Computer Modeling Of Processes Research Articles

Bringing computational models of bone regeneration to the clinic.

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient-specific information into a personalized diagnosis and optimal treatment remains a challenging task due to the large number of variables that affect bone regeneration. Computational models have the potential to cope with this complexity and to improve the fundamental understanding of the bone regeneration processes as well as to predict and optimize the patient-specific treatment strategies. However, the current use of computational models in daily orthopedic practice is very limited or inexistent. We have identified three key hurdles that limit the translation of computational models of bone regeneration from bench to bed side. First, there exists a clear mismatch between the scope of the existing and the clinically required models. Second, most computational models are confronted with limited quantitative information of insufficient quality thereby hampering the determination of patient-specific parameter values. Third, current computational models are only corroborated with animal models, whereas a thorough (retrospective and prospective) assessment of the computational model will be crucial to convince the health care providers of the capabilities thereof. These challenges must be addressed so that computational models of bone regeneration can reach their true potential, resulting in the advancement of individualized care and reduction of the associated health care costs.

Understanding and predicting the cognitive effects of sleep loss through simulation.

Sleep loss impacts cognitive functioning, and the resulting performance changes can have dramatic consequences in the real world. The increased risk to property and human life has motivated decades of empirical research on fatigue and its effects on performance. Models now exist that can predict the general time course and magnitude of changes in cognitive function caused by fatigue. These models have enabled the development of tools that are useful for shift work and sleep scheduling to improve safety. However, these models are incapable of making a priori predictions regarding the precise, task-specific effects that sleep loss and circadian rhythms will have on performance. Such a capability would make it possible to perform simulation-based risk assessments by conducting systematic evaluations over spaces of system designs, training approaches, policy interventions, and sleep/work schedules. It would also support monitoring technologies to detect behavioral evidence of fatigue. To develop such applications, computational process models that run in simulation are needed to produce behavior predictions in the domains of interest. In this article we review and summarize research committed to precisely this goal, we assess progress to date, and we describe remaining challenges on the path to application.

External fixation using locking plate in distal tibial fracture: a finite element analysis.

External fixation of tibial fractures using a locking plate has been reported with favorable results in some selected patients. However, the stability of external plate fixation in this fracture pattern has not been previously demonstrated. We investigated the stability of external plate fixation with different plate-bone distances. In this study, the computational processing model of external fixation of a distal tibial metaphyseal fracture utilizing the contralateral femoral less invasive stabilization system plate was analyzed. The plate was placed on the anteromedial aspect of tibia with different plate-bone distances: 1, 10, 20, and 30mm. Under axial load, the stiffness of construct in all groups was higher than intact tibia. Under axial load with an internal rotational force, the stiffness of construct with 1 and 10mm plate-bone distances was similar to that of an intact tibia and the stiffness of the construct with 20 and 30mm distances was lower than that of an intact tibia. Under axial load with an external rotational force, the stiffness of the construct in all groups was lower than that of an intact tibia. The maximum plate stresses were concentrated at the two most distal screws and were highest in the construct with the 10mm plate-bone distance, and least in the construct with a 1mm plate-bone distance. To guarantee a stable external plate fixation in distal tibial fracture, the plate-bone distance should be less than 30mm.

Design tool selecting aid: a neuronal approach

The objective of this paper is to prove that it is possible to integrate knowledge engineering in the development of computational design process models. The main contribution of our research is to propose a parametric conceptual model for the design process, adapting neural networks to the design context, and finally an algorithm to aid design tool choice. This algorithm is modular and allows the integration of other functionalities. We were particularly interested in the problem of design tool selection aid and we developed an algorithm that integrates a learning modelling approach (multilayer neural networks). The application stemming from this algorithm gives a computer tool which offers the designers, according to their design problems, a set of adequate design tools. This computer-based design tool selection aid gives very encouraging results.

Recent developments in hydroforming technology

This article is the review of hydroforming technologies that have been used increasingly in various industries including automotive applications. General concepts and technological developments in tube hydroforming and sheet hydroforming are presented with recent research and development activities. Then, the theoretical background associated with the plasticity and constitutive laws and their implementation to the computational modeling of the hydroforming process are discussed.

