Event-driven simulation in SELMON: An overview of EDSE

NASA Technical Reports Server (NTRS)

Rouquette, Nicolas F.; Chien, Steve A.; Charest, Leonard, Jr.

1992-01-01

EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.

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.

Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry

PubMed Central

Meyer, Andrew J.; Patten, Carolynn

2017-01-01

Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient’s lower extremity muscle excitations contribute to the patient’s lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient’s musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process. PMID:28700708

A Model-Driven Development Method for Management Information Systems

NASA Astrophysics Data System (ADS)

Mizuno, Tomoki; Matsumoto, Keinosuke; Mori, Naoki

Traditionally, a Management Information System (MIS) has been developed without using formal methods. By the informal methods, the MIS is developed on its lifecycle without having any models. It causes many problems such as lack of the reliability of system design specifications. In order to overcome these problems, a model theory approach was proposed. The approach is based on an idea that a system can be modeled by automata and set theory. However, it is very difficult to generate automata of the system to be developed right from the start. On the other hand, there is a model-driven development method that can flexibly correspond to changes of business logics or implementing technologies. In the model-driven development, a system is modeled using a modeling language such as UML. This paper proposes a new development method for management information systems applying the model-driven development method to a component of the model theory approach. The experiment has shown that a reduced amount of efforts is more than 30% of all the efforts.

Surface tension driven flow in glass melts and model fluids

NASA Technical Reports Server (NTRS)

Mcneil, T. J.; Cole, R.; Subramanian, R. S.

1982-01-01

Surface tension driven flow has been investigated analytically and experimentally using an apparatus where a free column of molten glass or model fluids was supported at its top and bottom faces by solid surfaces. The glass used in the experiments was sodium diborate, and the model fluids were silicone oils. In both the model fluid and glass melt experiments, conclusive evidence was obtained to prove that the observed flow was driven primarily by surface tension forces. The experimental observations are in qualitative agreement with predictions from the theoretical model.

Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study

NASA Astrophysics Data System (ADS)

Li, Jun; Fu, Siyao; He, Haibo; Jia, Hongfei; Li, Yanzhong; Guo, Yi

2015-11-01

Large-scale regional evacuation is an important part of national security emergency response plan. Large commercial shopping area, as the typical service system, its emergency evacuation is one of the hot research topics. A systematic methodology based on Cellular Automata with the Dynamic Floor Field and event driven model has been proposed, and the methodology has been examined within context of a case study involving the evacuation within a commercial shopping mall. Pedestrians walking is based on Cellular Automata and event driven model. In this paper, the event driven model is adopted to simulate the pedestrian movement patterns, the simulation process is divided into normal situation and emergency evacuation. The model is composed of four layers: environment layer, customer layer, clerk layer and trajectory layer. For the simulation of movement route of pedestrians, the model takes into account purchase intention of customers and density of pedestrians. Based on evacuation model of Cellular Automata with Dynamic Floor Field and event driven model, we can reflect behavior characteristics of customers and clerks at the situations of normal and emergency evacuation. The distribution of individual evacuation time as a function of initial positions and the dynamics of the evacuation process is studied. Our results indicate that the evacuation model using the combination of Cellular Automata with Dynamic Floor Field and event driven scheduling can be used to simulate the evacuation of pedestrian flows in indoor areas with complicated surroundings and to investigate the layout of shopping mall.

Mutational landscape of EGFR-, MYC-, and Kras-driven genetically engineered mouse models of lung adenocarcinoma

PubMed Central

McFadden, David G.; Politi, Katerina; Bhutkar, Arjun; Chen, Frances K.; Song, Xiaoling; Pirun, Mono; Santiago, Philip M.; Kim-Kiselak, Caroline; Platt, James T.; Lee, Emily; Hodges, Emily; Rosebrock, Adam P.; Bronson, Roderick T.; Socci, Nicholas D.; Hannon, Gregory J.; Jacks, Tyler; Varmus, Harold

2016-01-01

Genetically engineered mouse models (GEMMs) of cancer are increasingly being used to assess putative driver mutations identified by large-scale sequencing of human cancer genomes. To accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole-exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant epidermal growth factor receptor (EGFR), mutant Kirsten rat sarcoma viral oncogene homolog (Kras), or overexpression of MYC proto-oncogene. Tumors from EGFR- and Kras-driven models exhibited, respectively, 0.02 and 0.07 nonsynonymous mutations per megabase, a dramatically lower average mutational frequency than observed in human lung adenocarcinomas. Tumors from models driven by strong cancer drivers (mutant EGFR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker initiating oncogene in the murine lung, acquired recurrent clonal oncogenic Kras mutations. In addition, although EGFR- and Kras-driven models both exhibited recurrent whole-chromosome DNA copy number alterations, the specific chromosomes altered by gain or loss were different in each model. These data demonstrate that GEMM tumors exhibit relatively simple somatic genotypes compared with human cancers of a similar type, making these autochthonous model systems useful for additive engineering approaches to assess the potential of novel mutations on tumorigenesis, cancer progression, and drug sensitivity. PMID:27702896

Mutational landscape of EGFR-, MYC-, and Kras-driven genetically engineered mouse models of lung adenocarcinoma.

PubMed

McFadden, David G; Politi, Katerina; Bhutkar, Arjun; Chen, Frances K; Song, Xiaoling; Pirun, Mono; Santiago, Philip M; Kim-Kiselak, Caroline; Platt, James T; Lee, Emily; Hodges, Emily; Rosebrock, Adam P; Bronson, Roderick T; Socci, Nicholas D; Hannon, Gregory J; Jacks, Tyler; Varmus, Harold

2016-10-18

Genetically engineered mouse models (GEMMs) of cancer are increasingly being used to assess putative driver mutations identified by large-scale sequencing of human cancer genomes. To accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole-exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant epidermal growth factor receptor (EGFR), mutant Kirsten rat sarcoma viral oncogene homolog (Kras), or overexpression of MYC proto-oncogene. Tumors from EGFR- and Kras-driven models exhibited, respectively, 0.02 and 0.07 nonsynonymous mutations per megabase, a dramatically lower average mutational frequency than observed in human lung adenocarcinomas. Tumors from models driven by strong cancer drivers (mutant EGFR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker initiating oncogene in the murine lung, acquired recurrent clonal oncogenic Kras mutations. In addition, although EGFR- and Kras-driven models both exhibited recurrent whole-chromosome DNA copy number alterations, the specific chromosomes altered by gain or loss were different in each model. These data demonstrate that GEMM tumors exhibit relatively simple somatic genotypes compared with human cancers of a similar type, making these autochthonous model systems useful for additive engineering approaches to assess the potential of novel mutations on tumorigenesis, cancer progression, and drug sensitivity.

Using Data-Driven Model-Brain Mappings to Constrain Formal Models of Cognition

PubMed Central

Borst, Jelmer P.; Nijboer, Menno; Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John R.

2015-01-01

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings. PMID:25747601

Note: Model-based identification method of a cable-driven wearable device for arm rehabilitation

NASA Astrophysics Data System (ADS)

Cui, Xiang; Chen, Weihai; Zhang, Jianbin; Wang, Jianhua

2015-09-01

Cable-driven exoskeletons have used active cables to actuate the system and are worn on subjects to provide motion assistance. However, this kind of wearable devices usually contains uncertain kinematic parameters. In this paper, a model-based identification method has been proposed for a cable-driven arm exoskeleton to estimate its uncertainties. The identification method is based on the linearized error model derived from the kinematics of the exoskeleton. Experiment has been conducted to demonstrate the feasibility of the proposed model-based method in practical application.

Integrated modeling and analysis of the multiple electromechanical couplings for the direct driven feed system in machine tools

NASA Astrophysics Data System (ADS)

Yang, Xiaojun; Lu, Dun; Liu, Hui; Zhao, Wanhua

2018-06-01

The complicated electromechanical coupling phenomena due to different kinds of causes have significant influences on the dynamic precision of the direct driven feed system in machine tools. In this paper, a novel integrated modeling and analysis method of the multiple electromechanical couplings for the direct driven feed system in machine tools is presented. At first, four different kinds of electromechanical coupling phenomena in the direct driven feed system are analyzed systematically. Then a novel integrated modeling and analysis method of the electromechanical coupling which is influenced by multiple factors is put forward. In addition, the effects of multiple electromechanical couplings on the dynamic precision of the feed system and their main influencing factors are compared and discussed, respectively. Finally, the results of modeling and analysis are verified by the experiments. It finds out that multiple electromechanical coupling loops, which are overlapped and influenced by each other, are the main reasons of the displacement fluctuations in the direct driven feed system.

The NTeQ ISD Model: A Tech-Driven Model for Digital Natives (DNs)

ERIC Educational Resources Information Center

Williams, C.; Anekwe, J. U.

2017-01-01

Integrating Technology for enquiry (NTeQ) instructional development model (ISD), is believed to be a technology-driven model. The authors x-rayed the ten-step model to reaffirm the ICT knowledge demand of the learner and the educator; hence computer-based activities at various stages of the model are core elements. The model also is conscious of…

Performance Analysis of a Ring Current Model Driven by Global MHD

NASA Astrophysics Data System (ADS)

Falasca, A.; Keller, K. A.; Fok, M.; Hesse, M.; Gombosi, T.

2003-12-01

Effectively modeling the high-energy particles in Earth's inner magnetosphere has the potential to improve safety in both manned and unmanned spacecraft. One model of this environment is the Fok Ring Current Model. This model can utilize as inputs both solar wind data, and empirical ionospheric electric field and magnetic field models. Alternatively, we have a procedure which allows the model to be driven by outputs from the BATS-R-US global MHD model. By using in-situ satellite data we will compare the predictive capability of this model in its original stand-alone form, to that of the model when driven by the BATS-R-US Global Magnetosphere Model. As a basis for comparison we use the April 2002 and May 2003 storms where suitable LANL geosynchronous data are available.

Consistent data-driven computational mechanics

NASA Astrophysics Data System (ADS)

González, D.; Chinesta, F.; Cueto, E.

2018-05-01

We present a novel method, within the realm of data-driven computational mechanics, to obtain reliable and thermodynamically sound simulation from experimental data. We thus avoid the need to fit any phenomenological model in the construction of the simulation model. This kind of techniques opens unprecedented possibilities in the framework of data-driven application systems and, particularly, in the paradigm of industry 4.0.

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

PubMed Central

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. PMID:25427176

Toward computational cumulative biology by combining models of biological datasets.

PubMed

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Modeling the sustainable development of innovation in transport construction based on the communication approach

NASA Astrophysics Data System (ADS)

Revunova, Svetlana; Vlasenko, Vyacheslav; Bukreev, Anatoly

2017-10-01

The article proposes the models of innovative activity development, which is driven by the formation of “points of innovation-driven growth”. The models are based on the analysis of the current state and dynamics of innovative development of construction enterprises in the transport sector and take into account a number of essential organizational and economic changes in management. The authors substantiate implementing such development models as an organizational innovation that has a communication genesis. The use of the communication approach to the formation of “points of innovation-driven growth” allowed the authors to apply the mathematical tools of the graph theory in order to activate the innovative activity of the transport industry in the region. As a result, the authors have proposed models that allow constructing an optimal mechanism for the formation of “points of innovation-driven growth”.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

Negative differential mobility in interacting particle systems

NASA Astrophysics Data System (ADS)

Chatterjee, Amit Kumar; Basu, Urna; Mohanty, P. K.

2018-05-01

Driven particles in the presence of crowded environment, obstacles, or kinetic constraints often exhibit negative differential mobility (NDM) due to their decreased dynamical activity. Based on the empirical studies of conserved lattice gas model, two species exclusion model and other interacting particle systems we propose a new mechanism for complex many-particle systems where slowing down of certain non-driven degrees of freedom by the external field can give rise to NDM. To prove that the slowing down of the non-driven degrees is indeed the underlying cause, we consider several driven diffusive systems including two species exclusion models, misanthrope process, and show from the exact steady state results that NDM indeed appears when some non-driven modes are slowed down deliberately. For clarity, we also provide a simple pedagogical example of two interacting random walkers on a ring which conforms to the proposed scenario.

