Parameters Of Computational Model Research Articles

A triangular radial cubic spline deformation model for efficient 3D beating heart tracking

A novel deformable model is proposed for efficient 3D visual tracking of beating heart. The model is parameterized by the 3D coordinates of four control points: the three vertices and the circumcenter of a triangular target region. Nonlinear deformation on heart surfaces is handled by cubic spline interpolation based on radial pixel distances from the circumcenter. With a pre-computable design matrix, the model can be represented efficiently by a simple matrix equation. An iterative algorithm is developed based on the efficient second-order minimization to compute model parameters at each frame. The proposed tracking method is validated on the stereo-endoscopic videos of phantom heart and in vivo heart that are recorded by the da Vinci\(^{\tiny \textregistered }\) surgical system.

Modeling and Experiment to Determine the Role of Passive Stiffness on Mechanical (Strain Rate) Control of Relaxation

Diastolic dysfunction is increasingly linked to exercise intolerance and heart failure-like symptoms. The serendipitous discovery of a rat with a spontaneous mutation in the RNA Binding Motif-20 (RBM20) has led to new studies showing the effects of reduced titin-based passive tension. Decreased passive stiffness in the heart is associated with increased exercise tolerance, but also slowed crossbridge attachment and detachment rates. The influence on relaxation, and specifically mechanical control of relaxation by fast end systolic stretch, is not yet known. We utilized MyoSim, a computational modeling environment, and experiments using intact trabeculae to investigate the relationship between titin-based passive stiffness and mechanical control of relaxation. We have previously obtained physiological myofilament parameters by fitting a 2-state crossbridge model to experimental data using MyoSim. These model parameters were held fixed except for passive stiffness, which was reduced by 50 and 75% to match passive stiffness in heterozygous and homozygous RBM20 mutant tissues. Isometric relaxation was slowed by 4 and 5%, respectively, but relaxation rate became 19 and 23% more sensitive to mechanical control of relaxation (slope of relationship between relaxation rate and end systolic strain rate). These data predict that titin based stiffness may modify relaxation rate. Trabeculae experiments using wild-type, heterozygous, and homozygous RBM20 mutant rats, show no significant change in the mechanical control of relaxation. However, reduced titin stiffness was associated with a later time to peak contraction (shortening). Reducing crossbridge attachment and detachment rates in the computational model parameters replicates the experimental results. These data suggest that passive stiffness may play a role on mechanical control of relaxation and confirm that crossbridge properties significantly contribute to early diastolic relaxation.

Optimisation of Hidden Markov Model using Baum–Welch algorithm for prediction of maximum and minimum temperature over Indian Himalaya

Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively.

A nondeterministic GHM based model applied to sandwich beams

In order to obtain computational models to aid the design of viscoelastic sandwich structures used as passive damping treatment, many aspects should be considered. A crucial aspect is the incertitude inherent to experimental characterization tests of the complex modulus. Usually the computational model parameters are determined by curve fitting mean values of experimental dispersions. Considering that these dispersions are inherent to experimental tests and also those small variations in the fitted parameters lead to considerable changes on the dynamic behavior of a Viscoelastic Material (VEM), hence a nondeterministic model is more suitable than the usual deterministic ones. In that way, starting from dynamic properties of a VEM, a nondeterministic numerical model, which takes into account incertitudes in the VEM curve fitting procedure, is proposed.

The generic modeling fallacy: Average biomechanical models often produce non-average results!

Computational biomechanics models constructed using nominal or average input parameters are often assumed to produce average results that are representative of a target population of interest. To investigate this assumption a stochastic Monte Carlo analysis of two common biomechanical models was conducted. Consistent discrepancies were found between the behavior of average models and the average behavior of the population from which the average models׳ input parameters were derived. More interestingly, broadly distributed sets of non-average input parameters were found to produce average or near average model behaviors. In other words, average models did not produce average results, and models that did produce average results possessed non-average input parameters. These findings have implications on the prevalent practice of employing average input parameters in computational models. To facilitate further discussion on the topic, the authors have termed this phenomenon the “Generic Modeling Fallacy”. The mathematical explanation of the Generic Modeling Fallacy is presented and suggestions for avoiding it are provided. Analytical and empirical examples of the Generic Modeling Fallacy are also given.

