Comparisons of Multilevel Modeling and Structural Equation Modeling Approaches to Actor-Partner Interdependence Model.

PubMed

Hong, Sehee; Kim, Soyoung

2018-01-01

There are basically two modeling approaches applicable to analyzing an actor-partner interdependence model: the multilevel modeling (hierarchical linear model) and the structural equation modeling. This article explains how to use these two models in analyzing an actor-partner interdependence model and how these two approaches work differently. As an empirical example, marital conflict data were used to analyze an actor-partner interdependence model. The multilevel modeling and the structural equation modeling produced virtually identical estimates for a basic model. However, the structural equation modeling approach allowed more realistic assumptions on measurement errors and factor loadings, rendering better model fit indices.

A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

PubMed

Jalaleddini, Kian; Tehrani, Ehsan Sobhani; Kearney, Robert E

2017-06-01

The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.

A least-squares parameter estimation algorithm for switched hammerstein systems with applications to the VOR

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Kearney, Robert E.; Galiana, Henrietta L.

2005-01-01

A "Multimode" or "switched" system is one that switches between various modes of operation. When a switch occurs from one mode to another, a discontinuity may result followed by a smooth evolution under the new regime. Characterizing the switching behavior of these systems is not well understood and, therefore, identification of multimode systems typically requires a preprocessing step to classify the observed data according to a mode of operation. A further consequence of the switched nature of these systems is that data available for parameter estimation of any subsystem may be inadequate. As such, identification and parameter estimation of multimode systems remains an unresolved problem. In this paper, we 1) show that the NARMAX model structure can be used to describe the impulsive-smooth behavior of switched systems, 2) propose a modified extended least squares (MELS) algorithm to estimate the coefficients of such models, and 3) demonstrate its applicability to simulated and real data from the Vestibulo-Ocular Reflex (VOR). The approach will also allow the identification of other nonlinear bio-systems, suspected of containing "hard" nonlinearities.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

SLS Navigation Model-Based Design Approach

NASA Technical Reports Server (NTRS)

Oliver, T. Emerson; Anzalone, Evan; Geohagan, Kevin; Bernard, Bill; Park, Thomas

2018-01-01

The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and

Imputation approaches for animal movement modeling

USGS Publications Warehouse

Scharf, Henry; Hooten, Mevin B.; Johnson, Devin S.

2017-01-01

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pasqualini, Donatella

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimatedmore » stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.« less

Application of various FLD modelling approaches

NASA Astrophysics Data System (ADS)

Banabic, D.; Aretz, H.; Paraianu, L.; Jurco, P.

2005-07-01

This paper focuses on a comparison between different modelling approaches to predict the forming limit diagram (FLD) for sheet metal forming under a linear strain path using the recently introduced orthotropic yield criterion BBC2003 (Banabic D et al 2005 Int. J. Plasticity 21 493-512). The FLD models considered here are a finite element based approach, the well known Marciniak-Kuczynski model, the modified maximum force criterion according to Hora et al (1996 Proc. Numisheet'96 Conf. (Dearborn/Michigan) pp 252-6), Swift's diffuse (Swift H W 1952 J. Mech. Phys. Solids 1 1-18) and Hill's classical localized necking approach (Hill R 1952 J. Mech. Phys. Solids 1 19-30). The FLD of an AA5182-O aluminium sheet alloy has been determined experimentally in order to quantify the predictive capabilities of the models mentioned above.

Technical note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models

NASA Astrophysics Data System (ADS)

Peltola, Olli; Raivonen, Maarit; Li, Xuefei; Vesala, Timo

2018-02-01

Emission via bubbling, i.e. ebullition, is one of the main methane (CH4) emission pathways from wetlands to the atmosphere. Direct measurement of gas bubble formation, growth and release in the peat-water matrix is challenging and in consequence these processes are relatively unknown and are coarsely represented in current wetland CH4 emission models. In this study we aimed to evaluate three ebullition modelling approaches and their effect on model performance. This was achieved by implementing the three approaches in one process-based CH4 emission model. All the approaches were based on some kind of threshold: either on CH4 pore water concentration (ECT), pressure (EPT) or free-phase gas volume (EBG) threshold. The model was run using 4 years of data from a boreal sedge fen and the results were compared with eddy covariance measurements of CH4 fluxes.

Modelled annual CH4 emissions were largely unaffected by the different ebullition modelling approaches; however, temporal variability in CH4 emissions varied an order of magnitude between the approaches. Hence the ebullition modelling approach drives the temporal variability in modelled CH4 emissions and therefore significantly impacts, for instance, high-frequency (daily scale) model comparison and calibration against measurements. The modelling approach based on the most recent knowledge of the ebullition process (volume threshold, EBG) agreed the best with the measured fluxes (R2 = 0.63) and hence produced the most reasonable results, although there was a scale mismatch between the measurements (ecosystem scale with heterogeneous ebullition locations) and model results (single horizontally homogeneous peat column). The approach should be favoured over the two other more widely used ebullition modelling approaches and researchers are encouraged to implement it into their CH4 emission models.

A Unified Approach to Modeling Multidisciplinary Interactions

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.; Bhatia, Kumar G.

2000-01-01

There are a number of existing methods to transfer information among various disciplines. For a multidisciplinary application with n disciplines, the traditional methods may be required to model (n(exp 2) - n) interactions. This paper presents a unified three-dimensional approach that reduces the number of interactions from (n(exp 2) - n) to 2n by using a computer-aided design model. The proposed modeling approach unifies the interactions among various disciplines. The approach is independent of specific discipline implementation, and a number of existing methods can be reformulated in the context of the proposed unified approach. This paper provides an overview of the proposed unified approach and reformulations for two existing methods. The unified approach is specially tailored for application environments where the geometry is created and managed through a computer-aided design system. Results are presented for a blended-wing body and a high-speed civil transport.

Towards new approaches in phenological modelling

NASA Astrophysics Data System (ADS)

Chmielewski, Frank-M.; Götz, Klaus-P.; Rawel, Harshard M.; Homann, Thomas

2014-05-01

Modelling of phenological stages is based on temperature sums for many decades, describing both the chilling and the forcing requirement of woody plants until the beginning of leafing or flowering. Parts of this approach go back to Reaumur (1735), who originally proposed the concept of growing degree-days. Now, there is a growing body of opinion that asks for new methods in phenological modelling and more in-depth studies on dormancy release of woody plants. This requirement is easily understandable if we consider the wide application of phenological models, which can even affect the results of climate models. To this day, in phenological models still a number of parameters need to be optimised on observations, although some basic physiological knowledge of the chilling and forcing requirement of plants is already considered in these approaches (semi-mechanistic models). Limiting, for a fundamental improvement of these models, is the lack of knowledge about the course of dormancy in woody plants, which cannot be directly observed and which is also insufficiently described in the literature. Modern metabolomic methods provide a solution for this problem and allow both, the validation of currently used phenological models as well as the development of mechanistic approaches. In order to develop this kind of models, changes of metabolites (concentration, temporal course) must be set in relation to the variability of environmental (steering) parameters (weather, day length, etc.). This necessarily requires multi-year (3-5 yr.) and high-resolution (weekly probes between autumn and spring) data. The feasibility of this approach has already been tested in a 3-year pilot-study on sweet cherries. Our suggested methodology is not only limited to the flowering of fruit trees, it can be also applied to tree species of the natural vegetation, where even greater deficits in phenological modelling exist.

Challenges and opportunities for integrating lake ecosystem modelling approaches

USGS Publications Warehouse

Mooij, Wolf M.; Trolle, Dennis; Jeppesen, Erik; Arhonditsis, George; Belolipetsky, Pavel V.; Chitamwebwa, Deonatus B.R.; Degermendzhy, Andrey G.; DeAngelis, Donald L.; Domis, Lisette N. De Senerpont; Downing, Andrea S.; Elliott, J. Alex; Ruberto, Carlos Ruberto; Gaedke, Ursula; Genova, Svetlana N.; Gulati, Ramesh D.; Hakanson, Lars; Hamilton, David P.; Hipsey, Matthew R.; Hoen, Jochem 't; Hulsmann, Stephan; Los, F. Hans; Makler-Pick, Vardit; Petzoldt, Thomas; Prokopkin, Igor G.; Rinke, Karsten; Schep, Sebastiaan A.; Tominaga, Koji; Van Dam, Anne A.; Van Nes, Egbert H.; Wells, Scott A.; Janse, Jan H.

2010-01-01

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative

A simplified approach to quasi-linear viscoelastic modeling

PubMed Central

Nekouzadeh, Ali; Pryse, Kenneth M.; Elson, Elliot L.; Genin, Guy M.

2007-01-01

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in one dimension is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of “ramp-and-hold” stretching tests were applied to rectangular collagen specimens. The relaxation force data from the “hold” was used to calibrate a new “adaptive QLV model” and several models from literature, and the force data from the “ramp” was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The “adaptive QLV model” based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation. PMID:17499254

Predictive control of thermal state of blast furnace

NASA Astrophysics Data System (ADS)

Barbasova, T. A.; Filimonova, A. A.

2018-05-01

The work describes the structure of the model for predictive control of the thermal state of a blast furnace. The proposed model contains the following input parameters: coke rate; theoretical combustion temperature, comprising: natural gas consumption, blasting temperature, humidity, oxygen, blast furnace cooling water; blast furnace gas utilization rate. The output parameter is the cast iron temperature. The results for determining the cast iron temperature were obtained following the identification using the Hammerstein-Wiener model. The result of solving the cast iron temperature stabilization problem was provided for the calculated values of process parameters of the target area of the respective blast furnace operation mode.

Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

PubMed Central

Golkar, Mahsa A.; Sobhani Tehrani, Ehsan; Kearney, Robert E.

2017-01-01

Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from unperturbed trials. The LPV-PC algorithm identified (i) a non-parametric LPV impulse response function (LPV IRF) for intrinsic stiffness and (ii) a LPV-Hammerstein model for reflex stiffness consisting of a LPV static nonlinearity followed by a time-invariant state-space model of reflex dynamics. The results demonstrated that: (a) intrinsic stiffness, in particular ankle elasticity, increased significantly and monotonically with activation level; (b) the gain of the reflex pathway increased from rest to around 10–20% of subject's MVC and then declined; and (c) the reflex dynamics were second order. These findings suggest that in healthy human ankle, reflex stiffness contributes most at low muscle contraction levels, whereas, intrinsic contributions monotonically increase with activation level. PMID:28579954

A hybrid agent-based approach for modeling microbiological systems.

PubMed

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

Electrification Futures Study Modeling Approach | Energy Analysis | NREL

Science.gov Websites

Electrification Futures Study Modeling Approach Electrification Futures Study Modeling Approach To quantitatively answer the research questions of the Electrification Futures Study, researchers will use multiple accounting for infrastructure inertia through stock turnover. Load Modeling The Electrification Futures Study

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Modeling and Compensating Temperature-Dependent Non-Uniformity Noise in IR Microbolometer Cameras

PubMed Central

Wolf, Alejandro; Pezoa, Jorge E.; Figueroa, Miguel

2016-01-01

Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ∘C, when the array’s temperature varies by approximately 15 ∘C. PMID:27447637

An approach to solving large reliability models

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Veeraraghavan, Malathi; Dugan, Joanne Bechta; Trivedi, Kishor S.

1988-01-01

This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).

Mathematical Modeling Approaches in Plant Metabolomics.

PubMed

Fürtauer, Lisa; Weiszmann, Jakob; Weckwerth, Wolfram; Nägele, Thomas

2018-01-01

The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.

Challenges in structural approaches to cell modeling.

PubMed

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A

2016-07-31

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. Copyright © 2016 Elsevier Ltd. All rights reserved.

Challenges in structural approaches to cell modeling

PubMed Central

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A.

2016-01-01

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863

Formulation of consumables management models. Development approach for the mission planning processor working model

NASA Technical Reports Server (NTRS)

Connelly, L. C.

1977-01-01

The mission planning processor is a user oriented tool for consumables management and is part of the total consumables subsystem management concept. The approach to be used in developing a working model of the mission planning processor is documented. The approach includes top-down design, structured programming techniques, and application of NASA approved software development standards. This development approach: (1) promotes cost effective software development, (2) enhances the quality and reliability of the working model, (3) encourages the sharing of the working model through a standard approach, and (4) promotes portability of the working model to other computer systems.

Modeling healthcare authorization and claim submissions using the openEHR dual-model approach

PubMed Central

2011-01-01

Background The TISS standard is a set of mandatory forms and electronic messages for healthcare authorization and claim submissions among healthcare plans and providers in Brazil. It is not based on formal models as the new generation of health informatics standards suggests. The objective of this paper is to model the TISS in terms of the openEHR archetype-based approach and integrate it into a patient-centered EHR architecture. Methods Three approaches were adopted to model TISS. In the first approach, a set of archetypes was designed using ENTRY subclasses. In the second one, a set of archetypes was designed using exclusively ADMIN_ENTRY and CLUSTERs as their root classes. In the third approach, the openEHR ADMIN_ENTRY is extended with classes designed for authorization and claim submissions, and an ISM_TRANSITION attribute is added to the COMPOSITION class. Another set of archetypes was designed based on this model. For all three approaches, templates were designed to represent the TISS forms. Results The archetypes based on the openEHR RM (Reference Model) can represent all TISS data structures. The extended model adds subclasses and an attribute to the COMPOSITION class to represent information on authorization and claim submissions. The archetypes based on all three approaches have similar structures, although rooted in different classes. The extended openEHR RM model is more semantically aligned with the concepts involved in a claim submission, but may disrupt interoperability with other systems and the current tools must be adapted to deal with it. Conclusions Modeling the TISS standard by means of the openEHR approach makes it aligned with ISO recommendations and provides a solid foundation on which the TISS can evolve. Although there are few administrative archetypes available, the openEHR RM is expressive enough to represent the TISS standard. This paper focuses on the TISS but its results may be extended to other billing processes. A complete

HABITAT MODELING APPROACHES FOR RESTORATION SITE SELECTION

EPA Science Inventory

Numerous modeling approaches have been used to develop predictive models of species-environment and species-habitat relationships. These models have been used in conservation biology and habitat or species management, but their application to restoration efforts has been minimal...

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language. PMID:22615554

The layer-oriented approach to declarative languages for biological modeling.

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

Modelling approaches for evaluating multiscale tendon mechanics

PubMed Central

Fang, Fei; Lake, Spencer P.

2016-01-01

Tendon exhibits anisotropic, inhomogeneous and viscoelastic mechanical properties that are determined by its complicated hierarchical structure and varying amounts/organization of different tissue constituents. Although extensive research has been conducted to use modelling approaches to interpret tendon structure–function relationships in combination with experimental data, many issues remain unclear (i.e. the role of minor components such as decorin, aggrecan and elastin), and the integration of mechanical analysis across different length scales has not been well applied to explore stress or strain transfer from macro- to microscale. This review outlines mathematical and computational models that have been used to understand tendon mechanics at different scales of the hierarchical organization. Model representations at the molecular, fibril and tissue levels are discussed, including formulations that follow phenomenological and microstructural approaches (which include evaluations of crimp, helical structure and the interaction between collagen fibrils and proteoglycans). Multiscale modelling approaches incorporating tendon features are suggested to be an advantageous methodology to understand further the physiological mechanical response of tendon and corresponding adaptation of properties owing to unique in vivo loading environments. PMID:26855747

A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.

A multi-resolution approach to electromagnetic modeling.

NASA Astrophysics Data System (ADS)

Cherevatova, M.; Egbert, G. D.; Smirnov, M. Yu

2018-04-01

We present a multi-resolution approach for three-dimensional magnetotelluric forward modeling. Our approach is motivated by the fact that fine grid resolution is typically required at shallow levels to adequately represent near surface inhomogeneities, topography, and bathymetry, while a much coarser grid may be adequate at depth where the diffusively propagating electromagnetic fields are much smoother. This is especially true for forward modeling required in regularized inversion, where conductivity variations at depth are generally very smooth. With a conventional structured finite-difference grid the fine discretization required to adequately represent rapid variations near the surface are continued to all depths, resulting in higher computational costs. Increasing the computational efficiency of the forward modeling is especially important for solving regularized inversion problems. We implement a multi-resolution finite-difference scheme that allows us to decrease the horizontal grid resolution with depth, as is done with vertical discretization. In our implementation, the multi-resolution grid is represented as a vertical stack of sub-grids, with each sub-grid being a standard Cartesian tensor product staggered grid. Thus, our approach is similar to the octree discretization previously used for electromagnetic modeling, but simpler in that we allow refinement only with depth. The major difficulty arose in deriving the forward modeling operators on interfaces between adjacent sub-grids. We considered three ways of handling the interface layers and suggest a preferable one, which results in similar accuracy as the staggered grid solution, while retaining the symmetry of coefficient matrix. A comparison between multi-resolution and staggered solvers for various models show that multi-resolution approach improves on computational efficiency without compromising the accuracy of the solution.

Emulation of the MBM-MEDUSA model: exploring the sea level and the basin-to-shelf transfer influence on the system dynamics

NASA Astrophysics Data System (ADS)

Ermakov, Ilya; Crucifix, Michel; Munhoven, Guy

2013-04-01

Complex climate models require high computational burden. However, computational limitations may be avoided by using emulators. In this work we present several approaches for dynamical emulation (also called metamodelling) of the Multi-Box Model (MBM) coupled to the Model of Early Diagenesis in the Upper Sediment A (MEDUSA) that simulates the carbon cycle of the ocean and atmosphere [1]. We consider two experiments performed on the MBM-MEDUSA that explore the Basin-to-Shelf Transfer (BST) dynamics. In both experiments the sea level is varied according to a paleo sea level reconstruction. Such experiments are interesting because the BST is an important cause of the CO2 variation and the dynamics is potentially nonlinear. The output that we are interested in is the variation of the carbon dioxide partial pressure in the atmosphere over the Pleistocene. The first experiment considers that the BST is fixed constant during the simulation. In the second experiment the BST is interactively adjusted according to the sea level, since the sea level is the primary control of the growth and decay of coral reefs and other shelf carbon reservoirs. The main aim of the present contribution is to create a metamodel of the MBM-MEDUSA using the Dynamic Emulation Modelling methodology [2] and compare the results obtained using linear and non-linear methods. The first step in the emulation methodology used in this work is to identify the structure of the metamodel. In order to select an optimal approach for emulation we compare the results of identification obtained by the simple linear and more complex nonlinear models. In order to identify the metamodel in the first experiment the simple linear regression and the least-squares method is sufficient to obtain a 99,9% fit between the temporal outputs of the model and the metamodel. For the second experiment the MBM's output is highly nonlinear. In this case we apply nonlinear models, such as, NARX, Hammerstein model, and an 'ad

A Conceptual Modeling Approach for OLAP Personalization

NASA Astrophysics Data System (ADS)

Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan

Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

Leveraging Modeling Approaches: Reaction Networks and Rules

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

Leveraging modeling approaches: reaction networks and rules.

PubMed

Blinov, Michael L; Moraru, Ion I

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

A new approach for developing adjoint models

NASA Astrophysics Data System (ADS)

Farrell, P. E.; Funke, S. W.

2011-12-01

Many data assimilation algorithms rely on the availability of gradients of misfit functionals, which can be efficiently computed with adjoint models. However, the development of an adjoint model for a complex geophysical code is generally very difficult. Algorithmic differentiation (AD, also called automatic differentiation) offers one strategy for simplifying this task: it takes the abstraction that a model is a sequence of primitive instructions, each of which may be differentiated in turn. While extremely successful, this low-level abstraction runs into time-consuming difficulties when applied to the whole codebase of a model, such as differentiating through linear solves, model I/O, calls to external libraries, language features that are unsupported by the AD tool, and the use of multiple programming languages. While these difficulties can be overcome, it requires a large amount of technical expertise and an intimate familiarity with both the AD tool and the model. An alternative to applying the AD tool to the whole codebase is to assemble the discrete adjoint equations and use these to compute the necessary gradients. With this approach, the AD tool must be applied to the nonlinear assembly operators, which are typically small, self-contained units of the codebase. The disadvantage of this approach is that the assembly of the discrete adjoint equations is still very difficult to perform correctly, especially for complex multiphysics models that perform temporal integration; as it stands, this approach is as difficult and time-consuming as applying AD to the whole model. In this work, we have developed a library which greatly simplifies and automates the alternate approach of assembling the discrete adjoint equations. We propose a complementary, higher-level abstraction to that of AD: that a model is a sequence of linear solves. The developer annotates model source code with library calls that build a 'tape' of the operators involved and their dependencies, and

A multi-resolution approach to electromagnetic modelling

NASA Astrophysics Data System (ADS)

Cherevatova, M.; Egbert, G. D.; Smirnov, M. Yu

2018-07-01

We present a multi-resolution approach for 3-D magnetotelluric forward modelling. Our approach is motivated by the fact that fine-grid resolution is typically required at shallow levels to adequately represent near surface inhomogeneities, topography and bathymetry, while a much coarser grid may be adequate at depth where the diffusively propagating electromagnetic fields are much smoother. With a conventional structured finite difference grid, the fine discretization required to adequately represent rapid variations near the surface is continued to all depths, resulting in higher computational costs. Increasing the computational efficiency of the forward modelling is especially important for solving regularized inversion problems. We implement a multi-resolution finite difference scheme that allows us to decrease the horizontal grid resolution with depth, as is done with vertical discretization. In our implementation, the multi-resolution grid is represented as a vertical stack of subgrids, with each subgrid being a standard Cartesian tensor product staggered grid. Thus, our approach is similar to the octree discretization previously used for electromagnetic modelling, but simpler in that we allow refinement only with depth. The major difficulty arose in deriving the forward modelling operators on interfaces between adjacent subgrids. We considered three ways of handling the interface layers and suggest a preferable one, which results in similar accuracy as the staggered grid solution, while retaining the symmetry of coefficient matrix. A comparison between multi-resolution and staggered solvers for various models shows that multi-resolution approach improves on computational efficiency without compromising the accuracy of the solution.

Towards a Multiscale Approach to Cybersecurity Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogan, Emilie A.; Hui, Peter SY; Choudhury, Sutanay

2013-11-12

We propose a multiscale approach to modeling cyber networks, with the goal of capturing a view of the network and overall situational awareness with respect to a few key properties--- connectivity, distance, and centrality--- for a system under an active attack. We focus on theoretical and algorithmic foundations of multiscale graphs, coming from an algorithmic perspective, with the goal of modeling cyber system defense as a specific use case scenario. We first define a notion of \\emph{multiscale} graphs, in contrast with their well-studied single-scale counterparts. We develop multiscale analogs of paths and distance metrics. As a simple, motivating example ofmore » a common metric, we present a multiscale analog of the all-pairs shortest-path problem, along with a multiscale analog of a well-known algorithm which solves it. From a cyber defense perspective, this metric might be used to model the distance from an attacker's position in the network to a sensitive machine. In addition, we investigate probabilistic models of connectivity. These models exploit the hierarchy to quantify the likelihood that sensitive targets might be reachable from compromised nodes. We believe that our novel multiscale approach to modeling cyber-physical systems will advance several aspects of cyber defense, specifically allowing for a more efficient and agile approach to defending these systems.« less

Towards a 3d Spatial Urban Energy Modelling Approach

NASA Astrophysics Data System (ADS)

Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.

2013-09-01

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies

A Novel Triggerless Approach for Modeling Mass Wasting Susceptibility

NASA Astrophysics Data System (ADS)

Aly, M. H.; Rowden, K. W.

2017-12-01

Common approaches for modeling mass wasting susceptibility rely on using triggers, which are catalysts for failure, as critical inputs. Frequently used triggers include removal of the toe of a slope or vegetation and time correlated events such as seismicity or heavy precipitation. When temporal data are unavailable, correlating triggers with a particular mass wasting event (MWE) is futile. Meanwhile, geologic structures directly influence slope stability and are typically avoided in alternative modeling approaches. Depending on strata's dip direction, underlying geology can make a slope either stronger or weaker. To heuristically understand susceptibility and reliably infer risk, without being constrained by the previously mentioned limitations, a novel triggerless approach is conceived in this study. Core requisites include a digital elevation model and digitized geologic maps containing geologic formations delineated as polygons encompassing adequate distribution of structural attitudes. Tolerably simple geology composed of gently deformed, relatively flat-lying Carboniferous strata with minimal faulting or monoclines, ideal for applying this new triggerless approach, is found in the Boston Mountains, NW Arkansas, where 47 MWEs are documented. Two models are then created; one model has integrated Empirical Bayesian Kriging (EBK) and fuzzy logic, while the second model has employed a standard implementation of a weighted overlay. Statistical comparisons show that the first model has identified 83%, compared to only 28% for the latter model, of the failure events in categories ranging from moderate to very high susceptibility. These results demonstrate that the introduced triggerless approach is efficiently capable of modeling mass wasting susceptibility, by incorporating EBK and fuzzy logic, in areas lacking temporal datasets.

Multi-model approach to characterize human handwriting motion.

PubMed

Chihi, I; Abdelkrim, A; Benrejeb, M

2016-02-01

This paper deals with characterization and modelling of human handwriting motion from two forearm muscle activity signals, called electromyography signals (EMG). In this work, an experimental approach was used to record the coordinates of a pen tip moving on the (x, y) plane and EMG signals during the handwriting act. The main purpose is to design a new mathematical model which characterizes this biological process. Based on a multi-model approach, this system was originally developed to generate letters and geometric forms written by different writers. A Recursive Least Squares algorithm is used to estimate the parameters of each sub-model of the multi-model basis. Simulations show good agreement between predicted results and the recorded data.

An Instructional Approach to Modeling in Microevolution.

ERIC Educational Resources Information Center

Thompson, Steven R.

1988-01-01

Describes an approach to teaching population genetics and evolution and some of the ways models can be used to enhance understanding of the processes being studied. Discusses the instructional plan, and the use of models including utility programs and analysis with models. Provided are a basic program and sample program outputs. (CW)

A model-driven approach to information security compliance

NASA Astrophysics Data System (ADS)

Correia, Anacleto; Gonçalves, António; Teodoro, M. Filomena

2017-06-01

The availability, integrity and confidentiality of information are fundamental to the long-term survival of any organization. Information security is a complex issue that must be holistically approached, combining assets that support corporate systems, in an extended network of business partners, vendors, customers and other stakeholders. This paper addresses the conception and implementation of information security systems, conform the ISO/IEC 27000 set of standards, using the model-driven approach. The process begins with the conception of a domain level model (computation independent model) based on information security vocabulary present in the ISO/IEC 27001 standard. Based on this model, after embedding in the model mandatory rules for attaining ISO/IEC 27001 conformance, a platform independent model is derived. Finally, a platform specific model serves the base for testing the compliance of information security systems with the ISO/IEC 27000 set of standards.

Lightweight approach to model traceability in a CASE tool

NASA Astrophysics Data System (ADS)

Vileiniskis, Tomas; Skersys, Tomas; Pavalkis, Saulius; Butleris, Rimantas; Butkiene, Rita

2017-07-01

A term "model-driven" is not at all a new buzzword within the ranks of system development community. Nevertheless, the ever increasing complexity of model-driven approaches keeps fueling all kinds of discussions around this paradigm and pushes researchers forward to research and develop new and more effective ways to system development. With the increasing complexity, model traceability, and model management as a whole, becomes indispensable activities of model-driven system development process. The main goal of this paper is to present a conceptual design and implementation of a practical lightweight approach to model traceability in a CASE tool.

MODELING APPROACHES TO POPULATION-LEVEL RISK AESSESSMENT

EPA Science Inventory

A SETAC Pellston Workshop on Population-Level Risk Assessment was held in Roskilde, Denmark on 23-27 August 2003. One aspect of this workshop focused on modeling approaches for characterizing population-level effects of chemical exposure. The modeling work group identified th...

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of

An Alternative Approach to the Extended Drude Model

NASA Astrophysics Data System (ADS)

Gantzler, N. J.; Dordevic, S. V.

2018-05-01

The original Drude model, proposed over a hundred years ago, is still used today for the analysis of optical properties of solids. Within this model, both the plasma frequency and quasiparticle scattering rate are constant, which makes the model rather inflexible. In order to circumvent this problem, the so-called extended Drude model was proposed, which allowed for the frequency dependence of both the quasiparticle scattering rate and the effective mass. In this work we will explore an alternative approach to the extended Drude model. Here, one also assumes that the quasiparticle scattering rate is frequency dependent; however, instead of the effective mass, the plasma frequency becomes frequency-dependent. This alternative model is applied to the high Tc superconductor Bi2Sr2CaCu2O8+δ (Bi2212) with Tc = 92 K, and the results are compared and contrasted with the ones obtained from the conventional extended Drude model. The results point to several advantages of this alternative approach to the extended Drude model.

Teaching EFL Writing: An Approach Based on the Learner's Context Model

ERIC Educational Resources Information Center

Lin, Zheng

2017-01-01

This study aims to examine qualitatively a new approach to teaching English as a foreign language (EFL) writing based on the learner's context model. It investigates the context model-based approach in class and identifies key characteristics of the approach delivered through a four-phase teaching and learning cycle. The model collects research…

Modeling Approaches in Planetary Seismology

NASA Technical Reports Server (NTRS)

Weber, Renee; Knapmeyer, Martin; Panning, Mark; Schmerr, Nick

2014-01-01

Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. Given that the seismic data gathered on the Moon over 40 years ago revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure on and of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this chapter, we will review these approaches.

