSI analysis.

Revolute joint approach to model joint mechanism in water-filled road safety barriers

Portable water-filled road barriers (PWFB) are roadside structures placed on temporary construction zones to separate work site from traffic. Recent changes in governing standards require PWFB to adhere to strict compliance in terms of lateral displacement and vehicle redirectionality. Actual PWFB test can be very costly, thus researchers resort to Finite Element Analysis (FEA) in the initial designs phase. There has been many research conducted on concrete barriers and flexible steel barriers using FEA, however not many was done pertaining to PWFB. This research probes a new technique to model joints in PWFB. Two methods to model the joining mechanism are presented and discussed in relation to its practicality and accuracy. Moreover, the study of the physical gap and mass of the barrier was investigated. Outcome from this research will benefit PWFB research and allow road barrier designers better knowledge in developing the next generation of road safety structures.

Cohabitation, marriage, and divorce in a model of match quality

The objective of this research is to further our understanding of how and why individuals enter and leave coresidential relationships. We develop and estimate an economic model of nonmarital cohabitation, marriage, and divorce that is consistent with current data on the formation and dissolution of relationships. Jovanovic's (Journal of Political Economy 87 (1979), 972-90) theoretical matching model is extended to help explain household formation and dissolution behavior. Implications of the model reveal what factors influence the decision to start a relationship, what form this relationship will take, and the relative stability of the various types of unions. The structural parameters of the model are estimated using longitudinal data from a sample of female high school seniors from the United States. New numerical methods are developed to reduce computational costs associated with estimation. The empirical results have interesting interpretations given the structural model. They show that a significant cause of cohabitation is the need to learn about potential partners and to hedge against future bad shocks. The estimated parameters are used to conduct several comparative dynamic experiments. For example, we show that policy experiments changing the cost of divorce have little effect on relationship choices.

Nonlinear Model Predictive Control for a multi-rotor with heavy slung load

In this paper a novel controller for stable and precise operation of multi-rotors with heavy slung loads is introduced. First, simplified equations of motions for the multi-rotor and slung load are derived. The model is then used to design a Nonlinear Model Predictive Controller (NMPC) that can manage the highly nonlinear dynamics whilst accounting for system constraints. The controller is shown to simultaneously track specified waypoints whilst actively damping large slung load oscillations. A Linear-quadratic regulator (LQR) controller is also derived, and control performance is compared in simulation. Results show the improved performance of the Nonlinear Model Predictive Control (NMPC) controller over a larger flight envelope, including aggressive maneuvers and large slung load displacements. Computational cost remains relatively small, amenable to practical implementation. Such systems for small Unmanned Aerial Vehicles (UAVs) may provide significant benefit to several applications in agriculture, law enforcement and construction.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.

A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Bridging experiments, models and simulations : an integrative approach to validation in computational cardiac electrophysiology

Computational models in physiology often integrate functional and structural information from a large range of spatio-temporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and scepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace and refine animal experiments. A fundamental requirement to fulfil these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations between experiments, models and simulations in cardiac electrophysiology. We describe the processes, data and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. Validation must therefore take into account the complex interplay between models, simulations and experiments. Key points for developing strategies for validation are: 1) understanding sources of bio-variability is crucial to the comparison between simulation and experimental results; 2) robustness of techniques and tools is a pre-requisite to conducting physiological investigations using the MSE system; 3) definition and adoption of standards facilitates interoperability of experiments, models and simulations; 4) physiological validation must be understood as an iterative process that defines the specific aspects of electrophysiology the MSE system targets, and is driven by advancements in experimental and computational methods and the combination of both.