Second-principles method for materials simulations including electron and lattice degrees of freedom

We present a first-principles-based (second-principles) scheme that permits large-scale materials simulations including both atomic and electronic degrees of freedom on the same footing. The method is based on a predictive quantum-mechanical theory, e.g., Density Functional Theory, and its accuracy can be systematically improved at a very modest computational cost. Our approach is based on dividing the electron density of the system into a reference part - typically corresponding to the system's neutral, geometry-dependent ground state - and a deformation part - defined as the difference between the actual and reference densities. We then take advantage of the fact that the bulk part of the system's energy depends on the reference density alone; this part can be efficiently and accurately described by a force field, thus avoiding explicit consideration of the electrons. Then, the effects associated to the difference density can be treated perturbatively with good precision by working in a suitably chosen Wannier function basis. Further, the electronic model can be restricted to the bands of interest. All these features combined yield a very flexible and computationally very efficient scheme. Here we present the basic formulation of this approach, as well as a practical strategy to compute model parameters for realistic materials. We illustrate the accuracy and scope of the proposed method with two case studies, namely, the relative stability of various spin arrangements in NiO and the formation of a two-dimensional electron gas at the interface between band insulators LaAlO$_3$ and SrTiO$_3$. We conclude by discussing ways to overcome the limitations of the present approach (most notably, the assumption of a fixed bonding topology), as well as its many envisioned possibilities and future extensions.

How hierarchical models improve point estimates of model parameters at the individual level

Computational models have been used to analyze the data from behavioral experiments. One objective of the use of computational models is to estimate model parameters or internal variables for individual subjects from behavioral data. The estimates are often correlated with other variables that characterize subjects in order to investigate which computational processes are associated with specific personal or physiological traits. Although the accuracy of the estimates is important for these purposes, the parameter estimates obtained from individual subject data are often unreliable. To solve this problem, researchers have begun to use hierarchical modeling approaches to estimate parameters of computational models from multiple-subject data. It is widely accepted that the hierarchical model provides reliable estimates compared to other non-hierarchical approaches. However, how and under what conditions the hierarchical models provide better estimates than other approaches has yet to be systematically investigated. This study attempts to investigate these issues, focusing on two measures of estimation accuracy: the correlation between estimates of individual parameters and subject trait variables and the absolute measures of error (root mean squared error, RMSE) of the estimates. An analytical calculation based on a simple Gaussian model clarifies how the hierarchical model improves the point estimates of these two measures. We also performed simulation studies employing several realistic computational models based on the synthesized data to confirm that the theoretical properties hold in realistic situations.

A Frequentist Approach to Computer Model Calibration

Summary The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates of convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. The practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.

Electronic health record analysis via deep Poisson factor models

Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. Information from different modalities is shared via a deep hierarchy of common hidden units. Activation of these binary units occurs with probability characterized as Bernoulli-Poisson link functions, instead of more traditional logistic link functions. In addition, we demonstrate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 16,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.

A computational analysis of bone formation in the cranial vault in the mouse.

Bones of the cranial vault are formed by the differentiation of mesenchymal cells into osteoblasts on a surface that surrounds the brain, eventually forming mineralized bone. Signaling pathways causative for cell differentiation include the actions of extracellular proteins driven by information from genes. We assume that the interaction of cells and extracellular molecules, which are associated with cell differentiation, can be modeled using Turing’s reaction–diffusion model, a mathematical model for pattern formation controlled by two interacting molecules (activator and inhibitor). In this study, we hypothesize that regions of high concentration of an activator develop into primary centers of ossification, the earliest sites of cranial vault bone. In addition to the Turing model, we use another diffusion equation to model a morphogen (potentially the same as the morphogen associated with formation of ossification centers) associated with bone growth. These mathematical models were solved using the finite volume method. The computational domain and model parameters are determined using a large collection of experimental data showing skull bone formation in mouse at different embryonic days in mice carrying disease causing mutations and their unaffected littermates. The results show that the relative locations of the five ossification centers that form in our model occur at the same position as those identified in experimental data. As bone grows from these ossification centers, sutures form between the bones.