A Final Approach Trajectory Model for Current Operations

NASA Technical Reports Server (NTRS)

Gong, Chester; Sadovsky, Alexander

2010-01-01

Predicting accurate trajectories with limited intent information is a challenge faced by air traffic management decision support tools in operation today. One such tool is the FAA's Terminal Proximity Alert system which is intended to assist controllers in maintaining safe separation of arrival aircraft during final approach. In an effort to improve the performance of such tools, two final approach trajectory models are proposed; one based on polynomial interpolation, the other on the Fourier transform. These models were tested against actual traffic data and used to study effects of the key final approach trajectory modeling parameters of wind, aircraft type, and weight class, on trajectory prediction accuracy. Using only the limited intent data available to today's ATM system, both the polynomial interpolation and Fourier transform models showed improved trajectory prediction accuracy over a baseline dead reckoning model. Analysis of actual arrival traffic showed that this improved trajectory prediction accuracy leads to improved inter-arrival separation prediction accuracy for longer look ahead times. The difference in mean inter-arrival separation prediction error between the Fourier transform and dead reckoning models was 0.2 nmi for a look ahead time of 120 sec, a 33 percent improvement, with a corresponding 32 percent improvement in standard deviation.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches

Targeted versus statistical approaches to selecting parameters for modelling sediment provenance

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick

2017-04-01

One effective field-based approach to modelling sediment provenance is the source fingerprinting technique. Arguably, one of the most important steps for this approach is selecting the appropriate suite of parameters or fingerprints used to model source contributions. Accordingly, approaches to selecting parameters for sediment source fingerprinting will be reviewed. Thereafter, opportunities and limitations of these approaches and some future research directions will be presented. For properties to be effective tracers of sediment, they must discriminate between sources whilst behaving conservatively. Conservative behavior is characterized by constancy in sediment properties, where the properties of sediment sources remain constant, or at the very least, any variation in these properties should occur in a predictable and measurable way. Therefore, properties selected for sediment source fingerprinting should remain constant through sediment detachment, transportation and deposition processes, or vary in a predictable and measurable way. One approach to select conservative properties for sediment source fingerprinting is to identify targeted tracers, such as caesium-137, that provide specific source information (e.g. surface versus subsurface origins). A second approach is to use statistical tests to select an optimal suite of conservative properties capable of modelling sediment provenance. In general, statistical approaches use a combination of a discrimination (e.g. Kruskal Wallis H-test, Mann-Whitney U-test) and parameter selection statistics (e.g. Discriminant Function Analysis or Principle Component Analysis). The challenge is that modelling sediment provenance is often not straightforward and there is increasing debate in the literature surrounding the most appropriate approach to selecting elements for modelling. Moving forward, it would be beneficial if researchers test their results with multiple modelling approaches, artificial mixtures, and multiple

Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models.

PubMed

Ajelli, Marco; Gonçalves, Bruno; Balcan, Duygu; Colizza, Vittoria; Hu, Hao; Ramasco, José J; Merler, Stefano; Vespignani, Alessandro

2010-06-29

In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used. We provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels. The results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, R0, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are

Modeling Negotiation by a Paticipatory Approach

NASA Astrophysics Data System (ADS)

Torii, Daisuke; Ishida, Toru; Bousquet, François

In a participatory approach by social scientists, role playing games (RPG) are effectively used to understand real thinking and behavior of stakeholders, but RPG is not sufficient to handle a dynamic process like negotiation. In this study, a participatory simulation where user-controlled avatars and autonomous agents coexist is introduced to the participatory approach for modeling negotiation. To establish a modeling methodology of negotiation, we have tackled the following two issues. First, for enabling domain experts to concentrate interaction design for participatory simulation, we have adopted the architecture in which an interaction layer controls agents and have defined three types of interaction descriptions (interaction protocol, interaction scenario and avatar control scenario) to be described. Second, for enabling domain experts and stakeholders to capitalize on participatory simulation, we have established a four-step process for acquiring negotiation model: 1) surveys and interviews to stakeholders, 2) RPG, 3) interaction design, and 4) participatory simulation. Finally, we discussed our methodology through a case study of agricultural economics in the northeast Thailand.

Development of a Conservative Model Validation Approach for Reliable Analysis

DTIC Science & Technology

2015-01-01

CIE 2015 August 2-5, 2015, Boston, Massachusetts, USA [DRAFT] DETC2015-46982 DEVELOPMENT OF A CONSERVATIVE MODEL VALIDATION APPROACH FOR RELIABLE...obtain a conservative simulation model for reliable design even with limited experimental data. Very little research has taken into account the...3, the proposed conservative model validation is briefly compared to the conventional model validation approach. Section 4 describes how to account

A multilevel approach to modeling of porous bioceramics

NASA Astrophysics Data System (ADS)

Mikushina, Valentina A.; Sidorenko, Yury N.

2015-10-01

The paper is devoted to discussion of multiscale models of heterogeneous materials using principles. The specificity of approach considered is the using of geometrical model of composites representative volume, which must be generated with taking the materials reinforcement structure into account. In framework of such model may be considered different physical processes which have influence on the effective mechanical properties of composite, in particular, the process of damage accumulation. It is shown that such approach can be used to prediction the value of composite macroscopic ultimate strength. As an example discussed the particular problem of the study the mechanical properties of biocomposite representing porous ceramics matrix filled with cortical bones tissue.

A robust quantitative near infrared modeling approach for blend monitoring.

PubMed

Mohan, Shikhar; Momose, Wataru; Katz, Jeffrey M; Hossain, Md Nayeem; Velez, Natasha; Drennen, James K; Anderson, Carl A

2018-01-30

This study demonstrates a material sparing Near-Infrared modeling approach for powder blend monitoring. In this new approach, gram scale powder mixtures are subjected to compression loads to simulate the effect of scale using an Instron universal testing system. Models prepared by the new method development approach (small-scale method) and by a traditional method development (blender-scale method) were compared by simultaneously monitoring a 1kg batch size blend run. Both models demonstrated similar model performance. The small-scale method strategy significantly reduces the total resources expended to develop Near-Infrared calibration models for on-line blend monitoring. Further, this development approach does not require the actual equipment (i.e., blender) to which the method will be applied, only a similar optical interface. Thus, a robust on-line blend monitoring method can be fully developed before any large-scale blending experiment is viable, allowing the blend method to be used during scale-up and blend development trials. Copyright © 2017. Published by Elsevier B.V.

Popularity Modeling for Mobile Apps: A Sequential Approach.

PubMed

Zhu, Hengshu; Liu, Chuanren; Ge, Yong; Xiong, Hui; Chen, Enhong

2015-07-01

The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with mobile Apps, learn the process of adoption of mobile Apps, and thus enables better mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of mobile Apps toward mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Geo-referenced multimedia environmental fate model (G-CIEMS): model formulation and comparison to the generic model and monitoring approaches.

PubMed

Suzuki, Noriyuki; Murasawa, Kaori; Sakurai, Takeo; Nansai, Keisuke; Matsuhashi, Keisuke; Moriguchi, Yuichi; Tanabe, Kiyoshi; Nakasugi, Osami; Morita, Masatoshi

2004-11-01

A spatially resolved and geo-referenced dynamic multimedia environmental fate model, G-CIEMS (Grid-Catchment Integrated Environmental Modeling System) was developed on a geographical information system (GIS). The case study for Japan based on the air grid cells of 5 x 5 km resolution and catchments with an average area of 9.3 km2, which corresponds to about 40,000 air grid cells and 38,000 river segments/catchment polygons, were performed for dioxins, benzene, 1,3-butadiene, and di-(2-ethyhexyl)phthalate. The averaged concentration of the model and monitoring output were within a factor of 2-3 for all the media. Outputs from G-CIEMS and the generic model were essentially comparable when identical parameters were employed, whereas the G-CIEMS model gave explicit information of distribution of chemicals in the environment. Exposure-weighted averaged concentrations (EWAC) in air were calculated to estimate the exposure ofthe population, based on the results of generic, G-CIEMS, and monitoring approaches. The G-CIEMS approach showed significantly better agreement with the monitoring-derived EWAC than the generic model approach. Implication for the use of a geo-referenced modeling approach in the risk assessment scheme is discussed as a generic-spatial approach, which can be used to provide more accurate exposure estimation with distribution information, using generally available data sources for a wide range of chemicals.

A hybrid modelling approach for predicting ground vibration from trains

NASA Astrophysics Data System (ADS)

Triepaischajonsak, N.; Thompson, D. J.

2015-01-01

The prediction of ground vibration from trains presents a number of difficulties. The ground is effectively an infinite medium, often with a layered structure and with properties that may vary greatly from one location to another. The vibration from a passing train forms a transient event, which limits the usefulness of steady-state frequency domain models. Moreover, there is often a need to consider vehicle/track interaction in more detail than is commonly used in frequency domain models, such as the 2.5D approach, while maintaining the computational efficiency of the latter. However, full time-domain approaches involve large computation times, particularly where three-dimensional ground models are required. Here, a hybrid modelling approach is introduced. The vehicle/track interaction is calculated in the time domain in order to be able t account directly for effects such as the discrete sleeper spacing. Forces acting on the ground are extracted from this first model and used in a second model to predict the ground response at arbitrary locations. In the present case the second model is a layered ground model operating in the frequency domain. Validation of the approach is provided by comparison with an existing frequency domain model. The hybrid model is then used to study the sleeper-passing effect, which is shown to be less significant than excitation due to track unevenness in all the cases considered.

A Model-Driven Approach to Teaching Concurrency

ERIC Educational Resources Information Center

Carro, Manuel; Herranz, Angel; Marino, Julio

2013-01-01

We present an undergraduate course on concurrent programming where formal models are used in different stages of the learning process. The main practical difference with other approaches lies in the fact that the ability to develop correct concurrent software relies on a systematic transformation of formal models of inter-process interaction (so…

Post-16 Biology--Some Model Approaches?

ERIC Educational Resources Information Center

Lock, Roger

1997-01-01

Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)

Dynamics and control of quadcopter using linear model predictive control approach

NASA Astrophysics Data System (ADS)

Islam, M.; Okasha, M.; Idres, M. M.

2017-12-01

This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.

Flexible Approximation Model Approach for Bi-Level Integrated System Synthesis

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Kim, Hongman; Ragon, Scott; Soremekun, Grant; Malone, Brett

2004-01-01

Bi-Level Integrated System Synthesis (BLISS) is an approach that allows design problems to be naturally decomposed into a set of subsystem optimizations and a single system optimization. In the BLISS approach, approximate mathematical models are used to transfer information from the subsystem optimizations to the system optimization. Accurate approximation models are therefore critical to the success of the BLISS procedure. In this paper, new capabilities that are being developed to generate accurate approximation models for BLISS procedure will be described. The benefits of using flexible approximation models such as Kriging will be demonstrated in terms of convergence characteristics and computational cost. An approach of dealing with cases where subsystem optimization cannot find a feasible design will be investigated by using the new flexible approximation models for the violated local constraints.

Model-centric approaches for the development of health information systems.

PubMed

Tuomainen, Mika; Mykkänen, Juha; Luostarinen, Heli; Pöyhölä, Assi; Paakkanen, Esa

2007-01-01

Modeling is used increasingly in healthcare to increase shared knowledge, to improve the processes, and to document the requirements of the solutions related to health information systems (HIS). There are numerous modeling approaches which aim to support these aims, but a careful assessment of their strengths, weaknesses and deficiencies is needed. In this paper, we compare three model-centric approaches in the context of HIS development: the Model-Driven Architecture, Business Process Modeling with BPMN and BPEL and the HL7 Development Framework. The comparison reveals that all these approaches are viable candidates for the development of HIS. However, they have distinct strengths and abstraction levels, they require local and project-specific adaptation and offer varying levels of automation. In addition, illustration of the solutions to the end users must be improved.

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

NASA Astrophysics Data System (ADS)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

The Person Approach: Concepts, Measurement Models, and Research Strategy

ERIC Educational Resources Information Center

Magnusson, David

2003-01-01

This chapter discusses the "person approach" to studying developmental processes by focusing on the distinction and complementarity between this holistic-interactionistic framework and what has become designated as the variable approach. Particular attention is given to measurement models for use in the person approach. The discussion on the…

A Comparison of Three Approaches to Model Human Behavior

NASA Astrophysics Data System (ADS)

Palmius, Joel; Persson-Slumpi, Thomas

2010-11-01

One way of studying social processes is through the use of simulations. The use of simulations for this purpose has been established as its own field, social simulations, and has been used for studying a variety of phenomena. A simulation of a social setting can serve as an aid for thinking about that social setting, and for experimenting with different parameters and studying the outcomes caused by them. When using the simulation as an aid for thinking and experimenting, the chosen simulation approach will implicitly steer the simulationist towards thinking in a certain fashion in order to fit the model. To study the implications of model choice on the understanding of a setting where human anticipation comes into play, a simulation scenario of a coffee room was constructed using three different simulation approaches: Cellular Automata, Systems Dynamics and Agent-based modeling. The practical implementations of the models were done in three different simulation packages: Stella for Systems Dynamic, CaFun for Cellular automata and SesAM for Agent-based modeling. The models were evaluated both using Randers' criteria for model evaluation, and through introspection where the authors reflected upon how their understanding of the scenario was steered through the model choice. Further the software used for implementing the simulation models was evaluated, and practical considerations for the choice of software package are listed. It is concluded that the models have very different strengths. The Agent-based modeling approach offers the most intuitive support for thinking about and modeling a social setting where the behavior of the individual is in focus. The Systems Dynamics model would be preferable in situations where populations and large groups would be studied as wholes, but where individual behavior is of less concern. The Cellular Automata models would be preferable where processes need to be studied from the basis of a small set of very simple rules. It is

An Estimating Equations Approach for the LISCOMP Model.

ERIC Educational Resources Information Center

Reboussin, Beth A.; Liang, Kung-Lee

1998-01-01

A quadratic estimating equations approach for the LISCOMP model is proposed that only requires specification of the first two moments. This method is compared with a three-stage generalized least squares approach through a numerical study and application to a study of life events and neurotic illness. (SLD)

COMPREHENSIVE PBPK MODELING APPROACH USING THE EXPOSURE RELATED DOSE ESTIMATING MODEL (ERDEM)

EPA Science Inventory

ERDEM, a complex PBPK modeling system, is the result of the implementation of a comprehensive PBPK modeling approach. ERDEM provides a scalable and user-friendly environment that enables researchers to focus on data input values rather than writing program code. It efficiently ...

A vector space model approach to identify genetically related diseases.

PubMed

Sarkar, Indra Neil

2012-01-01

The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models. A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome. In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration. This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature. The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.

A model-based reasoning approach to sensor placement for monitorability

NASA Technical Reports Server (NTRS)

Chien, Steve; Doyle, Richard; Homemdemello, Luiz

1992-01-01

An approach is presented to evaluating sensor placements to maximize monitorability of the target system while minimizing the number of sensors. The approach uses a model of the monitored system to score potential sensor placements on the basis of four monitorability criteria. The scores can then be analyzed to produce a recommended sensor set. An example from our NASA application domain is used to illustrate our model-based approach to sensor placement.

A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling

USGS Publications Warehouse

Leavesley, G.H.; Markstrom, S.L.; Restrepo, Pedro J.; Viger, R.J.

2002-01-01

A modular approach to model design and construction provides a flexible framework in which to focus the multidisciplinary research and operational efforts needed to facilitate the development, selection, and application of the most robust distributed modelling methods. A variety of modular approaches have been developed, but with little consideration for compatibility among systems and concepts. Several systems are proprietary, limiting any user interaction. The US Geological Survey modular modelling system (MMS) is a modular modelling framework that uses an open source software approach to enable all members of the scientific community to address collaboratively the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. Implementation of a common modular concept is not a trivial task. However, it brings the resources of a larger community to bear on the problems of distributed modelling, provides a framework in which to compare alternative modelling approaches objectively, and provides a means of sharing the latest modelling advances. The concepts and components of the MMS are described and an example application of the MMS, in a decision-support system context, is presented to demonstrate current system capabilities. Copyright ?? 2002 John Wiley and Sons, Ltd.

An approach for modeling sediment budgets in supply-limited rivers

USGS Publications Warehouse

Wright, Scott A.; Topping, David J.; Rubin, David M.; Melis, Theodore S.

2010-01-01

Reliable predictions of sediment transport and river morphology in response to variations in natural and human-induced drivers are necessary for river engineering and management. Because engineering and management applications may span a wide range of space and time scales, a broad spectrum of modeling approaches has been developed, ranging from suspended-sediment "rating curves" to complex three-dimensional morphodynamic models. Suspended sediment rating curves are an attractive approach for evaluating changes in multi-year sediment budgets resulting from changes in flow regimes because they are simple to implement, computationally efficient, and the empirical parameters can be estimated from quantities that are commonly measured in the field (i.e., suspended sediment concentration and water discharge). However, the standard rating curve approach assumes a unique suspended sediment concentration for a given water discharge. This assumption is not valid in rivers where sediment supply varies enough to cause changes in particle size or changes in areal coverage of sediment on the bed; both of these changes cause variations in suspended sediment concentration for a given water discharge. More complex numerical models of hydraulics and morphodynamics have been developed to address such physical changes of the bed. This additional complexity comes at a cost in terms of computations as well as the type and amount of data required for model setup, calibration, and testing. Moreover, application of the resulting sediment-transport models may require observations of bed-sediment boundary conditions that require extensive (and expensive) observations or, alternatively, require the use of an additional model (subject to its own errors) merely to predict the bed-sediment boundary conditions for use by the transport model. In this paper we present a hybrid approach that combines aspects of the rating curve method and the more complex morphodynamic models. Our primary objective

Hypercompetitive Environments: An Agent-based model approach

NASA Astrophysics Data System (ADS)

Dias, Manuel; Araújo, Tanya

Information technology (IT) environments are characterized by complex changes and rapid evolution. Globalization and the spread of technological innovation have increased the need for new strategic information resources, both from individual firms and management environments. Improvements in multidisciplinary methods and, particularly, the availability of powerful computational tools, are giving researchers an increasing opportunity to investigate management environments in their true complex nature. The adoption of a complex systems approach allows for modeling business strategies from a bottom-up perspective — understood as resulting from repeated and local interaction of economic agents — without disregarding the consequences of the business strategies themselves to individual behavior of enterprises, emergence of interaction patterns between firms and management environments. Agent-based models are at the leading approach of this attempt.

Development of a category 2 approach system model

NASA Technical Reports Server (NTRS)

Johnson, W. A.; Mcruer, D. T.

1972-01-01

An analytical model is presented which provides, as its primary output, the probability of a successful Category II approach. Typical applications are included using several example systems (manual and automatic) which are subjected to random gusts and deterministic wind shear. The primary purpose of the approach system model is to establish a structure containing the system elements, command inputs, disturbances, and their interactions in an analytical framework so that the relative effects of changes in the various system elements on precision of control and available margins of safety can be estimated. The model is intended to provide insight for the design and integration of suitable autopilot, display, and navigation elements; and to assess the interaction of such elements with the pilot/copilot.

Numeric, Agent-based or System Dynamics Model? Which Modeling Approach is the Best for Vast Population Simulation?

PubMed

Cimler, Richard; Tomaskova, Hana; Kuhnova, Jitka; Dolezal, Ondrej; Pscheidl, Pavel; Kuca, Kamil

2018-01-01

Alzheimer's disease is one of the most common mental illnesses. It is posited that more than 25% of the population is affected by some mental disease during their lifetime. Treatment of each patient draws resources from the economy concerned. Therefore, it is important to quantify the potential economic impact. Agent-based, system dynamics and numerical approaches to dynamic modeling of the population of the European Union and its patients with Alzheimer's disease are presented in this article. Simulations, their characteristics, and the results from different modeling tools are compared. The results of these approaches are compared with EU population growth predictions from the statistical office of the EU by Eurostat. The methodology of a creation of the models is described and all three modeling approaches are compared. The suitability of each modeling approach for the population modeling is discussed. In this case study, all three approaches gave us the results corresponding with the EU population prediction. Moreover, we were able to predict the number of patients with AD and, based on the modeling method, we were also able to monitor different characteristics of the population. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Expeditionary Learning Approach in Integrated Teacher Education: Model Effectiveness and Dilemma.

ERIC Educational Resources Information Center

Hyun, Eunsook

This paper introduces an integrated teacher education model based on the Expeditionary Learning Outward Bound Project model. It integrates early childhood, elementary, and special education and uses inquiry-oriented and social constructive approaches. It models a team approach, with all teachers unified in their mutually shared philosophy of…

Meta-Modeling: A Knowledge-Based Approach to Facilitating Model Construction and Reuse

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Dungan, Jennifer L.

1997-01-01

In this paper, we introduce a new modeling approach called meta-modeling and illustrate its practical applicability to the construction of physically-based ecosystem process models. As a critical adjunct to modeling codes meta-modeling requires explicit specification of certain background information related to the construction and conceptual underpinnings of a model. This information formalizes the heretofore tacit relationship between the mathematical modeling code and the underlying real-world phenomena being investigated, and gives insight into the process by which the model was constructed. We show how the explicit availability of such information can make models more understandable and reusable and less subject to misinterpretation. In particular, background information enables potential users to better interpret an implemented ecosystem model without direct assistance from the model author. Additionally, we show how the discipline involved in specifying background information leads to improved management of model complexity and fewer implementation errors. We illustrate the meta-modeling approach in the context of the Scientists' Intelligent Graphical Modeling Assistant (SIGMA) a new model construction environment. As the user constructs a model using SIGMA the system adds appropriate background information that ties the executable model to the underlying physical phenomena under investigation. Not only does this information improve the understandability of the final model it also serves to reduce the overall time and programming expertise necessary to initially build and subsequently modify models. Furthermore, SIGMA's use of background knowledge helps eliminate coding errors resulting from scientific and dimensional inconsistencies that are otherwise difficult to avoid when building complex models. As a. demonstration of SIGMA's utility, the system was used to reimplement and extend a well-known forest ecosystem dynamics model: Forest-BGC.

Simple Heuristic Approach to Introduction of the Black-Scholes Model

ERIC Educational Resources Information Center

Yalamova, Rossitsa

2010-01-01

A heuristic approach to explaining of the Black-Scholes option pricing model in undergraduate classes is described. The approach draws upon the method of protocol analysis to encourage students to "think aloud" so that their mental models can be surfaced. It also relies upon extensive visualizations to communicate relationships that are…

SAM Works! A Systems Approach Model for Adult Education Programming.

ERIC Educational Resources Information Center

Murk, Peter J.; Wells, John H.

The Systems Approach Model (SAM) is a dynamic approach to planning adult and continuing education that is intended to provide the flexibility, creativity, and meaningfulness necessary to meet the needs and interests of an ever-expanding and ever-aging student population. The SAM model consists of the following dynamically interrelated and…

The standard data model approach to patient record transfer.

PubMed Central

Canfield, K.; Silva, M.; Petrucci, K.

1994-01-01

This paper develops an approach to electronic data exchange of patient records from Ambulatory Encounter Systems (AESs). This approach assumes that the AES is based upon a standard data model. The data modeling standard used here is IDEFIX for Entity/Relationship (E/R) modeling. Each site that uses a relational database implementation of this standard data model (or a subset of it) can exchange very detailed patient data with other such sites using industry standard tools and without excessive programming efforts. This design is detailed below for a demonstration project between the research-oriented geriatric clinic at the Baltimore Veterans Affairs Medical Center (BVAMC) and the Laboratory for Healthcare Informatics (LHI) at the University of Maryland. PMID:7949973

Adapting a Framework for Assessing Students' Approaches to Modeling

ERIC Educational Resources Information Center

Bennett, Steven Carl

2017-01-01

We used an "approach to learning" theoretical framework to explicate the ways students engage in scientific modeling. Approach to learning theory suggests that when students approach learning deeply, they link science concepts with prior knowledge and experiences. Conversely, when students engage in a surface approach to learning, they…

Learning the Task Management Space of an Aircraft Approach Model

NASA Technical Reports Server (NTRS)

Krall, Joseph; Menzies, Tim; Davies, Misty

2014-01-01

Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

Receiving water quality assessment: comparison between simplified and detailed integrated urban modelling approaches.

PubMed

Mannina, Giorgio; Viviani, Gaspare

2010-01-01

Urban water quality management often requires use of numerical models allowing the evaluation of the cause-effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches

Optimizing technology investments: a broad mission model approach

NASA Technical Reports Server (NTRS)

Shishko, R.

2003-01-01

A long-standing problem in NASA is how to allocate scarce technology development resources across advanced technologies in order to best support a large set of future potential missions. Within NASA, two orthogonal paradigms have received attention in recent years: the real-options approach and the broad mission model approach. This paper focuses on the latter.

Modelling household finances: A Bayesian approach to a multivariate two-part model

PubMed Central

Brown, Sarah; Ghosh, Pulak; Su, Li; Taylor, Karl

2016-01-01

We contribute to the empirical literature on household finances by introducing a Bayesian multivariate two-part model, which has been developed to further our understanding of household finances. Our flexible approach allows for the potential interdependence between the holding of assets and liabilities at the household level and also encompasses a two-part process to allow for differences in the influences on asset or liability holding and on the respective amounts held. Furthermore, the framework is dynamic in order to allow for persistence in household finances over time. Our findings endorse the joint modelling approach and provide evidence supporting the importance of dynamics. In addition, we find that certain independent variables exert different influences on the binary and continuous parts of the model thereby highlighting the flexibility of our framework and revealing a detailed picture of the nature of household finances. PMID:27212801

Towards a whole-cell modeling approach for synthetic biology

NASA Astrophysics Data System (ADS)

Purcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.

2013-06-01

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Putting the psychology back into psychological models: mechanistic versus rational approaches.

PubMed

Sakamoto, Yasuaki; Jones, Mattr; Love, Bradley C

2008-09-01

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.

A novel approach of modeling continuous dark hydrogen fermentation.

PubMed

Alexandropoulou, Maria; Antonopoulou, Georgia; Lyberatos, Gerasimos

2018-02-01

In this study a novel modeling approach for describing fermentative hydrogen production in a continuous stirred tank reactor (CSTR) was developed, using the Aquasim modeling platform. This model accounts for the key metabolic reactions taking place in a fermentative hydrogen producing reactor, using fixed stoichiometry but different reaction rates. Biomass yields are determined based on bioenergetics. The model is capable of describing very well the variation in the distribution of metabolic products for a wide range of hydraulic retention times (HRT). The modeling approach is demonstrated using the experimental data obtained from a CSTR, fed with food industry waste (FIW), operating at different HRTs. The kinetic parameters were estimated through fitting to the experimental results. Hydrogen and total biogas production rates were predicted very well by the model, validating the basic assumptions regarding the implicated stoichiometric biochemical reactions and their kinetic rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Porous Media Approach for Modeling Closed Cell Foam

NASA Technical Reports Server (NTRS)

Ghosn, Louis J.; Sullivan, Roy M.

2006-01-01

In order to minimize boil off of the liquid oxygen and liquid hydrogen and to prevent the formation of ice on its exterior surface, the Space Shuttle External Tank (ET) is insulated using various low-density, closed-cell polymeric foams. Improved analysis methods for these foam materials are needed to predict the foam structural response and to help identify the foam fracture behavior in order to help minimize foam shedding occurrences. This presentation describes a continuum based approach to modeling the foam thermo-mechanical behavior that accounts for the cellular nature of the material and explicitly addresses the effect of the internal cell gas pressure. A porous media approach is implemented in a finite element frame work to model the mechanical behavior of the closed cell foam. The ABAQUS general purpose finite element program is used to simulate the continuum behavior of the foam. The soil mechanics element is implemented to account for the cell internal pressure and its effect on the stress and strain fields. The pressure variation inside the closed cells is calculated using the ideal gas laws. The soil mechanics element is compatible with an orthotropic materials model to capture the different behavior between the rise and in-plane directions of the foam. The porous media approach is applied to model the foam thermal strain and calculate the foam effective coefficient of thermal expansion. The calculated foam coefficients of thermal expansion were able to simulate the measured thermal strain during heat up from cryogenic temperature to room temperature in vacuum. The porous media approach was applied to an insulated substrate with one inch foam and compared to a simple elastic solution without pore pressure. The porous media approach is also applied to model the foam mechanical behavior during subscale laboratory experiments. In this test, a foam layer sprayed on a metal substrate is subjected to a temperature variation while the metal substrate is

An interdisciplinary approach for earthquake modelling and forecasting

NASA Astrophysics Data System (ADS)

Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

2016-12-01

Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

A fuzzy logic approach to modeling a vehicle crash test

NASA Astrophysics Data System (ADS)

Pawlus, Witold; Karimi, Hamid Reza; Robbersmyr, Kjell G.

2013-03-01

This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle's kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model's output and how they can be adjusted to improve the model's fidelity.

BioModels: expanding horizons to include more modelling approaches and formats

PubMed Central

Nguyen, Tung V N; Graesslin, Martin; Hälke, Robert; Ali, Raza; Schramm, Jochen; Wimalaratne, Sarala M; Kothamachu, Varun B; Rodriguez, Nicolas; Swat, Maciej J; Eils, Jurgen; Eils, Roland; Laibe, Camille; Chelliah, Vijayalakshmi

2018-01-01

Abstract BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing. PMID:29106614

A screening-level modeling approach to estimate nitrogen ...

EPA Pesticide Factsheets

This paper presents a screening-level modeling approach that can be used to rapidly estimate nutrient loading and assess numerical nutrient standard exceedance risk of surface waters leading to potential classification as impaired for designated use. It can also be used to explore best management practice (BMP) implementation to reduce loading. The modeling framework uses a hybrid statistical and process based approach to estimate source of pollutants, their transport and decay in the terrestrial and aquatic parts of watersheds. The framework is developed in the ArcGIS environment and is based on the total maximum daily load (TMDL) balance model. Nitrogen (N) is currently addressed in the framework, referred to as WQM-TMDL-N. Loading for each catchment includes non-point sources (NPS) and point sources (PS). NPS loading is estimated using export coefficient or event mean concentration methods depending on the temporal scales, i.e., annual or daily. Loading from atmospheric deposition is also included. The probability of a nutrient load to exceed a target load is evaluated using probabilistic risk assessment, by including the uncertainty associated with export coefficients of various land uses. The computed risk data can be visualized as spatial maps which show the load exceedance probability for all stream segments. In an application of this modeling approach to the Tippecanoe River watershed in Indiana, USA, total nitrogen (TN) loading and risk of standard exce

A validated approach for modeling collapse of steel structures

NASA Astrophysics Data System (ADS)

Saykin, Vitaliy Victorovich

A civil engineering structure is faced with many hazardous conditions such as blasts, earthquakes, hurricanes, tornadoes, floods, and fires during its lifetime. Even though structures are designed for credible events that can happen during a lifetime of the structure, extreme events do happen and cause catastrophic failures. Understanding the causes and effects of structural collapse is now at the core of critical areas of national need. One factor that makes studying structural collapse difficult is the lack of full-scale structural collapse experimental test results against which researchers could validate their proposed collapse modeling approaches. The goal of this work is the creation of an element deletion strategy based on fracture models for use in validated prediction of collapse of steel structures. The current work reviews the state-of-the-art of finite element deletion strategies for use in collapse modeling of structures. It is shown that current approaches to element deletion in collapse modeling do not take into account stress triaxiality in vulnerable areas of the structure, which is important for proper fracture and element deletion modeling. The report then reviews triaxiality and its role in fracture prediction. It is shown that fracture in ductile materials is a function of triaxiality. It is also shown that, depending on the triaxiality range, different fracture mechanisms are active and should be accounted for. An approach using semi-empirical fracture models as a function of triaxiality are employed. The models to determine fracture initiation, softening and subsequent finite element deletion are outlined. This procedure allows for stress-displacement softening at an integration point of a finite element in order to subsequently remove the element. This approach avoids abrupt changes in the stress that would create dynamic instabilities, thus making the results more reliable and accurate. The calibration and validation of these models are

Unraveling the Mechanisms of Manual Therapy: Modeling an Approach.