Chemical reaction path modeling of hydrothermal processes on Mars: Preliminary results

NASA Technical Reports Server (NTRS)

Plumlee, Geoffrey S.; Ridley, W. Ian

1992-01-01

Hydrothermal processes are thought to have had significant roles in the development of surficial mineralogies and morphological features on Mars. For example, a significant proportion of the Martian soil could consist of the erosional products of hydrothermally altered impact melt sheets. In this model, impact-driven, vapor-dominated hydrothermal systems hydrothermally altered the surrounding rocks and transported volatiles such as S and Cl to the surface. Further support for impact-driven hydrothermal alteration on Mars was provided by studies of the Ries crater, Germany, where suevite deposits were extensively altered to montmorillonite clays by inferred low-temperature (100-130 C) hydrothermal fluids. It was also suggested that surface outflow from both impact-driven and volcano-driven hydrothermal systems could generate the valley networks, thereby eliminating the need for an early warm wet climate. We use computer-driven chemical reaction path calculation to model chemical processes which were likely associated with postulated Martian hydrothermal systems.

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

PubMed Central

Naveros, Francisco; Garrido, Jesus A.; Carrillo, Richard R.; Ros, Eduardo; Luque, Niceto R.

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity. PMID:28223930

Incorporating Temperature-driven Seasonal Variation in Survival, Growth, and Reproduction Models for Small Fish

EPA Science Inventory

Seasonal variation in survival and reproduction can be a large source of prediction uncertainty in models used for conservation and management. A seasonally varying matrix population model is developed that incorporates temperature-driven differences in mortality and reproduction...

Experiences in Teaching a Graduate Course on Model-Driven Software Development

ERIC Educational Resources Information Center

Tekinerdogan, Bedir

2011-01-01

Model-driven software development (MDSD) aims to support the development and evolution of software intensive systems using the basic concepts of model, metamodel, and model transformation. In parallel with the ongoing academic research, MDSD is more and more applied in industrial practices. After being accepted both by a broad community of…

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

PubMed

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

PubMed

Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing

2011-12-01

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

Studies of ion kinetic effects in OMEGA shock-driven implosions using fusion burn imaging

NASA Astrophysics Data System (ADS)

Rosenberg, M. J.; Seguin, F. H.; Rinderknecht, H. G.; Sio, H.; Zylstra, A. B.; Gatu Johnson, M.; Frenje, J. A.; Li, C. K.; Petrasso, R. D.; Amendt, P. A.; Wilks, S. C.; Zimmerman, G.; Hoffman, N. M.; Kagan, G.; Molvig, K.; Glebov, V. Yu.; Stoeckl, C.; Marshall, F. J.; Seka, W.; Delettrez, J. A.; Sangster, T. C.; Betti, R.; Meyerhofer, D. D.; Atzeni, S.; Nikroo, A.

2014-10-01

Ion kinetic effects have been inferred in a series of shock-driven implosions at OMEGA from an increasing yield discrepancy between observations and hydrodynamic simulations as the ion-ion mean free path increases. To more precisely identify the nature and impact of ion kinetic effects, spatial burn profile measurements of DD and D3He reactions in these D3He-filled shock-driven implosions are presented and contrasted to both purely hydrodynamic models and models that include ion kinetic effects. It is shown that in implosions where the ion mean free path is equal to or greater than the size of the fuel region, purely hydrodynamic models fail to capture the observed burn profiles, while a model that includes ion diffusion is able to recover the observed burn profile shape. These results further elucidate the ion kinetic mechanisms that are present under long mean-free-path conditions after shock convergence in both shock-driven and ablatively-driven implosions. This work was supported in part by the U.S. DOE, NLUF, LLE, and LLNL.

Event-driven simulations of nonlinear integrate-and-fire neurons.

PubMed

Tonnelier, Arnaud; Belmabrouk, Hana; Martinez, Dominique

2007-12-01

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

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…

Nonlinear Friction Compensation of Ball Screw Driven Stage Based on Variable Natural Length Spring Model and Disturbance Observer

NASA Astrophysics Data System (ADS)

Asaumi, Hiroyoshi; Fujimoto, Hiroshi

Ball screw driven stages are used for industrial equipments such as machine tools and semiconductor equipments. Fast and precise positioning is necessary to enhance productivity and microfabrication technology of the system. The rolling friction of the ball screw driven stage deteriorate the positioning performance. Therefore, the control system based on the friction model is necessary. In this paper, we propose variable natural length spring model (VNLS model) as the friction model. VNLS model is simple and easy to implement as friction controller. Next, we propose multi variable natural length spring model (MVNLS model) as the friction model. MVNLS model can represent friction characteristic of the stage precisely. Moreover, the control system based on MVNLS model and disturbance observer is proposed. Finally, the simulation results and experimental results show the advantages of the proposed method.

Comment on "Heat transfer and fluid flow in microchannels and nanochannels at high Knudsen number using thermal lattice-Boltzmann method".

PubMed

Luo, Li-Shi

2011-10-01

In this Comment we reveal the falsehood of the claim that the lattice Bhatnagar-Gross-Krook (BGK) model "is capable of modeling shear-driven, pressure-driven, and mixed shear-pressure-driven rarified [sic] flows and heat transfer up to Kn=1 in the transitional regime" made in a recent paper [Ghazanfarian and Abbassi, Phys. Rev. E 82, 026307 (2010)]. In particular, we demonstrate that the so-called "Knudsen effects" described are merely numerical artifacts of the lattice BGK model and they are unphysical. Specifically, we show that the erroneous results for the pressure-driven flow in a microchannel imply the false and unphysical condition that 6σKn<-1, where Kn is the Knudsen number σ=(2-σ(v))/σ(v) and σ(v)∈(0,1] is the tangential momentum accommodation coefficient. We also show explicitly that the defects of the lattice BGK model can be completely removed by using the multiple-relaxation-time collision model.

Nonlocal integral elasticity in nanostructures, mixtures, boundary effects and limit behaviours

NASA Astrophysics Data System (ADS)

Romano, Giovanni; Luciano, Raimondo; Barretta, Raffaele; Diaco, Marina

2018-02-01

Nonlocal elasticity is addressed in terms of integral convolutions for structural models of any dimension, that is bars, beams, plates, shells and 3D continua. A characteristic feature of the treatment is the recourse to the theory of generalised functions (distributions) to provide a unified presentation of previous proposals. Local-nonlocal mixtures are also included in the analysis. Boundary effects of convolutions on bounded domains are investigated, and analytical evaluations are provided in the general case. Methods for compensation of boundary effects are compared and discussed with a comprehensive treatment. Estimates of limit behaviours for extreme values of the nonlocal parameter are shown to give helpful information on model properties, allowing for new comments on previous proposals. Strain-driven and stress-driven models are shown to emerge by swapping the mechanical role of input and output fields in the constitutive convolution, with stress-driven elastic model leading to well-posed problems. Computations of stress-driven nonlocal one-dimensional elastic models are performed to exemplify the theoretical results.

An IRT Model with a Parameter-Driven Process for Change

ERIC Educational Resources Information Center

Rijmen, Frank; De Boeck, Paul; van der Maas, Han L. J.

2005-01-01

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative inter-individual differences and auto-dependencies are accounted for by assuming within-subject variability with respect to the…

Evaluating an Expectation-Driven Question-under-Discussion Model of Discourse Interpretation

ERIC Educational Resources Information Center

Kehler, Andrew; Rohde, Hannah

2017-01-01

According to Question-Under-Discussion (QUD) models of discourse interpretation, clauses cohere with the preceding context by virtue of providing answers to (usually implicit) questions that are situated within a speaker's goal-driven strategy of inquiry. In this article we present four experiments that examine the predictions of a QUD model of…

Teachers Develop CLIL Materials in Argentina: A Workshop Experience

ERIC Educational Resources Information Center

Banegas, Darío Luis

2016-01-01

Content and language integrated learning (CLIL) is a Europe-born approach. Nevertheless, CLIL as a language learning approach has been implemented in Latin America in different ways and models: content-driven models and language-driven models. As regards the latter, new school curricula demand that CLIL be used in secondary education in Argentina…

Assessing Argumentative Representation with Bayesian Network Models in Debatable Social Issues

ERIC Educational Resources Information Center

Zhang, Zhidong; Lu, Jingyan

2014-01-01

This study seeks to obtain argumentation models, which represent argumentative processes and an assessment structure in secondary school debatable issues in the social sciences. The argumentation model was developed based on mixed methods, a combination of both theory-driven and data-driven methods. The coding system provided a combing point by…

A Conceptual Systems Model to Facilitate Hypothesis-driven Ecotoxicogenomics Research on the Teleost Brain-pituitary-gonadal Axis

EPA Science Inventory

This provides an overview of a novel open-source conceptuial model of molecular and biochemical pathways involved in the regulation of fish reproduction. Further, it provides concrete examples of how such models can be used to design and conduct hypothesis-driven "omics" experim...

Adjustment of Adaptive Gain with Bounded Linear Stability Analysis to Improve Time-Delay Margin for Metrics-Driven Adaptive Control

NASA Technical Reports Server (NTRS)

Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje Srinvas

2009-01-01

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a linear damaged twin-engine generic transport model of aircraft. The analysis shows that the system with the adjusted adaptive gain becomes more robust to unmodeled dynamics or time delay.

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

NASA Astrophysics Data System (ADS)

Frankovský, P.; Dominik, L.; Gmiterko, A.; Virgala, I.; Kurylo, P.; Perminova, O.

2017-08-01

This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot's mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

Model-Driven Theme/UML

NASA Astrophysics Data System (ADS)

Carton, Andrew; Driver, Cormac; Jackson, Andrew; Clarke, Siobhán

Theme/UML is an existing approach to aspect-oriented modelling that supports the modularisation and composition of concerns, including crosscutting ones, in design. To date, its lack of integration with model-driven engineering (MDE) techniques has limited its benefits across the development lifecycle. Here, we describe our work on facilitating the use of Theme/UML as part of an MDE process. We have developed a transformation tool that adopts model-driven architecture (MDA) standards. It defines a concern composition mechanism, implemented as a model transformation, to support the enhanced modularisation features of Theme/UML. We evaluate our approach by applying it to the development of mobile, context-aware applications-an application area characterised by many non-functional requirements that manifest themselves as crosscutting concerns.

Model‐Based Approach to Predict Adherence to Protocol During Antiobesity Trials

PubMed Central

Sharma, Vishnu D.; Combes, François P.; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J.

2017-01-01

Abstract Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect‐response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose‐ and time‐dependent pharmacodynamic (DTPD) model. Additionally, a population‐pharmacokinetic parameter‐ and data (PPPD)‐driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD‐MM and PPPD‐MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body‐weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model‐driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic‐driven approach. PMID:28858397

Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

PubMed

Sharma, Vishnu D; Combes, François P; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J; Trame, Mirjam N

2018-02-01

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave ® trials were included in this analysis. An indirect-response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose- and time-dependent pharmacodynamic (DTPD) model. Additionally, a population-pharmacokinetic parameter- and data (PPPD)-driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave ® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD-MM and PPPD-MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body-weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model-driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic-driven approach. © 2017, The Authors. The Journal of Clinical Pharmacology published by Wiley Periodicals, Inc. on behalf of American College of Clinical Pharmacology.

Arbitrage with fractional Gaussian processes

NASA Astrophysics Data System (ADS)

Zhang, Xili; Xiao, Weilin

2017-04-01

While the arbitrage opportunity in the Black-Scholes model driven by fractional Brownian motion has a long history, the arbitrage strategy in the Black-Scholes model driven by general fractional Gaussian processes is in its infancy. The development of stochastic calculus with respect to fractional Gaussian processes allowed us to study such models. In this paper, following the idea of Shiryaev (1998), an arbitrage strategy is constructed for the Black-Scholes model driven by fractional Gaussian processes, when the stochastic integral is interpreted in the Riemann-Stieltjes sense. Arbitrage opportunities in some fractional Gaussian processes, including fractional Brownian motion, sub-fractional Brownian motion, bi-fractional Brownian motion, weighted-fractional Brownian motion and tempered fractional Brownian motion, are also investigated.