Characterization of ring-like F-actin structure as a mechanical partner for spindle positioning in mitosis.

Proper spindle positioning and orientation are essential for accurate mitosis which requires dynamic interactions between microtubule and actin filament (F-actin). Although mounting evidence demonstrates the role of F-actin in cortical cytoskeleton dynamics, it remains elusive as to the structure and function of F-actin-based networks in spindle geometry. Here we showed a ring-like F-actin structure surrounding the mitotic spindle which forms since metaphase and maintains in MG132-arrested metaphase HeLa cells. This cytoplasmic F-actin structure is relatively isotropic and less dynamic. Our computational modeling of spindle position process suggests a possible mechanism by which the ring-like F-actin structure can regulate astral microtubule dynamics and thus mitotic spindle orientation. We further demonstrated that inhibiting Plk1, Mps1 or Myosin, and disruption of microtubules or F-actin polymerization perturbs the formation of the ring-like F-actin structure and alters spindle position and symmetric division. These findings reveal a previously unrecognized but important link between mitotic spindle and ring-like F-actin network in accurate mitosis and enables the development of a method to theoretically illustrate the relationship between mitotic spindle and cytoplasmic F-actin.

Challenges in Concrete Structures Health Monitoring


 
 
 Structural health monitoring needs to produce actionable information regarding structural integrity that supports operational and maintenance decision making that is individualized for a given structure and its performance objectives. An effective Prognostics and Health Management (PHM) framework for aging structures (subjected to physical, chemical, environmental, and mechanical degradation) needs to integrate four elements – damage modeling, monitoring, data analytics, and uncertainty quantification. This paper briefly discusses available techniques and ongoing challenges in each of these four elements of PHM, in the context of concrete structures. A Bayesian network approach is discussed for integrating heterogeneous information from multi-physics computational models of degradation processes, full-field measurement techniques, big data analytics, and various data and model uncertainty sources. Such a comprehensive framework can quantitatively support decisions regarding appropriate risk management actions.
 
 

Prediction of cell growth rate over scaffold strands inside a perfusion bioreactor.

Mathematical and computational modeling of the dynamic process where tissue scaffolds are cultured in perfusion bioreactors is able to provide insight into the cell and tissue growth which can facilitate the design of tissue scaffolds and selection of optimal operating conditions. To date, a resolved-scale simulation of cell growth in the culture process, by taking account of the influences of the supply of nutrients and fluid shear stress on the cells, is not yet available in the literature. This paper presents such a simulation study specifically on cartilage tissue regeneration by numerically solving the momentum, scalar transport and cell growth equations, simultaneously, based on the lattice Boltzmann method. The simulation uses a simplified scaffold that consists of two circular strands placed in tandem inside a microchannel, with the object of identifying the effect of one strand on the other. The results indicate that the presence of the front strand can reduce the cell growth rate on the surface of the rear strand, depending on the distance between them. As such, the present study allows for investigation into the influence of the scaffold geometry on the cell growth rate within scaffolds, thus providing a means to improve the scaffold design and the culture process.