PubMed

Bialosky, Joel E; Beneciuk, Jason M; Bishop, Mark D; Coronado, Rogelio A; Penza, Charles W; Simon, Corey B; George, Steven Z

2018-01-01

Synopsis Manual therapy interventions are popular among individual health care providers and their patients; however, systematic reviews do not strongly support their effectiveness. Small treatment effect sizes of manual therapy interventions may result from a "one-size-fits-all" approach to treatment. Mechanistic-based treatment approaches to manual therapy offer an intriguing alternative for identifying patients likely to respond to manual therapy. However, the current lack of knowledge of the mechanisms through which manual therapy interventions inhibit pain limits such an approach. The nature of manual therapy interventions further confounds such an approach, as the related mechanisms are likely a complex interaction of factors related to the patient, the provider, and the environment in which the intervention occurs. Therefore, a model to guide both study design and the interpretation of findings is necessary. We have previously proposed a model suggesting that the mechanical force from a manual therapy intervention results in systemic neurophysiological responses leading to pain inhibition. In this clinical commentary, we provide a narrative appraisal of the model and recommendations to advance the study of manual therapy mechanisms. J Orthop Sports Phys Ther 2018;48(1):8-18. doi:10.2519/jospt.2018.7476.

A fully probabilistic approach to extreme rainfall modeling

NASA Astrophysics Data System (ADS)

Coles, Stuart; Pericchi, Luis Raúl; Sisson, Scott

2003-03-01

It is an embarrassingly frequent experience that statistical practice fails to foresee historical disasters. It is all too easy to blame global trends or some sort of external intervention, but in this article we argue that statistical methods that do not take comprehensive account of the uncertainties involved in both model and predictions, are bound to produce an over-optimistic appraisal of future extremes that is often contradicted by observed hydrological events. Based on the annual and daily rainfall data on the central coast of Venezuela, different modeling strategies and inference approaches show that the 1999 rainfall which caused the worst environmentally related tragedy in Venezuelan history was extreme, but not implausible given the historical evidence. We follow in turn a classical likelihood and Bayesian approach, arguing that the latter is the most natural approach for taking into account all uncertainties. In each case we emphasize the importance of making inference on predicted levels of the process rather than model parameters. Our most detailed model comprises of seasons with unknown starting points and durations for the extremes of daily rainfall whose behavior is described using a standard threshold model. Based on a Bayesian analysis of this model, so that both prediction uncertainty and process heterogeneity are properly modeled, we find that the 1999 event has a sizeable probability which implies that such an occurrence within a reasonably short time horizon could have been anticipated. Finally, since accumulation of extreme rainfall over several days is an additional difficulty—and indeed, the catastrophe of 1999 was exaggerated by heavy rainfall on successive days—we examine the effect of timescale on our broad conclusions, finding results to be broadly similar across different choices.

A Model-Driven Approach to e-Course Management

ERIC Educational Resources Information Center

Savic, Goran; Segedinac, Milan; Milenkovic, Dušica; Hrin, Tamara; Segedinac, Mirjana

2018-01-01

This paper presents research on using a model-driven approach to the development and management of electronic courses. We propose a course management system which stores a course model represented as distinct machine-readable components containing domain knowledge of different course aspects. Based on this formally defined platform-independent…

Research in Distance Education: A System Modeling Approach.

ERIC Educational Resources Information Center

Saba, Farhad; Twitchell, David

1988-01-01

Describes how a computer simulation research method can be used for studying distance education systems. Topics discussed include systems research in distance education; a technique of model development using the System Dynamics approach and DYNAMO simulation language; and a computer simulation of a prototype model. (18 references) (LRW)

Datamining approaches for modeling tumor control probability.

PubMed

Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

2010-11-01

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Fast Geometric Consensus Approach for Protein Model Quality Assessment

PubMed Central

Adamczak, Rafal; Pillardy, Jaroslaw; Vallat, Brinda K.

2011-01-01

Abstract Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well. PMID:21244273

Designing water demand management schemes using a socio-technical modelling approach.

PubMed

Baki, Sotiria; Rozos, Evangelos; Makropoulos, Christos

2018-05-01

Although it is now widely acknowledged that urban water systems (UWSs) are complex socio-technical systems and that a shift towards a socio-technical approach is critical in achieving sustainable urban water management, still, more often than not, UWSs are designed using a segmented modelling approach. As such, either the analysis focuses on the description of the purely technical sub-system, without explicitly taking into account the system's dynamic socio-economic processes, or a more interdisciplinary approach is followed, but delivered through relatively coarse models, which often fail to provide a thorough representation of the urban water cycle and hence cannot deliver accurate estimations of the hydrosystem's responses. In this work we propose an integrated modelling approach for the study of the complete socio-technical UWS that also takes into account socio-economic and climatic variability. We have developed an integrated model, which is used to investigate the diffusion of household water conservation technologies and its effects on the UWS, under different socio-economic and climatic scenarios. The integrated model is formed by coupling a System Dynamics model that simulates the water technology adoption process, and the Urban Water Optioneering Tool (UWOT) for the detailed simulation of the urban water cycle. The model and approach are tested and demonstrated in an urban redevelopment area in Athens, Greece under different socio-economic scenarios and policy interventions. It is suggested that the proposed approach can establish quantifiable links between socio-economic change and UWS responses and therefore assist decision makers in designing more effective and resilient long-term strategies for water conservation. Copyright © 2017 Elsevier B.V. All rights reserved.

A Constructive Neural-Network Approach to Modeling Psychological Development

ERIC Educational Resources Information Center

Shultz, Thomas R.

2012-01-01

This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

Estimating, Testing, and Comparing Specific Effects in Structural Equation Models: The Phantom Model Approach

ERIC Educational Resources Information Center

Macho, Siegfried; Ledermann, Thomas

2011-01-01

The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main…

An approach to multiscale modelling with graph grammars.

PubMed

Ong, Yongzhi; Streit, Katarína; Henke, Michael; Kurth, Winfried

2014-09-01

Functional-structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.

Continuity-based model interfacing for plant-wide simulation: a general approach.

PubMed

Volcke, Eveline I P; van Loosdrecht, Mark C M; Vanrolleghem, Peter A

2006-08-01

In plant-wide simulation studies of wastewater treatment facilities, often existing models from different origin need to be coupled. However, as these submodels are likely to contain different state variables, their coupling is not straightforward. The continuity-based interfacing method (CBIM) provides a general framework to construct model interfaces for models of wastewater systems, taking into account conservation principles. In this contribution, the CBIM approach is applied to study the effect of sludge digestion reject water treatment with a SHARON-Anammox process on a plant-wide scale. Separate models were available for the SHARON process and for the Anammox process. The Benchmark simulation model no. 2 (BSM2) is used to simulate the behaviour of the complete WWTP including sludge digestion. The CBIM approach is followed to develop three different model interfaces. At the same time, the generally applicable CBIM approach was further refined and particular issues when coupling models in which pH is considered as a state variable, are pointed out.

Matrix approach to land carbon cycle modeling: A case study with the Community Land Model.

PubMed

Huang, Yuanyuan; Lu, Xingjie; Shi, Zheng; Lawrence, David; Koven, Charles D; Xia, Jianyang; Du, Zhenggang; Kluzek, Erik; Luo, Yiqi

2018-03-01

The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO 2 fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective. © 2017 John Wiley & Sons Ltd.

Exponential series approaches for nonparametric graphical models

NASA Astrophysics Data System (ADS)

Janofsky, Eric

Markov Random Fields (MRFs) or undirected graphical models are parsimonious representations of joint probability distributions. This thesis studies high-dimensional, continuous-valued pairwise Markov Random Fields. We are particularly interested in approximating pairwise densities whose logarithm belongs to a Sobolev space. For this problem we propose the method of exponential series which approximates the log density by a finite-dimensional exponential family with the number of sufficient statistics increasing with the sample size. We consider two approaches to estimating these models. The first is regularized maximum likelihood. This involves optimizing the sum of the log-likelihood of the data and a sparsity-inducing regularizer. We then propose a variational approximation to the likelihood based on tree-reweighted, nonparametric message passing. This approximation allows for upper bounds on risk estimates, leverages parallelization and is scalable to densities on hundreds of nodes. We show how the regularized variational MLE may be estimated using a proximal gradient algorithm. We then consider estimation using regularized score matching. This approach uses an alternative scoring rule to the log-likelihood, which obviates the need to compute the normalizing constant of the distribution. For general continuous-valued exponential families, we provide parameter and edge consistency results. As a special case we detail a new approach to sparse precision matrix estimation which has statistical performance competitive with the graphical lasso and computational performance competitive with the state-of-the-art glasso algorithm. We then describe results for model selection in the nonparametric pairwise model using exponential series. The regularized score matching problem is shown to be a convex program; we provide scalable algorithms based on consensus alternating direction method of multipliers (ADMM) and coordinate-wise descent. We use simulations to compare our

A computational language approach to modeling prose recall in schizophrenia

PubMed Central

Rosenstein, Mark; Diaz-Asper, Catherine; Foltz, Peter W.; Elvevåg, Brita

2014-01-01

Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall. PMID:24709122

Post-closure biosphere assessment modelling: comparison of complex and more stylised approaches.

PubMed

Walke, Russell C; Kirchner, Gerald; Xu, Shulan; Dverstorp, Björn

2015-10-01

Geological disposal facilities are the preferred option for high-level radioactive waste, due to their potential to provide isolation from the surface environment (biosphere) on very long timescales. Assessments need to strike a balance between stylised models and more complex approaches that draw more extensively on site-specific information. This paper explores the relative merits of complex versus more stylised biosphere models in the context of a site-specific assessment. The more complex biosphere modelling approach was developed by the Swedish Nuclear Fuel and Waste Management Co (SKB) for the Formark candidate site for a spent nuclear fuel repository in Sweden. SKB's approach is built on a landscape development model, whereby radionuclide releases to distinct hydrological basins/sub-catchments (termed 'objects') are represented as they evolve through land rise and climate change. Each of seventeen of these objects is represented with more than 80 site specific parameters, with about 22 that are time-dependent and result in over 5000 input values per object. The more stylised biosphere models developed for this study represent releases to individual ecosystems without environmental change and include the most plausible transport processes. In the context of regulatory review of the landscape modelling approach adopted in the SR-Site assessment in Sweden, the more stylised representation has helped to build understanding in the more complex modelling approaches by providing bounding results, checking the reasonableness of the more complex modelling, highlighting uncertainties introduced through conceptual assumptions and helping to quantify the conservatisms involved. The more stylised biosphere models are also shown capable of reproducing the results of more complex approaches. A major recommendation is that biosphere assessments need to justify the degree of complexity in modelling approaches as well as simplifying and conservative assumptions. In light of

Centrifuge Rotor Models: A Comparison of the Euler-Lagrange and the Bond Graph Modeling Approach

NASA Technical Reports Server (NTRS)

Granda, Jose J.; Ramakrishnan, Jayant; Nguyen, Louis H.

2006-01-01

A viewgraph presentation on centrifuge rotor models with a comparison using Euler-Lagrange and bond graph methods is shown. The topics include: 1) Objectives; 2) MOdeling Approach Comparisons; 3) Model Structures; and 4) Application.

Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.

PubMed

Ocone, Andrea; Millar, Andrew J; Sanguinetti, Guido

2013-04-01

Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem. Here, we develop a general statistical inference framework for stochastic transcription-translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions. http://homepages.inf.ed.ac.uk/gsanguin/software.html. Supplementary data are available at Bioinformatics online.

An Alternative Approach for Nonlinear Latent Variable Models

ERIC Educational Resources Information Center

Mooijaart, Ab; Bentler, Peter M.

2010-01-01

In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and…

Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches

ERIC Educational Resources Information Center

Erbas, Ayhan Kürsat; Kertil, Mahmut; Çetinkaya, Bülent; Çakiroglu, Erdinç; Alacaci, Cengiz; Bas, Sinem

2014-01-01

Mathematical modeling and its role in mathematics education have been receiving increasing attention in Turkey, as in many other countries. The growing body of literature on this topic reveals a variety of approaches to mathematical modeling and related concepts, along with differing perspectives on the use of mathematical modeling in teaching and…

A Model-Based Prognostics Approach Applied to Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Goebel, Kai

2011-01-01

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

An approach to multiscale modelling with graph grammars

PubMed Central

Ong, Yongzhi; Streit, Katarína; Henke, Michael; Kurth, Winfried

2014-01-01

Background and Aims Functional–structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. Methods A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. Key Results Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. Conclusions The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models. PMID:25134929

A rule-based approach to model checking of UML state machines

NASA Astrophysics Data System (ADS)

Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

2016-12-01

In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

A model comparison approach shows stronger support for economic models of fertility decline

PubMed Central

Shenk, Mary K.; Towner, Mary C.; Kress, Howard C.; Alam, Nurul

2013-01-01

The demographic transition is an ongoing global phenomenon in which high fertility and mortality rates are replaced by low fertility and mortality. Despite intense interest in the causes of the transition, especially with respect to decreasing fertility rates, the underlying mechanisms motivating it are still subject to much debate. The literature is crowded with competing theories, including causal models that emphasize (i) mortality and extrinsic risk, (ii) the economic costs and benefits of investing in self and children, and (iii) the cultural transmission of low-fertility social norms. Distinguishing between models, however, requires more comprehensive, better-controlled studies than have been published to date. We use detailed demographic data from recent fieldwork to determine which models produce the most robust explanation of the rapid, recent demographic transition in rural Bangladesh. To rigorously compare models, we use an evidence-based statistical approach using model selection techniques derived from likelihood theory. This approach allows us to quantify the relative evidence the data give to alternative models, even when model predictions are not mutually exclusive. Results indicate that fertility, measured as either total fertility or surviving children, is best explained by models emphasizing economic factors and related motivations for parental investment. Our results also suggest important synergies between models, implicating multiple causal pathways in the rapidity and degree of recent demographic transitions. PMID:23630293

Bystander Approaches: Empowering Students to Model Ethical Sexual Behavior

ERIC Educational Resources Information Center

Lynch, Annette; Fleming, Wm. Michael

2005-01-01

Sexual violence on college campuses is well documented. Prevention education has emerged as an alternative to victim-- and perpetrator--oriented approaches used in the past. One sexual violence prevention education approach focuses on educating and empowering the bystander to become a point of ethical intervention. In this model, bystanders to…

A hybrid modeling approach for option pricing

NASA Astrophysics Data System (ADS)

Hajizadeh, Ehsan; Seifi, Abbas

2011-11-01

The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

Approaches to modelling uranium (VI) adsorption on natural mineral assemblages

USGS Publications Warehouse

Waite, T.D.; Davis, J.A.; Fenton, B.R.; Payne, T.E.

2000-01-01

Component additivity (CA) and generalised composite (GC) approaches to deriving a suitable surface complexation model for description of U(VI) adsorption to natural mineral assemblages are pursued in this paper with good success. A single, ferrihydrite-like component is found to reasonably describe uranyl uptake to a number of kaolinitic iron-rich natural substrates at pH > 4 in the CA approach with previously published information on nature of surface complexes, acid-base properties of surface sites and electrostatic effects used in the model. The GC approach, in which little pre-knowledge about generic surface sites is assumed, gives even better fits and would appear to be a method of particular strength for application in areas such as performance assessment provided the model is developed in a careful, stepwise manner with simplicity and goodness of fit as the major criteria for acceptance.

Sandia fracture challenge 2: Sandia California's modeling approach

DOE PAGES

Karlson, Kyle N.; James W. Foulk, III; Brown, Arthur A.; ...

2016-03-09

The second Sandia Fracture Challenge illustrates that predicting the ductile fracture of Ti-6Al-4V subjected to moderate and elevated rates of loading requires thermomechanical coupling, elasto-thermo-poro-viscoplastic constitutive models with the physics of anisotropy and regularized numerical methods for crack initiation and propagation. We detail our initial approach with an emphasis on iterative calibration and systematically increasing complexity to accommodate anisotropy in the context of an isotropic material model. Blind predictions illustrate strengths and weaknesses of our initial approach. We then revisit our findings to illustrate the importance of including anisotropy in the failure process. Furthermore, mesh-independent solutions of continuum damage modelsmore » having both isotropic and anisotropic yields surfaces are obtained through nonlocality and localization elements.« less

Towards a Semantic E-Learning Theory by Using a Modelling Approach

ERIC Educational Resources Information Center

Yli-Luoma, Pertti V. J.; Naeve, Ambjorn

2006-01-01

In the present study, a semantic perspective on e-learning theory is advanced and a modelling approach is used. This modelling approach towards the new learning theory is based on the four SECI phases of knowledge conversion: Socialisation, Externalisation, Combination and Internalisation, introduced by Nonaka in 1994, and involving two levels of…

Reusable Component Model Development Approach for Parallel and Distributed Simulation

PubMed Central

Zhu, Feng; Yao, Yiping; Chen, Huilong; Yao, Feng

2014-01-01

Model reuse is a key issue to be resolved in parallel and distributed simulation at present. However, component models built by different domain experts usually have diversiform interfaces, couple tightly, and bind with simulation platforms closely. As a result, they are difficult to be reused across different simulation platforms and applications. To address the problem, this paper first proposed a reusable component model framework. Based on this framework, then our reusable model development approach is elaborated, which contains two phases: (1) domain experts create simulation computational modules observing three principles to achieve their independence; (2) model developer encapsulates these simulation computational modules with six standard service interfaces to improve their reusability. The case study of a radar model indicates that the model developed using our approach has good reusability and it is easy to be used in different simulation platforms and applications. PMID:24729751

Query Language for Location-Based Services: A Model Checking Approach

NASA Astrophysics Data System (ADS)

Hoareau, Christian; Satoh, Ichiro

We present a model checking approach to the rationale, implementation, and applications of a query language for location-based services. Such query mechanisms are necessary so that users, objects, and/or services can effectively benefit from the location-awareness of their surrounding environment. The underlying data model is founded on a symbolic model of space organized in a tree structure. Once extended to a semantic model for modal logic, we regard location query processing as a model checking problem, and thus define location queries as hybrid logicbased formulas. Our approach is unique to existing research because it explores the connection between location models and query processing in ubiquitous computing systems, relies on a sound theoretical basis, and provides modal logic-based query mechanisms for expressive searches over a decentralized data structure. A prototype implementation is also presented and will be discussed.

Merging Digital Surface Models Implementing Bayesian Approaches

NASA Astrophysics Data System (ADS)

Sadeq, H.; Drummond, J.; Li, Z.

2016-06-01

In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

Box-wing model approach for solar radiation pressure modelling in a multi-GNSS scenario

NASA Astrophysics Data System (ADS)

Tobias, Guillermo; Jesús García, Adrián

2016-04-01

The solar radiation pressure force is the largest orbital perturbation after the gravitational effects and the major error source affecting GNSS satellites. A wide range of approaches have been developed over the years for the modelling of this non gravitational effect as part of the orbit determination process. These approaches are commonly divided into empirical, semi-analytical and analytical, where their main difference relies on the amount of knowledge of a-priori physical information about the properties of the satellites (materials and geometry) and their attitude. It has been shown in the past that the pre-launch analytical models fail to achieve the desired accuracy mainly due to difficulties in the extrapolation of the in-orbit optical and thermic properties, the perturbations in the nominal attitude law and the aging of the satellite's surfaces, whereas empirical models' accuracies strongly depend on the amount of tracking data used for deriving the models, and whose performances are reduced as the area to mass ratio of the GNSS satellites increases, as it happens for the upcoming constellations such as BeiDou and Galileo. This paper proposes to use basic box-wing model for Galileo complemented with empirical parameters, based on the limited available information about the Galileo satellite's geometry. The satellite is modelled as a box, representing the satellite bus, and a wing representing the solar panel. The performance of the model will be assessed for GPS, GLONASS and Galileo constellations. The results of the proposed approach have been analyzed over a one year period. In order to assess the results two different SRP models have been used. Firstly, the proposed box-wing model and secondly, the new CODE empirical model, ECOM2. The orbit performances of both models are assessed using Satellite Laser Ranging (SLR) measurements, together with the evaluation of the orbit prediction accuracy. This comparison shows the advantages and disadvantages of

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Using VCL as an Aspect-Oriented Approach to Requirements Modelling

NASA Astrophysics Data System (ADS)

Amálio, Nuno; Kelsen, Pierre; Ma, Qin; Glodt, Christian

Software systems are becoming larger and more complex. By tackling the modularisation of crosscutting concerns, aspect orientation draws attention to modularity as a means to address the problems of scalability, complexity and evolution in software systems development. Aspect-oriented modelling (AOM) applies aspect-orientation to the construction of models. Most existing AOM approaches are designed without a formal semantics, and use multi-view partial descriptions of behaviour. This paper presents an AOM approach based on the Visual Contract Language (VCL): a visual language for abstract and precise modelling, designed with a formal semantics, and comprising a novel approach to visual behavioural modelling based on design by contract where behavioural descriptions are total. By applying VCL to a large case study of a car-crash crisis management system, the paper demonstrates how modularity of VCL's constructs, at different levels of granularity, help to tackle complexity. In particular, it shows how VCL's package construct and its associated composition mechanisms are key in supporting separation of concerns, coarse-grained problem decomposition and aspect-orientation. The case study's modelling solution has a clear and well-defined modular structure; the backbone of this structure is a collection of packages encapsulating local solutions to concerns.

Numerical modeling of hydrodynamics and sediment transport—an integrated approach

NASA Astrophysics Data System (ADS)

Gic-Grusza, Gabriela; Dudkowska, Aleksandra

2017-10-01

Point measurement-based estimation of bedload transport in the coastal zone is very difficult. The only way to assess the magnitude and direction of bedload transport in larger areas, particularly those characterized by complex bottom topography and hydrodynamics, is to use a holistic approach. This requires modeling of waves, currents, and the critical bed shear stress and bedload transport magnitude, with a due consideration to the realistic bathymetry and distribution of surface sediment types. Such a holistic approach is presented in this paper which describes modeling of bedload transport in the Gulf of Gdańsk. Extreme storm conditions defined based on 138-year NOAA data were assumed. The SWAN model (Booij et al. 1999) was used to define wind-wave fields, whereas wave-induced currents were calculated using the Kołodko and Gic-Grusza (2015) model, and the magnitude of bedload transport was estimated using the modified Meyer-Peter and Müller (1948) formula. The calculations were performed using a GIS model. The results obtained are innovative. The approach presented appears to be a valuable source of information on bedload transport in the coastal zone.

Geospatial Modelling Approach for 3d Urban Densification Developments

NASA Astrophysics Data System (ADS)

Koziatek, O.; Dragićević, S.; Li, S.

2016-06-01

With growing populations, economic pressures, and the need for sustainable practices, many urban regions are rapidly densifying developments in the vertical built dimension with mid- and high-rise buildings. The location of these buildings can be projected based on key factors that are attractive to urban planners, developers, and potential buyers. Current research in this area includes various modelling approaches, such as cellular automata and agent-based modelling, but the results are mostly linked to raster grids as the smallest spatial units that operate in two spatial dimensions. Therefore, the objective of this research is to develop a geospatial model that operates on irregular spatial tessellations to model mid- and high-rise buildings in three spatial dimensions (3D). The proposed model is based on the integration of GIS, fuzzy multi-criteria evaluation (MCE), and 3D GIS-based procedural modelling. Part of the City of Surrey, within the Metro Vancouver Region, Canada, has been used to present the simulations of the generated 3D building objects. The proposed 3D modelling approach was developed using ESRI's CityEngine software and the Computer Generated Architecture (CGA) language.

A Survey of Model Evaluation Approaches with a Tutorial on Hierarchical Bayesian Methods

ERIC Educational Resources Information Center

Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan

2008-01-01

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…

Single-particle dynamics of the Anderson model: a local moment approach

NASA Astrophysics Data System (ADS)

Glossop, Matthew T.; Logan, David E.

2002-07-01

A non-perturbative local moment approach to single-particle dynamics of the general asymmetric Anderson impurity model is developed. The approach encompasses all energy scales and interaction strengths. It captures thereby strong coupling Kondo behaviour, including the resultant universal scaling behaviour of the single-particle spectrum; as well as the mixed valence and essentially perturbative empty orbital regimes. The underlying approach is physically transparent and innately simple, and as such is capable of practical extension to lattice-based models within the framework of dynamical mean-field theory.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Fresh approaches to Earth surface modeling

NASA Astrophysics Data System (ADS)

Kopylova, N. S.; Starikov, I. P.

2018-05-01

The paper considers modelling of the surface when fixing objects in the geocentric coordinate systems in the course of GLONASS satellite system development. The authors revealed new approaches to presentation of geographical data to a user, transformation of map properties and the leading role of ERS (Earth remote sensing) as a source of mapping information; change of scientific paradigms aimed at improvement of high-accuracy cartographic objects representation in the plane.

Evaluation of various modelling approaches in flood routing simulation and flood area mapping

NASA Astrophysics Data System (ADS)

Papaioannou, George; Loukas, Athanasios; Vasiliades, Lampros; Aronica, Giuseppe

2016-04-01

An essential process of flood hazard analysis and mapping is the floodplain modelling. The selection of the modelling approach, especially, in complex riverine topographies such as urban and suburban areas, and ungauged watersheds may affect the accuracy of the outcomes in terms of flood depths and flood inundation area. In this study, a sensitivity analysis implemented using several hydraulic-hydrodynamic modelling approaches (1D, 2D, 1D/2D) and the effect of modelling approach on flood modelling and flood mapping was investigated. The digital terrain model (DTMs) used in this study was generated from Terrestrial Laser Scanning (TLS) point cloud data. The modelling approaches included 1-dimensional hydraulic-hydrodynamic models (1D), 2-dimensional hydraulic-hydrodynamic models (2D) and the coupled 1D/2D. The 1D hydraulic-hydrodynamic models used were: HECRAS, MIKE11, LISFLOOD, XPSTORM. The 2D hydraulic-hydrodynamic models used were: MIKE21, MIKE21FM, HECRAS (2D), XPSTORM, LISFLOOD and FLO2d. The coupled 1D/2D models employed were: HECRAS(1D/2D), MIKE11/MIKE21(MIKE FLOOD platform), MIKE11/MIKE21 FM(MIKE FLOOD platform), XPSTORM(1D/2D). The validation process of flood extent achieved with the use of 2x2 contingency tables between simulated and observed flooded area for an extreme historical flash flood event. The skill score Critical Success Index was used in the validation process. The modelling approaches have also been evaluated for simulation time and requested computing power. The methodology has been implemented in a suburban ungauged watershed of Xerias river at Volos-Greece. The results of the analysis indicate the necessity of sensitivity analysis application with the use of different hydraulic-hydrodynamic modelling approaches especially for areas with complex terrain.

Experimental Validation of Various Temperature Modells for Semi-Physical Tyre Model Approaches

NASA Astrophysics Data System (ADS)

Hackl, Andreas; Scherndl, Christoph; Hirschberg, Wolfgang; Lex, Cornelia

2017-10-01

With increasing level of complexity and automation in the area of automotive engineering, the simulation of safety relevant Advanced Driver Assistance Systems (ADAS) leads to increasing accuracy demands in the description of tyre contact forces. In recent years, with improvement in tyre simulation, the needs for coping with tyre temperatures and the resulting changes in tyre characteristics are rising significantly. Therefore, experimental validation of three different temperature model approaches is carried out, discussed and compared in the scope of this article. To investigate or rather evaluate the range of application of the presented approaches in combination with respect of further implementation in semi-physical tyre models, the main focus lies on the a physical parameterisation. Aside from good modelling accuracy, focus is held on computational time and complexity of the parameterisation process. To evaluate this process and discuss the results, measurements from a Hoosier racing tyre 6.0 / 18.0 10 LCO C2000 from an industrial flat test bench are used. Finally the simulation results are compared with the measurement data.

Setting conservation management thresholds using a novel participatory modeling approach.

PubMed

Addison, P F E; de Bie, K; Rumpff, L

2015-10-01

We devised a participatory modeling approach for setting management thresholds that show when management intervention is required to address undesirable ecosystem changes. This approach was designed to be used when management thresholds: must be set for environmental indicators in the face of multiple competing objectives; need to incorporate scientific understanding and value judgments; and will be set by participants with limited modeling experience. We applied our approach to a case study where management thresholds were set for a mat-forming brown alga, Hormosira banksii, in a protected area management context. Participants, including management staff and scientists, were involved in a workshop to test the approach, and set management thresholds to address the threat of trampling by visitors to an intertidal rocky reef. The approach involved trading off the environmental objective, to maintain the condition of intertidal reef communities, with social and economic objectives to ensure management intervention was cost-effective. Ecological scenarios, developed using scenario planning, were a key feature that provided the foundation for where to set management thresholds. The scenarios developed represented declines in percent cover of H. banksii that may occur under increased threatening processes. Participants defined 4 discrete management alternatives to address the threat of trampling and estimated the effect of these alternatives on the objectives under each ecological scenario. A weighted additive model was used to aggregate participants' consequence estimates. Model outputs (decision scores) clearly expressed uncertainty, which can be considered by decision makers and used to inform where to set management thresholds. This approach encourages a proactive form of conservation, where management thresholds and associated actions are defined a priori for ecological indicators, rather than reacting to unexpected ecosystem changes in the future. © 2015 The

Inverse problems and computational cell metabolic models: a statistical approach

NASA Astrophysics Data System (ADS)

Calvetti, D.; Somersalo, E.