AST: Activity-Security-Trust driven modeling of time varying networks.

PubMed

Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen

2016-02-18

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents' interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes.

An analytical study of hybrid ejector/internal combustion engine-driven heat pumps

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

Murphy, R.W.

1988-01-01

Because ejectors can combine high reliability with low maintenance cost in a package requiring little capital investment, they may provide attractive heat pumping capability in situations where the importance of their inefficiencies is minimized. One such concept, a hybrid system in which an ejector driven by engine reject heat is used to increase the performance of an internal combustion engine-driven heat pump, was analyzed by modifying an existing ejector heat pump model and combining it with generic compressor and internal combustion engine models. Under the model assumptions for nominal cooling mode conditions, the results showed that hybrid systems could providemore » substantial performance augmentation/emdash/up to 17/percent/ increase in system coefficient of performance for a parallel arrangement of an enhanced ejector with the engine-driven compressor. 4 refs., 4 figs., 4 tabs.« less

Effects of a Data-Driven District-Level Reform Model

ERIC Educational Resources Information Center

Slavin, Robert E.; Holmes, GwenCarol; Madden, Nancy A.; Chamberlain, Anne; Cheung, Alan

2010-01-01

Despite a quarter-century of reform, US schools serving students in poverty continue to lag far behind other schools. There are proven programs, but these are not widely used. This large-scale experiment evaluated a district-level reform model created by the Center for DataDriven Reform in Education (CDDRE). The CDDRE model provided consultation…

Designing Cognitively Diagnostic Assessment for Algebraic Content Knowledge and Thinking Skills

ERIC Educational Resources Information Center

Zhang, Zhidong

2018-01-01

This study explored a diagnostic assessment method that emphasized the cognitive process of algebra learning. The study utilized a design and a theory-driven model to examine the content knowledge. Using the theory driven model, the thinking skills of algebra learning was also examined. A Bayesian network model was applied to represent the theory…

Current fluctuations in periodically driven systems

NASA Astrophysics Data System (ADS)

Barato, Andre C.; Chetrite, Raphael

2018-05-01

Small nonequelibrium systems driven by an external periodic protocol can be described by Markov processes with time-periodic transition rates. In general, current fluctuations in such small systems are large and may play a crucial role. We develop a theoretical formalism to evaluate the rate of such large deviations in periodically driven systems. We show that the scaled cumulant generating function that characterizes current fluctuations is given by a maximal Floquet exponent. Comparing deterministic protocols with stochastic protocols, we show that, with respect to large deviations, systems driven by a stochastic protocol with an infinitely large number of jumps are equivalent to systems driven by deterministic protocols. Our results are illustrated with three case studies: a two-state model for a heat engine, a three-state model for a molecular pump, and a biased random walk with a time-periodic affinity.

Hybridized Kibble-Zurek scaling in the driven critical dynamics across an overlapping critical region

NASA Astrophysics Data System (ADS)

Zhai, Liang-Jun; Wang, Huai-Yu; Yin, Shuai

2018-04-01

The conventional Kibble-Zurek scaling describes the scaling behavior in the driven dynamics across a single critical region. In this paper, we study the driven dynamics across an overlapping critical region, in which a critical region (Region A) is overlaid by another critical region (Region B). We develop a hybridized Kibble-Zurek scaling (HKZS) to characterize the scaling behavior in the driven process. According to the HKZS, the driven dynamics in the overlapping region can be described by the critical theories for both Region A and Region B simultaneously. This results in a constraint on the scaling function in the overlapping critical region. We take the quantum Ising chain in an imaginary longitudinal field as an example. In this model, the critical region of the Yang-Lee edge singularity and the critical region of the ferromagnetic-paramagnetic phase transition overlap with each other. We numerically confirm the HKZS by simulating the driven dynamics in this overlapping critical region. The HKZSs in other models are also discussed.

Yield Hardening of Electrorheological Fluids in Channel Flow

NASA Astrophysics Data System (ADS)

Helal, Ahmed; Qian, Bian; McKinley, Gareth H.; Hosoi, A. E.

2016-06-01

Electrorheological fluids offer potential for developing rapidly actuated hydraulic devices where shear forces or pressure-driven flow are present. In this study, the Bingham yield stress of electrorheological fluids with different particle volume fractions is investigated experimentally in wall-driven and pressure-driven flow modes using measurements in a parallel-plate rheometer and a microfluidic channel, respectively. A modified Krieger-Dougherty model can be used to describe the effects of the particle volume fraction on the yield stress and is in good agreement with the viscometric data. However, significant yield hardening in pressure-driven channel flow is observed and attributed to an increase and eventual saturation of the particle volume fraction in the channel. A phenomenological physical model linking the densification and consequent microstructure to the ratio of the particle aggregation time scale compared to the convective time scale is presented and used to predict the enhancement in yield stress in channel flow, enabling us to reconcile discrepancies in the literature between wall-driven and pressure-driven flows.

Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution and Eruption

NASA Astrophysics Data System (ADS)

Leake, J. E.; Linton, M.; Schuck, P. W.

2017-12-01

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the recent development of coronal models which are "data-driven" at the photosphere. Using magnetohydrodynamic simulations of active region formation and our recently created validation framework we investigate the source of errors in data-driven models that use surface measurements of the magnetic field, and derived MHD quantities, to model the coronal magnetic field. The primary sources of errors in these studies are the temporal and spatial resolution of the surface measurements. We will discuss the implications of theses studies for accurately modeling the build up and release of coronal magnetic energy based on photospheric magnetic field observations.

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Kalamangalam, Giridhar; Tandon, Nitin; Aazhang, Behnaam

2016-10-01

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems

NASA Astrophysics Data System (ADS)

Hunter, Jason M.; Maier, Holger R.; Gibbs, Matthew S.; Foale, Eloise R.; Grosvenor, Naomi A.; Harders, Nathan P.; Kikuchi-Miller, Tahali C.

2018-05-01

Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process- or data-driven. The primary problem with process-based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically. There are also often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs) in comparison, is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a generic framework for developing hybrid process and data-driven models of salinity in river systems is introduced and applied in this paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, the degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the Murray River in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, accession of saline groundwater along the length of the reach and the flushing of three waterbodies in the floodplain during overbank flows of various magnitudes. Based on trade-offs between the degree of process understanding and data availability, a process-driven model is developed for in-stream salt transport, an ANN model is used to model saline groundwater accession and three linear regression models are used to account for the flushing of the different floodplain storages. The resulting hybrid model performs very well on approximately 3 years of daily validation data, with a Nash-Sutcliffe efficiency (NSE) of 0.89 and a root mean squared error (RMSE) of 12.62 mg L-1 (over a range from approximately 50 to 250 mg L-1). Each component of the hybrid model results in noticeable improvements in model performance corresponding to the range of flows for which they are developed. The predictive performance of the hybrid model is significantly better than that of a benchmark process-driven model (NSE = -0.14, RMSE = 41.10 mg L-1, Gbench index = 0.90) and slightly better than that of a benchmark data-driven (ANN) model (NSE = 0.83, RMSE = 15.93 mg L-1, Gbench index = 0.36). Apart from improved predictive performance, the hybrid model also has advantages over the ANN benchmark model in terms of increased capacity for improving system understanding and greater ability to support management decisions.

Study on the CO2 electric driven fixed swash plate type compressor for eco-friendly vehicles

NASA Astrophysics Data System (ADS)

Nam, Donglim; Kim, Kitae; Lee, Jehie; Kwon, Yunki; Lee, Geonho

2017-08-01

The purpose of this study is to experiment and to performance analysis about the electric-driven fixed swash plate compressor using alternate refrigerant(R744). Comprehensive simulation model for an electric driven compressor using CO2 for eco-friendly vehicle is presented. This model consists of compression model and dynamic model. The compression model included valve dynamics, leakage, and heat transfer models. And the dynamic model included frictional loss between piston ring and cylinder wall, frictional loss between shoe and swash plate, frictional loss of bearings, and electric efficiency. Especially, because the efficiency of an electric parts(motor and inverter) in the compressor affects the loss of the compressor, the dynamo test was performed. We made the designed compressor, and tested the performance of the compressor about the variety pressure conditions. Also we compared the performance analysis result and performance test result.

Toward a user-driven approach to radiology software solutions: putting the wag back in the dog.

PubMed

Morgan, Matthew; Mates, Jonathan; Chang, Paul

2006-09-01

The relationship between healthcare providers and the software industry is evolving. In many cases, industry's traditional, market-driven model is failing to meet the increasingly sophisticated and appropriately individualized needs of providers. Advances in both technology infrastructure and development methodologies have set the stage for the transition from a vendor-driven to a more user-driven process of solution engineering. To make this transition, providers must take an active role in the development process and vendors must provide flexible frameworks on which to build. Only then can the provider/vendor relationship mature from a purchaser/supplier to a codesigner/partner model, where true insight and innovation can occur.

Spin Seebeck effect in a metal-single-molecule-magnet-metal junction

NASA Astrophysics Data System (ADS)

Niu, Pengbin; Liu, Lixiang; Su, Xiaoqiang; Dong, Lijuan; Luo, Hong-Gang

2018-01-01

We investigate the nonlinear regime of temperature-driven spin-related currents through a single molecular magnet (SMM), which is connected with two metal electrodes. Under a large spin approximation, the SMM is simplified to a natural two-channel model possessing spin-opposite configuration and Coulomb interaction. We find that in temperature-driven case the system can generate spin-polarized currents. More interestingly, at electron-hole symmetry point, the competition of the two channels induces a temperature-driven pure spin current. This device demonstrates that temperature-driven SMM junction shows some results different from the usual quantum dot model, which may be useful in the future design of thermal-based molecular spintronic devices.

Anomalous metastability in a temperature-driven transition

NASA Astrophysics Data System (ADS)

Ibáñez Berganza, M.; Coletti, P.; Petri, A.

2014-06-01

The Langer theory of metastability provides a description of the lifetime and properties of the metastable phase of the Ising model field-driven transition, describing the magnetic-field-driven transition in ferromagnets and the chemical-potential-driven transition of fluids. An immediate further step is to apply it to the study of a transition driven by the temperature, as the one exhibited by the two-dimensional Potts model. For this model, a study based on the analytical continuation of the free energy (Meunier J. L. and Morel A., Eur. Phys. J. B, 13 (2000) 341) predicts the anomalous vanishing of the metastable temperature range in the large-system-size limit, an issue that has been controversial since the eighties. By a GPU algorithm we compare the Monte Carlo dynamics with the theory. For temperatures close to the transition we obtain agreement and characterize the dependence on the system size, which is essentially different with respect to the Ising case. For smaller temperatures, we observe the onset of stationary states with non-Boltzmann statistics, not predicted by the theory.

Tidal flushing and wind driven circulation of Ahe atoll lagoon (Tuamotu Archipelago, French Polynesia) from in situ observations and numerical modelling.

PubMed

Dumas, F; Le Gendre, R; Thomas, Y; Andréfouët, S

2012-01-01

Hydrodynamic functioning and water circulation of the semi-closed deep lagoon of Ahe atoll (Tuamotu Archipelago, French Polynesia) were investigated using 1 year of field data and a 3D hydrodynamical model. Tidal amplitude averaged less than 30 cm, but tide generated very strong currents (2 ms(-1)) in the pass, creating a jet-like circulation that partitioned the lagoon into three residual circulation cells. The pass entirely flushed excess water brought by waves-induced radiation stress. Circulation patterns were computed for climatological meteorological conditions and summarized with stream function and flushing time. Lagoon hydrodynamics and general overturning circulation was driven by wind. Renewal time was 250 days, whereas the e-flushing time yielded a lagoon-wide 80-days average. Tide-driven flush through the pass and wind-driven overturning circulation designate Ahe as a wind-driven, tidally and weakly wave-flushed deep lagoon. The 3D model allows studying pearl oyster larvae dispersal in both realistic and climatological conditions for aquaculture applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer.