Integrating Systems Biology Markup Language(SBML) with NEURON

The NEURON simulator software is widely used by the computational neuroscience community for neuronal electrophysiology modeling and we have recently extended it to support reaction-diffusion dynamics [1]. This class of dynamics is ideal for studying intracellular dynamics, such as calcium-induced calcium release. NEURON’s original reaction-diffusion support introduced a custom syntax; although this allowed taking advantage of NEURON’s full capabilities, it required existing cell biology models to be rewritten. To eliminate this barrier, we have introduced SBML support for NEURON. SBML [2] is an XML-based representation format used for specifying computational models of biological processes. This format is one of the most widely used, and is supported by over 200 software packages. It has become a standard used by systems biologists for the exchange of information about chemical reaction dynamics. We have introduced SBML support to the reaction diffusion module of the NEURON simulation software. This enables NEURON users to import a large number of previously developed cell biology models and use them in computational neuroscience research. We have added the capability to import and export models in SBML format to NEURON’s reaction-diffusion module using the libSBML library [3]. This allows one to combine models from ModelDB [4] with cell models from the BioModels database [5] in a way that makes it possible to match state variables and to readily change parameters. The procedure is as follows: • Electrophysiology model is loaded from ModelDB or constructed de novo. • NEURON loads SBML data and instantiates appropriate reaction diffusion objects: rxd.Region, rxd.Species, rxd.Reaction. User must interactively or preemptively match state-variable names across combined models to combine the two models into one. • SBML models do not include diffusion so diffusion constants can be added to provide the spatial element of the combined model. • User can adjust the parameters and make simulation runs of the model. We have used these new features to study the interaction of a calcium induced calcium release model from BioModels with electrical activity in a detailed model of a hippocampal CA1 pyramidal neuron.

Collaborative Experimental and Computational Study of a Dual-Mode Scramjet Combustor

Advanced computational models of hypersonic air-breathing combustion processes are being developed to better understand and predict the complex flows within a dual-mode scramjet combustor. However, the accuracy of these models can only be quantified through comparison to experimental databases. Moreover, the quality of computational results is dependent on accurate and detailed knowledge of the combustor inflow and boundary conditions. Toward these ends, this paper describes results from a collaboration of experimental and computational investigators. Detailed computational fluid dynamics and finite element analyses were performed throughout the design and implementation of experiments involving a direct-connect scramjet combustor operating at steady state during long duration testing. The test section hardware was designed to provide substantial access for optical laser diagnostics. Measurement locations included the inflow plane and several locations downstream of fuel injection. A suite of advanced in-stream diagnostics were applied, many of which are described in companion papers. Significant results in this paper include measured static wall pressures and temperatures, stereoscopic particle image velocimetry, and focused schlieren imaging. Validated thermal finite element calculations in the scramjet hardware and temperature maps of the flow path boundaries are also presented. Comparison of experimental results with computational fluid dynamics predictions are discussed in a separate paper.

Personalizing oncology treatments by predicting drug efficacy, side‐effects, and improved therapy: mathematics, statistics, and their integration

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology.

A Framework for Different Levels of Integration of Computational Models Into Web-Based Virtual Patients

BackgroundVirtual patients are increasingly common tools used in health care education to foster learning of clinical reasoning skills. One potential way to expand their functionality is to augment virtual patients’ interactivity by enriching them with computational models of physiological and pathological processes.ObjectiveThe primary goal of this paper was to propose a conceptual framework for the integration of computational models within virtual patients, with particular focus on (1) characteristics to be addressed while preparing the integration, (2) the extent of the integration, (3) strategies to achieve integration, and (4) methods for evaluating the feasibility of integration. An additional goal was to pilot the first investigation of changing framework variables on altering perceptions of integration.MethodsThe framework was constructed using an iterative process informed by Soft System Methodology. The Virtual Physiological Human (VPH) initiative has been used as a source of new computational models. The technical challenges associated with development of virtual patients enhanced by computational models are discussed from the perspectives of a number of different stakeholders. Concrete design and evaluation steps are discussed in the context of an exemplar virtual patient employing the results of the VPH ARCH project, as well as improvements for future iterations.ResultsThe proposed framework consists of four main elements. The first element is a list of feasibility features characterizing the integration process from three perspectives: the computational modelling researcher, the health care educationalist, and the virtual patient system developer. The second element included three integration levels: basic, where a single set of simulation outcomes is generated for specific nodes in the activity graph; intermediate, involving pre-generation of simulation datasets over a range of input parameters; advanced, including dynamic solution of the model. The third element is the description of four integration strategies, and the last element consisted of evaluation profiles specifying the relevant feasibility features and acceptance thresholds for specific purposes. The group of experts who evaluated the virtual patient exemplar found higher integration more interesting, but at the same time they were more concerned with the validity of the result. The observed differences were not statistically significant.ConclusionsThis paper outlines a framework for the integration of computational models into virtual patients. The opportunities and challenges of model exploitation are discussed from a number of user perspectives, considering different levels of model integration. The long-term aim for future research is to isolate the most crucial factors in the framework and to determine their influence on the integration outcome.