2008-07-01

In this article, we give an overview of the Bayesian modelling of metabolic systems at the cellular and subcellular level. The models are based on detailed description of key biochemical reactions occurring in tissue, which may in turn be compartmentalized into cytosol and mitochondria, and of transports between the compartments. The classical deterministic approach which models metabolic systems as dynamical systems with Michaelis-Menten kinetics, is replaced by a stochastic extension where the model parameters are interpreted as random variables with an appropriate probability density. The inverse problem of cell metabolism in this setting consists of estimating the density of the model parameters. After discussing some possible approaches to solving the problem, we address the issue of how to assess the reliability of the predictions of a stochastic model by proposing an output analysis in terms of model uncertainties. Visualization modalities for organizing the large amount of information provided by the Bayesian dynamic sensitivity analysis are also illustrated.

Finite Element Model Calibration Approach for Area I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Gaspar, James L.; Lazor, Daniel R.; Parks, Russell A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Finite Element Model Calibration Approach for Ares I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Lazor, Daniel R.; Gaspar, James L.; Parks, Russel A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of nonconventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pre-test predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Extracting business vocabularies from business process models: SBVR and BPMN standards-based approach

NASA Astrophysics Data System (ADS)

Skersys, Tomas; Butleris, Rimantas; Kapocius, Kestutis

2013-10-01

Approaches for the analysis and specification of business vocabularies and rules are very relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, basic aspects of the approach for business vocabularies' semi-automated extraction from business process models are presented. The approach is based on novel business modeling-level OMG standards "Business Process Model and Notation" (BPMN) and "Semantics for Business Vocabularies and Business Rules" (SBVR), thus contributing to OMG's vision about Model-Driven Architecture (MDA) and to model-driven development in general.

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Ice Accretion Modeling using an Eulerian Approach for Droplet Impingement

NASA Technical Reports Server (NTRS)

Kim, Joe Woong; Garza, Dennis P.; Sankar, Lakshmi N.; Kreeger, Richard E.

2012-01-01

A three-dimensional Eulerian analysis has been developed for modeling droplet impingement on lifting bodes. The Eulerian model solves the conservation equations of mass and momentum to obtain the droplet flow field properties on the same mesh used in CFD simulations. For complex configurations such as a full rotorcraft, the Eulerian approach is more efficient because the Lagrangian approach would require a significant amount of seeding for accurate estimates of collection efficiency. Simulations are done for various benchmark cases such as NACA0012 airfoil, MS317 airfoil and oscillating SC2110 airfoil to illustrate its use. The present results are compared with results from the Lagrangian approach used in an industry standard analysis called LEWICE.

Bayesian Approach for Flexible Modeling of Semicompeting Risks Data

PubMed Central

Han, Baoguang; Yu, Menggang; Dignam, James J.; Rathouz, Paul J.

2016-01-01

Summary Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis. PMID:25274445

System integration of wind and solar power in integrated assessment models: A cross-model evaluation of new approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pietzcker, Robert C.; Ueckerdt, Falko; Carrara, Samuel

Mitigation-Process Integrated Assessment Models (MP-IAMs) are used to analyze long-term transformation pathways of the energy system required to achieve stringent climate change mitigation targets. Due to their substantial temporal and spatial aggregation, IAMs cannot explicitly represent all detailed challenges of integrating the variable renewable energies (VRE) wind and solar in power systems, but rather rely on parameterized modeling approaches. In the ADVANCE project, six international modeling teams have developed new approaches to improve the representation of power sector dynamics and VRE integration in IAMs. In this study, we qualitatively and quantitatively evaluate the last years' modeling progress and study themore » impact of VRE integration modeling on VRE deployment in IAM scenarios. For a comprehensive and transparent qualitative evaluation, we first develop a framework of 18 features of power sector dynamics and VRE integration. We then apply this framework to the newly-developed modeling approaches to derive a detailed map of strengths and limitations of the different approaches. For the quantitative evaluation, we compare the IAMs to the detailed hourly-resolution power sector model REMIX. We find that the new modeling approaches manage to represent a large number of features of the power sector, and the numerical results are in reasonable agreement with those derived from the detailed power sector model. Updating the power sector representation and the cost and resources of wind and solar substantially increased wind and solar shares across models: Under a carbon price of 30$/tCO2 in 2020 (increasing by 5% per year), the model-average cost-minimizing VRE share over the period 2050-2100 is 62% of electricity generation, 24%-points higher than with the old model version.« less

Systems and context modeling approach to requirements analysis

NASA Astrophysics Data System (ADS)

Ahuja, Amrit; Muralikrishna, G.; Patwari, Puneet; Subhrojyoti, C.; Swaminathan, N.; Vin, Harrick

2014-08-01

Ensuring completeness and correctness of the requirements for a complex system such as the SKA is challenging. Current system engineering practice includes developing a stakeholder needs definition, a concept of operations, and defining system requirements in terms of use cases and requirements statements. We present a method that enhances this current practice into a collection of system models with mutual consistency relationships. These include stakeholder goals, needs definition and system-of-interest models, together with a context model that participates in the consistency relationships among these models. We illustrate this approach by using it to analyze the SKA system requirements.

A global sensitivity analysis approach for morphogenesis models.

PubMed

Boas, Sonja E M; Navarro Jimenez, Maria I; Merks, Roeland M H; Blom, Joke G

2015-11-21

Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.

Vector-model-supported approach in prostate plan optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100more » previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

Bridging process-based and empirical approaches to modeling tree growth

Treesearch

Harry T. Valentine; Annikki Makela; Annikki Makela

2005-01-01

The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...

Initial assessment of a multi-model approach to spring flood forecasting in Sweden

NASA Astrophysics Data System (ADS)

Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

2015-06-01

Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing.

A Machine Learning Approach to Student Modeling.

DTIC Science & Technology

1984-05-01

machine learning , and describe ACN, a student modeling system that incorporates this approach. This system begins with a set of overly general rules, which it uses to search a problem space until it arrives at the same answer as the student. The ACM computer program then uses the solution path it has discovered to determine positive and negative instances of its initial rules, and employs a discrimination learning mechanism to place additional conditions on these rules. The revised rules will reproduce the solution path without search, and constitute a cognitive model of

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

A Model-Based Approach to Support Validation of Medical Cyber-Physical Systems

PubMed Central

Silva, Lenardo C.; Almeida, Hyggo O.; Perkusich, Angelo; Perkusich, Mirko

2015-01-01

Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different contexts. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage. PMID:26528982

A Model-Based Approach to Support Validation of Medical Cyber-Physical Systems.

PubMed

Silva, Lenardo C; Almeida, Hyggo O; Perkusich, Angelo; Perkusich, Mirko

2015-10-30

Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different contexts. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage.

A Bayesian Approach for Analyzing Longitudinal Structural Equation Models

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lu, Zhao-Hua; Hser, Yih-Ing; Lee, Sik-Yum

2011-01-01

This article considers a Bayesian approach for analyzing a longitudinal 2-level nonlinear structural equation model with covariates, and mixed continuous and ordered categorical variables. The first-level model is formulated for measures taken at each time point nested within individuals for investigating their characteristics that are dynamically…

Crime Modeling using Spatial Regression Approach

NASA Astrophysics Data System (ADS)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

Configurational coupled cluster approach with applications to magnetic model systems

NASA Astrophysics Data System (ADS)

Wu, Siyuan; Nooijen, Marcel

2018-05-01

A general exponential, coupled cluster like, approach is discussed to extract an effective Hamiltonian in configurational space, as a sum of 1-body, 2-body up to n-body operators. The simplest two-body approach is illustrated by calculations on simple magnetic model systems. A key feature of the approach is that equations up to a certain rank do not depend on higher body cluster operators.

A Robust Sound Source Localization Approach for Microphone Array with Model Errors

NASA Astrophysics Data System (ADS)

Xiao, Hua; Shao, Huai-Zong; Peng, Qi-Cong

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i. e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.

[Individual growth modeling of the penshell Atrina maura (Bivalvia: Pinnidae) using a multi model inference approach].

PubMed

Aragón-Noriega, Eugenio Alberto

2013-09-01

Growth models of marine animals, for fisheries and/or aquaculture purposes, are based on the popular von Bertalanffy model. This tool is mostly used because its parameters are used to evaluate other fisheries models, such as yield per recruit; nevertheless, there are other alternatives (such as Gompertz, Logistic, Schnute) not yet used by fishery scientists, that may result useful depending on the studied species. The penshell Atrina maura, has been studied for fisheries or aquaculture supplies, but its individual growth has not yet been studied before. The aim of this study was to model the absolute growth of the penshell A. maura using length-age data. For this, five models were assessed to obtain growth parameters: von Bertalanffy, Gompertz, Logistic, Schnute case 1 and Schnute and Richards. The criterion used to select the best models was the Akaike information criterion, as well as the residual squared sum and R2 adjusted. To get the average asymptotic length, the multi model inference approach was used. According to Akaike information criteria, the Gompertz model better described the absolute growth of A. maura. Following the multi model inference approach the average asymptotic shell length was 218.9 mm (IC 212.3-225.5) of shell length. I concluded that the use of the multi model approach and the Akaike information criteria represented the most robust method for growth parameter estimation of A. maura and the von Bertalanffy growth model should not be selected a priori as the true model to obtain the absolute growth in bivalve mollusks like in the studied species in this paper.

A Simple Approach to Account for Climate Model Interdependence in Multi-Model Ensembles

NASA Astrophysics Data System (ADS)

Herger, N.; Abramowitz, G.; Angelil, O. M.; Knutti, R.; Sanderson, B.

2016-12-01

Multi-model ensembles are an indispensable tool for future climate projection and its uncertainty quantification. Ensembles containing multiple climate models generally have increased skill, consistency and reliability. Due to the lack of agreed-on alternatives, most scientists use the equally-weighted multi-model mean as they subscribe to model democracy ("one model, one vote").Different research groups are known to share sections of code, parameterizations in their model, literature, or even whole model components. Therefore, individual model runs do not represent truly independent estimates. Ignoring this dependence structure might lead to a false model consensus, wrong estimation of uncertainty and effective number of independent models.Here, we present a way to partially address this problem by selecting a subset of CMIP5 model runs so that its climatological mean minimizes the RMSE compared to a given observation product. Due to the cancelling out of errors, regional biases in the ensemble mean are reduced significantly.Using a model-as-truth experiment we demonstrate that those regional biases persist into the future and we are not fitting noise, thus providing improved observationally-constrained projections of the 21st century. The optimally selected ensemble shows significantly higher global mean surface temperature projections than the original ensemble, where all the model runs are considered. Moreover, the spread is decreased well beyond that expected from the decreased ensemble size.Several previous studies have recommended an ensemble selection approach based on performance ranking of the model runs. Here, we show that this approach can perform even worse than randomly selecting ensemble members and can thus be harmful. We suggest that accounting for interdependence in the ensemble selection process is a necessary step for robust projections for use in impact assessments, adaptation and mitigation of climate change.

Classical Michaelis-Menten and system theory approach to modeling metabolite formation kinetics.

PubMed

Popović, Jovan

2004-01-01

When single doses of drug are administered and kinetics are linear, techniques, which are based on the compartment approach and the linear system theory approach, in modeling the formation of the metabolite from the parent drug are proposed. Unlike the purpose-specific compartment approach, the methodical, conceptual and computational uniformity in modeling various linear biomedical systems is the dominant characteristic of the linear system approach technology. Saturation of the metabolic reaction results in nonlinear kinetics according to the Michaelis-Menten equation. The two compartment open model with Michaelis-Menten elimination kinetics is theorethicaly basic when single doses of drug are administered. To simulate data or to fit real data using this model, one must resort to numerical integration. A biomathematical model for multiple dosage regimen calculations of nonlinear metabolic systems in steady-state and a working example with phenytoin are presented. High correlation between phenytoin steady-state serum levels calculated from individual Km and Vmax values in the 15 adult epileptic outpatients and the observed levels at the third adjustment of phenytoin daily dose (r=0.961, p<0.01) were found.

The system-resonance approach in modeling genetic structures.

PubMed

Petoukhov, Sergey V

2016-01-01

The founder of the theory of resonance in structural chemistry Linus Pauling established the importance of resonance patterns in organization of living systems. Any living organism is a great chorus of coordinated oscillatory processes. From the formal point of view, biological organism is an oscillatory system with a great number of degrees of freedom. Such systems are studied in the theory of oscillations using matrix mathematics of their resonance characteristics. This study is devoted to a new approach for modeling genetically inherited structures and processes in living organisms using mathematical tools of the theory of resonances. This approach reveals hidden relationships in a number of genetic phenomena and gives rise to a new class of bio-mathematical models, which contribute to a convergence of biology with physics and informatics. In addition some relationships of molecular-genetic ensembles with mathematics of noise-immunity coding of information in modern communications technology are shown. Perspectives of applications of the phenomena of vibrational mechanics for modeling in biology are discussed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

Modelling approaches: the case of schizophrenia.

PubMed

Heeg, Bart M S; Damen, Joep; Buskens, Erik; Caleo, Sue; de Charro, Frank; van Hout, Ben A

2008-01-01

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.

Approaches to modeling landscape-scale drought-induced forest mortality

USGS Publications Warehouse

Gustafson, Eric J.; Shinneman, Douglas

2015-01-01

Drought stress is an important cause of tree mortality in forests, and drought-induced disturbance events are projected to become more common in the future due to climate change. Landscape Disturbance and Succession Models (LDSM) are becoming widely used to project climate change impacts on forests, including potential interactions with natural and anthropogenic disturbances, and to explore the efficacy of alternative management actions to mitigate negative consequences of global changes on forests and ecosystem services. Recent studies incorporating drought-mortality effects into LDSMs have projected significant potential changes in forest composition and carbon storage, largely due to differential impacts of drought on tree species and interactions with other disturbance agents. In this chapter, we review how drought affects forest ecosystems and the different ways drought effects have been modeled (both spatially and aspatially) in the past. Building on those efforts, we describe several approaches to modeling drought effects in LDSMs, discuss advantages and shortcomings of each, and include two case studies for illustration. The first approach features the use of empirically derived relationships between measures of drought and the loss of tree biomass to drought-induced mortality. The second uses deterministic rules of species mortality for given drought events to project changes in species composition and forest distribution. A third approach is more mechanistic, simulating growth reductions and death caused by water stress. Because modeling of drought effects in LDSMs is still in its infancy, and because drought is expected to play an increasingly important role in forest health, further development of modeling drought-forest dynamics is urgently needed.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three

Agent-based modeling: a new approach for theory building in social psychology.

PubMed

Smith, Eliot R; Conrey, Frederica R

2007-02-01

Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach.

Meta-analysis of diagnostic test data: a bivariate Bayesian modeling approach.

PubMed

Verde, Pablo E

2010-12-30

In the last decades, the amount of published results on clinical diagnostic tests has expanded very rapidly. The counterpart to this development has been the formal evaluation and synthesis of diagnostic results. However, published results present substantial heterogeneity and they can be regarded as so far removed from the classical domain of meta-analysis, that they can provide a rather severe test of classical statistical methods. Recently, bivariate random effects meta-analytic methods, which model the pairs of sensitivities and specificities, have been presented from the classical point of view. In this work a bivariate Bayesian modeling approach is presented. This approach substantially extends the scope of classical bivariate methods by allowing the structural distribution of the random effects to depend on multiple sources of variability. Meta-analysis is summarized by the predictive posterior distributions for sensitivity and specificity. This new approach allows, also, to perform substantial model checking, model diagnostic and model selection. Statistical computations are implemented in the public domain statistical software (WinBUGS and R) and illustrated with real data examples. Copyright © 2010 John Wiley & Sons, Ltd.

An endorsement-based approach to student modeling for planner-controlled intelligent tutoring systems

NASA Technical Reports Server (NTRS)

Murray, William R.

1990-01-01

An approach is described to student modeling for intelligent tutoring systems based on an explicit representation of the tutor's beliefs about the student and the arguments for and against those beliefs (called endorsements). A lexicographic comparison of arguments, sorted according to evidence reliability, provides a principled means of determining those beliefs that are considered true, false, or uncertain. Each of these beliefs is ultimately justified by underlying assessment data. The endorsement-based approach to student modeling is particularly appropriate for tutors controlled by instructional planners. These tutors place greater demands on a student model than opportunistic tutors. Numerical calculi approaches are less well-suited because it is difficult to correctly assign numbers for evidence reliability and rule plausibility. It may also be difficult to interpret final results and provide suitable combining functions. When numeric measures of uncertainty are used, arbitrary numeric thresholds are often required for planning decisions. Such an approach is inappropriate when robust context-sensitive planning decisions must be made. A TMS-based implementation of the endorsement-based approach to student modeling is presented, this approach is compared to alternatives, and a project history is provided describing the evolution of this approach.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Wei-Cheng; Yang, Zhaoqing; Wang, Taiping

This paper presents a modeling study conducted on the central Oregon coast for wave resource characterization using the unstructured-grid SWAN model coupled with a nested-grid WWIII model. The flexibility of models of various spatial resolutions and the effects of open- boundary conditions simulated by a nested-grid WWIII model with different physics packages were evaluated. The model results demonstrate the advantage of the unstructured-grid modeling approach for flexible model resolution and good model skills in simulating the six wave resource parameters recommended by the International Electrotechnical Commission in comparison to the observed data in Year 2009 at National Data Buoy Centermore » Buoy 46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the model skill of the ST2 physics package for predicting wave power density for large waves, which is important for wave resource assessment, device load calculation, and risk management. In addition, bivariate distributions show the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than that with the ST2 physics package. This study demonstrated that the unstructured-grid wave modeling approach, driven by the nested-grid regional WWIII outputs with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10^2 km).« less

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

DOE PAGES

Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas; ...

2015-02-05

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based modelmore » $$\\eta (\\theta )$$, where θ denotes the uncertain, best input setting. Hence the statistical model is of the form $$y=\\eta (\\theta )+\\epsilon ,$$ where $$\\epsilon $$ accounts for measurement, and possibly other, error sources. When nonlinearity is present in $$\\eta (\\cdot )$$, the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model $$\\eta (\\cdot )$$. This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. Lastly, we also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.« less

Intercomparison Of Approaches For Modeling Second Order Ionospheric Corrections Using Gnss Measurements

NASA Astrophysics Data System (ADS)

Garcia Fernandez, M.; Butala, M.; Komjathy, A.; Desai, S. D.

2012-12-01

Correcting GNSS tracking data for the effects of second order ionospheric effects have been shown to cause a southward shift in GNSS-based precise point positioning solutions by as much as 10 mm, depending on the solar cycle conditions. The most commonly used approaches for modeling the higher order ionospheric effect include, (a) the use of global ionosphere maps to determine vertical total electron content (VTEC) and convert to slant TEC (STEC) assuming a thin shell ionosphere, and (b) using the dual-frequency measurements themselves to determine STEC. The latter approach benefits from not requiring ionospheric mapping functions between VTEC and STEC. However, this approach will require calibrations with receiver and transmitter Differential Code Biases (DCBs). We present results from comparisons of the two approaches. For the first approach, we also compare the use of VTEC observations from IONEX maps compared to climatological model-derived VTEC as provided by the International Reference Ionosphere (IRI2012). We consider various metrics to evaluate the relative performance of the different approaches, including station repeatability, GNSS-based reference frame recovery, and post-fit measurement residuals. Overall, the GIM-based approaches tend to provide lower noise in second order ionosphere correction and positioning solutions. The use of IONEX and IRI2012 models of VTEC provide similar results, especially in periods of low solar activity periods. The use of the IRI2012 model provides a convenient approach for operational scenarios by eliminating the dependence on routine updates of the GIMs, and also serves as a useful source of VTEC when IONEX maps may not be readily available.

Proposal: A Hybrid Dictionary Modelling Approach for Malay Tweet Normalization

NASA Astrophysics Data System (ADS)

Muhamad, Nor Azlizawati Binti; Idris, Norisma; Arshi Saloot, Mohammad

2017-02-01

Malay Twitter message presents a special deviation from the original language. Malay Tweet widely used currently by Twitter users, especially at Malaya archipelago. Thus, it is important to make a normalization system which can translated Malay Tweet language into the standard Malay language. Some researchers have conducted in natural language processing which mainly focuses on normalizing English Twitter messages, while few studies have been done for normalize Malay Tweets. This paper proposes an approach to normalize Malay Twitter messages based on hybrid dictionary modelling methods. This approach normalizes noisy Malay twitter messages such as colloquially language, novel words, and interjections into standard Malay language. This research will be used Language Model and N-grams model.

The Intersystem Model of Psychotherapy: An Integrated Systems Treatment Approach

ERIC Educational Resources Information Center

Weeks, Gerald R.; Cross, Chad L.

2004-01-01

This article introduces the intersystem model of psychotherapy and discusses its utility as a truly integrative and comprehensive approach. The foundation of this conceptually complex approach comes from dialectic metatheory; hence, its derivation requires an understanding of both foundational and integrational constructs. The article provides a…

A reduced order, test verified component mode synthesis approach for system modeling applications

NASA Astrophysics Data System (ADS)

Butland, Adam; Avitabile, Peter

2010-05-01

Component mode synthesis (CMS) is a very common approach used for the generation of large system models. In general, these modeling techniques can be separated into two categories: those utilizing a combination of constraint modes and fixed interface normal modes and those based on a combination of free interface normal modes and residual flexibility terms. The major limitation of the methods utilizing constraint modes and fixed interface normal modes is the inability to easily obtain the required information from testing; the result of this limitation is that constraint mode-based techniques are primarily used with numerical models. An alternate approach is proposed which utilizes frequency and shape information acquired from modal testing to update reduced order finite element models using exact analytical model improvement techniques. The connection degrees of freedom are then rigidly constrained in the test verified, reduced order model to provide the boundary conditions necessary for constraint modes and fixed interface normal modes. The CMS approach is then used with this test verified, reduced order model to generate the system model for further analysis. A laboratory structure is used to show the application of the technique with both numerical and simulated experimental components to describe the system and validate the proposed approach. Actual test data is then used in the approach proposed. Due to typical measurement data contaminants that are always included in any test, the measured data is further processed to remove contaminants and is then used in the proposed approach. The final case using improved data with the reduced order, test verified components is shown to produce very acceptable results from the Craig-Bampton component mode synthesis approach. Use of the technique with its strengths and weaknesses are discussed.

River Export of Plastic from Land to Sea: A Global Modeling Approach

NASA Astrophysics Data System (ADS)

Siegfried, Max; Gabbert, Silke; Koelmans, Albert A.; Kroeze, Carolien; Löhr, Ansje; Verburg, Charlotte

2016-04-01

Plastic is increasingly considered a serious cause of water pollution. It is a threat to aquatic ecosystems, including rivers, coastal waters and oceans. Rivers transport considerable amounts of plastic from land to sea. The quantity and its main sources, however, are not well known. Assessing the amount of macro- and microplastic transport from river to sea is, therefore, important for understanding the dimension and the patterns of plastic pollution of aquatic ecosystems. In addition, it is crucial for assessing short- and long-term impacts caused by plastic pollution. Here we present a global modelling approach to quantify river export of plastic from land to sea. Our approach accounts for different types of plastic, including both macro- and micro-plastics. Moreover, we distinguish point sources and diffuse sources of plastic in rivers. Our modelling approach is inspired by global nutrient models, which include more than 6000 river basins. In this paper, we will present our modelling approach, as well as first model results for micro-plastic pollution in European rivers. Important sources of micro-plastics include personal care products, laundry, household dust and car tyre wear. We combine information on these sources with information on sewage management, and plastic retention during river transport for the largest European rivers. Our modelling approach may help to better understand and prevent water pollution by plastic , and at the same time serves as 'proof of concept' for future application on global scale.

Mathematically guided approaches to distinguish models of periodic patterning

PubMed Central

Hiscock, Tom W.; Megason, Sean G.

2015-01-01

How periodic patterns are generated is an open question. A number of mechanisms have been proposed – most famously, Turing's reaction-diffusion model. However, many theoretical and experimental studies focus on the Turing mechanism while ignoring other possible mechanisms. Here, we use a general model of periodic patterning to show that different types of mechanism (molecular, cellular, mechanical) can generate qualitatively similar final patterns. Observation of final patterns is therefore not sufficient to favour one mechanism over others. However, we propose that a mathematical approach can help to guide the design of experiments that can distinguish between different mechanisms, and illustrate the potential value of this approach with specific biological examples. PMID:25605777

An approach for modelling snowcover ablation and snowmelt runoff in cold region environments

NASA Astrophysics Data System (ADS)

Dornes, Pablo Fernando

Reliable hydrological model simulations are the result of numerous complex interactions among hydrological inputs, landscape properties, and initial conditions. Determination of the effects of these factors is one of the main challenges in hydrological modelling. This situation becomes even more difficult in cold regions due to the ungauged nature of subarctic and arctic environments. This research work is an attempt to apply a new approach for modelling snowcover ablation and snowmelt runoff in complex subarctic environments with limited data while retaining integrity in the process representations. The modelling strategy is based on the incorporation of both detailed process understanding and inputs along with information gained from observations of basin-wide streamflow phenomenon; essentially a combination of deductive and inductive approaches. The study was conducted in the Wolf Creek Research Basin, Yukon Territory, using three models, a small-scale physically based hydrological model, a land surface scheme, and a land surface hydrological model. The spatial representation was based on previous research studies and observations, and was accomplished by incorporating landscape units, defined according to topography and vegetation, as the spatial model elements. Comparisons between distributed and aggregated modelling approaches showed that simulations incorporating distributed initial snowcover and corrected solar radiation were able to properly simulate snowcover ablation and snowmelt runoff whereas the aggregated modelling approaches were unable to represent the differential snowmelt rates and complex snowmelt runoff dynamics. Similarly, the inclusion of spatially distributed information in a land surface scheme clearly improved simulations of snowcover ablation. Application of the same modelling approach at a larger scale using the same landscape based parameterisation showed satisfactory results in simulating snowcover ablation and snowmelt runoff with

PROCRU: A model for analyzing flight crew procedures in approach to landing

NASA Technical Reports Server (NTRS)

Baron, S.; Zacharias, G.; Muraidharan, R.; Lancraft, R.

1982-01-01

A model for the human performance of approach and landing tasks that would provide a means for systematic exploration of questions concerning the impact of procedural and equipment design and the allocation of resources in the cockpit on performance and safety in approach-to-landing is discussed. A system model is needed that accounts for the interactions of crew, procedures, vehicle, approach geometry, and environment. The issues of interest revolve principally around allocation of tasks in the cockpit and crew performance with respect to the cognitive aspects of the tasks. The model must, therefore, deal effectively with information processing and decision-making aspects of human performance.

Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches.

PubMed

Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S

2017-08-01

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.

A secured e-tendering modeling using misuse case approach

NASA Astrophysics Data System (ADS)

Mohd, Haslina; Robie, Muhammad Afdhal Muhammad; Baharom, Fauziah; Darus, Norida Muhd; Saip, Mohamed Ali; Yasin, Azman

2016-08-01

Major risk factors relating to electronic transactions may lead to destructive impacts on trust and transparency in the process of tendering. Currently, electronic tendering (e-tendering) systems still remain uncertain in issues relating to legal and security compliance and most importantly it has an unclear security framework. Particularly, the available systems are lacking in addressing integrity, confidentiality, authentication, and non-repudiation in e-tendering requirements. Thus, one of the challenges in developing an e-tendering system is to ensure the system requirements include the function for secured and trusted environment. Therefore, this paper aims to model a secured e-tendering system using misuse case approach. The modeling process begins with identifying the e-tendering process, which is based on the Australian Standard Code of Tendering (AS 4120-1994). It is followed by identifying security threats and their countermeasure. Then, the e-tendering was modelled using misuse case approach. The model can contribute to e-tendering developers and also to other researchers or experts in the e-tendering domain.

EPA and EFSA approaches for Benchmark Dose modeling

EPA Science Inventory

Benchmark dose (BMD) modeling has become the preferred approach in the analysis of toxicological dose-response data for the purpose of deriving human health toxicity values. The software packages most often used are Benchmark Dose Software (BMDS, developed by EPA) and PROAST (de...

Validation of Slosh Modeling Approach Using STAR-CCM+

NASA Technical Reports Server (NTRS)

Benson, David J.; Ng, Wanyi

2018-01-01

Without an adequate understanding of propellant slosh, the spacecraft attitude control system may be inadequate to control the spacecraft or there may be an unexpected loss of science observation time due to higher slosh settling times. Computational fluid dynamics (CFD) is used to model propellant slosh. STAR-CCM+ is a commercially available CFD code. This paper seeks to validate the CFD modeling approach via a comparison between STAR-CCM+ liquid slosh modeling results and experimental, empirically, and analytically derived results. The geometries examined are a bare right cylinder tank and a right cylinder with a single ring baffle.

Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach

NASA Astrophysics Data System (ADS)

Yuniarto, Budi; Kurniawan, Robert

2017-03-01

PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.

Anthropomorphic Coding of Speech and Audio: A Model Inversion Approach

NASA Astrophysics Data System (ADS)

Feldbauer, Christian; Kubin, Gernot; Kleijn, W. Bastiaan

2005-12-01

Auditory modeling is a well-established methodology that provides insight into human perception and that facilitates the extraction of signal features that are most relevant to the listener. The aim of this paper is to provide a tutorial on perceptual speech and audio coding using an invertible auditory model. In this approach, the audio signal is converted into an auditory representation using an invertible auditory model. The auditory representation is quantized and coded. Upon decoding, it is then transformed back into the acoustic domain. This transformation converts a complex distortion criterion into a simple one, thus facilitating quantization with low complexity. We briefly review past work on auditory models and describe in more detail the components of our invertible model and its inversion procedure, that is, the method to reconstruct the signal from the output of the auditory model. We summarize attempts to use the auditory representation for low-bit-rate coding. Our approach also allows the exploitation of the inherent redundancy of the human auditory system for the purpose of multiple description (joint source-channel) coding.