PubMed

Coates, James; Jeyaseelan, Asha K; Ybarra, Norma; David, Marc; Faria, Sergio; Souhami, Luis; Cury, Fabio; Duclos, Marie; El Naqa, Issam

2015-04-01

We explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients. Sixty-two patients who underwent curative hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman-Kutcher-Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data. Integration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models. As a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Argument-Driven Inquiry

ERIC Educational Resources Information Center

Sampson, Victor; Grooms, Jonathon; Walker, Joi

2009-01-01

Argument-Driven Inquiry (ADI) is an instructional model that enables science teachers to transform a traditional laboratory activity into a short integrated instructional unit. To illustrate how the ADI instructional model works, this article describes an ADI lesson developed for a 10th-grade chemistry class. This example lesson was designed to…

Review on the Modeling of Electrostatic MEMS

PubMed Central

Chuang, Wan-Chun; Lee, Hsin-Li; Chang, Pei-Zen; Hu, Yuh-Chung

2010-01-01

Electrostatic-driven microelectromechanical systems devices, in most cases, consist of couplings of such energy domains as electromechanics, optical electricity, thermoelectricity, and electromagnetism. Their nonlinear working state makes their analysis complex and complicated. This article introduces the physical model of pull-in voltage, dynamic characteristic analysis, air damping effect, reliability, numerical modeling method, and application of electrostatic-driven MEMS devices. PMID:22219707

Consistent Evolution of Software Artifacts and Non-Functional Models

DTIC Science & Technology

2014-11-14

induce bad software performance)? 15. SUBJECT TERMS EOARD, Nano particles, Photo-Acoustic Sensors, Model-Driven Engineering ( MDE ), Software Performance...Università degli Studi dell’Aquila, Via Vetoio, 67100 L’Aquila, Italy Email: vittorio.cortellessa@univaq.it Web : http: // www. di. univaq. it/ cortelle/ Phone...Model-Driven Engineering ( MDE ), Software Performance Engineering (SPE), Change Propagation, Performance Antipatterns. For sake of readability of the

Insights into asthenospheric anisotropy and deformation in Mainland China

NASA Astrophysics Data System (ADS)

Zhu, Tao

2018-03-01

Seismic anisotropy can provide direct constraints on asthenospheric deformation which also can be induced by the inherent mantle flow within our planet. Mantle flow calculations thus have been an effective tool to probe asthenospheric anisotropy. To explore the source of seismic anisotropy, asthenospheric deformation and the effects of mantle flow on seismic anisotropy in Mainland China, mantle flow models driven by plate motion (plate-driven) and by a combination of plate motion and mantle density heterogeneity (plate-density-driven) are used to predict the fast polarization direction of shear wave splitting. Our results indicate that: (1) plate-driven or plate-density-driven mantle flow significantly affects the predicted fast polarization direction when compared with simple asthenospheric flow commonly used in interpreting the asthenospheric source of seismic anisotropy, and thus new insights are presented; (2) plate-driven flow controls the fast polarization direction while thermal mantle flow affects asthenospheric deformation rate and local deformation direction significantly; (3) asthenospheric flow is an assignable contributor to seismic anisotropy, and the asthenosphere is undergoing low, large or moderate shear deformation controlled by the strain model, the flow plane/flow direction model or both in most regions of central and eastern China; and (4) the asthenosphere is under more rapid extension deformation in eastern China than in western China.

The application of domain-driven design in NMS

NASA Astrophysics Data System (ADS)

Zhang, Jinsong; Chen, Yan; Qin, Shengjun

2011-12-01

In the traditional design approach of data-model-driven, system analysis and design phases are often separated which makes the demand information can not be expressed explicitly. The method is also easy to lead developer to the process-oriented programming, making codes between the modules or between hierarchies disordered. So it is hard to meet requirement of system scalability. The paper proposes a software hiberarchy based on rich domain model according to domain-driven design named FHRDM, then the Webwork + Spring + Hibernate (WSH) framework is determined. Domain-driven design aims to construct a domain model which not only meets the demand of the field where the software exists but also meets the need of software development. In this way, problems in Navigational Maritime System (NMS) development like big system business volumes, difficulty of requirement elicitation, high development costs and long development cycle can be resolved successfully.

Floquet prethermalization in the resonantly driven Hubbard model

NASA Astrophysics Data System (ADS)

Herrmann, Andreas; Murakami, Yuta; Eckstein, Martin; Werner, Philipp

2017-12-01

We demonstrate the existence of long-lived prethermalized states in the Mott insulating Hubbard model driven by periodic electric fields. These states, which also exist in the resonantly driven case with a large density of photo-induced doublons and holons, are characterized by a nonzero current and an effective temperature of the doublons and holons which depends sensitively on the driving condition. Focusing on the specific case of resonantly driven models whose effective time-independent Hamiltonian in the high-frequency driving limit corresponds to noninteracting fermions, we show that the time evolution of the double occupation can be reproduced by the effective Hamiltonian, and that the prethermalization plateaus at finite driving frequency are controlled by the next-to-leading-order correction in the high-frequency expansion of the effective Hamiltonian. We propose a numerical procedure to determine an effective Hubbard interaction that mimics the correlation effects induced by these higher-order terms.

Spatial correlations in driven-dissipative photonic lattices

NASA Astrophysics Data System (ADS)

Biondi, Matteo; Lienhard, Saskia; Blatter, Gianni; Türeci, Hakan E.; Schmidt, Sebastian

2017-12-01

We study the nonequilibrium steady-state of interacting photons in cavity arrays as described by the driven-dissipative Bose–Hubbard and spin-1/2 XY model. For this purpose, we develop a self-consistent expansion in the inverse coordination number of the array (∼ 1/z) to solve the Lindblad master equation of these systems beyond the mean-field approximation. Our formalism is compared and benchmarked with exact numerical methods for small systems based on an exact diagonalization of the Liouvillian and a recently developed corner-space renormalization technique. We then apply this method to obtain insights beyond mean-field in two particular settings: (i) we show that the gas–liquid transition in the driven-dissipative Bose–Hubbard model is characterized by large density fluctuations and bunched photon statistics. (ii) We study the antibunching–bunching transition of the nearest-neighbor correlator in the driven-dissipative spin-1/2 XY model and provide a simple explanation of this phenomenon.

The steady state solutions of radiatively driven stellar winds for a non-Sobolev, pure absorption model

NASA Technical Reports Server (NTRS)

Poe, C. H.; Owocki, S. P.; Castor, J. I.

1990-01-01

The steady state solution topology for absorption line-driven flows is investigated for the condition that the Sobolev approximation is not used to compute the line force. The solution topology near the sonic point is of the nodal type with two positive slope solutions. The shallower of these slopes applies to reasonable lower boundary conditions and realistic ion thermal speed v(th) and to the Sobolev limit of zero of the usual Castor, Abbott, and Klein model. At finite v(th), this solution consists of a family of very similar solutions converging on the sonic point. It is concluded that a non-Sobolev, absorption line-driven flow with a realistic values of v(th) has no uniquely defined steady state. To the extent that a pure absorption model of the outflow of stellar winds is applicable, radiatively driven winds should be intrinsically variable.

Lessons Learned from Stakeholder-Driven Modeling in the Western Lake Erie Basin

NASA Astrophysics Data System (ADS)

Muenich, R. L.; Read, J.; Vaccaro, L.; Kalcic, M. M.; Scavia, D.

2017-12-01

Lake Erie's history includes a great environmental success story. Recognizing the impact of high phosphorus loads from point sources, the United States and Canada 1972 Great Lakes Water Quality Agreement set load reduction targets to reduce algae blooms and hypoxia. The Lake responded quickly to those reductions and it was declared a success. However, since the mid-1990s, Lake Erie's algal blooms and hypoxia have returned, and this time with a dominant algae species that produces toxins. Return of the algal blooms and hypoxia is again driven by phosphorus loads, but this time a major source is the agriculturally-dominated Maumee River watershed that covers NW Ohio, NE Indiana, and SE Michigan, and the hypoxic extent has been shown to be driven by Maumee River loads plus those from the bi-national and multiple land-use St. Clair - Detroit River system. Stakeholders in the Lake Erie watershed have a long history of engagement with environmental policy, including modeling and monitoring efforts. This talk will focus on the application of interdisciplinary, stakeholder-driven modeling efforts aimed at understanding the primary phosphorus sources and potential pathways to reduce these sources and the resulting algal blooms and hypoxia in Lake Erie. We will discuss the challenges, such as engaging users with different goals, benefits to modeling, such as improvements in modeling data, and new research questions emerging from these modeling efforts that are driven by end-user needs.

A more rational, theory-driven approach to analysing the factor structure of the Edinburgh Postnatal Depression Scale.

PubMed

Kozinszky, Zoltan; Töreki, Annamária; Hompoth, Emőke A; Dudas, Robert B; Németh, Gábor

2017-04-01

We endeavoured to analyze the factor structure of the Edinburgh Postnatal Depression Scale (EPDS) during a screening programme in Hungary, using exploratory (EFA) and confirmatory factor analysis (CFA), testing both previously published models and newly developed theory-driven ones, after a critical analysis of the literature. Between April 2011 and January 2015, a sample of 2967 pregnant women (between 12th and 30th weeks of gestation) and 714 women 6 weeks after delivery completed the Hungarian version of the EPDS in South-East Hungary. EFAs suggested unidimensionality in both samples. 33 out of 42 previously published models showed good and 6 acceptable fit with our antepartum data in CFAs, whilst 10 of them showed good and 28 acceptable fit in our postpartum sample. Using multiple fit indices, our theory-driven anhedonia (items 1,2) - anxiety (items 4,5) - low mood (items 8,9) model provided the best fit in the antepartum sample. In the postpartum sample, our theory-driven models were again among the best performing models, including an anhedonia and an anxiety factor together with either a low mood or a suicidal risk factor (items 3,6,10). The EPDS showed moderate within- and between-culture invariability, although this would also need to be re-examined with a theory-driven approach. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Multi-Fluid Interpenetration Mixing in X-ray and Directly Laser driven ICF Capsule Implosions

NASA Astrophysics Data System (ADS)

Wilson, Douglas

2003-10-01

Mix between a surrounding shell and the fuel leads to degradation in ICF capsule performance. Both indirectly (X-ray) and directly laser driven implosions provide a wealth of data to test mix models. One model, the multi-fluid interpenetration mix model of Scannapieco and Cheng (Phys. Lett. A., 299, 49, 2002), was implemented in an ICF code and applied to a wide variety of experiments (e.g. J. D. Kilkenny et al., Proc. Conf Plasm. Phys. Contr. Nuc. Fus. Res. 3, 29(1988), P. Amendt, R. E. Turner, O. L. Landen, Phy. Rev. Lett., 89, 165001 (2002), or Li et al., Phy. Rev. Lett, 89, 165002 (2002)). With its single adjustable parameter fixed, it replicates well the yield degradation with increasing convergence ratio for both directly and indirectly driven capsules. Often, but not always the ion temperatures with mixing are calculated to be higher than in an unmixed implosion, agreeing with observations. Comparison with measured directly driven implosion yield rates ( from the neutron temporal diagnostic or NTD) shows mixing increases rapidly during the burn. The model also reproduces the decrease of the fuel "rho-r" with fill gas pressure, measured by observing escaping deuterons or secondary neutrons. The mix model assumes fully atomically mixed constituents, but when experiments with deuterated plastic layers and 3He fuel are modeled, less that full atomic mix is appropriate. Applying the mix model to the ablator - solid DT interface in indirectly driven ignition capsules for the NIF or LMJ suggests that the capsules will ignite, but that burn after ignition may be somewhat degraded. Situations in which the Scannapieco and Cheng model fails to agree with experiments can guide us to improvements or the development of other models. Some directly driven symmetric implosions suggest that in highly mixed situations, a higher value of the mix parameter may needed. Others show the model underestimating the fuel burn temperature. This work was performed by the Los Alamos National Laboratory under DOE contract number W-7405-Eng-36.

Data to Decisions: Creating a Culture of Model-Driven Drug Discovery.

PubMed

Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L

2017-09-01

Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery predictive models were made available from familiar desktop tools, such as ChemDraw. This was built using a workflow-driven service-oriented architecture (SOA) on top of the rigorous registration of all underlying model entities.