Computer simulation and experimental study of transient processes in a single-phase voltage transformer

We perform simulation and experimental investigation of transient processes emerging in a single-phase transformer when it is connected to the network. The transformer model constructed taking into account the saturation of the steel of the core differs from standard models in detailed accounting for the magnetic flux leakage, which is required, for example, for a correct description of inrush current. Universality of the model for the type of transformers under study is ensured by the allowance for eddy current losses and calculation of the magnetic hysteresis in the steel core. The latter makes it possible to estimate the effect of residual magnetization of the core on the form of a transient process, which is studied in detail. The methods for computing model parameters are described and its experimental verification is carried out.

Simulation of radiofrequency ablation in real human anatomy

Purpose: The objective of the current work was to simulate radiofrequency ablation treatment in computational models with realistic human anatomy, in order to investigate the effect of realistic geometry in the treatment outcome.Materials and methods: The body sites considered in the study were liver, lung and kidney. One numerical model for each body site was obtained from Duke, member of the IT’IS Virtual Family. A spherical tumour was embedded in each model and a single electrode was inserted into the tumour. The same excitation voltage was used in all cases to underline the differences in the resulting temperature rise, due to different anatomy at each body site investigated. The same numerical calculations were performed for a two-compartment model of the tissue geometry, as well as with the use of an analytical approximation for a single tissue compartment.Results: Radiofrequency ablation (RFA) therapy appears efficient for tumours in liver and lung, but less efficient in kidney. Moreover, the time evolution of temperature for a realistic geometry differs from that for a two-compartment model, but even more for an infinite homogenous tissue model. However, it appears that the most critical parameters of computational models for RFA treatment planning are tissue properties rather than tissue geometry.Conclusions: Computational simulations of realistic anatomy models show that the conventional technique of a single electrode inside the tumour volume requires a careful choice of both the excitation voltage and treatment time in order to achieve effective treatment, since the ablation zone differs considerably for various body sites.

Transcranial direct current stimulation over the left prefrontal cortex increases randomness of choice in instrumental learning

IntroductionThere is growing evidence from neuro-computational studies that instrumental learning involves the dynamic interaction of a computationally rigid, low-level striatal and a more flexible, high-level prefrontal component. MethodsTo evaluate the role of the prefrontal cortex in instrumental learning, we applied anodal transcranial direct current stimulation (tDCS) optimized for the left dorsolateral prefrontal cortex, by using realistic MR-derived finite element model-based electric field simulations. In a study with a double-blind, sham-controlled, repeated-measures design, sixteen male participants performed a probabilistic learning task while receiving anodal and sham tDCS in a counterbalanced order. ResultsCompared to sham tDCS, anodal tDCS significantly increased the amount of maladaptive shifting behavior after optimal outcomes during learning when reward probabilities were highly dissociable. Derived parameters of the Q-learning computational model further revealed a significantly increased model parameter that was sensitive to random action selection in the anodal compared to the sham tDCS session, whereas the learning rate parameter was not influenced significantly by tDCS. ConclusionThese results congruently indicate that prefrontal tDCS during instrumental learning increased randomness of choice, possibly reflecting the influence of the cognitive prefrontal component.

Electromagnetic field investigation on different cancer cell lines

There is a strong interest in the investigation of extremely low frequency Electromagnetic Fields (EMF) in the clinic. While evidence about anticancer effects exists, the mechanism explaining this effect is still unknown. We investigated in vitro, and with computer simulation, the influence of a 50 Hz EMF on three cancer cell lines: breast cancer MDA-MB-231, and colon cancer SW-480 and HCT-116. After 24 h preincubation, cells were exposed to 50 Hz extremely low frequency (ELF) radiofrequency EMF using in vitro exposure systems for 24 and 72 h. A computer reaction-diffusion model with the net rate of cell proliferation and effect of EMF in time was developed. The fitting procedure for estimation of the computer model parameters was implemented. Experimental results clearly showed disintegration of cells treated with a 50 Hz EMF, compared to untreated control cells. A large percentage of treated cells resulted in increased early apoptosis after 24 h and 72 h, compared to the controls. Computer model have shown good comparison with experimental data. Using EMF at specific frequencies may represent a new approach in controlling the growth of cancer cells, while computer modelling could be used to predict such effects and make optimisation for complex experimental design. Further studies are required before testing this approach in humans.