Spectral Estimation: An Overdetermined Rational Model Equation Approach.

DTIC Science & Technology

1982-09-15

A-A123 122 SPECTRAL ESTIMATION: AN OVERDETERMINEO RATIONAL MODEL 1/2 EQUATION APPROACH..(U) ARIZONA STATE UNIV TEMPE DEPT OF ELECTRICAL AND COMPUTER...2 0 447,_______ 4. TITLE (mAd Sabile) S. TYPE or REPORT a PEP40D COVERED Spectral Estimation; An Overdeteruined Rational Final Report 9/3 D/8 to...andmmd&t, by uwek 7a5 4 Rational Spectral Estimation, ARMA mo~Ie1, AR model, NMA Mdle, Spectrum, Singular Value Decomposition. Adaptivb Implementatlan

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Validation of a Parametric Approach for 3d Fortification Modelling: Application to Scale Models

NASA Astrophysics Data System (ADS)

Jacquot, K.; Chevrier, C.; Halin, G.

2013-02-01

Parametric modelling approach applied to cultural heritage virtual representation is a field of research explored for years since it can address many limitations of digitising tools. For example, essential historical sources for fortification virtual reconstructions like plans-reliefs have several shortcomings when they are scanned. To overcome those problems, knowledge based-modelling can be used: knowledge models based on the analysis of theoretical literature of a specific domain such as bastioned fortification treatises can be the cornerstone of the creation of a parametric library of fortification components. Implemented in Grasshopper, these components are manually adjusted on the data available (i.e. 3D surveys of plans-reliefs or scanned maps). Most of the fortification area is now modelled and the question of accuracy assessment is raised. A specific method is used to evaluate the accuracy of the parametric components. The results of the assessment process will allow us to validate the parametric approach. The automation of the adjustment process can finally be planned. The virtual model of fortification is part of a larger project aimed at valorising and diffusing a very unique cultural heritage item: the collection of plans-reliefs. As such, knowledge models are precious assets when automation and semantic enhancements will be considered.

The Elastic Body Model: A Pedagogical Approach Integrating Real Time Measurements and Modelling Activities

ERIC Educational Resources Information Center

Fazio, C.; Guastella, I.; Tarantino, G.

2007-01-01

In this paper, we describe a pedagogical approach to elastic body movement based on measurements of the contact times between a metallic rod and small bodies colliding with it and on modelling of the experimental results by using a microcomputer-based laboratory and simulation tools. The experiments and modelling activities have been built in the…

A Model-Driven Approach for Telecommunications Network Services Definition

NASA Astrophysics Data System (ADS)

Chiprianov, Vanea; Kermarrec, Yvon; Alff, Patrick D.

Present day Telecommunications market imposes a short concept-to-market time for service providers. To reduce it, we propose a computer-aided, model-driven, service-specific tool, with support for collaborative work and for checking properties on models. We started by defining a prototype of the Meta-model (MM) of the service domain. Using this prototype, we defined a simple graphical modeling language specific for service designers. We are currently enlarging the MM of the domain using model transformations from Network Abstractions Layers (NALs). In the future, we will investigate approaches to ensure the support for collaborative work and for checking properties on models.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity

Effective use of integrated hydrological models in basin-scale water resources management: surrogate modeling approaches

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Wu, X.

2015-12-01

Integrated hydrological models (IHMs) consider surface water and subsurface water as a unified system, and have been widely adopted in basin-scale water resources studies. However, due to IHMs' mathematical complexity and high computational cost, it is difficult to implement them in an iterative model evaluation process (e.g., Monte Carlo Simulation, simulation-optimization analysis, etc.), which diminishes their applicability for supporting decision-making in real-world situations. Our studies investigated how to effectively use complex IHMs to address real-world water issues via surrogate modeling. Three surrogate modeling approaches were considered, including 1) DYCORS (DYnamic COordinate search using Response Surface models), a well-established response surface-based optimization algorithm; 2) SOIM (Surrogate-based Optimization for Integrated surface water-groundwater Modeling), a response surface-based optimization algorithm that we developed specifically for IHMs; and 3) Probabilistic Collocation Method (PCM), a stochastic response surface approach. Our investigation was based on a modeling case study in the Heihe River Basin (HRB), China's second largest endorheic river basin. The GSFLOW (Coupled Ground-Water and Surface-Water Flow Model) model was employed. Two decision problems were discussed. One is to optimize, both in time and in space, the conjunctive use of surface water and groundwater for agricultural irrigation in the middle HRB region; and the other is to cost-effectively collect hydrological data based on a data-worth evaluation. Overall, our study results highlight the value of incorporating an IHM in making decisions of water resources management and hydrological data collection. An IHM like GSFLOW can provide great flexibility to formulating proper objective functions and constraints for various optimization problems. On the other hand, it has been demonstrated that surrogate modeling approaches can pave the path for such incorporation in real

Modelling road accidents: An approach using structural time series

NASA Astrophysics Data System (ADS)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

PubMed

Patri, Jean-François; Diard, Julien; Perrier, Pascal

2015-12-01

The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

Accurate analytical modeling of junctionless DG-MOSFET by green's function approach

NASA Astrophysics Data System (ADS)

Nandi, Ashutosh; Pandey, Nilesh

2017-11-01

An accurate analytical model of Junctionless double gate MOSFET (JL-DG-MOSFET) in the subthreshold regime of operation is developed in this work using green's function approach. The approach considers 2-D mixed boundary conditions and multi-zone techniques to provide an exact analytical solution to 2-D Poisson's equation. The Fourier coefficients are calculated correctly to derive the potential equations that are further used to model the channel current and subthreshold slope of the device. The threshold voltage roll-off is computed from parallel shifts of Ids-Vgs curves between the long channel and short-channel devices. It is observed that the green's function approach of solving 2-D Poisson's equation in both oxide and silicon region can accurately predict channel potential, subthreshold current (Isub), threshold voltage (Vt) roll-off and subthreshold slope (SS) of both long & short channel devices designed with different doping concentrations and higher as well as lower tsi/tox ratio. All the analytical model results are verified through comparisons with TCAD Sentaurus simulation results. It is observed that the model matches quite well with TCAD device simulations.

A compartmental-spatial system dynamics approach to ground water modeling.

PubMed

Roach, Jesse; Tidwell, Vince

2009-01-01

High-resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental-spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real-time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51-compartment CSSD model was built and calibrated from a 100,0001 cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy-seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.

Modeling the cometary environment using a fluid approach

NASA Astrophysics Data System (ADS)

Shou, Yinsi

Comets are believed to have preserved the building material of the early solar system and to hold clues to the origin of life on Earth. Abundant remote observations of comets by telescopes and the in-situ measurements by a handful of space missions reveal that the cometary environments are complicated by various physical and chemical processes among the neutral gases and dust grains released from comets, cometary ions, and the solar wind in the interplanetary space. Therefore, physics-based numerical models are in demand to interpret the observational data and to deepen our understanding of the cometary environment. In this thesis, three models using a fluid approach, which include important physical and chemical processes underlying the cometary environment, have been developed to study the plasma, neutral gas, and the dust grains, respectively. Although models based on the fluid approach have limitations in capturing all of the correct physics for certain applications, especially for very low gas density environment, they are computationally much more efficient than alternatives. In the simulations of comet 67P/Churyumov-Gerasimenko at various heliocentric distances with a wide range of production rates, our multi-fluid cometary neutral gas model and multi-fluid cometary dust model have achieved comparable results to the Direct Simulation Monte Carlo (DSMC) model, which is based on a kinetic approach that is valid in all collisional regimes. Therefore, our model is a powerful alternative to the particle-based model, especially for some computationally intensive simulations. Capable of accounting for the varying heating efficiency under various physical conditions in a self-consistent way, the multi-fluid cometary neutral gas model is a good tool to study the dynamics of the cometary coma with different production rates and heliocentric distances. The modeled H2O expansion speeds reproduce the general trend and the speed's nonlinear dependencies of production rate

Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

USGS Publications Warehouse

Farley, Kevin J.; Meyer, Joe; Balistrieri, Laurie S.; DeSchamphelaere, Karl; Iwasaki, Yuichi; Janssen, Colin; Kamo, Masashi; Lofts, Steve; Mebane, Christopher A.; Naito, Wataru; Ryan, Adam C.; Santore, Robert C.; Tipping, Edward

2015-01-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the U.S. Geological Survey (USA), HDR⎪HydroQual, Inc. (USA), and the Centre for Ecology and Hydrology (UK) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME Workshop in Brussels, Belgium (May 2012), is provided herein. Overall, the models were found to be similar in structure (free ion activities computed by WHAM; specific or non-specific binding of metals/cations in or on the organism; specification of metal potency factors and/or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single versus multiple types of binding site on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong inter-relationships among the model parameters (log KM values, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Reduced modeling of signal transduction – a modular approach

PubMed Central

Koschorreck, Markus; Conzelmann, Holger; Ebert, Sybille; Ederer, Michael; Gilles, Ernst Dieter

2007-01-01

Background Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. Results We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. Conclusion The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for

A moni-modelling approach to manage groundwater risk to pesticide leaching at regional scale.

PubMed

Di Guardo, Andrea; Finizio, Antonio

2016-03-01

Historically, the approach used to manage risk of chemical contamination of water bodies is based on the use of monitoring programmes, which provide a snapshot of the presence/absence of chemicals in water bodies. Monitoring is required in the current EU regulations, such as the Water Framework Directive (WFD), as a tool to record temporal variation in the chemical status of water bodies. More recently, a number of models have been developed and used to forecast chemical contamination of water bodies. These models combine information of chemical properties, their use, and environmental scenarios. Both approaches are useful for risk assessors in decision processes. However, in our opinion, both show flaws and strengths when taken alone. This paper proposes an integrated approach (moni-modelling approach) where monitoring data and modelling simulations work together in order to provide a common decision framework for the risk assessor. This approach would be very useful, particularly for the risk management of pesticides at a territorial level. It fulfils the requirement of the recent Sustainable Use of Pesticides Directive. In fact, the moni-modelling approach could be used to identify sensible areas where implement mitigation measures or limitation of use of pesticides, but even to effectively re-design future monitoring networks or to better calibrate the pedo-climatic input data for the environmental fate models. A case study is presented, where the moni-modelling approach is applied in Lombardy region (North of Italy) to identify groundwater vulnerable areas to pesticides. The approach has been applied to six active substances with different leaching behaviour, in order to highlight the advantages in using the proposed methodology. Copyright © 2015 Elsevier B.V. All rights reserved.

Numerical modeling of axi-symmetrical cold forging process by ``Pseudo Inverse Approach''

NASA Astrophysics Data System (ADS)

Halouani, A.; Li, Y. M.; Abbes, B.; Guo, Y. Q.

2011-05-01

The incremental approach is widely used for the forging process modeling, it gives good strain and stress estimation, but it is time consuming. A fast Inverse Approach (IA) has been developed for the axi-symmetric cold forging modeling [1-2]. This approach exploits maximum the knowledge of the final part's shape and the assumptions of proportional loading and simplified tool actions make the IA simulation very fast. The IA is proved very useful for the tool design and optimization because of its rapidity and good strain estimation. However, the assumptions mentioned above cannot provide good stress estimation because of neglecting the loading history. A new approach called "Pseudo Inverse Approach" (PIA) was proposed by Batoz, Guo et al.. [3] for the sheet forming modeling, which keeps the IA's advantages but gives good stress estimation by taking into consideration the loading history. Our aim is to adapt the PIA for the cold forging modeling in this paper. The main developments in PIA are resumed as follows: A few intermediate configurations are generated for the given tools' positions to consider the deformation history; the strain increment is calculated by the inverse method between the previous and actual configurations. An incremental algorithm of the plastic integration is used in PIA instead of the total constitutive law used in the IA. An example is used to show the effectiveness and limitations of the PIA for the cold forging process modeling.

Finding your inner modeler: An NSF-sponsored workshop to introduce cell biologists to modeling/computational approaches.

PubMed

Stone, David E; Haswell, Elizabeth S; Sztul, Elizabeth

2017-01-01

In classical Cell Biology, fundamental cellular processes are revealed empirically, one experiment at a time. While this approach has been enormously fruitful, our understanding of cells is far from complete. In fact, the more we know, the more keenly we perceive our ignorance of the profoundly complex and dynamic molecular systems that underlie cell structure and function. Thus, it has become apparent to many cell biologists that experimentation alone is unlikely to yield major new paradigms, and that empiricism must be combined with theory and computational approaches to yield major new discoveries. To facilitate those discoveries, three workshops will convene annually for one day in three successive summers (2017-2019) to promote the use of computational modeling by cell biologists currently unconvinced of its utility or unsure how to apply it. The first of these workshops was held at the University of Illinois, Chicago in July 2017. Organized to facilitate interactions between traditional cell biologists and computational modelers, it provided a unique educational opportunity: a primer on how cell biologists with little or no relevant experience can incorporate computational modeling into their research. Here, we report on the workshop and describe how it addressed key issues that cell biologists face when considering modeling including: (1) Is my project appropriate for modeling? (2) What kind of data do I need to model my process? (3) How do I find a modeler to help me in integrating modeling approaches into my work? And, perhaps most importantly, (4) why should I bother?

Electromagnetic forward modelling for realistic Earth models using unstructured tetrahedral meshes and a meshfree approach

NASA Astrophysics Data System (ADS)

Farquharson, C.; Long, J.; Lu, X.; Lelievre, P. G.

2017-12-01

Real-life geology is complex, and so, even when allowing for the diffusive, low resolution nature of geophysical electromagnetic methods, we need Earth models that can accurately represent this complexity when modelling and inverting electromagnetic data. This is particularly the case for the scales, detail and conductivity contrasts involved in mineral and hydrocarbon exploration and development, but also for the larger scale of lithospheric studies. Unstructured tetrahedral meshes provide a flexible means of discretizing a general, arbitrary Earth model. This is important when wanting to integrate a geophysical Earth model with a geological Earth model parameterized in terms of surfaces. Finite-element and finite-volume methods can be derived for computing the electric and magnetic fields in a model parameterized using an unstructured tetrahedral mesh. A number of such variants have been proposed and have proven successful. However, the efficiency and accuracy of these methods can be affected by the "quality" of the tetrahedral discretization, that is, how many of the tetrahedral cells in the mesh are long, narrow and pointy. This is particularly the case if one wants to use an iterative technique to solve the resulting linear system of equations. One approach to deal with this issue is to develop sophisticated model and mesh building and manipulation capabilities in order to ensure that any mesh built from geological information is of sufficient quality for the electromagnetic modelling. Another approach is to investigate other methods of synthesizing the electromagnetic fields. One such example is a "meshfree" approach in which the electromagnetic fields are synthesized using a mesh that is distinct from the mesh used to parameterized the Earth model. There are then two meshes, one describing the Earth model and one used for the numerical mathematics of computing the fields. This means that there are no longer any quality requirements on the model mesh, which

A model predictive speed tracking control approach for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Zhu, Min; Chen, Huiyan; Xiong, Guangming

2017-03-01

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

Comparative study of two approaches to model the offshore fish cages

NASA Astrophysics Data System (ADS)

Zhao, Yun-peng; Wang, Xin-xin; Decew, Jud; Tsukrov, Igor; Bai, Xiao-dong; Bi, Chun-wei

2015-06-01

The goal of this paper is to provide a comparative analysis of two commonly used approaches to discretize offshore fish cages: the lumped-mass approach and the finite element technique. Two case studies are chosen to compare predictions of the LMA (lumped-mass approach) and FEA (finite element analysis) based numerical modeling techniques. In both case studies, we consider several loading conditions consisting of different uniform currents and monochromatic waves. We investigate motion of the cage, its deformation, and the resultant tension in the mooring lines. Both model predictions are sufficient close to the experimental data, but for the first experiment, the DUT-FlexSim predictions are slightly more accurate than the ones provided by Aqua-FE™. According to the comparisons, both models can be successfully utilized to the design and analysis of the offshore fish cages provided that an appropriate safety factor is chosen.

Practical modeling approaches for geological storage of carbon dioxide.

PubMed

Celia, Michael A; Nordbotten, Jan M

2009-01-01

The relentless increase of anthropogenic carbon dioxide emissions and the associated concerns about climate change have motivated new ideas about carbon-constrained energy production. One technological approach to control carbon dioxide emissions is carbon capture and storage, or CCS. The underlying idea of CCS is to capture the carbon before it emitted to the atmosphere and store it somewhere other than the atmosphere. Currently, the most attractive option for large-scale storage is in deep geological formations, including deep saline aquifers. Many physical and chemical processes can affect the fate of the injected CO2, with the overall mathematical description of the complete system becoming very complex. Our approach to the problem has been to reduce complexity as much as possible, so that we can focus on the few truly important questions about the injected CO2, most of which involve leakage out of the injection formation. Toward this end, we have established a set of simplifying assumptions that allow us to derive simplified models, which can be solved numerically or, for the most simplified cases, analytically. These simplified models allow calculation of solutions to large-scale injection and leakage problems in ways that traditional multicomponent multiphase simulators cannot. Such simplified models provide important tools for system analysis, screening calculations, and overall risk-assessment calculations. We believe this is a practical and important approach to model geological storage of carbon dioxide. It also serves as an example of how complex systems can be simplified while retaining the essential physics of the problem.

The Common Factors Discrimination Model: An Integrated Approach to Counselor Supervision

ERIC Educational Resources Information Center

Crunk, A. Elizabeth; Barden, Sejal M.

2017-01-01

Numerous models of clinical supervision have been developed; however, there is little empirical support indicating that any one model is superior. Therefore, common factors approaches to supervision integrate essential components that are shared among counseling and supervision models. The purpose of this paper is to present an innovative model of…

Research on teacher education programs: logic model approach.

PubMed

Newton, Xiaoxia A; Poon, Rebecca C; Nunes, Nicole L; Stone, Elisa M

2013-02-01

Teacher education programs in the United States face increasing pressure to demonstrate their effectiveness through pupils' learning gains in classrooms where program graduates teach. The link between teacher candidates' learning in teacher education programs and pupils' learning in K-12 classrooms implicit in the policy discourse suggests a one-to-one correspondence. However, the logical steps leading from what teacher candidates have learned in their programs to what they are doing in classrooms that may contribute to their pupils' learning are anything but straightforward. In this paper, we argue that the logic model approach from scholarship on evaluation can enhance research on teacher education by making explicit the logical links between program processes and intended outcomes. We demonstrate the usefulness of the logic model approach through our own work on designing a longitudinal study that focuses on examining the process and impact of an undergraduate mathematics and science teacher education program. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Feature-Based Approach to Modeling Protein–DNA Interactions

PubMed Central

Segal, Eran

2008-01-01

Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/. PMID:18725950

Longitudinal Models of Reliability and Validity: A Latent Curve Approach.

ERIC Educational Resources Information Center

Tisak, John; Tisak, Marie S.

1996-01-01

Dynamic generalizations of reliability and validity that will incorporate longitudinal or developmental models, using latent curve analysis, are discussed. A latent curve model formulated to depict change is incorporated into the classical definitions of reliability and validity. The approach is illustrated with sociological and psychological…

Human systems immunology: hypothesis-based modeling and unbiased data-driven approaches.

PubMed

Arazi, Arnon; Pendergraft, William F; Ribeiro, Ruy M; Perelson, Alan S; Hacohen, Nir

2013-10-31

Systems immunology is an emerging paradigm that aims at a more systematic and quantitative understanding of the immune system. Two major approaches have been utilized to date in this field: unbiased data-driven modeling to comprehensively identify molecular and cellular components of a system and their interactions; and hypothesis-based quantitative modeling to understand the operating principles of a system by extracting a minimal set of variables and rules underlying them. In this review, we describe applications of the two approaches to the study of viral infections and autoimmune diseases in humans, and discuss possible ways by which these two approaches can synergize when applied to human immunology. Copyright © 2012 Elsevier Ltd. All rights reserved.

Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato.

PubMed

Tran, Dinh T; Hertog, Maarten L A T M; Tran, Thi L H; Quyen, Nguyen T; Van de Poel, Bram; Mata, Clara I; Nicolaï, Bart M

2017-01-01

In this study, the aim is to develop a population model based approach to optimize fruit harvesting strategies with regard to fruit quality and its derived economic value. This approach was applied to the case of tomato fruit harvesting under Vietnamese conditions. Fruit growth and development of tomato (cv. "Savior") was monitored in terms of fruit size and color during both the Vietnamese winter and summer growing seasons. A kinetic tomato fruit growth model was applied to quantify biological fruit-to-fruit variation in terms of their physiological maturation. This model was successfully calibrated. Finally, the model was extended to translate the fruit-to-fruit variation at harvest into the economic value of the harvested crop. It can be concluded that a model based approach to the optimization of harvest date and harvest frequency with regard to economic value of the crop as such is feasible. This approach allows growers to optimize their harvesting strategy by harvesting the crop at more uniform maturity stages meeting the stringent retail demands for homogeneous high quality product. The total farm profit would still depend on the impact a change in harvesting strategy might have on related expenditures. This model based harvest optimisation approach can be easily transferred to other fruit and vegetable crops improving homogeneity of the postharvest product streams.

Nonrelativistic approaches derived from point-coupling relativistic models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lourenco, O.; Dutra, M.; Delfino, A.

2010-03-15

We construct nonrelativistic versions of relativistic nonlinear hadronic point-coupling models, based on new normalized spinor wave functions after small component reduction. These expansions give us energy density functionals that can be compared to their relativistic counterparts. We show that the agreement between the nonrelativistic limit approach and the Skyrme parametrizations becomes strongly dependent on the incompressibility of each model. We also show that the particular case A=B=0 (Walecka model) leads to the same energy density functional of the Skyrme parametrizations SV and ZR2, while the truncation scheme, up to order {rho}{sup 3}, leads to parametrizations for which {sigma}=1.

A Cybernetic Approach to the Modeling of Agent Communities

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Karlin, Jay

2000-01-01

In an earlier paper [1] examples of agent technology in a NASA context were presented. Both groundbased and space-based applications were addressed. This paper continues the discussion of one aspect of the Goddard Space Flight Center's continuing efforts to develop a community of agents that can support both ground-based and space-based systems autonomy. The paper focuses on an approach to agent-community modeling based on the theory of viable systems developed by Stafford Beer. It gives the status of an initial attempt to capture some of the agent-community behaviors in a viable system context. This paper is expository in nature and focuses on a discussion of the modeling of some of the underlying concepts and infrastructure that will serve as the basis of more detailed investigative work into the behavior of agent communities. The paper is organized as follows. First, a general introduction to agent community requirements is presented. Secondly, a brief introduction to the cybernetic concept of a viable system is given. This concept forms the foundation of the modeling approach. Then the concept of an agent community is modeled in the cybernetic context.

Teaching Service Modelling to a Mixed Class: An Integrated Approach

ERIC Educational Resources Information Center

Deng, Jeremiah D.; Purvis, Martin K.

2015-01-01

Service modelling has become an increasingly important area in today's telecommunications and information systems practice. We have adapted a Network Design course in order to teach service modelling to a mixed class of both the telecommunication engineering and information systems backgrounds. An integrated approach engaging mathematics teaching…

Physiology-based modelling approaches to characterize fish habitat suitability: Their usefulness and limitations

NASA Astrophysics Data System (ADS)

Teal, Lorna R.; Marras, Stefano; Peck, Myron A.; Domenici, Paolo

2018-02-01

Models are useful tools for predicting the impact of global change on species distribution and abundance. As ectotherms, fish are being challenged to adapt or track changes in their environment, either in time through a phenological shift or in space by a biogeographic shift. Past modelling efforts have largely been based on correlative Species Distribution Models, which use known occurrences of species across landscapes of interest to define sets of conditions under which species are likely to maintain populations. The practical advantages of this correlative approach are its simplicity and the flexibility in terms of data requirements. However, effective conservation management requires models that make projections beyond the range of available data. One way to deal with such an extrapolation is to use a mechanistic approach based on physiological processes underlying climate change effects on organisms. Here we illustrate two approaches for developing physiology-based models to characterize fish habitat suitability. (i) Aerobic Scope Models (ASM) are based on the relationship between environmental factors and aerobic scope (defined as the difference between maximum and standard (basal) metabolism). This approach is based on experimental data collected by using a number of treatments that allow a function to be derived to predict aerobic metabolic scope from the stressor/environmental factor(s). This function is then integrated with environmental (oceanographic) data of current and future scenarios. For any given species, this approach allows habitat suitability maps to be generated at various spatiotemporal scales. The strength of the ASM approach relies on the estimate of relative performance when comparing, for example, different locations or different species. (ii) Dynamic Energy Budget (DEB) models are based on first principles including the idea that metabolism is organised in the same way within all animals. The (standard) DEB model aims to describe

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches.

PubMed

Farley, Kevin J; Meyer, Joseph S; Balistrieri, Laurie S; De Schamphelaere, Karel A C; Iwasaki, Yuichi; Janssen, Colin R; Kamo, Masashi; Lofts, Stephen; Mebane, Christopher A; Naito, Wataru; Ryan, Adam C; Santore, Robert C; Tipping, Edward

2015-04-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed. © 2014 SETAC.

COMPARISON OF PBPK MODELING SOFTWARE FEATURES AND APPROACHES TO MODELING IMPORTNAT PHYSIOLOGICAL AND BIOCHEMICAL BEHAVIORS

EPA Science Inventory

Abstract for 40th Annual Meeting of the Society of Toxicology, March 25-29, 2001

COMPARISON OF PBPK MODELING SOFTWARE FEATURES AND APPROACHES TO MODELING IMPORTANT PHYSIOLOGICAL AND BIOCHEMICAL BEHAVIORS. R S DeWoskin and R W Setzer. USEPA/ORD/NHEERL, RTP, NC, USA.

...

Biomass transformation webs provide a unified approach to consumer–resource modelling

PubMed Central

Getz, Wayne M.

2011-01-01

An approach to modelling food web biomass flows among live and dead compartments within and among species is formulated using metaphysiological principles that characterise population growth in terms of basal metabolism, feeding, senescence and exploitation. This leads to a unified approach to modelling interactions among plants, herbivores, carnivores, scavengers, parasites and their resources. Also, dichotomising sessile miners from mobile gatherers of resources, with relevance to feeding and starvation time scales, suggests a new classification scheme involving 10 primary categories of consumer types. These types, in various combinations, rigorously distinguish scavenger from parasite, herbivory from phytophagy and detritivore from decomposer. Application of the approach to particular consumer–resource interactions is demonstrated, culminating in the construction of an anthrax-centred food web model, with parameters applicable to Etosha National Park, Namibia, where deaths of elephants and zebra from the bacterial pathogen, Bacillus anthracis, provide significant subsidies to jackals, vultures and other scavengers. PMID:21199247

A chain reaction approach to modelling gene pathways.

PubMed

Cheng, Gary C; Chen, Dung-Tsa; Chen, James J; Soong, Seng-Jaw; Lamartiniere, Coral; Barnes, Stephen

2012-08-01

BACKGROUND: Of great interest in cancer prevention is how nutrient components affect gene pathways associated with the physiological events of puberty. Nutrient-gene interactions may cause changes in breast or prostate cells and, therefore, may result in cancer risk later in life. Analysis of gene pathways can lead to insights about nutrient-gene interactions and the development of more effective prevention approaches to reduce cancer risk. To date, researchers have relied heavily upon experimental assays (such as microarray analysis, etc.) to identify genes and their associated pathways that are affected by nutrient and diets. However, the vast number of genes and combinations of gene pathways, coupled with the expense of the experimental analyses, has delayed the progress of gene-pathway research. The development of an analytical approach based on available test data could greatly benefit the evaluation of gene pathways, and thus advance the study of nutrient-gene interactions in cancer prevention. In the present study, we have proposed a chain reaction model to simulate gene pathways, in which the gene expression changes through the pathway are represented by the species undergoing a set of chemical reactions. We have also developed a numerical tool to solve for the species changes due to the chain reactions over time. Through this approach we can examine the impact of nutrient-containing diets on the gene pathway; moreover, transformation of genes over time with a nutrient treatment can be observed numerically, which is very difficult to achieve experimentally. We apply this approach to microarray analysis data from an experiment which involved the effects of three polyphenols (nutrient treatments), epigallo-catechin-3-O-gallate (EGCG), genistein, and resveratrol, in a study of nutrient-gene interaction in the estrogen synthesis pathway during puberty. RESULTS: In this preliminary study, the estrogen synthesis pathway was simulated by a chain reaction model. By

Exploring a microbial ecosystem approach to modeling deep ocean biogeochemical cycles

NASA Astrophysics Data System (ADS)

Zakem, E.; Follows, M. J.

2014-12-01

Though microbial respiration of organic matter in the deep ocean governs ocean and atmosphere biogeochemistry, it is not represented mechanistically in current global biogeochemical models. We seek approaches that are feasible for a global resolution, yet still reflect the enormous biodiversity of the deep microbial community and its associated metabolic pathways. We present a modeling framework grounded in thermodynamics and redox reaction stoichiometry that represents diverse microbial metabolisms explicitly. We describe a bacterial/archaeal functional type with two parameters: a growth efficiency representing the chemistry underlying a bacterial metabolism, and a rate limitation given by the rate of uptake of each of the necessary substrates for that metabolism. We then apply this approach to answer questions about microbial ecology. As a start, we resolve two dominant heterotrophic respiratory pathways- reduction of oxygen and nitrate- and associated microbial functional types. We combine these into an ecological model and a two-dimensional ocean circulation model to explore the organization, biogeochemistry, and ecology of oxygen minimum zones. Intensified upwelling and lateral transport conspire to produce an oxygen minimum at mid-depth, populated by anaerobic denitrifiers. This modeling approach should ultimately allow for the emergence of bacterial biogeography from competition of metabolisms and for the incorporation of microbial feedbacks to the climate system.

Transient high frequency signal estimation: A model-based processing approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnes, F.L.