Heat-driven spin torques in antiferromagnets

NASA Astrophysics Data System (ADS)

Białek, Marcin; Bréchet, Sylvain; Ansermet, Jean-Philippe

2018-04-01

Heat-driven magnetization damping, which is a linear function of a temperature gradient, is predicted in antiferromagnets by considering the sublattice dynamics subjected to a heat-driven spin torque. This points to the possibility of achieving spin torque oscillator behavior. The model is based on the magnetic Seebeck effect acting on sublattices which are exchange coupled. The heat-driven spin torque is estimated and the feasibility of detecting this effect is discussed.

An efficient soil water balance model based on hybrid numerical and statistical methods

NASA Astrophysics Data System (ADS)

Mao, Wei; Yang, Jinzhong; Zhu, Yan; Ye, Ming; Liu, Zhao; Wu, Jingwei

2018-04-01

Most soil water balance models only consider downward soil water movement driven by gravitational potential, and thus cannot simulate upward soil water movement driven by evapotranspiration especially in agricultural areas. In addition, the models cannot be used for simulating soil water movement in heterogeneous soils, and usually require many empirical parameters. To resolve these problems, this study derives a new one-dimensional water balance model for simulating both downward and upward soil water movement in heterogeneous unsaturated zones. The new model is based on a hybrid of numerical and statistical methods, and only requires four physical parameters. The model uses three governing equations to consider three terms that impact soil water movement, including the advective term driven by gravitational potential, the source/sink term driven by external forces (e.g., evapotranspiration), and the diffusive term driven by matric potential. The three governing equations are solved separately by using the hybrid numerical and statistical methods (e.g., linear regression method) that consider soil heterogeneity. The four soil hydraulic parameters required by the new models are as follows: saturated hydraulic conductivity, saturated water content, field capacity, and residual water content. The strength and weakness of the new model are evaluated by using two published studies, three hypothetical examples and a real-world application. The evaluation is performed by comparing the simulation results of the new model with corresponding results presented in the published studies, obtained using HYDRUS-1D and observation data. The evaluation indicates that the new model is accurate and efficient for simulating upward soil water flow in heterogeneous soils with complex boundary conditions. The new model is used for evaluating different drainage functions, and the square drainage function and the power drainage function are recommended. Computational efficiency of the new model makes it particularly suitable for large-scale simulation of soil water movement, because the new model can be used with coarse discretization in space and time.

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Purpose-Driven Leadership: Defining, Defending and Sustaining a School's Purpose

ERIC Educational Resources Information Center

Holloman, Harold L., Jr.; Rouse, William A., Jr.; Farrington, Vernon

2007-01-01

Purpose-driven leadership is a constructive leadership model that challenges an organization to: define its purpose, maintain integrity, encourage character, prevent burnout and sustain vitality. The model incorporates "best practice language" and the tools needed to foster a meaningful discourse. As school leaders strive to define, defend, and…

Model-Driven Design: Systematically Building Integrated Blended Learning Experiences

ERIC Educational Resources Information Center

Laster, Stephen

2010-01-01

Developing and delivering curricula that are integrated and that use blended learning techniques requires a highly orchestrated design. While institutions have demonstrated the ability to design complex curricula on an ad-hoc basis, these projects are generally successful at a great human and capital cost. Model-driven design provides a…

Lightning-driven electric and magnetic fields measured in the stratosphere: Implications for sprites

NASA Astrophysics Data System (ADS)

Thomas, Jeremy Norman

A well accepted model for sprite production involves quasi-electrostatic fields (QSF) driven by large positive cloud-to-ground (+CG) strokes that can cause electrical breakdown in the middle atmosphere. A new high voltage, high impedance, double Langmuir probe instrument is designed specifically for measuring these large lightning-driven electric field changes at altitudes above 30 km. This High Voltage (HV) Electric Field Detector measured 200 nearby (<75 km) lightning-driven electric field changes, up to 140 V/m in magnitude, during the Brazil Sprite Balloon Campaign 2002--03. A numerical QSF model is developed and compared to the in situ measurements. It is found that the amplitudes and relaxation times of the electric fields driven by these nearby lightning events generally agree with the numerical QSF model, which suggests that the QSF approach is valid for modeling lightning-driven fields. Using the best fit parameters of this comparison, it is predicted that the electric fields at sprite altitudes (60--90 km) never surpass conventional breakdown in the mesosphere for each of these 200 nearby lightning events. Lightning-driven ELF to VLF (25 Hz--8 kHz) electric field changes were measured for each of the 2467 cloud-to-ground lightning (CGs) detected by the Brazilian Integrated Lightning Network (BIN) at distances of 75--600 km, and magnetic field changes (300 Hz--8 kHz) above the background noise were measured for about 35% (858) of these CGs. ELF pulses that occur 4--12 ms after the retarded time of the lightning sferic, which have been previously attributed to sprites, were found for 1.4% of 934 CGs examined with a strong bias towards +CGs (4.9% or 9/184) compared to -CGs (0.5% or 4/750). These results disagree with results from the Sprites99 Balloon Campaign [Bering et al., 2004b], in which the lightning-driven electric and magnetic field changes were rare, while the CG delayed ELF pulses were frequent. The Brazil Campaign results thus suggest that mesospheric currents are likely the result of the QSF driven by large charge moment strokes, which are usually +CG strokes, initiating breakdown in the middle atmosphere.

The fiber walk: a model of tip-driven growth with lateral expansion.

PubMed

Bucksch, Alexander; Turk, Greg; Weitz, Joshua S

2014-01-01

Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modeled as an elongating path or series of segments, without taking into account lateral expansion during elongation. Instead, models of growth often introduce an explicit thickness by expanding the area around the completed elongated path. Modeling expansion in this way can lead to contradictions in the physical plausibility of the resulting surface and to uncertainty about how the object reached certain regions of space. Here, we introduce fiber walks as a self-avoiding random walk model for tip-driven growth processes that includes lateral expansion. In 2D, the fiber walk takes place on a square lattice and the space occupied by the fiber is modeled as a lateral contraction of the lattice. This contraction influences the possible subsequent steps of the fiber walk. The boundary of the area consumed by the contraction is derived as the dual of the lattice faces adjacent to the fiber. We show that fiber walks generate fibers that have well-defined curvatures, and thus enable the identification of the process underlying the occupancy of physical space. Hence, fiber walks provide a base from which to model both the extension and expansion of physical biological objects with finite thickness.

Simulation and Sensitivity in a Nested Modeling System for South America. Part II: GCM Boundary Forcing.

NASA Astrophysics Data System (ADS)

Rojas, Maisa; Seth, Anji

2003-08-01

of this study, the RegCM's ability to simulate circulation and rainfall observed in the two extreme seasons was demonstrated when driven at the lateral boundaries by reanalyzed forcing. Seasonal integrations with the RegCM driven by GCM ensemble-derived lateral boundary forcing demonstrate that the nested model responds well to the SST forcing, by capturing the major features of the circulation and rainfall differences between the two years. The GCM-driven model also improves upon the monthly evolution of rainfall compared with that from the GCM. However, the nested model rainfall simulations for the two seasons are degraded compared with those from the reanalyses-driven RegCM integrations. The poor location of the Atlantic intertropical convergence zone (ITCZ) in the GCM leads to excess rainfall in Nordeste in the nested model.An expanded domain was tested, wherein the RegCM was permitted more internal freedom to respond to SST and regional orographic forcing. Results show that the RegCM is able to improve the location of the ITCZ, and the seasonal evolution of rainfall in Nordeste, the Amazon region, and the southeastern region of Brazil. However, it remains that the limiting factor in the skill of the nested modeling system is the quality of the lateral boundary forcing provided by the global model.

AST: Activity-Security-Trust driven modeling of time varying networks

PubMed Central

Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen

2016-01-01

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents’ interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes. PMID:26888717

The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion

PubMed Central

Bucksch, Alexander; Turk, Greg; Weitz, Joshua S.

2014-01-01

Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modeled as an elongating path or series of segments, without taking into account lateral expansion during elongation. Instead, models of growth often introduce an explicit thickness by expanding the area around the completed elongated path. Modeling expansion in this way can lead to contradictions in the physical plausibility of the resulting surface and to uncertainty about how the object reached certain regions of space. Here, we introduce fiber walks as a self-avoiding random walk model for tip-driven growth processes that includes lateral expansion. In 2D, the fiber walk takes place on a square lattice and the space occupied by the fiber is modeled as a lateral contraction of the lattice. This contraction influences the possible subsequent steps of the fiber walk. The boundary of the area consumed by the contraction is derived as the dual of the lattice faces adjacent to the fiber. We show that fiber walks generate fibers that have well-defined curvatures, and thus enable the identification of the process underlying the occupancy of physical space. Hence, fiber walks provide a base from which to model both the extension and expansion of physical biological objects with finite thickness. PMID:24465607

Comparison between collective coordinate models for domain wall motion in PMA nanostrips in the presence of the Dzyaloshinskii-Moriya interaction

NASA Astrophysics Data System (ADS)

Vandermeulen, J.; Nasseri, S. A.; Van de Wiele, B.; Durin, G.; Van Waeyenberge, B.; Dupré, L.

2018-03-01

Lagrangian-based collective coordinate models for magnetic domain wall (DW) motion rely on an ansatz for the DW profile and a Lagrangian approach to describe the DW motion in terms of a set of time-dependent collective coordinates: the DW position, the DW magnetization angle, the DW width and the DW tilting angle. Another approach was recently used to derive similar equations of motion by averaging the Landau-Lifshitz-Gilbert equation without any ansatz, and identifying the relevant collective coordinates afterwards. In this paper, we use an updated version of the semi-analytical equations to compare the Lagrangian-based collective coordinate models with micromagnetic simulations for field- and STT-driven (spin-transfer torque-driven) DW motion in Pt/CoFe/MgO and Pt/Co/AlOx nanostrips. Through this comparison, we assess the accuracy of the different models, and provide insight into the deviations of the models from simulations. It is found that the lack of terms related to DW asymmetry in the Lagrangian-based collective coordinate models significantly contributes to the discrepancy between the predictions of the most accurate Lagrangian-based model and the micromagnetic simulations in the field-driven case. This is in contrast to the STT-driven case where the DW remains symmetric.

Irreducible Uncertainty in Terrestrial Carbon Projections

NASA Astrophysics Data System (ADS)

Lovenduski, N. S.; Bonan, G. B.

2016-12-01

We quantify and isolate the sources of uncertainty in projections of carbon accumulation by the ocean and terrestrial biosphere over 2006-2100 using output from Earth System Models participating in the 5th Coupled Model Intercomparison Project. We consider three independent sources of uncertainty in our analysis of variance: (1) internal variability, driven by random, internal variations in the climate system, (2) emission scenario, driven by uncertainty in future radiative forcing, and (3) model structure, wherein different models produce different projections given the same emission scenario. Whereas uncertainty in projections of ocean carbon accumulation by 2100 is 100 Pg C and driven primarily by emission scenario, uncertainty in projections of terrestrial carbon accumulation by 2100 is 50% larger than that of the ocean, and driven primarily by model structure. This structural uncertainty is correlated with emission scenario: the variance associated with model structure is an order of magnitude larger under a business-as-usual scenario (RCP8.5) than a mitigation scenario (RCP2.6). In an effort to reduce this structural uncertainty, we apply various model weighting schemes to our analysis of variance in terrestrial carbon accumulation projections. The largest reductions in uncertainty are achieved when giving all the weight to a single model; here the uncertainty is of a similar magnitude to the ocean projections. Such an analysis suggests that this structural uncertainty is irreducible given current terrestrial model development efforts.

Master-slave control with trajectory planning and Bouc-Wen model for tracking control of piezo-driven stage

NASA Astrophysics Data System (ADS)

Lu, Xiaojun; Liu, Changli; Chen, Lei

2018-04-01

In this paper, a redundant Piezo-driven stage having 3RRR compliant mechanism is introduced, we propose the master-slave control with trajectory planning (MSCTP) strategy and Bouc-Wen model to improve its micro-motion tracking performance. The advantage of the proposed controller lies in that its implementation only requires a simple control strategy without the complexity of modeling to avoid the master PEA's tracking error. The dynamic model of slave PEA system with Bouc-Wen hysteresis is established and identified via particle swarm optimization (PSO) approach. The Piezo-driven stage with operating period T=1s and 2s is implemented to track a prescribed circle. The simulation results show that MSCTP with Bouc-Wen model reduces the trajectory tracking errors to the range of the accuracy of our available measurement.