Nonlinear load–strain modeling of polypropylene geogrids during constant rate‐of‐strain loading

This article describes a rate‐dependent hyperbolic model that was developed to predict the tensile load–strain behavior of a polypropylene geogrid reinforcement material under monotonic and stepped constant rate‐of‐strain testing. A more general three‐component model previously reported in the literature was also used in the current study but with some modifications to compute model parameters. Details of the trial and error procedure to select three‐component model parameters, not previously reported in the literature, are explained. Both models gave similar good agreement between measured and predicted constant rate‐of‐strain tests. The accuracy of the three‐component model to simulate stepped constant rate‐of‐strain tests was judged to be better, but for practical purposes, the simpler hyperbolic model was judged to be satisfactory. An advantage of the hyperbolic model is that the model parameters are easy to determine, only monotonic constant rate‐of‐strain tests are required, and numerical implementation is simple. However, the hyperbolic model is restricted to monotonic or stepped constant rate‐of‐strain load paths. An advantage of the more complicated three‐component model is that it has been demonstrated in previous studies to be more general and thus can be used for other load paths and other polymeric reinforcement material types that do not have characteristic hyperbolic load–strain behavior. POLYM. ENG. SCI., 55:1617–1627, 2015. © 2014 Society of Plastics Engineers

Model identification in computational stochastic dynamics using experimental modal data

This paper deals with the identification of a stochastic computational model using experimental eigenfrequencies and mode shapes. In the presence of randomness, it is difficult to construct a one-to-one correspondence between the results provided by the stochastic computational model and the experimental data because of the random modes crossing and veering phenomena that may occur from one realization to another one. In this paper, this correspondence is constructed by introducing an adapted transformation for the computed modal quantities. Then the transformed computed modal quantities can be compared with the experimental data in order to identify the parameters of the stochastic computational model. The methodology is applied to a booster pump of thermal units for which experimental modal data have been measured on several sites.

Safety Analysis of the Soil Beds of Foundations Based on Bearing-Capacity Criterion

Methods of in-service analyses of the safety of the soil beds of structures based on the bearing-capacity criterion are examined when statistical information on controllable parameters of computational limiting-state models is restricted (incomplete). Approaches are proposed for analysis with use of adjunct functions (from possibility theory) and distribution functions of random numbers obtained from a Chebyshev inequality.

Hidden Markov Models for background clutter

The development of a target acquisition performance model for an electro-optical imaging system is seriously affected by the description of the target and background characteristics at present. Based on the Hidden Markov Model (HMM), a different clutter metric is proposed to quantify the influence of background on target detection in this article. It first simulates the process of recording a target in the human brain by optimizing the HMM parameters to represent the target as far as possible. And then the background clutter is defined to be the similarity, estimated by the computed model parameters, between the target and background. Finally, the newly proposed clutter metric is applied to the Search2 database, and the experiment results prove its superiority to other metrics.

Evaluation of hydrogen sorption models for AB5-type metal alloys by employing a gravimetric technique

This paper is concerned with hydrogen absorption and desorption in AB5-type hydrogen storage metal alloys. We give a brief overview on models which have been proposed for hydrogen sorption in metals over the past decades. We choose three models based on different perspectives, i.e. thermodynamics, reaction kinetics, and mere observation (empiricism), and evaluate their applicability in order to describe the sorption behaviour. Additionally, we propose a model which is based on a cumulative distribution function. In order to evaluate the models, the hydrogen absorption and desorption isotherms of LaNi5 and LaNi4.5Co0.5 are measured by means of a gravimetric technique. A nonlinear regression is performed to fit the models to experimental data. The computed model parameters are compared to values reported in the literature. The emphasis is given to the applicability of the models with respect to describing the non-ideality of the plateau region and the continuity/smoothness of phase transition regions.

Mixtures of experts for understanding model discrepancy in dynamic computer models

There are many areas of science and engineering where research and decision making are performed using computer models. These computer models are usually deterministic and may take minutes, hours or days to produce an output for a single value of the model inputs. Fitting mixtures of experts of computer models where the expert components use different values of the computer model parameters is considered. The efficient calibration of such models using emulators, which are fast statistical surrogates for the computer model, is discussed. It is argued that mixtures of experts are often insightful for describing model discrepancy and ways in which the computer model can be improved. This is not a strength of standard approaches to the statistical analysis of computer models where a certain “best input” assumption is usually made and model discrepancy is often described through a stationary Gaussian process prior on the discrepancy function. Application of the framework is presented for a dynamic hydrological rainfall–runoff model in which the mixture approach is helpful for highlighting model deficiencies.