1985-03-22

By utilizing the superposition property of linear systems a method of estimating the incident signal from reflective nondispersive data is developed. One of the basic merits of this approach is that, the reflections were removed by direct application of a Weiner type estimation algorithm, after the appropriate input was synthesized. The structure of the nondispersive signal model is well documented, and thus its' credence is established. The model is stated and more effort is devoted to practical methods of estimating the model parameters. Though a general approach was developed for obtaining the reflection weights, a simpler approach was employed here,more » since a fairly good reflection model is available. The technique essentially consists of calculating ratios of the autocorrelation function at lag zero and that lag where the incident and first reflection coincide. We initially performed our processing procedure on a measurement of a single signal. Multiple application of the processing procedure was required when we applied the reflection removal technique on a measurement containing information from the interaction of two physical phenomena. All processing was performed using SIG, an interactive signal processing package. One of the many consequences of using SIG was that repetitive operations were, for the most part, automated. A custom menu was designed to perform the deconvolution process.« less

Different Approaches to Covariate Inclusion in the Mixture Rasch Model

ERIC Educational Resources Information Center

Li, Tongyun; Jiao, Hong; Macready, George B.

2016-01-01

The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo…

Comparison of Two Analysis Approaches for Measuring Externalized Mental Models

ERIC Educational Resources Information Center

Al-Diban, Sabine; Ifenthaler, Dirk

2011-01-01

Mental models are basic cognitive constructs that are central for understanding phenomena of the world and predicting future events. Our comparison of two analysis approaches, SMD and QFCA, for measuring externalized mental models reveals different levels of abstraction and different perspectives. The advantages of the SMD include possibilities…

Comparison of individual-based modeling and population approaches for prediction of foodborne pathogens growth.

PubMed

Augustin, Jean-Christophe; Ferrier, Rachel; Hezard, Bernard; Lintz, Adrienne; Stahl, Valérie

2015-02-01

Individual-based modeling (IBM) approach combined with the microenvironment modeling of vacuum-packed cold-smoked salmon was more effective to describe the variability of the growth of a few Listeria monocytogenes cells contaminating irradiated salmon slices than the traditional population models. The IBM approach was particularly relevant to predict the absence of growth in 25% (5 among 20) of artificially contaminated cold-smoked salmon samples stored at 8 °C. These results confirmed similar observations obtained with smear soft cheese (Ferrier et al., 2013). These two different food models were used to compare the IBM/microscale and population/macroscale modeling approaches in more global exposure and risk assessment frameworks taking into account the variability and/or the uncertainty of the factors influencing the growth of L. monocytogenes. We observed that the traditional population models significantly overestimate exposure and risk estimates in comparison to IBM approach when contamination of foods occurs with a low number of cells (<100 per serving). Moreover, the exposure estimates obtained with the population model were characterized by a great uncertainty. The overestimation was mainly linked to the ability of IBM to predict no growth situations rather than the consideration of microscale environment. On the other hand, when the aim of quantitative risk assessment studies is only to assess the relative impact of changes in control measures affecting the growth of foodborne bacteria, the two modeling approach gave similar results and the simplest population approach was suitable. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

A probabilistic approach to the drag-based model

NASA Astrophysics Data System (ADS)

Napoletano, Gianluca; Forte, Roberta; Moro, Dario Del; Pietropaolo, Ermanno; Giovannelli, Luca; Berrilli, Francesco

2018-02-01

The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.

An overview of modelling approaches and potential solution towards an endgame of tobacco

NASA Astrophysics Data System (ADS)

Halim, Tisya Farida Abdul; Sapiri, Hasimah; Abidin, Norhaslinda Zainal

2015-12-01

A high number of premature mortality due to tobacco use has increased worldwide. Despite control policies being implemented to reduce premature mortality, the rate of smoking prevalence is still high. Moreover, tobacco issues become increasingly difficult since many aspects need to be considered simultaneously. Thus, the purpose of this paper is to present an overview of existing modelling studies on tobacco control system. The background section describes the tobacco issues and its current trends. These models have been categorised according to their modelling approaches either individual or integrated approaches. Next, a framework of modelling approaches based on the integration of multi-criteria decision making, system dynamics and nonlinear programming is proposed, expected to reduce the smoking prevalence. This framework provides guideline for modelling the interaction between smoking behaviour and its impacts, tobacco control policies and the effectiveness of each strategy in healthcare.

Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach

ERIC Educational Resources Information Center

Selig, James P.; Preacher, Kristopher J.; Little, Todd D.

2012-01-01

We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in favor of this approach on the potential for better understanding the associations between variables by…

A novel approach to multihazard modeling and simulation.

PubMed

Smith, Silas W; Portelli, Ian; Narzisi, Giuseppe; Nelson, Lewis S; Menges, Fabian; Rekow, E Dianne; Mincer, Joshua S; Mishra, Bhubaneswar; Goldfrank, Lewis R

2009-06-01

To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment. An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims. In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible. An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events.

An integrated biomechanical modeling approach to the ergonomic evaluation of drywall installation.

PubMed

Yuan, Lu; Buchholz, Bryan; Punnett, Laura; Kriebel, David

2016-03-01

Three different methodologies: work sampling, computer simulation and biomechanical modeling, were integrated to study the physical demands of drywall installation. PATH (Posture, Activity, Tools, and Handling), a work-sampling based method, was used to quantify the percent of time that the drywall installers were conducting different activities with different body segment (trunk, arm, and leg) postures. Utilizing Monte-Carlo simulation to convert the categorical PATH data into continuous variables as inputs for the biomechanical models, the required muscle contraction forces and joint reaction forces at the low back (L4/L5) and shoulder (glenohumeral and sternoclavicular joints) were estimated for a typical eight-hour workday. To demonstrate the robustness of this modeling approach, a sensitivity analysis was conducted to examine the impact of some quantitative assumptions that have been made to facilitate the modeling approach. The results indicated that the modeling approach seemed to be the most sensitive to both the distribution of work cycles for a typical eight-hour workday and the distribution and values of Euler angles that are used to determine the "shoulder rhythm." Other assumptions including the distribution of trunk postures did not appear to have a significant impact on the model outputs. It was concluded that the integrated approach might provide an applicable examination of physical loads during the non-routine construction work, especially for those operations/tasks that have certain patterns/sequences for the workers to follow. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Modeling an alkaline electrolysis cell through reduced-order and loss-estimate approaches

NASA Astrophysics Data System (ADS)

Milewski, Jaroslaw; Guandalini, Giulio; Campanari, Stefano

2014-12-01

The paper presents two approaches to the mathematical modeling of an Alkaline Electrolyzer Cell. The presented models were compared and validated against available experimental results taken from a laboratory test and against literature data. The first modeling approach is based on the analysis of estimated losses due to the different phenomena occurring inside the electrolytic cell, and requires careful calibration of several specific parameters (e.g. those related to the electrochemical behavior of the electrodes) some of which could be hard to define. An alternative approach is based on a reduced-order equivalent circuit, resulting in only two fitting parameters (electrodes specific resistance and parasitic losses) and calculation of the internal electric resistance of the electrolyte. Both models yield satisfactory results with an average error limited below 3% vs. the considered experimental data and show the capability to describe with sufficient accuracy the different operating conditions of the electrolyzer; the reduced-order model could be preferred thanks to its simplicity for implementation within plant simulation tools dealing with complex systems, such as electrolyzers coupled with storage facilities and intermittent renewable energy sources.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric

Evaluating scaling models in biology using hierarchical Bayesian approaches

PubMed Central

Price, Charles A; Ogle, Kiona; White, Ethan P; Weitz, Joshua S

2009-01-01

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morphology within a hierarchical Bayesian framework that simultaneously fits multiple scaling relationships to three large allometric datasets. The scaling models include: inflexible universal models derived from biophysical assumptions (e.g. elastic similarity or fractal networks), a flexible variation of a fractal network model, and a highly flexible model constrained only by basic algebraic relationships. We demonstrate that variation in intraspecific allometric scaling exponents is inconsistent with the universal models, and that more flexible approaches that allow for biological variability at the species level outperform universal models, even when accounting for relative increases in model complexity. PMID:19453621

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

2010-01-01

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

NASA Astrophysics Data System (ADS)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United

Approaches to the structural modelling of insect wings.

PubMed Central

Wootton, R J; Herbert, R C; Young, P G; Evans, K E

2003-01-01

Insect wings lack internal muscles, and the orderly, necessary deformations which they undergo in flight and folding are in part remotely controlled, in part encoded in their structure. This factor is crucial in understanding their complex, extremely varied morphology. Models have proved particularly useful in clarifying the facilitation and control of wing deformation. Their development has followed a logical sequence from conceptual models through physical and simple analytical to numerical models. All have value provided their limitations are realized and constant comparisons made with the properties and mechanical behaviour of real wings. Numerical modelling by the finite element method is by far the most time-consuming approach, but has real potential in analysing the adaptive significance of structural details and interpreting evolutionary trends. Published examples are used to review the strengths and weaknesses of each category of model, and a summary is given of new work using finite element modelling to investigate the vibration properties and response to impact of hawkmoth wings. PMID:14561349

Model-based approach to partial tracking for musical transcription

NASA Astrophysics Data System (ADS)

Sterian, Andrew; Wakefield, Gregory H.

1998-10-01

We present a new method for musical partial tracking in the context of musical transcription using a time-frequency Kalman filter structure. The filter is based upon a model for the evolution of a partial behavior across a wide range of pitch from four brass instruments. Statistics are computed independently for the partial attributes of frequency and log-power first differences. We present observed power spectral density shapes, total powers, and histograms, as well as least-squares approximations to these. We demonstrate that a Kalman filter tracker using this partial model is capable of tracking partials in music. We discuss how the filter structure naturally provides quality-of-fit information about the data for use in further processing and how this information can be used to perform partial track initiation and termination within a common framework. We propose that a model-based approach to partial tracking is preferable to existing approaches which generally use heuristic rules or birth/death notions over a small time neighborhood. The advantages include better performance in the presence of cluttered data and simplified tracking over missed observations.

Computational modeling of an endovascular approach to deep brain stimulation

NASA Astrophysics Data System (ADS)

Teplitzky, Benjamin A.; Connolly, Allison T.; Bajwa, Jawad A.; Johnson, Matthew D.

2014-04-01

Objective. Deep brain stimulation (DBS) therapy currently relies on a transcranial neurosurgical technique to implant one or more electrode leads into the brain parenchyma. In this study, we used computational modeling to investigate the feasibility of using an endovascular approach to target DBS therapy. Approach. Image-based anatomical reconstructions of the human brain and vasculature were used to identify 17 established and hypothesized anatomical targets of DBS, of which five were found adjacent to a vein or artery with intraluminal diameter ≥1 mm. Two of these targets, the fornix and subgenual cingulate white matter (SgCwm) tracts, were further investigated using a computational modeling framework that combined segmented volumes of the vascularized brain, finite element models of the tissue voltage during DBS, and multi-compartment axon models to predict the direct electrophysiological effects of endovascular DBS. Main results. The models showed that: (1) a ring-electrode conforming to the vessel wall was more efficient at neural activation than a guidewire design, (2) increasing the length of a ring-electrode had minimal effect on neural activation thresholds, (3) large variability in neural activation occurred with suboptimal placement of a ring-electrode along the targeted vessel, and (4) activation thresholds for the fornix and SgCwm tracts were comparable for endovascular and stereotactic DBS, though endovascular DBS was able to produce significantly larger contralateral activation for a unilateral implantation. Significance. Together, these results suggest that endovascular DBS can serve as a complementary approach to stereotactic DBS in select cases.

Modelling of Sub-daily Hydrological Processes Using Daily Time-Step Models: A Distribution Function Approach to Temporal Scaling

NASA Astrophysics Data System (ADS)

Kandel, D. D.; Western, A. W.; Grayson, R. B.

2004-12-01

Mismatches in scale between the fundamental processes, the model and supporting data are a major limitation in hydrologic modelling. Surface runoff generation via infiltration excess and the process of soil erosion are fundamentally short time-scale phenomena and their average behaviour is mostly determined by the short time-scale peak intensities of rainfall. Ideally, these processes should be simulated using time-steps of the order of minutes to appropriately resolve the effect of rainfall intensity variations. However, sub-daily data support is often inadequate and the processes are usually simulated by calibrating daily (or even coarser) time-step models. Generally process descriptions are not modified but rather effective parameter values are used to account for the effect of temporal lumping, assuming that the effect of the scale mismatch can be counterbalanced by tuning the parameter values at the model time-step of interest. Often this results in parameter values that are difficult to interpret physically. A similar approach is often taken spatially. This is problematic as these processes generally operate or interact non-linearly. This indicates a need for better techniques to simulate sub-daily processes using daily time-step models while still using widely available daily information. A new method applicable to many rainfall-runoff-erosion models is presented. The method is based on temporal scaling using statistical distributions of rainfall intensity to represent sub-daily intensity variations in a daily time-step model. This allows the effect of short time-scale nonlinear processes to be captured while modelling at a daily time-step, which is often attractive due to the wide availability of daily forcing data. The approach relies on characterising the rainfall intensity variation within a day using a cumulative distribution function (cdf). This cdf is then modified by various linear and nonlinear processes typically represented in hydrological and

A biologically inspired approach to modeling unmanned vehicle teams

NASA Astrophysics Data System (ADS)

Cortesi, Roger S.; Galloway, Kevin S.; Justh, Eric W.

2008-04-01

Cooperative motion control of teams of agile unmanned vehicles presents modeling challenges at several levels. The "microscopic equations" describing individual vehicle dynamics and their interaction with the environment may be known fairly precisely, but are generally too complicated to yield qualitative insights at the level of multi-vehicle trajectory coordination. Interacting particle models are suitable for coordinating trajectories, but require care to ensure that individual vehicles are not driven in a "costly" manner. From the point of view of the cooperative motion controller, the individual vehicle autopilots serve to "shape" the microscopic equations, and we have been exploring the interplay between autopilots and cooperative motion controllers using a multivehicle hardware-in-the-loop simulator. Specifically, we seek refinements to interacting particle models in order to better describe observed behavior, without sacrificing qualitative understanding. A recent analogous example from biology involves introducing a fixed delay into a curvature-control-based feedback law for prey capture by an echolocating bat. This delay captures both neural processing time and the flight-dynamic response of the bat as it uses sensor-driven feedback. We propose a comparable approach for unmanned vehicle modeling; however, in contrast to the bat, with unmanned vehicles we have an additional freedom to modify the autopilot. Simulation results demonstrate the effectiveness of this biologically guided modeling approach.

Bridging the divide: a model-data approach to Polar and Alpine microbiology.

PubMed

Bradley, James A; Anesio, Alexandre M; Arndt, Sandra

2016-03-01

Advances in microbial ecology in the cryosphere continue to be driven by empirical approaches including field sampling and laboratory-based analyses. Although mathematical models are commonly used to investigate the physical dynamics of Polar and Alpine regions, they are rarely applied in microbial studies. Yet integrating modelling approaches with ongoing observational and laboratory-based work is ideally suited to Polar and Alpine microbial ecosystems given their harsh environmental and biogeochemical characteristics, simple trophic structures, distinct seasonality, often difficult accessibility, geographical expansiveness and susceptibility to accelerated climate changes. In this opinion paper, we explain how mathematical modelling ideally complements field and laboratory-based analyses. We thus argue that mathematical modelling is a powerful tool for the investigation of these extreme environments and that fully integrated, interdisciplinary model-data approaches could help the Polar and Alpine microbiology community address some of the great research challenges of the 21st century (e.g. assessing global significance and response to climate change). However, a better integration of field and laboratory work with model design and calibration/validation, as well as a stronger focus on quantitative information is required to advance models that can be used to make predictions and upscale processes and fluxes beyond what can be captured by observations alone. © FEMS 2016.

Bridging the divide: a model-data approach to Polar and Alpine microbiology

PubMed Central

Bradley, James A.; Anesio, Alexandre M.; Arndt, Sandra

2016-01-01

Advances in microbial ecology in the cryosphere continue to be driven by empirical approaches including field sampling and laboratory-based analyses. Although mathematical models are commonly used to investigate the physical dynamics of Polar and Alpine regions, they are rarely applied in microbial studies. Yet integrating modelling approaches with ongoing observational and laboratory-based work is ideally suited to Polar and Alpine microbial ecosystems given their harsh environmental and biogeochemical characteristics, simple trophic structures, distinct seasonality, often difficult accessibility, geographical expansiveness and susceptibility to accelerated climate changes. In this opinion paper, we explain how mathematical modelling ideally complements field and laboratory-based analyses. We thus argue that mathematical modelling is a powerful tool for the investigation of these extreme environments and that fully integrated, interdisciplinary model-data approaches could help the Polar and Alpine microbiology community address some of the great research challenges of the 21st century (e.g. assessing global significance and response to climate change). However, a better integration of field and laboratory work with model design and calibration/validation, as well as a stronger focus on quantitative information is required to advance models that can be used to make predictions and upscale processes and fluxes beyond what can be captured by observations alone. PMID:26832206

Toward a More Pragmatic Approach to Morality: A Critical Evaluation of Kohlberg's Model

ERIC Educational Resources Information Center

Krebs, Dennis L.; Denton, Kathy

2005-01-01

In this article, the authors evaluate L. Kohlberg's (1984) cognitive-developmental approach to morality, find it wanting, and introduce a more pragmatic approach. They review research designed to evaluate Kohlberg's model, describe how they revised the model to accommodate discrepant findings, and explain why they concluded that it is poorly…

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

PubMed

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Approach to Computer Implementation of Mathematical Model of 3-Phase Induction Motor

NASA Astrophysics Data System (ADS)

Pustovetov, M. Yu

2018-03-01

This article discusses the development of the computer model of an induction motor based on the mathematical model in a three-phase stator reference frame. It uses an approach that allows combining during preparation of the computer model dual methods: means of visual programming circuitry (in the form of electrical schematics) and logical one (in the form of block diagrams). The approach enables easy integration of the model of an induction motor as part of more complex models of electrical complexes and systems. The developed computer model gives the user access to the beginning and the end of a winding of each of the three phases of the stator and rotor. This property is particularly important when considering the asymmetric modes of operation or when powered by the special circuitry of semiconductor converters.

Behavioral facilitation: a cognitive model of individual differences in approach motivation.

PubMed

Robinson, Michael D; Meier, Brian P; Tamir, Maya; Wilkowski, Benjamin M; Ode, Scott

2009-02-01

Approach motivation consists of the active, engaged pursuit of one's goals. The purpose of the present three studies (N = 258) was to examine whether approach motivation could be cognitively modeled, thereby providing process-based insights into personality functioning. Behavioral facilitation was assessed in terms of faster (or facilitated) reaction time with practice. As hypothesized, such tendencies predicted higher levels of approach motivation, higher levels of positive affect, and lower levels of depressive symptoms and did so across cognitive, behavioral, self-reported, and peer-reported outcomes. Tendencies toward behavioral facilitation, on the other hand, did not correlate with self-reported traits (Study 1) and did not predict avoidance motivation or negative affect (all studies). The results indicate a systematic relationship between behavioral facilitation in cognitive tasks and approach motivation in daily life. Results are discussed in terms of the benefits of modeling the cognitive processes hypothesized to underlie individual differences motivation, affect, and depression. (c) 2009 APA, all rights reserved

Modeling AEC—New Approaches to Study Rare Genetic Disorders

PubMed Central

Koch, Peter J.; Dinella, Jason; Fete, Mary; Siegfried, Elaine C.; Koster, Maranke I.

2015-01-01

Ankyloblepharon-ectodermal defects-cleft lip/palate (AEC) syndrome is a rare monogenetic disorder that is characterized by severe abnormalities in ectoderm-derived tissues, such as skin and its appendages. A major cause of morbidity among affected infants is severe and chronic skin erosions. Currently, supportive care is the only available treatment option for AEC patients. Mutations in TP63, a gene that encodes key regulators of epidermal development, are the genetic cause of AEC. However, it is currently not clear how mutations in TP63 lead to the various defects seen in the patients’ skin. In this review, we will discuss current knowledge of the AEC disease mechanism obtained by studying patient tissue and genetically engineered mouse models designed to mimic aspects of the disorder. We will then focus on new approaches to model AEC, including the use of patient cells and stem cell technology to replicate the disease in a human tissue culture model. The latter approach will advance our understanding of the disease and will allow for the development of new in vitro systems to identify drugs for the treatment of skin erosions in AEC patients. Further, the use of stem cell technology, in particular induced pluripotent stem cells (iPSC), will enable researchers to develop new therapeutic approaches to treat the disease using the patient’s own cells (autologous keratinocyte transplantation) after correction of the disease-causing mutations. PMID:24665072

On the numerical modeling of sliding beams: A comparison of different approaches

NASA Astrophysics Data System (ADS)

Steinbrecher, Ivo; Humer, Alexander; Vu-Quoc, Loc

2017-11-01

The transient analysis of sliding beams represents a challenging problem of structural mechanics. Typically, the sliding motion superimposed by large flexible deformation requires numerical methods as, e.g., finite elements, to obtain approximate solutions. By means of the classical sliding spaghetti problem, the present paper provides a guideline to the numerical modeling with conventional finite element codes. For this purpose, two approaches, one using solid elements and one using beam elements, respectively, are employed in the analysis, and the characteristics of each approach are addressed. The contact formulation realizing the interaction of the beam with its support demands particular attention in the context of sliding structures. Additionally, the paper employs the sliding-beam formulation as a third approach, which avoids the numerical difficulties caused by the large sliding motion through a suitable coordinate transformation. The present paper briefly outlines the theoretical fundamentals of the respective approaches for the modeling of sliding structures and gives a detailed comparison by means of the sliding spaghetti serving as a representative example. The specific advantages and limitations of the different approaches with regard to accuracy and computational efficiency are discussed in detail. Through the comparison, the sliding-beam formulation, which proves as an effective approach for the modeling, can be validated for the general problem of a sliding structure subjected to large deformation.

Modeling Complex Marine Ecosystems: An Investigation of Two Teaching Approaches with Fifth Graders

ERIC Educational Resources Information Center

Papaevripidou, M.; Constantinou, C. P.; Zacharia, Z. C.

2007-01-01

This study investigated acquisition and transfer of the modeling ability of fifth graders in various domains. Teaching interventions concentrated on the topic of marine ecosystems either through a modeling-based approach or a worksheet-based approach. A quasi-experimental (pre-post comparison study) design was used. The control group (n = 17)…

Experimental and Numerical Analysis of Triaxially Braided Composites Utilizing a Modified Subcell Modeling Approach

NASA Technical Reports Server (NTRS)

Cater, Christopher; Xiao, Xinran; Goldberg, Robert K.; Kohlman, Lee W.

2015-01-01

A combined experimental and analytical approach was performed for characterizing and modeling triaxially braided composites with a modified subcell modeling strategy. Tensile coupon tests were conducted on a [0deg/60deg/-60deg] braided composite at angles of 0deg, 30deg, 45deg, 60deg and 90deg relative to the axial tow of the braid. It was found that measured coupon strength varied significantly with the angle of the applied load and each coupon direction exhibited unique final failures. The subcell modeling approach implemented into the finite element software LS-DYNA was used to simulate the various tensile coupon test angles. The modeling approach was successful in predicting both the coupon strength and reported failure mode for the 0deg, 30deg and 60deg loading directions. The model over-predicted the strength in the 90deg direction; however, the experimental results show a strong influence of free edge effects on damage initiation and failure. In the absence of these local free edge effects, the subcell modeling approach showed promise as a viable and computationally efficient analysis tool for triaxially braided composite structures. Future work will focus on validation of the approach for predicting the impact response of the braided composite against flat panel impact tests.

Experimental and Numerical Analysis of Triaxially Braided Composites Utilizing a Modified Subcell Modeling Approach

NASA Technical Reports Server (NTRS)

Cater, Christopher; Xiao, Xinran; Goldberg, Robert K.; Kohlman, Lee W.

2015-01-01

A combined experimental and analytical approach was performed for characterizing and modeling triaxially braided composites with a modified subcell modeling strategy. Tensile coupon tests were conducted on a [0deg/60deg/-60deg] braided composite at angles [0deg, 30deg, 45deg, 60deg and 90deg] relative to the axial tow of the braid. It was found that measured coupon strength varied significantly with the angle of the applied load and each coupon direction exhibited unique final failures. The subcell modeling approach implemented into the finite element software LS-DYNA was used to simulate the various tensile coupon test angles. The modeling approach was successful in predicting both the coupon strength and reported failure mode for the 0deg, 30deg and 60deg loading directions. The model over-predicted the strength in the 90deg direction; however, the experimental results show a strong influence of free edge effects on damage initiation and failure. In the absence of these local free edge effects, the subcell modeling approach showed promise as a viable and computationally efficient analysis tool for triaxially braided composite structures. Future work will focus on validation of the approach for predicting the impact response of the braided composite against flat panel impact tests.

Modeling languages for biochemical network simulation: reaction vs equation based approaches.

PubMed

Wiechert, Wolfgang; Noack, Stephan; Elsheikh, Atya

2010-01-01

Biochemical network modeling and simulation is an essential task in any systems biology project. The systems biology markup language (SBML) was established as a standardized model exchange language for mechanistic models. A specific strength of SBML is that numerous tools for formulating, processing, simulation and analysis of models are freely available. Interestingly, in the field of multidisciplinary simulation, the problem of model exchange between different simulation tools occurred much earlier. Several general modeling languages like Modelica have been developed in the 1990s. Modelica enables an equation based modular specification of arbitrary hierarchical differential algebraic equation models. Moreover, libraries for special application domains can be rapidly developed. This contribution compares the reaction based approach of SBML with the equation based approach of Modelica and explains the specific strengths of both tools. Several biological examples illustrating essential SBML and Modelica concepts are given. The chosen criteria for tool comparison are flexibility for constraint specification, different modeling flavors, hierarchical, modular and multidisciplinary modeling. Additionally, support for spatially distributed systems, event handling and network analysis features is discussed. As a major result it is shown that the choice of the modeling tool has a strong impact on the expressivity of the specified models but also strongly depends on the requirements of the application context.

Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach.

PubMed

Peng, Changhui; Guiot, Joel; Wu, Haibin; Jiang, Hong; Luo, Yiqi

2011-05-01

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e., palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services. © 2011 Blackwell Publishing Ltd/CNRS.

Moving university hydrology education forward with geoinformatics, data and modeling approaches

NASA Astrophysics Data System (ADS)

Merwade, V.; Ruddell, B. L.

2012-02-01

In this opinion paper, we review recent literature related to data and modeling driven instruction in hydrology, and present our findings from surveying the hydrology education community in the United States. This paper presents an argument that that Data and Modeling Driven Geoscience Cybereducation (DMDGC) approaches are valuable for teaching the conceptual and applied aspects of hydrology, as a part of the broader effort to improve Science, Technology, Engineering, and Mathematics (STEM) education at the university level. The authors have undertaken a series of surveys and a workshop involving the community of university hydrology educators to determine the state of the practice of DMDGC approaches to hydrology. We identify the most common tools and approaches currently utilized, quantify the extent of the adoption of DMDGC approaches in the university hydrology classroom, and explain the community's views on the challenges and barriers preventing DMDGC approaches from wider use. DMDGC approaches are currently emphasized at the graduate level of the curriculum, and only the most basic modeling and visualization tools are in widespread use. The community identifies the greatest barriers to greater adoption as a lack of access to easily adoptable curriculum materials and a lack of time and training to learn constantly changing tools and methods. The community's current consensus is that DMDGC approaches should emphasize conceptual learning, and should be used to complement rather than replace lecture-based pedagogies. Inadequate online material-publication and sharing systems, and a lack of incentives for faculty to develop and publish materials via such systems, is also identified as a challenge. Based on these findings, we suggest that a number of steps should be taken by the community to develop the potential of DMDGC in university hydrology education, including formal development and assessment of curriculum materials integrating lecture-format and DMDGC

Modeling urban building energy use: A review of modeling approaches and procedures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Wenliang; Zhou, Yuyu; Cetin, Kristen

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. This paper aims to provide an up-to-datemore » review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. This is followed by a discussion of challenging issues associated with model preparation and calibration.« less

Modeling urban building energy use: A review of modeling approaches and procedures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Wenliang; Zhou, Yuyu; Cetin, Kristen

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. Our paper aims to provide an up-to-datemore » review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. We then follow this with a discussion of challenging issues associated with model preparation and calibration.« less

Modeling urban building energy use: A review of modeling approaches and procedures

DOE PAGES

Li, Wenliang; Zhou, Yuyu; Cetin, Kristen; ...

2017-11-13

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. Our paper aims to provide an up-to-datemore » review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. We then follow this with a discussion of challenging issues associated with model preparation and calibration.« less

A multi-objective approach to improve SWAT model calibration in alpine catchments

NASA Astrophysics Data System (ADS)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

The comparative cost-effectiveness of an equity-focused approach to child survival, health, and nutrition: a modelling approach.

PubMed

Carrera, Carlos; Azrack, Adeline; Begkoyian, Genevieve; Pfaffmann, Jerome; Ribaira, Eric; O'Connell, Thomas; Doughty, Patricia; Aung, Kyaw Myint; Prieto, Lorena; Rasanathan, Kumanan; Sharkey, Alyssa; Chopra, Mickey; Knippenberg, Rudolf

2012-10-13

Progress on child mortality and undernutrition has seen widening inequities and a concentration of child deaths and undernutrition in the most deprived communities, threatening the achievement of the Millennium Development Goals. Conversely, a series of recent process and technological innovations have provided effective and efficient options to reach the most deprived populations. These trends raise the possibility that the perceived trade-off between equity and efficiency no longer applies for child health--that prioritising services for the poorest and most marginalised is now more effective and cost effective than mainstream approaches. We tested this hypothesis with a mathematical-modelling approach by comparing the cost-effectiveness in terms of child deaths and stunting events averted between two approaches (from 2011-15 in 14 countries and one province): an equity-focused approach that prioritises the most deprived communities, and a mainstream approach that is representative of current strategies. We combined some existing models, notably the Marginal Budgeting for Bottlenecks Toolkit and the Lives Saved Tool, to do our analysis. We showed that, with the same level of investment, disproportionately higher effects are possible by prioritising the poorest and most marginalised populations, for averting both child mortality and stunting. Our results suggest that an equity-focused approach could result in sharper decreases in child mortality and stunting and higher cost-effectiveness than mainstream approaches, while reducing inequities in effective intervention coverage, health outcomes, and out-of-pocket spending between the most and least deprived groups and geographic areas within countries. Our findings should be interpreted with caution due to uncertainties around some of the model parameters and baseline data. Further research is needed to address some of these gaps in the evidence base. Strategies for improving child nutrition and survival, however

SMALL POPULATIONS REQUIRE SPECIFIC MODELING APPROACHES FOR ASSESSING RISK

EPA Science Inventory

All populations face non-zero risks of extinction. However, the risks for small populations, and therefore the modeling approaches necessary to predict them, are different from those of large populations. These differences are currently hindering assessment of risk to small pop...