Organization-based Model-driven Development of High-assurance Multiagent Systems

DTIC Science & Technology

2009-02-27

based Model -driven Development of High-assurance Multiagent Systems " performed by Dr. Scott A . DeLoach and Dr Robby at Kansas State University... A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems . Volume 16, no. 1, February 2008, pp...Matson, E . T. (2007). A capabilities based theory of artificial organizations. Journal of Autonomous Agents and Multiagent Systems

Reflection of a Year Long Model-Driven Business and UI Modeling Development Project

NASA Astrophysics Data System (ADS)

Sukaviriya, Noi; Mani, Senthil; Sinha, Vibha

Model-driven software development enables users to specify an application at a high level - a level that better matches problem domain. It also promises the users with better analysis and automation. Our work embarks on two collaborating domains - business process and human interactions - to build an application. Business modeling expresses business operations and flows then creates business flow implementation. Human interaction modeling expresses a UI design, its relationship with business data, logic, and flow, and can generate working UI. This double modeling approach automates the production of a working system with UI and business logic connected. This paper discusses the human aspects of this modeling approach after a year long of building a procurement outsourcing contract application using the approach - the result of which was deployed in December 2008. The paper discusses in multiple areas the happy endings and some heartache. We end with insights on how a model-driven approach could do better for humans in the process.

Open data models for smart health interconnected applications: the example of openEHR.

PubMed

Demski, Hans; Garde, Sebastian; Hildebrand, Claudia

2016-10-22

Smart Health is known as a concept that enhances networking, intelligent data processing and combining patient data with other parameters. Open data models can play an important role in creating a framework for providing interoperable data services that support the development of innovative Smart Health applications profiting from data fusion and sharing. This article describes a model-driven engineering approach based on standardized clinical information models and explores its application for the development of interoperable electronic health record systems. The following possible model-driven procedures were considered: provision of data schemes for data exchange, automated generation of artefacts for application development and native platforms that directly execute the models. The applicability of the approach in practice was examined using the openEHR framework as an example. A comprehensive infrastructure for model-driven engineering of electronic health records is presented using the example of the openEHR framework. It is shown that data schema definitions to be used in common practice software development processes can be derived from domain models. The capabilities for automatic creation of implementation artefacts (e.g., data entry forms) are demonstrated. Complementary programming libraries and frameworks that foster the use of open data models are introduced. Several compatible health data platforms are listed. They provide standard based interfaces for interconnecting with further applications. Open data models help build a framework for interoperable data services that support the development of innovative Smart Health applications. Related tools for model-driven application development foster semantic interoperability and interconnected innovative applications.

Stable solutions of inflation driven by vector fields

NASA Astrophysics Data System (ADS)

Emami, Razieh; Mukohyama, Shinji; Namba, Ryo; Zhang, Ying-li

2017-03-01

Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost and/or gradient instabilities. In this work, we seek a new class of vector-driven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi de-Sitter solutions to be an attractor analogous to the standard slow-roll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models, we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.

Input-driven versus turnover-driven controls of simulated changes in soil carbon due to land-use change

NASA Astrophysics Data System (ADS)

Nyawira, Sylvia; Nabel, Julia; Brovkin, Victor; Pongratz, Julia

2017-04-01

Modelling studies estimate a global loss in soil carbon caused by land-use changes (LUCs) over the last century. Although it is known that this loss stems from the changes in quantity of litter inputs from the vegetation to the soil (input-driven) and the changes in turnover of carbon in the soil (turnover-driven) associated with LUC, the individual contribution of these two controls to the total changes have not been assessed. Using the dynamic global vegetation model JSBACH, we apply a factor separation approach to isolate the contribution of the input-driven and turnover-driven changes, as well as their synergies, to the total changes in soil carbon from LUC. To assess how land management through crop and wood harvest influences the controls, we compare our results for simulations with and without land management. Our results reveal that for the afforested regions both the input-driven and turnover-driven changes generally result in soil carbon gain, whereas deforested regions exhibit a loss. However, for regions where croplands have increased at the expense of grasslands and pastures, the input-driven changes result in a loss that is partly offset by a gain via the turnover-driven changes. This gain stems from a decrease in the fire-related carbon losses when grasslands or pastures are replaced with croplands. Omitting land management reduces the carbon losses in regions where natural vegetation has been converted to croplands and enhances the gain in afforested regions. The global simulated losses are substantially reduced from 54.0 Pg C to 22.0 Pg C, with the input-driven losses reducing from 54.7 Pg C to 24.9 Pg C. Our study shows that the dominating control of soil carbon losses is through the input-driven changes, which are more directly influenced by human management than the turnover-driven ones.

Multi-Model Comparison of Lateral Boundary Contributions to ...

EPA Pesticide Factsheets

As the National Ambient Air Quality Standards (NAAQS) for ozone become more stringent, there has been growing attention on characterizing the contributions and the uncertainties in ozone from outside the US to the ozone concentrations within the US. The third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) provides an opportunity to investigate this issue through the combined efforts of multiple research groups in the US and Europe. The model results cover a range of representations of chemical and physical processes, vertical and horizontal resolutions, and meteorological fields to drive the regional chemical transport models (CTMs), all of which are important components of model uncertainty (Solazzo and Galmarini, 2016). In AQMEII3, all groups were asked to track the contribution of ozone from lateral boundary through the use of chemically inert tracers. Though the inert tracer method tends to overestimate the impact of ozone boundary conditions compared with other methods such as chemically reactive tracers and source apportionment (Baker et al., 2015), the method takes the least effort to implement in different models, and is thus useful in highlighting and understanding the process-level differences amongst the models. In this study, results from four models were included (CMAQ driven by WRF, CAMx driven by WRF, CMAQ driven by CCLM, DEHM driven by WRF). At each site, the distribution of daily maximum 8-hour ozone, and the corre

Towards a Multi-Stakeholder-Driven Model for Excellence in Higher Education Curriculum Development

ERIC Educational Resources Information Center

Meyer, M. H.; Bushney, M. J.

2008-01-01

A multi-stakeholder-driven model for excellence in higher education curriculum development has been developed. It is based on the assumption that current efforts to curriculum development take place within a framework of limited stakeholder consultation. A total of 18 multiple stakeholders are identified, including learners, alumni, government,…

The MDE Diploma: First International Postgraduate Specialization in Model-Driven Engineering

ERIC Educational Resources Information Center

Cabot, Jordi; Tisi, Massimo

2011-01-01

Model-Driven Engineering (MDE) is changing the way we build, operate, and maintain our software-intensive systems. Several projects using MDE practices are reporting significant improvements in quality and performance but, to be able to handle these projects, software engineers need a set of technical and interpersonal skills that are currently…

A framework for the automated data-driven constitutive characterization of composites

Treesearch

J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos

2010-01-01

We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...

Pedagogy and Japanese Culture in a Distance Learning Environment

ERIC Educational Resources Information Center

Anderson, Bodi O.

2012-01-01

Current theoretical models of distance learning are driven by two impetuses: a technical CMC element, and a pedagogical foundation rooted strongly in the Western world, and driven by social constructivism. By and large these models have been exported throughout the world as-is. However, previous research has hinted at potential problems with these…

Do Teacher-Child Relationship and Friendship Quality Matter for Children's School Engagement and Academic Skills?

ERIC Educational Resources Information Center

Hosan, Naheed E.; Hoglund, Wendy

2017-01-01

This study examined three competing models assessing the directional associations between the quality of children's relationships with teachers and friends (i.e., closeness and conflict) and their emotional and behavioral school engagement (i.e., the relationship-driven, engagement-driven, and transactional models). The additive contributions of…

The "Village" Model: A Consumer-Driven Approach for Aging in Place

ERIC Educational Resources Information Center

Scharlach, Andrew; Graham, Carrie; Lehning, Amanda

2012-01-01

Purpose of the Study: This study examines the characteristics of the "Village" model, an innovative consumer-driven approach that aims to promote aging in place through a combination of member supports, service referrals, and consumer engagement. Design and Methods: Thirty of 42 fully operational Villages completed 2 surveys. One survey examined…

Applying cost accounting to operating room staffing in otolaryngology: time-driven activity-based costing and outpatient adenotonsillectomy.

PubMed

Balakrishnan, Karthik; Goico, Brian; Arjmand, Ellis M

2015-04-01

(1) To describe the application of a detailed cost-accounting method (time-driven activity-cased costing) to operating room personnel costs, avoiding the proxy use of hospital and provider charges. (2) To model potential cost efficiencies using different staffing models with the case study of outpatient adenotonsillectomy. Prospective cost analysis case study. Tertiary pediatric hospital. All otolaryngology providers and otolaryngology operating room staff at our institution. Time-driven activity-based costing demonstrated precise per-case and per-minute calculation of personnel costs. We identified several areas of unused personnel capacity in a basic staffing model. Per-case personnel costs decreased by 23.2% by allowing a surgeon to run 2 operating rooms, despite doubling all other staff. Further cost reductions up to a total of 26.4% were predicted with additional staffing rearrangements. Time-driven activity-based costing allows detailed understanding of not only personnel costs but also how personnel time is used. This in turn allows testing of alternative staffing models to decrease unused personnel capacity and increase efficiency. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects

PubMed Central

Zhang, Fan; Wu, Min; Kwoh, Chee Keong; Zheng, Jie

2016-01-01

Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model. PMID:27764199

A 1D ion species model for an RF driven negative ion source

NASA Astrophysics Data System (ADS)

Turner, I.; Holmes, A. J. T.

2017-08-01

A one-dimensional model for an RF driven negative ion source has been developed based on an inductive discharge. The RF source differs from traditional filament and arc ion sources because there are no primary electrons present, and is simply composed of an antenna region (driver) and a main plasma discharge region. However the model does still make use of the classical plasma transport equations for particle energy and flow, which have previously worked well for modelling DC driven sources. The model has been developed primarily to model the Small Negative Ion Facility (SNIF) ion source at CCFE, but may be easily adapted to model other RF sources. Currently the model considers the hydrogen ion species, and provides a detailed description of the plasma parameters along the source axis, i.e. plasma temperature, density and potential, as well as current densities and species fluxes. The inputs to the model are currently the RF power, the magnetic filter field and the source gas pressure. Results from the model are presented and where possible compared to existing experimental data from SNIF, with varying RF power, source pressure.

Petri net model for analysis of concurrently processed complex algorithms

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Mielke, Roland R.

1986-01-01

This paper presents a Petri-net model suitable for analyzing the concurrent processing of computationally complex algorithms. The decomposed operations are to be processed in a multiple processor, data driven architecture. Of particular interest is the application of the model to both the description of the data/control flow of a particular algorithm, and to the general specification of the data driven architecture. A candidate architecture is also presented.

Using a Time-Driven Activity-Based Costing Model To Determine the Actual Cost of Services Provided by a Transgenic Core.

PubMed

Gerwin, Philip M; Norinsky, Rada M; Tolwani, Ravi J

2018-03-01

Laboratory animal programs and core laboratories often set service rates based on cost estimates. However, actual costs may be unknown, and service rates may not reflect the actual cost of services. Accurately evaluating the actual costs of services can be challenging and time-consuming. We used a time-driven activity-based costing (ABC) model to determine the cost of services provided by a resource laboratory at our institution. The time-driven approach is a more efficient approach to calculating costs than using a traditional ABC model. We calculated only 2 parameters: the time required to perform an activity and the unit cost of the activity based on employee cost. This method allowed us to rapidly and accurately calculate the actual cost of services provided, including microinjection of a DNA construct, microinjection of embryonic stem cells, embryo transfer, and in vitro fertilization. We successfully implemented a time-driven ABC model to evaluate the cost of these services and the capacity of labor used to deliver them. We determined how actual costs compared with current service rates. In addition, we determined that the labor supplied to conduct all services (10,645 min/wk) exceeded the practical labor capacity (8400 min/wk), indicating that the laboratory team was highly efficient and that additional labor capacity was needed to prevent overloading of the current team. Importantly, this time-driven ABC approach allowed us to establish a baseline model that can easily be updated to reflect operational changes or changes in labor costs. We demonstrated that a time-driven ABC model is a powerful management tool that can be applied to other core facilities as well as to entire animal programs, providing valuable information that can be used to set rates based on the actual cost of services and to improve operating efficiency.