Pointing the way to new constraints on the dynamical claims of computational models.

The reach-to-touch paradigm has become an increasingly popular tool in the study of human cognition. It is widely held that reaching responses are able to reveal the moment-by-moment unfolding of decision processes by virtue of an assumed continuity between reaching trajectories and the underlying "cognitive trajectory." Yet the standard analysis of reaching trajectories aggregates the trajectories across stimulus viewing times, which yields ambiguous results. Here we introduce a new version of the reach-to-touch paradigm that incorporates the response-signal procedure to elicit reaching movements across a wide range of stimulus viewing times. We then analyze the direction of the initial movement by stimulus viewing time, which produces a sigmoidal growth pattern. Of note, we show how this sigmoidal relationship between stimulus viewing time and initial direction can be used to test and constrain the dynamical claims of computational models of basic cognitive processes. We introduce our new version of the reach-to-touch paradigm and analyses in the context of a lexical decision task and we compare our results with the dynamical claims of the dual-route cascaded model of reading.

Affective BICA: Challenges and open questions

Abstract In spite of the progress in emotion research over the past 20 years, emotions remain an elusive phenomenon. While some underlying circuitry has been identified for some aspects of affective processing (e.g., amygdala-mediated processing of threatening stimuli, the role of orbitofrontal cortex in emotion regulation), much remains unknown about the mechanisms of emotions. Computational models of cognitive and affective processes provide a unique and powerful means of refining psychological theories, and can help elucidate the mechanisms that mediate affective phenomena. This paper outlines a number of open questions and challenges associated with developing computational models of emotion, and with their integration within biologically-inspired cognitive architectures. These include the following: the extent to which mechanisms in biological affective agents should be simulated or emulated in affective BICAs; importance of more precise, design-based terminology; identification of fundamental affective processes, and the computational tasks necessary for their implementation; improved understanding of affective dynamics and development of more accurate models of these phenomena; and understanding the alternative means of integrating emotions within agent architectures. The challenges associated with data availability and model validation are also discussed.

ACT-RΦ: A cognitive architecture with physiology and affect

Abstract Current computational cognitive architectures lack a comprehensive representation of human physiology. Connecting a more realistic model of physiology to a cognitive architecture allows one to develop more realistic computational process models that include an account for the effect of physiology on cognition. It is also useful to include some theory of affect and emotion to act as a functional layer between some representations of physiology and cognition. We chose to extend the ACT-R (adaptive control of thought – rational) cognitive architecture with physiology and affect to make the ACT-RΦ hybrid architecture. We connected the HumMod physiological simulation system with the ACT-R architecture; ACT-RΦ has a module that communicates between ACT-R and HumMod. A new affect system was also developed using an existing theoretical framework to serve as a functional layer between some of the physiological and cognitive representations. With this new architecture one can model a wide range of human behavior (e.g., the effects of homeostasis on cognition) in a more tractable fashion.

Process Modeling of Low-Pressure Die Casting of Aluminum Alloy Automotive Wheels

Although on initial inspection, the aluminum alloy automotive wheel seems to be a relatively simple component to cast based on its shape, further insight reveals that this is not the case. Automotive wheels are in a select group of cast components that have strict specifications for both mechanical and aesthetic characteristics due to their important structural requirements and their visibility on a vehicle. The modern aluminum alloy automotive wheel continues to experience tightened tolerances relating to defects to improve mechanical performance and/or the physical appearance. Automotive aluminum alloy wheels are assessed against three main criteria: wheel cosmetics, mechanical performance, and air tightness. Failure to achieve the required standards in any one of these categories will lead to the wheel either requiring costly repair or being rejected and remelted. Manufacturers are becoming more reliant on computational process modeling as a design tool for the wheel casting process. This article discusses and details examples of the use of computational process modeling as a predictive tool to optimize the casting process from the standpoint of defect minimization with the emphasis on those defects that lead to failure of aluminum automotive wheels, namely, macroporosity, microporosity, and oxide films. The current state of applied computational process modeling and its limitations with regard to wheel casting are discussed.