Modeling Alaska boreal forests with a controlled trend surface approach

Treesearch

Mo Zhou; Jingjing Liang

2012-01-01

An approach of Controlled Trend Surface was proposed to simultaneously take into consideration large-scale spatial trends and nonspatial effects. A geospatial model of the Alaska boreal forest was developed from 446 permanent sample plots, which addressed large-scale spatial trends in recruitment, diameter growth, and mortality. The model was tested on two sets of...

A modeling approach to compare ΣPCB concentrations between congener-specific analyses

USGS Publications Warehouse

Gibson, Polly P.; Mills, Marc A.; Kraus, Johanna M.; Walters, David M.

2017-01-01

Changes in analytical methods over time pose problems for assessing long-term trends in environmental contamination by polychlorinated biphenyls (PCBs). Congener-specific analyses vary widely in the number and identity of the 209 distinct PCB chemical configurations (congeners) that are quantified, leading to inconsistencies among summed PCB concentrations (ΣPCB) reported by different studies. Here we present a modeling approach using linear regression to compare ΣPCB concentrations derived from different congener-specific analyses measuring different co-eluting groups. The approach can be used to develop a specific conversion model between any two sets of congener-specific analytical data from similar samples (similar matrix and geographic origin). We demonstrate the method by developing a conversion model for an example data set that includes data from two different analytical methods, a low resolution method quantifying 119 congeners and a high resolution method quantifying all 209 congeners. We used the model to show that the 119-congener set captured most (93%) of the total PCB concentration (i.e., Σ209PCB) in sediment and biological samples. ΣPCB concentrations estimated using the model closely matched measured values (mean relative percent difference = 9.6). General applications of the modeling approach include (a) generating comparable ΣPCB concentrations for samples that were analyzed for different congener sets; and (b) estimating the proportional contribution of different congener sets to ΣPCB. This approach may be especially valuable for enabling comparison of long-term remediation monitoring results even as analytical methods change over time. 

Integrating Environmental Genomics and Biogeochemical Models: a Gene-centric Approach

NASA Astrophysics Data System (ADS)

Reed, D. C.; Algar, C. K.; Huber, J. A.; Dick, G.

2013-12-01

Rapid advances in molecular microbial ecology have yielded an unprecedented amount of data about the evolutionary relationships and functional traits of microbial communities that regulate global geochemical cycles. Biogeochemical models, however, are trailing in the wake of the environmental genomics revolution and such models rarely incorporate explicit representations of bacteria and archaea, nor are they compatible with nucleic acid or protein sequence data. Here, we present a functional gene-based framework for describing microbial communities in biogeochemical models that uses genomics data and provides predictions that are readily testable using cutting-edge molecular tools. To demonstrate the approach in practice, nitrogen cycling in the Arabian Sea oxygen minimum zone (OMZ) was modelled to examine key questions about cryptic sulphur cycling and dinitrogen production pathways in OMZs. By directly linking geochemical dynamics to the genetic composition of microbial communities, the method provides mechanistic insights into patterns and biogeochemical consequences of marine microbes. Such an approach is critical for informing our understanding of the key role microbes play in modulating Earth's biogeochemistry.

The IDEA model: A single equation approach to the Ebola forecasting challenge.

PubMed

Tuite, Ashleigh R; Fisman, David N

2018-03-01

Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in the face of emerging infectious diseases, such as the 2014-2015 West African Ebola epidemic, but little is known about the relative performance of alternate forecasting approaches. The RAPIDD Ebola Forecasting Challenge (REFC) tested the ability of eight mathematical models to generate useful forecasts in the face of simulated Ebola outbreaks. We used a simple, phenomenological single-equation model (the "IDEA" model), which relies only on case counts, in the REFC. Model fits were performed using a maximum likelihood approach. We found that the model performed reasonably well relative to other more complex approaches, with performance metrics ranked on average 4th or 5th among participating models. IDEA appeared better suited to long- than short-term forecasts, and could be fit using nothing but reported case counts. Several limitations were identified, including difficulty in identifying epidemic peak (even retrospectively), unrealistically precise confidence intervals, and difficulty interpolating daily case counts when using a model scaled to epidemic generation time. More realistic confidence intervals were generated when case counts were assumed to follow a negative binomial, rather than Poisson, distribution. Nonetheless, IDEA represents a simple phenomenological model, easily implemented in widely available software packages that could be used by frontline public health personnel to generate forecasts with accuracy that approximates that which is achieved using more complex methodologies. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Interrater Agreement Evaluation: A Latent Variable Modeling Approach

ERIC Educational Resources Information Center

Raykov, Tenko; Dimitrov, Dimiter M.; von Eye, Alexander; Marcoulides, George A.

2013-01-01

A latent variable modeling method for evaluation of interrater agreement is outlined. The procedure is useful for point and interval estimation of the degree of agreement among a given set of judges evaluating a group of targets. In addition, the approach allows one to test for identity in underlying thresholds across raters as well as to identify…

A Probabilistic Model for Diagnosing Misconceptions by a Pattern Classification Approach.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi K.

A probabilistic approach is introduced to classify and diagnose erroneous rules of operation resulting from a variety of misconceptions ("bugs") in a procedural domain of arithmetic. The model is contrasted with the deterministic approach which has commonly been used in the field of artificial intelligence, and the advantage of treating the…

An integrated modeling approach to predict flooding on urban basin.

PubMed

Dey, Ashis Kumar; Kamioka, Seiji

2007-01-01

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.

Evaluation of the Combined AERCOARE/AERMOD Modeling Approach for Offshore Sources

EPA Science Inventory

ENVIRON conducted an evaluation of the combined AERCOARE/AERMOD (AERCOARE-MOD) modeling approach for offshore sources using tracer data from four field studies. AERCOARE processes overwater meteorological data for use by the AERMOD air quality dispersion model (EPA, 2004a). AERC...

Hierarchical approaches for systems modeling in cardiac development.

PubMed

Gould, Russell A; Aboulmouna, Lina M; Varner, Jeffrey D; Butcher, Jonathan T

2013-01-01

Ordered cardiac morphogenesis and function are essential for all vertebrate life. The heart begins as a simple contractile tube, but quickly grows and morphs into a multichambered pumping organ complete with valves, while maintaining regulation of blood flow and nutrient distribution. Though not identical, cardiac morphogenesis shares many molecular and morphological processes across vertebrate species. Quantitative data across multiple time and length scales have been gathered through decades of reductionist single variable analyses. These range from detailed molecular signaling pathways at the cellular levels to cardiac function at the tissue/organ levels. However, none of these components act in true isolation from others, and each, in turn, exhibits short- and long-range effects in both time and space. With the absence of a gene, entire signaling cascades and genetic profiles may be shifted, resulting in complex feedback mechanisms. Also taking into account local microenvironmental changes throughout development, it is apparent that a systems level approach is an essential resource to accelerate information generation concerning the functional relationships across multiple length scales (molecular data vs physiological function) and structural development. In this review, we discuss relevant in vivo and in vitro experimental approaches, compare different computational frameworks for systems modeling, and the latest information about systems modeling of cardiac development. Finally, we conclude with some important future directions for cardiac systems modeling. Copyright © 2013 Wiley Periodicals, Inc.

Algebraic approach to small-world network models

NASA Astrophysics Data System (ADS)

Rudolph-Lilith, Michelle; Muller, Lyle E.

2014-01-01

We introduce an analytic model for directed Watts-Strogatz small-world graphs and deduce an algebraic expression of its defining adjacency matrix. The latter is then used to calculate the small-world digraph's asymmetry index and clustering coefficient in an analytically exact fashion, valid nonasymptotically for all graph sizes. The proposed approach is general and can be applied to all algebraically well-defined graph-theoretical measures, thus allowing for an analytical investigation of finite-size small-world graphs.

A predictive modeling approach to increasing the economic effectiveness of disease management programs.

PubMed

Bayerstadler, Andreas; Benstetter, Franz; Heumann, Christian; Winter, Fabian

2014-09-01

Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-) effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.

Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models with Factor Structures

ERIC Educational Resources Information Center

Jeon, Minjeong; Rabe-Hesketh, Sophia

2012-01-01

In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be…

Computationally efficient and flexible modular modelling approach for river and urban drainage systems based on surrogate conceptual models

NASA Astrophysics Data System (ADS)

Wolfs, Vincent; Willems, Patrick

2015-04-01

Water managers rely increasingly on mathematical simulation models that represent individual parts of the water system, such as the river, sewer system or waste water treatment plant. The current evolution towards integral water management requires the integration of these distinct components, leading to an increased model scale and scope. Besides this growing model complexity, certain applications gained interest and importance, such as uncertainty and sensitivity analyses, auto-calibration of models and real time control. All these applications share the need for models with a very limited calculation time, either for performing a large number of simulations, or a long term simulation followed by a statistical post-processing of the results. The use of the commonly applied detailed models that solve (part of) the de Saint-Venant equations is infeasible for these applications or such integrated modelling due to several reasons, of which a too long simulation time and the inability to couple submodels made in different software environments are the main ones. Instead, practitioners must use simplified models for these purposes. These models are characterized by empirical relationships and sacrifice model detail and accuracy for increased computational efficiency. The presented research discusses the development of a flexible integral modelling platform that complies with the following three key requirements: (1) Include a modelling approach for water quantity predictions for rivers, floodplains, sewer systems and rainfall runoff routing that require a minimal calculation time; (2) A fast and semi-automatic model configuration, thereby making maximum use of data of existing detailed models and measurements; (3) Have a calculation scheme based on open source code to allow for future extensions or the coupling with other models. First, a novel and flexible modular modelling approach based on the storage cell concept was developed. This approach divides each

A Multiscale Modeling Approach to Analyze Filament-Wound Composite Pressure Vessels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Ba Nghiep; Simmons, Kevin L.

2013-07-22

A multiscale modeling approach to analyze filament-wound composite pressure vessels is developed in this article. The approach, which extends the Nguyen et al. model [J. Comp. Mater. 43 (2009) 217] developed for discontinuous fiber composites to continuous fiber ones, spans three modeling scales. The microscale considers the unidirectional elastic fibers embedded in an elastic-plastic matrix obeying the Ramberg-Osgood relation and J2 deformation theory of plasticity. The mesoscale behavior representing the composite lamina is obtained through an incremental Mori-Tanaka type model and the Eshelby equivalent inclusion method [Proc. Roy. Soc. Lond. A241 (1957) 376]. The implementation of the micro-meso constitutive relationsmore » in the ABAQUS® finite element package (via user subroutines) allows the analysis of a filament-wound composite pressure vessel (macroscale) to be performed. Failure of the composite lamina is predicted by a criterion that accounts for the strengths of the fibers and of the matrix as well as of their interface. The developed approach is demonstrated in the analysis of a filament-wound pressure vessel to study the effect of the lamina thickness on the burst pressure. The predictions are favorably compared to the numerical and experimental results by Lifshitz and Dayan [Comp. Struct. 32 (1995) 313].« less

Modelling parasite aggregation: disentangling statistical and ecological approaches.

PubMed

Yakob, Laith; Soares Magalhães, Ricardo J; Gray, Darren J; Milinovich, Gabriel; Wardrop, Nicola; Dunning, Rebecca; Barendregt, Jan; Bieri, Franziska; Williams, Gail M; Clements, Archie C A

2014-05-01

The overdispersion in macroparasite infection intensity among host populations is commonly simulated using a constant negative binomial aggregation parameter. We describe an alternative to utilising the negative binomial approach and demonstrate important disparities in intervention efficacy projections that can come about from opting for pattern-fitting models that are not process-explicit. We present model output in the context of the epidemiology and control of soil-transmitted helminths due to the significant public health burden imposed by these parasites, but our methods are applicable to other infections with demonstrable aggregation in parasite numbers among hosts. Copyright © 2014. Published by Elsevier Ltd.

A new epidemic modeling approach: Multi-regions discrete-time model with travel-blocking vicinity optimal control strategy.

PubMed

Zakary, Omar; Rachik, Mostafa; Elmouki, Ilias

2017-08-01

First, we devise in this paper, a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement. We suppose homogeneous Susceptible-Infected-Removed (SIR) populations, and we consider in our simulations, a grid of colored cells, which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region. Second, in order to minimize the number of infected individuals in one region, we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells. Thus, we show the influence of the optimal control approach on the controlled cell. We should also note that the cellular modeling approach we propose here, can also describes infection dynamics of regions which are not necessarily attached one to an other, even if no empty space can be viewed between cells. The theoretical method we follow for the characterization of the travel-locking optimal controls, is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here, is based on discrete progressive-regressive iterative schemes. We illustrate our modeling and control approaches by giving an example of 100 regions.

An Integrated Spin-Labeling/Computational-Modeling Approach for Mapping Global Structures of Nucleic Acids.

PubMed

Tangprasertchai, Narin S; Zhang, Xiaojun; Ding, Yuan; Tham, Kenneth; Rohs, Remo; Haworth, Ian S; Qin, Peter Z

2015-01-01

The technique of site-directed spin labeling (SDSL) provides unique information on biomolecules by monitoring the behavior of a stable radical tag (i.e., spin label) using electron paramagnetic resonance (EPR) spectroscopy. In this chapter, we describe an approach in which SDSL is integrated with computational modeling to map conformations of nucleic acids. This approach builds upon a SDSL tool kit previously developed and validated, which includes three components: (i) a nucleotide-independent nitroxide probe, designated as R5, which can be efficiently attached at defined sites within arbitrary nucleic acid sequences; (ii) inter-R5 distances in the nanometer range, measured via pulsed EPR; and (iii) an efficient program, called NASNOX, that computes inter-R5 distances on given nucleic acid structures. Following a general framework of data mining, our approach uses multiple sets of measured inter-R5 distances to retrieve "correct" all-atom models from a large ensemble of models. The pool of models can be generated independently without relying on the inter-R5 distances, thus allowing a large degree of flexibility in integrating the SDSL-measured distances with a modeling approach best suited for the specific system under investigation. As such, the integrative experimental/computational approach described here represents a hybrid method for determining all-atom models based on experimentally-derived distance measurements. © 2015 Elsevier Inc. All rights reserved.

An Integrated Spin-Labeling/Computational-Modeling Approach for Mapping Global Structures of Nucleic Acids

PubMed Central

Tangprasertchai, Narin S.; Zhang, Xiaojun; Ding, Yuan; Tham, Kenneth; Rohs, Remo; Haworth, Ian S.; Qin, Peter Z.

2015-01-01

The technique of site-directed spin labeling (SDSL) provides unique information on biomolecules by monitoring the behavior of a stable radical tag (i.e., spin label) using electron paramagnetic resonance (EPR) spectroscopy. In this chapter, we describe an approach in which SDSL is integrated with computational modeling to map conformations of nucleic acids. This approach builds upon a SDSL tool kit previously developed and validated, which includes three components: (i) a nucleotide-independent nitroxide probe, designated as R5, which can be efficiently attached at defined sites within arbitrary nucleic acid sequences; (ii) inter-R5 distances in the nanometer range, measured via pulsed EPR; and (iii) an efficient program, called NASNOX, that computes inter-R5 distances on given nucleic acid structures. Following a general framework of data mining, our approach uses multiple sets of measured inter-R5 distances to retrieve “correct” all-atom models from a large ensemble of models. The pool of models can be generated independently without relying on the inter-R5 distances, thus allowing a large degree of flexibility in integrating the SDSL-measured distances with a modeling approach best suited for the specific system under investigation. As such, the integrative experimental/computational approach described here represents a hybrid method for determining all-atom models based on experimentally-derived distance measurements. PMID:26477260

A model-based analysis of a display for helicopter landing approach. [control theoretical model of human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.; Wheat, L. W.

1975-01-01

A control theoretic model of the human pilot was used to analyze a baseline electronic cockpit display in a helicopter landing approach task. The head down display was created on a stroke written cathode ray tube and the vehicle was a UH-1H helicopter. The landing approach task consisted of maintaining prescribed groundspeed and glideslope in the presence of random vertical and horizontal turbulence. The pilot model was also used to generate and evaluate display quickening laws designed to improve pilot vehicle performance. A simple fixed base simulation provided comparative tracking data.

Dynamic pathway modeling of signal transduction networks: a domain-oriented approach.

PubMed

Conzelmann, Holger; Gilles, Ernst-Dieter

2008-01-01

Mathematical models of biological processes become more and more important in biology. The aim is a holistic understanding of how processes such as cellular communication, cell division, regulation, homeostasis, or adaptation work, how they are regulated, and how they react to perturbations. The great complexity of most of these processes necessitates the generation of mathematical models in order to address these questions. In this chapter we provide an introduction to basic principles of dynamic modeling and highlight both problems and chances of dynamic modeling in biology. The main focus will be on modeling of s transduction pathways, which requires the application of a special modeling approach. A common pattern, especially in eukaryotic signaling systems, is the formation of multi protein signaling complexes. Even for a small number of interacting proteins the number of distinguishable molecular species can be extremely high. This combinatorial complexity is due to the great number of distinct binding domains of many receptors and scaffold proteins involved in signal transduction. However, these problems can be overcome using a new domain-oriented modeling approach, which makes it possible to handle complex and branched signaling pathways.

A Generalized Hybrid Multiscale Modeling Approach for Flow and Reactive Transport in Porous Media

NASA Astrophysics Data System (ADS)

Yang, X.; Meng, X.; Tang, Y. H.; Guo, Z.; Karniadakis, G. E.

2017-12-01

Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale approaches, and there is strong interest in coupling models at different scales together in a hybrid multiscale simulation framework. A limited number of hybrid multiscale simulation methods have been developed for subsurface applications, mostly using application-specific approaches for model coupling. The proposed generalized hybrid multiscale approach is designed with minimal intrusiveness to the at-scale simulators (pre-selected) and provides a set of lightweight C++ scripts to manage a complex multiscale workflow utilizing a concurrent coupling approach. The workflow includes at-scale simulators (using the lattice-Boltzmann method, LBM, at the pore and Darcy scale, respectively), scripts for boundary treatment (coupling and kriging), and a multiscale universal interface (MUI) for data exchange. The current study aims to apply the generalized hybrid multiscale modeling approach to couple pore- and Darcy-scale models for flow and mixing-controlled reaction with precipitation/dissolution in heterogeneous porous media. The model domain is packed heterogeneously that the mixing front geometry is more complex and not known a priori. To address those challenges, the generalized hybrid multiscale modeling approach is further developed to 1) adaptively define the locations of pore-scale subdomains, 2) provide a suite of physical boundary coupling schemes and 3) consider the dynamic change of the pore structures due to mineral precipitation/dissolution. The results are validated and evaluated by comparing with single-scale simulations in terms of velocities, reactive concentrations and computing cost.

A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

NASA Astrophysics Data System (ADS)

Nanda, Tarun; Kumar, B. Ravi; Singh, Vishal

2017-11-01

Micromechanical modeling is used to predict material's tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, `dislocation-based strain-hardening method' was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using `rule of mixtures,' to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress-strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

Teaching Modeling with Partial Differential Equations: Several Successful Approaches

ERIC Educational Resources Information Center

Myers, Joseph; Trubatch, David; Winkel, Brian

2008-01-01

We discuss the introduction and teaching of partial differential equations (heat and wave equations) via modeling physical phenomena, using a new approach that encompasses constructing difference equations and implementing these in a spreadsheet, numerically solving the partial differential equations using the numerical differential equation…

Evaluation of approaches focused on modelling of organic carbon stocks using the RothC model

NASA Astrophysics Data System (ADS)

Koco, Štefan; Skalský, Rastislav; Makovníková, Jarmila; Tarasovičová, Zuzana; Barančíková, Gabriela

2014-05-01

The aim of current efforts in the European area is the protection of soil organic matter, which is included in all relevant documents related to the protection of soil. The use of modelling of organic carbon stocks for anticipated climate change, respectively for land management can significantly help in short and long-term forecasting of the state of soil organic matter. RothC model can be applied in the time period of several years to centuries and has been tested in long-term experiments within a large range of soil types and climatic conditions in Europe. For the initialization of the RothC model, knowledge about the carbon pool sizes is essential. Pool size characterization can be obtained from equilibrium model runs, but this approach is time consuming and tedious, especially for larger scale simulations. Due to this complexity we search for new possibilities how to simplify and accelerate this process. The paper presents a comparison of two approaches for SOC stocks modelling in the same area. The modelling has been carried out on the basis of unique input of land use, management and soil data for each simulation unit separately. We modeled 1617 simulation units of 1x1 km grid on the territory of agroclimatic region Žitný ostrov in the southwest of Slovakia. The first approach represents the creation of groups of simulation units based on the evaluation of results for simulation unit with similar input values. The groups were created after the testing and validation of modelling results for individual simulation units with results of modelling the average values of inputs for the whole group. Tests of equilibrium model for interval in the range 5 t.ha-1 from initial SOC stock showed minimal differences in results comparing with result for average value of whole interval. Management inputs data from plant residues and farmyard manure for modelling of carbon turnover were also the same for more simulation units. Combining these groups (intervals of initial

A Simulation Modeling Approach Method Focused on the Refrigerated Warehouses Using Design of Experiment

NASA Astrophysics Data System (ADS)

Cho, G. S.

2017-09-01

For performance optimization of Refrigerated Warehouses, design parameters are selected based on the physical parameters such as number of equipment and aisles, speeds of forklift for ease of modification. This paper provides a comprehensive framework approach for the system design of Refrigerated Warehouses. We propose a modeling approach which aims at the simulation optimization so as to meet required design specifications using the Design of Experiment (DOE) and analyze a simulation model using integrated aspect-oriented modeling approach (i-AOMA). As a result, this suggested method can evaluate the performance of a variety of Refrigerated Warehouses operations.

A Latent Class Approach to Fitting the Weighted Euclidean Model, CLASCAL.

ERIC Educational Resources Information Center

Winsberg, Suzanne; De Soete, Geert

1993-01-01

A weighted Euclidean distance model is proposed that incorporates a latent class approach (CLASCAL). The contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. A model selection strategy is proposed and illustrated. (SLD)

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

PubMed

Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

2014-01-01

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.

PubMed

Liu, Shuo; Zeng, Jinshu; Gong, Huizhou; Yang, Hongqin; Zhai, Jia; Cao, Yi; Liu, Junxiu; Luo, Yuling; Li, Yuhua; Maguire, Liam; Ding, Xuemei

2018-01-01

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Vertically-integrated Approaches for Carbon Sequestration Modeling

NASA Astrophysics Data System (ADS)

Bandilla, K.; Celia, M. A.; Guo, B.

2015-12-01

Carbon capture and sequestration (CCS) is being considered as an approach to mitigate anthropogenic CO2 emissions from large stationary sources such as coal fired power plants and natural gas processing plants. Computer modeling is an essential tool for site design and operational planning as it allows prediction of the pressure response as well as the migration of both CO2 and brine in the subsurface. Many processes, such as buoyancy, hysteresis, geomechanics and geochemistry, can have important impacts on the system. While all of the processes can be taken into account simultaneously, the resulting models are computationally very expensive and require large numbers of parameters which are often uncertain or unknown. In many cases of practical interest, the computational and data requirements can be reduced by choosing a smaller domain and/or by neglecting or simplifying certain processes. This leads to a series of models with different complexity, ranging from coupled multi-physics, multi-phase three-dimensional models to semi-analytical single-phase models. Under certain conditions the three-dimensional equations can be integrated in the vertical direction, leading to a suite of two-dimensional multi-phase models, termed vertically-integrated models. These models are either solved numerically or simplified further (e.g., assumption of vertical equilibrium) to allow analytical or semi-analytical solutions. This presentation focuses on how different vertically-integrated models have been applied to the simulation of CO2 and brine migration during CCS projects. Several example sites, such as the Illinois Basin and the Wabamun Lake region of the Alberta Basin, are discussed to show how vertically-integrated models can be used to gain understanding of CCS operations.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

PubMed

Dockendorf, Marissa F; Vargo, Ryan C; Gheyas, Ferdous; Chain, Anne S Y; Chatterjee, Manash S; Wenning, Larissa A

2018-06-01

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

Gutzwiller Monte Carlo approach for a critical dissipative spin model

NASA Astrophysics Data System (ADS)

Casteels, Wim; Wilson, Ryan M.; Wouters, Michiel

2018-06-01

We use the Gutzwiller Monte Carlo approach to simulate the dissipative X Y Z model in the vicinity of a dissipative phase transition. This approach captures classical spatial correlations together with the full on-site quantum behavior while neglecting nonlocal quantum effects. By considering finite two-dimensional lattices of various sizes, we identify a ferromagnetic and two paramagnetic phases, in agreement with earlier studies. The greatly reduced numerical complexity of the Gutzwiller Monte Carlo approach facilitates efficient simulation of relatively large lattice sizes. The inclusion of the spatial correlations allows to capture parts of the phase diagram that are completely missed by the widely applied Gutzwiller decoupling of the density matrix.

Toward a Model-Based Approach to Flight System Fault Protection

NASA Technical Reports Server (NTRS)

Day, John; Murray, Alex; Meakin, Peter

2012-01-01

Fault Protection (FP) is a distinct and separate systems engineering sub-discipline that is concerned with the off-nominal behavior of a system. Flight system fault protection is an important part of the overall flight system systems engineering effort, with its own products and processes. As with other aspects of systems engineering, the FP domain is highly amenable to expression and management in models. However, while there are standards and guidelines for performing FP related analyses, there are not standards or guidelines for formally relating the FP analyses to each other or to the system hardware and software design. As a result, the material generated for these analyses are effectively creating separate models that are only loosely-related to the system being designed. Development of approaches that enable modeling of FP concerns in the same model as the system hardware and software design enables establishment of formal relationships that has great potential for improving the efficiency, correctness, and verification of the implementation of flight system FP. This paper begins with an overview of the FP domain, and then continues with a presentation of a SysML/UML model of the FP domain and the particular analyses that it contains, by way of showing a potential model-based approach to flight system fault protection, and an exposition of the use of the FP models in FSW engineering. The analyses are small examples, inspired by current real-project examples of FP analyses.

Automated model integration at source code level: An approach for implementing models into the NASA Land Information System

NASA Astrophysics Data System (ADS)

Wang, S.; Peters-Lidard, C. D.; Mocko, D. M.; Kumar, S.; Nearing, G. S.; Arsenault, K. R.; Geiger, J. V.

2014-12-01

Model integration bridges the data flow between modeling frameworks and models. However, models usually do not fit directly into a particular modeling environment, if not designed for it. An example includes implementing different types of models into the NASA Land Information System (LIS), a software framework for land-surface modeling and data assimilation. Model implementation requires scientific knowledge and software expertise and may take a developer months to learn LIS and model software structure. Debugging and testing of the model implementation is also time-consuming due to not fully understanding LIS or the model. This time spent is costly for research and operational projects. To address this issue, an approach has been developed to automate model integration into LIS. With this in mind, a general model interface was designed to retrieve forcing inputs, parameters, and state variables needed by the model and to provide as state variables and outputs to LIS. Every model can be wrapped to comply with the interface, usually with a FORTRAN 90 subroutine. Development efforts need only knowledge of the model and basic programming skills. With such wrappers, the logic is the same for implementing all models. Code templates defined for this general model interface could be re-used with any specific model. Therefore, the model implementation can be done automatically. An automated model implementation toolkit was developed with Microsoft Excel and its built-in VBA language. It allows model specifications in three worksheets and contains FORTRAN 90 code templates in VBA programs. According to the model specification, the toolkit generates data structures and procedures within FORTRAN modules and subroutines, which transfer data between LIS and the model wrapper. Model implementation is standardized, and about 80 - 90% of the development load is reduced. In this presentation, the automated model implementation approach is described along with LIS programming

Modeling Individual Differences in Unfolding Preference Data: A Restricted Latent Class Approach.

ERIC Educational Resources Information Center

Bockenholt, Ulf; Bockenholt, Ingo

1990-01-01

A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)

Cellular communication and “non-targeted effects”: Modelling approaches

NASA Astrophysics Data System (ADS)

Ballarini, Francesca; Facoetti, Angelica; Mariotti, Luca; Nano, Rosanna; Ottolenghi, Andrea

2009-10-01

During the last decade, a large number of experimental studies on the so-called "non-targeted effects", in particular bystander effects, outlined that cellular communication plays a significant role in the pathways leading to radiobiological damage. Although it is known that two main types of cellular communication (i.e. via gap junctions and/or molecular messengers diffusing in the extra-cellular environment, such as cytokines, NO etc.) play a major role, it is of utmost importance to better understand the underlying mechanisms, and how such mechanisms can be modulated by ionizing radiation. Though the "final" goal is of course to elucidate the in vivo scenario, in the meanwhile also in vitro studies can provide useful insights. In the present paper we will discuss key issues on the mechanisms underlying non-targeted effects and cell communication, for which theoretical models and simulation codes can be of great help. In this framework, we will present in detail three literature models, as well as an approach under development at the University of Pavia. More specifically, we will first focus on a version of the "State-Vector Model" including bystander-induced apoptosis of initiated cells, which was successfully fitted to in vitro data on neoplastic transformation supporting the hypothesis of a protective bystander effect mediated by apoptosis. The second analyzed model, focusing on the kinetics of bystander effects in 3D tissues, was successfully fitted to data on bystander damage in an artificial 3D skin system, indicating a signal range of the order of 0.7-1 mm. A third model for bystander effect, taking into account of spatial location, cell killing and repopulation, showed dose-response curves increasing approximately linearly at low dose rates but quickly flattening out for higher dose rates, also predicting an effect augmentation following dose fractionation. Concerning the Pavia approach, which can model the release, diffusion and depletion/degradation of

TinkerPlots™ Model Construction Approaches for Comparing Two Groups: Student Perspectives

ERIC Educational Resources Information Center

Noll, Jennifer; Kirin, Dana

2017-01-01

Teaching introductory statistics using curricula focused on modeling and simulation is becoming increasingly common in introductory statistics courses and touted as a more beneficial approach for fostering students' statistical thinking. Yet, surprisingly little research has been conducted to study the impact of modeling and simulation curricula…

A Unified Approach to Quantifying Feedbacks in Earth System Models

NASA Astrophysics Data System (ADS)

Taylor, K. E.

2008-12-01

In order to speed progress in reducing uncertainty in climate projections, the processes that most strongly influence those projections must be identified. It is of some importance, therefore, to assess the relative strengths of various climate feedbacks and to determine the degree to which various earth system models (ESMs) agree in their simulations of these processes. Climate feedbacks have been traditionally quantified in terms of their impact on the radiative balance of the planet, whereas carbon cycle responses have been assessed in terms of the size of the perturbations to the surface fluxes of carbon dioxide. In this study we introduce a diagnostic strategy for unifying the two approaches, which allows us to directly compare the strength of carbon-climate feedbacks with other conventional climate feedbacks associated with atmospheric and surface changes. Applying this strategy to a highly simplified model of the carbon-climate system demonstrates the viability of the approach. In the simple model we find that even if the strength of the carbon-climate feedbacks is very large, the uncertainty associated with the overall response of the climate system is likely to be dominated by uncertainties in the much larger feedbacks associated with clouds. This does not imply that the carbon cycle itself is unimportant, only that changes in the carbon cycle that are associated with climate change have a relatively small impact on global temperatures. This new, unified diagnostic approach is suitable for assessing feedbacks in even the most sophisticated earth system models. It will be interesting to see whether our preliminary conclusions are confirmed when output from the more realistic models is analyzed. This work was carried out at the University of California Lawrence Livermore National Laboratory under Contract W-7405-Eng-48.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological

Population response to climate change: linear vs. non-linear modeling approaches.

PubMed

Ellis, Alicia M; Post, Eric

2004-03-31

Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.

Decision support models for solid waste management: Review and game-theoretic approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karmperis, Athanasios C., E-mail: athkarmp@mail.ntua.gr; Army Corps of Engineers, Hellenic Army General Staff, Ministry of Defence; Aravossis, Konstantinos

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decisionmore » support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.« less

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

PubMed

Ng, Edmond S-W; Diaz-Ordaz, Karla; Grieve, Richard; Nixon, Richard M; Thompson, Simon G; Carpenter, James R

2016-10-01

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data. © The Author(s) 2013.

Nested 1D-2D approach for urban surface flood modeling

NASA Astrophysics Data System (ADS)

Murla, Damian; Willems, Patrick

2015-04-01

Floods in urban areas as a consequence of sewer capacity exceedance receive increased attention because of trends in urbanization (increased population density and impermeability of the surface) and climate change. Despite the strong recent developments in numerical modeling of water systems, urban surface flood modeling is still a major challenge. Whereas very advanced and accurate flood modeling systems are in place and operation by many river authorities in support of flood management along rivers, this is not yet the case in urban water management. Reasons include the small scale of the urban inundation processes, the need to have very high resolution topographical information available, and the huge computational demands. Urban drainage related inundation modeling requires a 1D full hydrodynamic model of the sewer network to be coupled with a 2D surface flood model. To reduce the computational times, 0D (flood cones), 1D/quasi-2D surface flood modeling approaches have been developed and applied in some case studies. In this research, a nested 1D/2D hydraulic model has been developed for an urban catchment at the city of Gent (Belgium), linking the underground sewer (minor system) with the overland surface (major system). For the overland surface flood modelling, comparison was made of 0D, 1D/quasi-2D and full 2D approaches. The approaches are advanced by considering nested 1D-2D approaches, including infiltration in the green city areas, and allowing the effects of surface storm water storage to be simulated. An optimal nested combination of three different mesh resolutions was identified; based on a compromise between precision and simulation time for further real-time flood forecasting, warning and control applications. Main streets as mesh zones together with buildings as void regions constitute one of these mesh resolution (3.75m2 - 15m2); they have been included since they channel most of the flood water from the manholes and they improve the accuracy of

A new approach to modelling schistosomiasis transmission based on stratified worm burden.

PubMed

Gurarie, D; King, C H; Wang, X

2010-11-01

Multiple factors affect schistosomiasis transmission in distributed meta-population systems including age, behaviour, and environment. The traditional approach to modelling macroparasite transmission often exploits the 'mean worm burden' (MWB) formulation for human hosts. However, typical worm distribution in humans is overdispersed, and classic models either ignore this characteristic or make ad hoc assumptions about its pattern (e.g., by assuming a negative binomial distribution). Such oversimplifications can give wrong predictions for the impact of control interventions. We propose a new modelling approach to macro-parasite transmission by stratifying human populations according to worm burden, and replacing MWB dynamics with that of 'population strata'. We developed proper calibration procedures for such multi-component systems, based on typical epidemiological and demographic field data, and implemented them using Wolfram Mathematica. Model programming and calibration proved to be straightforward. Our calibrated system provided good agreement with the individual level field data from the Msambweni region of eastern Kenya. The Stratified Worm Burden (SWB) approach offers many advantages, in that it accounts naturally for overdispersion and accommodates other important factors and measures of human infection and demographics. Future work will apply this model and methodology to evaluate innovative control intervention strategies, including expanded drug treatment programmes proposed by the World Health Organization and its partners.

A Generic Modeling Approach to Biomass Dynamics of Sagittaria latifolia and Spartina alterniflora

DTIC Science & Technology

2011-01-01

ammonium nitrate pulse of the growth and elemental composition of natural stands of Spartina alterniflora and Juncus roemerianus. American Journal of...calibration values become available. This modelling approach was applied to submersed aquatic vegetation (SAV) also (Best and Boyd 2008). The approach is... the models. The DVS is dimensionless and its value increases gradually within a growing season. The development rate (DVR) has the dimension d-1

A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis.

PubMed

Zeng, Ziqiang; Zhu, Wenbo; Ke, Ruimin; Ash, John; Wang, Yinhai; Xu, Jiuping; Xu, Xinxin

2017-02-01

The mixed multinomial logit (MNL) approach, which can account for unobserved heterogeneity, is a promising unordered model that has been employed in analyzing the effect of factors contributing to crash severity. However, its basic assumption of using a linear function to explore the relationship between the probability of crash severity and its contributing factors can be violated in reality. This paper develops a generalized nonlinear model-based mixed MNL approach which is capable of capturing non-monotonic relationships by developing nonlinear predictors for the contributing factors in the context of unobserved heterogeneity. The crash data on seven Interstate freeways in Washington between January 2011 and December 2014 are collected to develop the nonlinear predictors in the model. Thirteen contributing factors in terms of traffic characteristics, roadway geometric characteristics, and weather conditions are identified to have significant mixed (fixed or random) effects on the crash density in three crash severity levels: fatal, injury, and property damage only. The proposed model is compared with the standard mixed MNL model. The comparison results suggest a slight superiority of the new approach in terms of model fit measured by the Akaike Information Criterion (12.06 percent decrease) and Bayesian Information Criterion (9.11 percent decrease). The predicted crash densities for all three levels of crash severities of the new approach are also closer (on average) to the observations than the ones predicted by the standard mixed MNL model. Finally, the significance and impacts of the contributing factors are analyzed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Introducing an osteopathic approach into neonatology ward: the NE-O model.

PubMed

Cerritelli, Francesco; Martelli, Marta; Renzetti, Cinzia; Pizzolorusso, Gianfranco; Cozzolino, Vincenzo; Barlafante, Gina

2014-01-01

Several studies showed the effect of osteopathic manipulative treatment on neonatal care in reducing length of stay in hospital, gastrointestinal problems, clubfoot complications and improving cranial asymmetry of infants affected by plagiocephaly. Despite several results obtained, there is still a lack of standardized osteopathic evaluation and treatment procedures for newborns recovered in neonatal intensive care unit (NICU). The aim of this paper is to suggest a protocol on osteopathic approach (NE-O model) in treating hospitalized newborns. The NE-O model is composed by specific evaluation tests and treatments to tailor osteopathic method according to preterm and term infants' needs, NICU environment, medical and paramedical assistance. This model was developed to maximize the effectiveness and the clinical use of osteopathy into NICU. The NE-O model was adopted in 2006 to evaluate the efficacy of OMT in neonatology. Results from research showed the effectiveness of this osteopathic model in reducing preterms' length of stay and hospital costs. Additionally the present model was demonstrated to be safe. The present paper defines the key steps for a rigorous and effective osteopathic approach into NICU setting, providing a scientific and methodological example of integrated medicine and complex intervention.

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee

2015-08-01

This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach

PubMed Central

Duarte, Belmiro P. M.; Wong, Weng Kee

2014-01-01

Summary This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted. PMID:26512159

An Approach to Average Modeling and Simulation of Switch-Mode Systems

ERIC Educational Resources Information Center

Abramovitz, A.

2011-01-01

This paper suggests a pedagogical approach to teaching the subject of average modeling of PWM switch-mode power electronics systems through simulation by general-purpose electronic circuit simulators. The paper discusses the derivation of PSPICE/ORCAD-compatible average models of the switch-mode power stages, their software implementation, and…

Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

NASA Technical Reports Server (NTRS)

Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

2010-01-01

Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

PubMed

Ebalunode, Jerry O; Zheng, Weifan; Tropsha, Alexander

2011-01-01

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Training young scientists across empirical and modeling approaches

NASA Astrophysics Data System (ADS)

Moore, D. J.

2014-12-01

The "fluxcourse," is a two-week program of study on Flux Measurements and Advanced Modeling (www.fluxcourse.org). Since 2007, this course has trained early career scientists to use both empirical observations and models to tackle terrestrial ecological questions. The fluxcourse seeks to cross train young scientists in measurement techniques and advanced modeling approaches for quantifying carbon and water fluxes between the atmosphere and the biosphere. We invited between ten and twenty volunteer instructors depending on the year ranging in experience and expertise, including representatives from industry, university professors and research specialists. The course combines online learning, lecture and discussion with hands on activities that range from measuring photosynthesis and installing an eddy covariance system to wrangling data and carrying out modeling experiments. Attendees are asked to develop and present two different group projects throughout the course. The overall goal is provide the next generation of scientists with the tools to tackle complex problems that require collaboration.

A graphical vector autoregressive modelling approach to the analysis of electronic diary data

PubMed Central

2010-01-01

Background In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. Methods We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models. Results The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours. Conclusion The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research. PMID:20359333

The Agent-based Approach: A New Direction for Computational Models of Development.

ERIC Educational Resources Information Center

Schlesinger, Matthew; Parisi, Domenico

2001-01-01

Introduces the concepts of online and offline sampling and highlights the role of online sampling in agent-based models of learning and development. Compares the strengths of each approach for modeling particular developmental phenomena and research questions. Describes a recent agent-based model of infant causal perception. Discusses limitations…

Modelling approaches for pipe inclination effect on deposition limit velocity of settling slurry flow

NASA Astrophysics Data System (ADS)

Matoušek, Václav; Kesely, Mikoláš; Vlasák, Pavel

2018-06-01

The deposition velocity is an important operation parameter in hydraulic transport of solid particles in pipelines. It represents flow velocity at which transported particles start to settle out at the bottom of the pipe and are no longer transported. A number of predictive models has been developed to determine this threshold velocity for slurry flows of different solids fractions (fractions of different grain size and density). Most of the models consider flow in a horizontal pipe only, modelling approaches for inclined flows are extremely scarce due partially to a lack of experimental information about the effect of pipe inclination on the slurry flow pattern and behaviour. We survey different approaches to modelling of particle deposition in flowing slurry and discuss mechanisms on which deposition-limit models are based. Furthermore, we analyse possibilities to incorporate the effect of flow inclination into the predictive models and select the most appropriate ones based on their ability to modify the modelled deposition mechanisms to conditions associated with the flow inclination. A usefulness of the selected modelling approaches and their modifications are demonstrated by comparing model predictions with experimental results for inclined slurry flows from our own laboratory and from the literature.

Distinguishing Continuous and Discrete Approaches to Multilevel Mixture IRT Models: A Model Comparison Perspective

ERIC Educational Resources Information Center

Zhu, Xiaoshu

2013-01-01

The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish…

[New approaches in pharmacology: numerical modelling and simulation].

PubMed

Boissel, Jean-Pierre; Cucherat, Michel; Nony, Patrice; Dronne, Marie-Aimée; Kassaï, Behrouz; Chabaud, Sylvie

2005-01-01

The complexity of pathophysiological mechanisms is beyond the capabilities of traditional approaches. Many of the decision-making problems in public health, such as initiating mass screening, are complex. Progress in genomics and proteomics, and the resulting extraordinary increase in knowledge with regard to interactions between gene expression, the environment and behaviour, the customisation of risk factors and the need to combine therapies that individually have minimal though well documented efficacy, has led doctors to raise new questions: how to optimise choice and the application of therapeutic strategies at the individual rather than the group level, while taking into account all the available evidence? This is essentially a problem of complexity with dimensions similar to the previous ones: multiple parameters with nonlinear relationships between them, varying time scales that cannot be ignored etc. Numerical modelling and simulation (in silico investigations) have the potential to meet these challenges. Such approaches are considered in drug innovation and development. They require a multidisciplinary approach, and this will involve modification of the way research in pharmacology is conducted.

Minimization of required model runs in the Random Mixing approach to inverse groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Hoerning, Sebastian; Bardossy, Andras; du Plessis, Jaco

2017-04-01

Most geostatistical inverse groundwater flow and transport modelling approaches utilize a numerical solver to minimize the discrepancy between observed and simulated hydraulic heads and/or hydraulic concentration values. The optimization procedure often requires many model runs, which for complex models lead to long run times. Random Mixing is a promising new geostatistical technique for inverse modelling. The method is an extension of the gradual deformation approach. It works by finding a field which preserves the covariance structure and maintains observed hydraulic conductivities. This field is perturbed by mixing it with new fields that fulfill the homogeneous conditions. This mixing is expressed as an optimization problem which aims to minimize the difference between the observed and simulated hydraulic heads and/or concentration values. To preserve the spatial structure, the mixing weights must lie on the unit hyper-sphere. We present a modification to the Random Mixing algorithm which significantly reduces the number of model runs required. The approach involves taking n equally spaced points on the unit circle as weights for mixing conditional random fields. Each of these mixtures provides a solution to the forward model at the conditioning locations. For each of the locations the solutions are then interpolated around the circle to provide solutions for additional mixing weights at very low computational cost. The interpolated solutions are used to search for a mixture which maximally reduces the objective function. This is in contrast to other approaches which evaluate the objective function for the n mixtures and then interpolate the obtained values. Keeping the mixture on the unit circle makes it easy to generate equidistant sampling points in the space; however, this means that only two fields are mixed at a time. Once the optimal mixture for two fields has been found, they are combined to form the input to the next iteration of the algorithm. This

A hidden Markov model approach to neuron firing patterns.

PubMed Central

Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

1996-01-01

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing. Images FIGURE 3 PMID:8913581

A hidden Markov model approach to neuron firing patterns.

PubMed

Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

1996-11-01

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

An approach for modeling thermal destruction of hazardous wastes in circulating fluidized bed incinerator.

PubMed

Patil, M P; Sonolikar, R L

2008-10-01

This paper presents a detailed computational fluid dynamics (CFD) based approach for modeling thermal destruction of hazardous wastes in a circulating fluidized bed (CFB) incinerator. The model is based on Eular - Lagrangian approach in which gas phase (continuous phase) is treated in a Eularian reference frame, whereas the waste particulate (dispersed phase) is treated in a Lagrangian reference frame. The reaction chemistry hasbeen modeled through a mixture fraction/ PDF approach. The conservation equations for mass, momentum, energy, mixture fraction and other closure equations have been solved using a general purpose CFD code FLUENT4.5. Afinite volume method on a structured grid has been used for solution of governing equations. The model provides detailed information on the hydrodynamics (gas velocity, particulate trajectories), gas composition (CO, CO2, O2) and temperature inside the riser. The model also allows different operating scenarios to be examined in an efficient manner.

A 2D flood inundation model based on cellular automata approach

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Todini, Ezio

2010-05-01

In the past years, the cellular automata approach has been successfully applied in two-dimensional modelling of flood events. When used in experimental applications, models based on such approach have provided good results, comparable to those obtained with more complex 2D models; moreover, CA models have proven significantly faster and easier to apply than most of existing models, and these features make them a valuable tool for flood analysis especially when dealing with large areas. However, to date the real degree of accuracy of such models has not been demonstrated, since they have been mainly used in experimental applications, while very few comparisons with theoretical solutions have been made. Also, the use of an explicit scheme of solution, which is inherent in cellular automata models, forces them to work only with small time steps, thus reducing model computation speed. The present work describes a cellular automata model based on the continuity and diffusive wave equations. Several model versions based on different solution schemes have been realized and tested in a number of numerical cases, both 1D and 2D, comparing the results with theoretical and numerical solutions. In all cases, the model performed well compared to the reference solutions, and proved to be both stable and accurate. Finally, the version providing the best results in terms of stability was tested in a real flood event and compared with different hydraulic models. Again, the cellular automata model provided very good results, both in term of computational speed and reproduction of the simulated event.

Modeling the Relations among Students' Epistemological Beliefs, Motivation, Learning Approach, and Achievement

ERIC Educational Resources Information Center

Kizilgunes, Berna; Tekkaya, Ceren; Sungur, Semra

2009-01-01

The authors proposed a model to explain how epistemological beliefs, achievement motivation, and learning approach related to achievement. The authors assumed that epistemological beliefs influence achievement indirectly through their effect on achievement motivation and learning approach. Participants were 1,041 6th-grade students. Results of the…

Computer Simulation of Breast Cancer Screening

DTIC Science & Technology

2001-07-01

21. Tompkins PA, Abreu CC, Carroll FE, Xiao therapeutic medical physics. Med Phys 14. Gentry JR, DeWerd LA. TLD measure- QF, MacDonald CA. Use of...capillary op- 1996; 23:1997-2005. ments of in vivo mammographic expo- tics as a beam intensifier for a Compton 28. Hammerstein GR, Miller DW, White DR...cm), and was only poorly correlated thicker slices. with breast thickness (r2 0.159). The For comparison images and dosimetry , magnification factor

Assessing Knowledge of Mathematical Equivalence: A Construct-Modeling Approach

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Matthews, Percival G.; Taylor, Roger S.; McEldoon, Katherine L.

2011-01-01

Knowledge of mathematical equivalence, the principle that 2 sides of an equation represent the same value, is a foundational concept in algebra, and this knowledge develops throughout elementary and middle school. Using a construct-modeling approach, we developed an assessment of equivalence knowledge. Second through sixth graders (N = 175)…

Systems engineering interfaces: A model based approach

NASA Astrophysics Data System (ADS)

Fosse, E.; Delp, C. L.

The engineering of interfaces is a critical function of the discipline of Systems Engineering. Included in interface engineering are instances of interaction. Interfaces provide the specifications of the relevant properties of a system or component that can be connected to other systems or components while instances of interaction are identified in order to specify the actual integration to other systems or components. Current Systems Engineering practices rely on a variety of documents and diagrams to describe interface specifications and instances of interaction. The SysML[1] specification provides a precise model based representation for interfaces and interface instance integration. This paper will describe interface engineering as implemented by the Operations Revitalization Task using SysML, starting with a generic case and culminating with a focus on a Flight System to Ground Interaction. The reusability of the interface engineering approach presented as well as its extensibility to more complex interfaces and interactions will be shown. Model-derived tables will support the case studies shown and are examples of model-based documentation products.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Characterization of potential security threats in modern automobiles: a composite modeling approach

DOT National Transportation Integrated Search

2014-10-01

The primary objective of the work detailed in this report is to describe a composite modeling approach for potential cybersecurity threats in modern vehicles. Threat models, threat descriptions, and examples of various types of conceivable threats to...

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

PubMed

Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

2017-03-20

Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort. To our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in

The Threshold Bias Model: A Mathematical Model for the Nomothetic Approach of Suicide

PubMed Central

Folly, Walter Sydney Dutra

2011-01-01

Background Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. Methodology/Principal Findings A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. Conclusions/Significance The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health. PMID:21909431

The threshold bias model: a mathematical model for the nomothetic approach of suicide.

PubMed

Folly, Walter Sydney Dutra

2011-01-01

Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health.

Development of a hybrid modeling approach for predicting intensively managed Douglas-fir growth at multiple scales.

Treesearch

A. Weiskittel; D. Maguire; R. Monserud

2007-01-01

Hybrid models offer the opportunity to improve future growth projections by combining advantages of both empirical and process-based modeling approaches. Hybrid models have been constructed in several regions and their performance relative to a purely empirical approach has varied. A hybrid model was constructed for intensively managed Douglas-fir plantations in the...

A weakly-constrained data assimilation approach to address rainfall-runoff model structural inadequacy in streamflow prediction

NASA Astrophysics Data System (ADS)

Lee, Haksu; Seo, Dong-Jun; Noh, Seong Jin

2016-11-01

This paper presents a simple yet effective weakly-constrained (WC) data assimilation (DA) approach for hydrologic models which accounts for model structural inadequacies associated with rainfall-runoff transformation processes. Compared to the strongly-constrained (SC) DA, WC DA adjusts the control variables less while producing similarly or more accurate analysis. Hence the adjusted model states are dynamically more consistent with those of the base model. The inadequacy of a rainfall-runoff model was modeled as an additive error to runoff components prior to routing and penalized in the objective function. Two example modeling applications, distributed and lumped, were carried out to investigate the effects of the WC DA approach on DA results. For distributed modeling, the distributed Sacramento Soil Moisture Accounting (SAC-SMA) model was applied to the TIFM7 Basin in Missouri, USA. For lumped modeling, the lumped SAC-SMA model was applied to nineteen basins in Texas. In both cases, the variational DA (VAR) technique was used to assimilate discharge data at the basin outlet. For distributed SAC-SMA, spatially homogeneous error modeling yielded updated states that are spatially much more similar to the a priori states, as quantified by Earth Mover's Distance (EMD), than spatially heterogeneous error modeling by up to ∼10 times. DA experiments using both lumped and distributed SAC-SMA modeling indicated that assimilating outlet flow using the WC approach generally produce smaller mean absolute difference as well as higher correlation between the a priori and the updated states than the SC approach, while producing similar or smaller root mean square error of streamflow analysis and prediction. Large differences were found in both lumped and distributed modeling cases between the updated and the a priori lower zone tension and primary free water contents for both WC and SC approaches, indicating possible model structural deficiency in describing low flows or

Assessing Climate Change Risks Using a Multi-Model Approach

NASA Astrophysics Data System (ADS)

Knorr, W.; Scholze, M.; Prentice, C.

2007-12-01

We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from the IPCC AR4 data archive using 16 climate models and mapping the proportions of model runs showing exceedance of natural variability in wildfire frequency and freshwater supply or shifts in vegetation cover. Our analysis does not assign probabilities to scenarios. Instead, we consider the distribution of outcomes within three sets of model runs grouped according to the amount of global warming they simulate: < 2 degree C (including committed climate change simulations), 2-3 degree C, and >3 degree C. Here, we are contrasting two different methods for calculating the risks: first we use an equal weighting approach giving every model within one of the three sets the same weight, and second, we weight the models according to their ability to model ENSO. The differences are underpinning the need for the development of more robust performance metrics for global climate models.

Using A Model-Based Systems Engineering Approach For Exploration Medical System Development

NASA Technical Reports Server (NTRS)

Hanson, A.; Mindock, J.; McGuire, K.; Reilly, J.; Cerro, J.; Othon, W.; Rubin, D.; Urbina, M.; Canga, M.

2017-01-01

NASA's Human Research Program's Exploration Medical Capabilities (ExMC) element is defining the medical system needs for exploration class missions. ExMC's Systems Engineering (SE) team will play a critical role in successful design and implementation of the medical system into exploration vehicles. The team's mission is to "Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds." Development of the medical system is being conducted in parallel with exploration mission architecture and vehicle design development. Successful implementation of the medical system in this environment will require a robust systems engineering approach to enable technical communication across communities to create a common mental model of the emergent engineering and medical systems. Model-Based Systems Engineering (MBSE) improves shared understanding of system needs and constraints between stakeholders and offers a common language for analysis. The ExMC SE team is using MBSE techniques to define operational needs, decompose requirements and architecture, and identify medical capabilities needed to support human exploration. Systems Modeling Language (SysML) is the specific language the SE team is utilizing, within an MBSE approach, to model the medical system functional needs, requirements, and architecture. Modeling methods are being developed through the practice of MBSE within the team, and tools are being selected to support meta-data exchange as integration points to other system models are identified. Use of MBSE is supporting the development of relationships across disciplines and NASA Centers to build trust and enable teamwork, enhance visibility of team goals, foster a culture of unbiased learning and serving, and be responsive to customer needs. The MBSE approach to medical system design offers a paradigm shift toward greater integration between

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Development of Continuum-Atomistic Approach for Modeling Metal Irradiation by Heavy Ions

NASA Astrophysics Data System (ADS)

Batgerel, Balt; Dimova, Stefka; Puzynin, Igor; Puzynina, Taisia; Hristov, Ivan; Hristova, Radoslava; Tukhliev, Zafar; Sharipov, Zarif

2018-02-01

Over the last several decades active research in the field of materials irradiation by high-energy heavy ions has been worked out. The experiments in this area are labor-consuming and expensive. Therefore the improvement of the existing mathematical models and the development of new ones based on the experimental data of interaction of high-energy heavy ions with materials are of interest. Presently, two approaches are used for studying these processes: a thermal spike model and molecular dynamics methods. The combination of these two approaches - the continuous-atomistic model - will give the opportunity to investigate more thoroughly the processes of irradiation of materials by high-energy heavy ions. To solve the equations of the continuous-atomistic model, a software package was developed and the block of molecular dynamics software was tested on the heterogeneous cluster HybriLIT.

Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

NASA Astrophysics Data System (ADS)

Engel, Dave W.; Reichardt, Thomas A.; Kulp, Thomas J.; Graff, David L.; Thompson, Sandra E.

2016-05-01

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.

Hierarchical Multi-Scale Approach To Validation and Uncertainty Quantification of Hyper-Spectral Image Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engel, David W.; Reichardt, Thomas A.; Kulp, Thomas J.

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensormore » level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.« less

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

Modeling of scale-dependent bacterial growth by chemical kinetics approach.

PubMed

Martínez, Haydee; Sánchez, Joaquín; Cruz, José-Manuel; Ayala, Guadalupe; Rivera, Marco; Buhse, Thomas

2014-01-01

We applied the so-called chemical kinetics approach to complex bacterial growth patterns that were dependent on the liquid-surface-area-to-volume ratio (SA/V) of the bacterial cultures. The kinetic modeling was based on current experimental knowledge in terms of autocatalytic bacterial growth, its inhibition by the metabolite CO2, and the relief of inhibition through the physical escape of the inhibitor. The model quantitatively reproduces kinetic data of SA/V-dependent bacterial growth and can discriminate between differences in the growth dynamics of enteropathogenic E. coli, E. coli JM83, and Salmonella typhimurium on one hand and Vibrio cholerae on the other hand. Furthermore, the data fitting procedures allowed predictions about the velocities of the involved key processes and the potential behavior in an open-flow bacterial chemostat, revealing an oscillatory approach to the stationary states.

Uranium adsorption on weathered schist - Intercomparison of modeling approaches

USGS Publications Warehouse

Payne, T.E.; Davis, J.A.; Ochs, M.; Olin, M.; Tweed, C.J.

2004-01-01

Experimental data for uranium adsorption on a complex weathered rock were simulated by twelve modelling teams from eight countries using surface complexation (SC) models. This intercomparison was part of an international project to evaluate the present capabilities and limitations of SC models in representing sorption by geologic materials. The models were assessed in terms of their predictive ability, data requirements, number of optimised parameters, ability to simulate diverse chemical conditions and transferability to other substrates. A particular aim was to compare the generalised composite (GC) and component additivity (CA) approaches for modelling sorption by complex substrates. Both types of SC models showed a promising capability to simulate sorption data obtained across a range of chemical conditions. However, the models incorporated a wide variety of assumptions, particularly in terms of input parameters such as site densities and surface site types. Furthermore, the methods used to extrapolate the model simulations to different weathered rock samples collected at the same field site tended to be unsatisfactory. The outcome of this modelling exercise provides an overview of the present status of adsorption modelling in the context of radionuclide migration as practised in a number of countries worldwide.

An Efficient Approach to Modeling the Topographic Control of Surface Hydrology for Regional and Global Climate Modeling.

NASA Astrophysics Data System (ADS)

Stieglitz, Marc; Rind, David; Famiglietti, James; Rosenzweig, Cynthia

1997-01-01

The current generation of land-surface models used in GCMs view the soil column as the fundamental hydrologic unit. While this may be effective in simulating such processes as the evolution of ground temperatures and the growth/ablation of a snowpack at the soil plot scale, it effectively ignores the role topography plays in the development of soil moisture heterogeneity and the subsequent impacts of this soil moisture heterogeneity on watershed evapotranspiration and the partitioning of surface fluxes. This view also ignores the role topography plays in the timing of discharge and the partitioning of discharge into surface runoff and baseflow. In this paper an approach to land-surface modeling is presented that allows us to view the watershed as the fundamental hydrologic unit. The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts the partitioning of surface fluxes, including evapotranspiration and runoff. The approach is computationally efficient, allows for a greatly improved simulation of the hydrologic cycle, and is easily coupled into the existing framework of the current generation of single column land-surface models. Because this approach uses the statistics of the topography rather than the details of the topography, it is compatible with the large spatial scales of today's regional and global climate models. Five years of meteorological and hydrological data from the Sleepers River watershed located in the northeastern United States where winter snow cover is significant were used to drive the new model. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and