On the analogical modelling of the English past-tense: A critical assessment

The formation of English past tenses has provided the arena in which alternative computational models of psycholinguistic processes have been most intensely tested. The debate has mainly revolved around accounts based on a single, analogising mechanism as exemplified by connectionist models and the dual mechanism theory of Pinker (1999) which combines elements of a pattern associator memory with a rule-based system. Prasada and Pinker (1993) provide evidence against the connectionist approach by showing that their networks failed to reproduce the behaviour of their English speaking subjects when forming the past tense of a subset of nonce verbs. Eddington (2000a) provides an alternative single mechanism account of this data using exemplar-based, analogising models which are claimed to produce behaviour comparable to Prasada and Pinker's subjects. On the basis of these results he argues that not only is a general rejection of single mechanism, analogising accounts premature but also that exemplar-based models ⿿may prove to have advantages over their connectionist counterparts⿿.This paper tests these theoretically important claims. It is shown that the contrast between the particular exemplar-based theory, analogical modelling (AM), and connectionist model which forms the basis of Eddington's arguments is, in fact, a function of differences in the mappings the two simulations compute and that once these are removed, the AM advantage disappears. A detailed analysis of the predictions of the particular AM simulation presented further shows that, although the output bears superficial similarities with those produced by Prasada and Pinker's subjects, other properties are more problematic.

Challenges in Computational Modeling of Affective Processes

In this special section we report on the workshop Standards in Emotion Modeling held in August 2011, Leiden, The Netherlands. An important goal of this workshop was to identify challenges related to the development and evaluation of computational models of affective processes. These challenges were approached from a psychological and a computational modeling perspective. In this introduction we present a summary of the results of this week-long workshop. In addition to that, we are proud to present an invited contribution that proposes solutions to several of these challenges.

Learning curves as strong evidence for testing models: The case of EBRW

This manuscript describes how learning curves can be used to provide a strong test for computational models of cognitive processes. As an example, we show how this method can be used to evaluate the Exemplar-Based Random-Walk model of categorization (EBRW; Nosofsky & Palmeri, 1997a). EBRW is an extension of the Generalized Context Model (GCM; Nosofsky, 1984, 1986). It predicts that the mean response times (RTs) follow a power function. It can be shown analytically, however, that the learning rate (i.e., the curvature) predicted by the model can only be equal to 1, a value rarely observed in empirical data analyses. We also explored an extended version of EBRW including background noise elements (Nosofsky & Alfonso-Reese, 1999) and identified conditions under which this model can predict curvatures different from 1. The limitation of these models to predict a wide variety of curvatures as observed in human data can be resolved by a simple extension to EBRW in which the original exponential distribution of retrieval times is replaced by a Weibull distribution. Additional predictions regarding learning curves are discussed.

The computational prediction of masking thresholds for ecologically valid interference scenarios

Auditory interference scenarios, where a listener wishes to attend to some target audio while being presented with interfering audio, are prevalent in daily life. The goal of developing an accurate computational model which can predict masking thresholds for such scenarios is still incomplete. While some sophisticated, physiologically inspired, masking prediction models exist, they are rarely tested with ecologically valid programs (such as music and speech). In order to test the accuracy of model predictions human listener data is required. To that end a masking threshold experiment was conducted for a variety of target and interferer programs. The results were analyzed alongside predictions made by the computational auditory signal processing and prediction model described by Jepsen et al. (2008). Masking thresholds were predicted to within 3 dB root mean squared error with the greatest prediction inaccuracies occurring in the presence of speech. These results are comparable to those of the model by Glasberg and Moore (2005) for predicting the audibility of time-varying sounds in the presence of background sounds, which otherwise represent the most accurate predictions of this type in the literature.