The influence of data-driven versus conceptually-driven processing on the development of PTSD-like symptoms.

PubMed

Kindt, Merel; van den Hout, Marcel; Arntz, Arnoud; Drost, Jolijn

2008-12-01

Ehlers and Clark [(2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38, 319-345] propose that a predominance of data-driven processing during the trauma predicts subsequent PTSD. We wondered whether, apart from data-driven encoding, sustained data-driven processing after the trauma is also crucial for the development of PTSD. Both hypotheses were tested in two analogue experiments. Experiment 1 demonstrated that relative to conceptually-driven processing (n=20), data-driven processing after the film (n=14), resulted in more intrusions. Experiment 2 demonstrated that relative to the neutral condition (n=24) and the data-driven encoding condition (n=24), conceptual encoding (n=25) reduced suppression of intrusions and a trend emerged for memory fragmentation. The difference between the two encoding styles was due to the beneficial effect of induced conceptual encoding and not to the detrimental effect of data-driven encoding. The data support the viability of the distinction between data-driven/conceptually-driven processing for the understanding of the development of PTSD.

Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force during Hot Rolling of Steel

NASA Astrophysics Data System (ADS)

Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.

2013-07-01

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

Modeling and control of a cable-suspended robot for inspection of vertical structures

NASA Astrophysics Data System (ADS)

Barry, Nicole; Fisher, Erin; Vaughan, Joshua

2016-09-01

In this paper, a cable-driven system is examined for the application of inspection of large, vertical-walled structures such as chemical storage tanks, large ship hulls, and high-rise buildings. Such cable-driven systems are not commonly used for these tasks due to vibration, which decreases inspection accuracy and degrades safety. The flexible nature of the cables make them difficult to control. In this paper, input shaping is implemented on a cable-driven system to reduce vibration. To design the input shapers, a model of the cable-driven system was developed. Analysis of the dominant dynamics and changes in them over the large workspace are also presented. The performance improvements provided by the input shaping controller are quantified through a series of simulations.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling.

PubMed

de Vries, Sjoerd J; Chauvot de Beauchêne, Isaure; Schindler, Christina E M; Zacharias, Martin

2016-02-23

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

PubMed Central

de Vries, Sjoerd J.; Chauvot de Beauchêne, Isaure; Schindler, Christina E.M.; Zacharias, Martin

2016-01-01

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling. PMID:26846888

Managing business compliance using model-driven security management

NASA Astrophysics Data System (ADS)

Lang, Ulrich; Schreiner, Rudolf

Compliance with regulatory and governance standards is rapidly becoming one of the hot topics of information security today. This is because, especially with regulatory compliance, both business and government have to expect large financial and reputational losses if compliance cannot be ensured and demonstrated. One major difficulty of implementing such regulations is caused the fact that they are captured at a high level of abstraction that is business-centric and not IT centric. This means that the abstract intent needs to be translated in a trustworthy, traceable way into compliance and security policies that the IT security infrastructure can enforce. Carrying out this mapping process manually is time consuming, maintenance-intensive, costly, and error-prone. Compliance monitoring is also critical in order to be able to demonstrate compliance at any given point in time. The problem is further complicated because of the need for business-driven IT agility, where IT policies and enforcement can change frequently, e.g. Business Process Modelling (BPM) driven Service Oriented Architecture (SOA). Model Driven Security (MDS) is an innovative technology approach that can solve these problems as an extension of identity and access management (IAM) and authorization management (also called entitlement management). In this paper we will illustrate the theory behind Model Driven Security for compliance, provide an improved and extended architecture, as well as a case study in the healthcare industry using our OpenPMF 2.0 technology.

A data driven control method for structure vibration suppression

NASA Astrophysics Data System (ADS)

Xie, Yangmin; Wang, Chao; Shi, Hang; Shi, Junwei

2018-02-01

High radio-frequency space applications have motivated continuous research on vibration suppression of large space structures both in academia and industry. This paper introduces a novel data driven control method to suppress vibrations of flexible structures and experimentally validates the suppression performance. Unlike model-based control approaches, the data driven control method designs a controller directly from the input-output test data of the structure, without requiring parametric dynamics and hence free of system modeling. It utilizes the discrete frequency response via spectral analysis technique and formulates a non-convex optimization problem to obtain optimized controller parameters with a predefined controller structure. Such approach is then experimentally applied on an end-driving flexible beam-mass structure. The experiment results show that the presented method can achieve competitive disturbance rejections compared to a model-based mixed sensitivity controller under the same design criterion but with much less orders and design efforts, demonstrating the proposed data driven control is an effective approach for vibration suppression of flexible structures.

Simulating Sources of Superstorm Plasmas

NASA Technical Reports Server (NTRS)

Fok, Mei-Ching

2008-01-01

We evaluated the contributions to magnetospheric pressure (ring current) of the solar wind, polar wind, auroral wind, and plasmaspheric wind, with the surprising result that the main phase pressure is dominated by plasmaspheric protons. We used global simulation fields from the LFM single fluid ideal MHD model. We embedded the Comprehensive Ring Current Model within it, driven by the LFM transpolar potential, and supplied with plasmas at its boundary including solar wind protons, polar wind protons, auroral wind O+, and plasmaspheric protons. We included auroral outflows and acceleration driven by the LFM ionospheric boundary condition, including parallel ion acceleration driven by upward currents. Our plasmasphere model runs within the CRCM and is driven by it. Ionospheric sources were treated using our Global Ion Kinetics code based on full equations of motion. This treatment neglects inertial loading and pressure exerted by the ionospheric plasmas, and will be superceded by multifluid simulations that include those effects. However, these simulations provide new insights into the respective role of ionospheric sources in storm-time magnetospheric dynamics.

HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.

PubMed

Bharath, A; Madhvanath, Sriganesh

2012-04-01

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

Data Driven Model Development for the Supersonic Semispan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Driven-dissipative quantum Monte Carlo method for open quantum systems

NASA Astrophysics Data System (ADS)

Nagy, Alexandra; Savona, Vincenzo

2018-05-01

We develop a real-time full configuration-interaction quantum Monte Carlo approach to model driven-dissipative open quantum systems with Markovian system-bath coupling. The method enables stochastic sampling of the Liouville-von Neumann time evolution of the density matrix thanks to a massively parallel algorithm, thus providing estimates of observables on the nonequilibrium steady state. We present the underlying theory and introduce an initiator technique and importance sampling to reduce the statistical error. Finally, we demonstrate the efficiency of our approach by applying it to the driven-dissipative two-dimensional X Y Z spin-1/2 model on a lattice.

Assessing Satisfaction with Selected Student Services Using SERVQUAL, a Market-Driven Model of Service Quality.

ERIC Educational Resources Information Center

Ruby, Carl A.

1998-01-01

Demonstrates how the use of SERVQUAL, a market-driven assessment model adapted from business, can be used to study student satisfaction with four areas of support services hypothetically related to enrollment management. The sample included 748 students enrolled in general education courses at ten different private institutions. (Contains 27…

Implementation of Argument-Driven Inquiry as an Instructional Model in a General Chemistry Laboratory Course

ERIC Educational Resources Information Center

Kadayifci, Hakki; Yalcin-Celik, Ayse

2016-01-01

This study examined the effectiveness of Argument-Driven Inquiry (ADI) as an instructional model in a general chemistry laboratory course. The study was conducted over the course of ten experimental sessions with 125 pre-service science teachers. The participants' level of reflective thinking about the ADI activities, changes in their science…

Leading Change: A Case Study of Alamo Academies--An Industry-Driven Workforce Partnership Program

ERIC Educational Resources Information Center

Hu, Xiaodan; Bowman, Gene

2016-01-01

In this study, the authors focus on the initiation and development of the Alamo Academies, aiming to illustrate an exemplary industry-driven model that addresses workforce development in local community. After a brief introduction of the context, the authors summarized major factors that contribute to the success of the collaboration model,…

Developing Students' Scientific Writing and Presentation Skills through Argument Driven Inquiry: An Exploratory Study

ERIC Educational Resources Information Center

C¸etin, Pinar Seda; Eymur, Gülüzar

2017-01-01

In this study, we employed a new instructional model that helps students develop scientific writing and presentation skills. Argument-driven inquiry (ADI) is one of the most novel instructional models that emphasizes the role of argumentation and inquiry in science education equally. This is an exploratory study where five ADI lab activities take…

Ice-Shelf Flexure and Tidal Forcing of Bindschadler Ice Stream, West Antarctica

NASA Technical Reports Server (NTRS)

Walker, Ryan T.; Parizek, Bryron R.; Alley, Richard B.; Brunt, Kelly M.; Anandakrishnan, Sridhar

2014-01-01

Viscoelastic models of ice-shelf flexure and ice-stream velocity perturbations are combined into a single efficient flowline model to study tidal forcing of grounded ice. The magnitude and timing of icestream response to tidally driven changes in hydrostatic pressure and/or basal drag are found to depend significantly on bed rheology, with only a perfectly plastic bed allowing instantaneous velocity response at the grounding line. The model can reasonably reproduce GPS observations near the grounding zone of Bindschadler Ice Stream (formerly Ice Stream D) on semidiurnal time scales; however, other forcings such as tidally driven ice-shelf slope transverse to the flowline and flexurally driven till deformation must also be considered if diurnal motion is to be matched

Model-driven meta-analyses for informing health care: a diabetes meta-analysis as an exemplar.

PubMed

Brown, Sharon A; Becker, Betsy Jane; García, Alexandra A; Brown, Adama; Ramírez, Gilbert

2015-04-01

A relatively novel type of meta-analysis, a model-driven meta-analysis, involves the quantitative synthesis of descriptive, correlational data and is useful for identifying key predictors of health outcomes and informing clinical guidelines. Few such meta-analyses have been conducted and thus, large bodies of research remain unsynthesized and uninterpreted for application in health care. We describe the unique challenges of conducting a model-driven meta-analysis, focusing primarily on issues related to locating a sample of published and unpublished primary studies, extracting and verifying descriptive and correlational data, and conducting analyses. A current meta-analysis of the research on predictors of key health outcomes in diabetes is used to illustrate our main points. © The Author(s) 2014.

MODEL-DRIVEN META-ANALYSES FOR INFORMING HEALTH CARE: A DIABETES META-ANALYSIS AS AN EXEMPLAR

PubMed Central

Brown, Sharon A.; Becker, Betsy Jane; García, Alexandra A.; Brown, Adama; Ramírez, Gilbert

2015-01-01

A relatively novel type of meta-analysis, a model-driven meta-analysis, involves the quantitative synthesis of descriptive, correlational data and is useful for identifying key predictors of health outcomes and informing clinical guidelines. Few such meta-analyses have been conducted and thus, large bodies of research remain unsynthesized and uninterpreted for application in health care. We describe the unique challenges of conducting a model-driven meta-analysis, focusing primarily on issues related to locating a sample of published and unpublished primary studies, extracting and verifying descriptive and correlational data, and conducting analyses. A current meta-analysis of the research on predictors of key health outcomes in diabetes is used to illustrate our main points. PMID:25142707

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN

DTIC Science & Technology

2014-09-01

AWARD NUMBER: W81XWH-13-1-0220 TITLE: A Genetically Engineered Mouse Model of Neuroblastoma ...CONTRACT NUMBER A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN 5b. GRANT NUMBER W81XWH-13-1-0220 5c...common ALK mutations in neuroblastoma , F1174L and R1275Q. We have determined that in tumors cells expressing mutated ALK, different downstream

Dynamical spike solutions in a nonlocal model of pattern formation

NASA Astrophysics Data System (ADS)

Marciniak-Czochra, Anna; Härting, Steffen; Karch, Grzegorz; Suzuki, Kanako

2018-05-01

Coupling a reaction-diffusion equation with ordinary differential equa- tions (ODE) may lead to diffusion-driven instability (DDI) which, in contrast to the classical reaction-diffusion models, causes destabilization of both, constant solutions and Turing patterns. Using a shadow-type limit of a reaction-diffusion-ODE model, we show that in such cases the instability driven by nonlocal terms (a counterpart of DDI) may lead to formation of unbounded spike patterns.

Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks

NASA Astrophysics Data System (ADS)

Gosangi, Rakesh; Gutierrez-Osuna, Ricardo

2011-09-01

We present a data-driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico-chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data-driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients.

Scenario and modelling uncertainty in global mean temperature change derived from emission driven Global Climate Models

NASA Astrophysics Data System (ADS)

Booth, B. B. B.; Bernie, D.; McNeall, D.; Hawkins, E.; Caesar, J.; Boulton, C.; Friedlingstein, P.; Sexton, D.

2012-09-01

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission driven rather than concentration driven perturbed parameter ensemble of a Global Climate Model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration driven simulations (with 10-90 percentile ranges of 1.7 K for the aggressive mitigation scenario up to 3.9 K for the high end business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 degrees (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission driven experiments, they do not change existing expectations (based on previous concentration driven experiments) on the timescale that different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration pathways used to drive GCM ensembles lies towards the lower end of our simulated distribution. This design decision (a legecy of previous assessments) is likely to lead concentration driven experiments to under-sample strong feedback responses in concentration driven projections. Our ensemble of emission driven simulations span the global temperature response of other multi-model frameworks except at the low end, where combinations of low climate sensitivity and low carbon cycle feedbacks lead to responses outside our ensemble range. The ensemble simulates a number of high end responses which lie above the CMIP5 carbon cycle range. These high end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real world climate sensitivity constraints which, if achieved, would lead to reductions on the uppper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present day observables and future changes while the large spread of future projected changes, highlights the ongoing need for such work.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Data-Driven Modeling and Rendering of Force Responses from Elastic Tool Deformation

PubMed Central

Rakhmatov, Ruslan; Ogay, Tatyana; Jeon, Seokhee

2018-01-01

This article presents a new data-driven model design for rendering force responses from elastic tool deformation. The new design incorporates a six-dimensional input describing the initial position of the contact, as well as the state of the tool deformation. The input-output relationship of the model was represented by a radial basis functions network, which was optimized based on training data collected from real tool-surface contact. Since the input space of the model is represented in the local coordinate system of a tool, the model is independent of recording and rendering devices and can be easily deployed to an existing simulator. The model also supports complex interactions, such as self and multi-contact collisions. In order to assess the proposed data-driven model, we built a custom data acquisition setup and developed a proof-of-concept rendering simulator. The simulator was evaluated through numerical and psychophysical experiments with four different real tools. The numerical evaluation demonstrated the perceptual soundness of the proposed model, meanwhile the user study revealed the force feedback of the proposed simulator to be realistic. PMID:29342964

Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

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

Lu, Qingda; Gao, Xiaoyang; Krishnamoorthy, Sriram

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empiricallymore » measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.« less

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

WE-EF-207-01: FEATURED PRESENTATION and BEST IN PHYSICS (IMAGING): Task-Driven Imaging for Cone-Beam CT in Interventional Guidance

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

Gang, G; Stayman, J; Ouadah, S

2015-06-15

Purpose: This work introduces a task-driven imaging framework that utilizes a patient-specific anatomical model, mathematical definition of the imaging task, and a model of the imaging system to prospectively design acquisition and reconstruction techniques that maximize task-based imaging performance. Utility of the framework is demonstrated in the joint optimization of tube current modulation and view-dependent reconstruction kernel in filtered-backprojection reconstruction and non-circular orbit design in model-based reconstruction. Methods: The system model is based on a cascaded systems analysis of cone-beam CT capable of predicting the spatially varying noise and resolution characteristics as a function of the anatomical model and amore » wide range of imaging parameters. Detectability index for a non-prewhitening observer model is used as the objective function in a task-driven optimization. The combination of tube current and reconstruction kernel modulation profiles were identified through an alternating optimization algorithm where tube current was updated analytically followed by a gradient-based optimization of reconstruction kernel. The non-circular orbit is first parameterized as a linear combination of bases functions and the coefficients were then optimized using an evolutionary algorithm. The task-driven strategy was compared with conventional acquisitions without modulation, using automatic exposure control, and in a circular orbit. Results: The task-driven strategy outperformed conventional techniques in all tasks investigated, improving the detectability of a spherical lesion detection task by an average of 50% in the interior of a pelvis phantom. The non-circular orbit design successfully mitigated photon starvation effects arising from a dense embolization coil in a head phantom, improving the conspicuity of an intracranial hemorrhage proximal to the coil. Conclusion: The task-driven imaging framework leverages a knowledge of the imaging task within a patient-specific anatomical model to optimize image acquisition and reconstruction techniques, thereby improving imaging performance beyond that achievable with conventional approaches. 2R01-CA-112163; R01-EB-017226; U01-EB-018758; Siemens Healthcare (Forcheim, Germany)« less

Accurate position estimation methods based on electrical impedance tomography measurements

NASA Astrophysics Data System (ADS)

Vergara, Samuel; Sbarbaro, Daniel; Johansen, T. A.

2017-08-01

Electrical impedance tomography (EIT) is a technology that estimates the electrical properties of a body or a cross section. Its main advantages are its non-invasiveness, low cost and operation free of radiation. The estimation of the conductivity field leads to low resolution images compared with other technologies, and high computational cost. However, in many applications the target information lies in a low intrinsic dimensionality of the conductivity field. The estimation of this low-dimensional information is addressed in this work. It proposes optimization-based and data-driven approaches for estimating this low-dimensional information. The accuracy of the results obtained with these approaches depends on modelling and experimental conditions. Optimization approaches are sensitive to model discretization, type of cost function and searching algorithms. Data-driven methods are sensitive to the assumed model structure and the data set used for parameter estimation. The system configuration and experimental conditions, such as number of electrodes and signal-to-noise ratio (SNR), also have an impact on the results. In order to illustrate the effects of all these factors, the position estimation of a circular anomaly is addressed. Optimization methods based on weighted error cost functions and derivate-free optimization algorithms provided the best results. Data-driven approaches based on linear models provided, in this case, good estimates, but the use of nonlinear models enhanced the estimation accuracy. The results obtained by optimization-based algorithms were less sensitive to experimental conditions, such as number of electrodes and SNR, than data-driven approaches. Position estimation mean squared errors for simulation and experimental conditions were more than twice for the optimization-based approaches compared with the data-driven ones. The experimental position estimation mean squared error of the data-driven models using a 16-electrode setup was less than 0.05% of the tomograph radius value. These results demonstrate that the proposed approaches can estimate an object’s position accurately based on EIT measurements if enough process information is available for training or modelling. Since they do not require complex calculations it is possible to use them in real-time applications without requiring high-performance computers.

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

Density-driven vertical transport of saltwater through the freshwater lens on the island of Baltrum (Germany) following the 1962 storm flood

NASA Astrophysics Data System (ADS)

Post, Vincent E. A.; Houben, Georg J.

2017-08-01

Due to the growing vulnerability of low-lying coastal zones to flooding by seawater, there is a current need for studies of the impact of such inundations on fresh groundwater resources. The knowledge from the literature is biased towards tropical atoll environments, and only few studies specifically investigated the effect of density-driven downward flow, even though its importance is widely acknowledged. The present study is based on previously unpublished hydrochemical data collected on the island of Baltrum following a devastating storm in 1962, which uniquely show the impact of seawater inundation on a freshwater lens in a siliciclastic aquifer. The field data show that about 3 kg of Cl per m2 of inundated land area, or 18 cm of seawater, infiltrated, and that elevated salinities persisted at the measurement depths of 4 and 6 m for at least 4 years, and at least for 6 years at greater depths. Numerical models support the assertion that the shape of the measured salinographs, i.e. an initial sharp rise in the salt concentration with time, followed by a continually-slowing decrease, must be attributed to density-driven salt fingering. Models that did not consider density effects fail to simulate the observed patterns. Transient recharge, model dimension and lateral flow modify the details of the simulation results, but in all models density-driven vertical flow dominates the overall system behaviour. The diminishing importance of density-driven flow at greater depths, however, in combination with slow recharge-driven flow rates prolongs flushing times, and enhances the risk of brackish-water up-coning when pumping is resumed too soon.

High Performance Automatic Character Skinning Based on Projection Distance

NASA Astrophysics Data System (ADS)

Li, Jun; Lin, Feng; Liu, Xiuling; Wang, Hongrui

2018-03-01

Skeleton-driven-deformation methods have been commonly used in the character deformations. The process of painting skin weights for character deformation is a long-winded task requiring manual tweaking. We present a novel method to calculate skinning weights automatically from 3D human geometric model and corresponding skeleton. The method first, groups each mesh vertex of 3D human model to a skeleton bone by the minimum distance from a mesh vertex to each bone. Secondly, calculates each vertex's weights to the adjacent bones by the vertex's projection point distance to the bone joints. Our method's output can not only be applied to any kind of skeleton-driven deformation, but also to motion capture driven (mocap-driven) deformation. Experiments results show that our method not only has strong generality and robustness, but also has high performance.

Efficient cold outflows driven by cosmic rays in high-redshift galaxies and their global effects on the IGM

NASA Astrophysics Data System (ADS)

Samui, Saumyadip; Subramanian, Kandaswamy; Srianand, Raghunathan

2018-05-01

We present semi-analytical models of galactic outflows in high-redshift galaxies driven by both hot thermal gas and non-thermal cosmic rays. Thermal pressure alone may not sustain a large-scale outflow in low-mass galaxies (i.e. M ˜ 108 M⊙), in the presence of supernovae feedback with large mass loading. We show that inclusion of cosmic ray pressure allows outflow solutions even in these galaxies. In massive galaxies for the same energy efficiency, cosmic ray-driven winds can propagate to larger distances compared to pure thermally driven winds. On an average gas in the cosmic ray-driven winds has a lower temperature which could aid detecting it through absorption lines in the spectra of background sources. Using our constrained semi-analytical models of galaxy formation (that explains the observed ultraviolet luminosity functions of galaxies), we study the influence of cosmic ray-driven winds on the properties of the intergalactic medium (IGM) at different redshifts. In particular, we study the volume filling factor, average metallicity, cosmic ray and magnetic field energy densities for models invoking atomic cooled and molecular cooled haloes. We show that the cosmic rays in the IGM could have enough energy that can be transferred to the thermal gas in presence of magnetic fields to influence the thermal history of the IGM. The significant volume filling and resulting strength of IGM magnetic fields can also account for recent γ-ray observations of blazars.

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.

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.

Simulation modelling of central order processing system under resource sharing strategy in demand-driven garment supply chains

NASA Astrophysics Data System (ADS)

Ma, K.; Thomassey, S.; Zeng, X.

2017-10-01

In this paper we proposed a central order processing system under resource sharing strategy for demand-driven garment supply chains to increase supply chain performances. We examined this system by using simulation technology. Simulation results showed that significant improvement in various performance indicators was obtained in new collaborative model with proposed system.

A Monthly Water-Balance Model Driven By a Graphical User Interface

USGS Publications Warehouse

McCabe, Gregory J.; Markstrom, Steven L.

2007-01-01

This report describes a monthly water-balance model driven by a graphical user interface, referred to as the Thornthwaite monthly water-balance program. Computations of monthly water-balance components of the hydrologic cycle are made for a specified location. The program can be used as a research tool, an assessment tool, and a tool for classroom instruction.

Parametric instabilities in resonantly-driven Bose–Einstein condensates

NASA Astrophysics Data System (ADS)

Lellouch, S.; Goldman, N.

2018-04-01

Shaking optical lattices in a resonant manner offers an efficient and versatile method to devise artificial gauge fields and topological band structures for ultracold atomic gases. This was recently demonstrated through the experimental realization of the Harper–Hofstadter model, which combined optical superlattices and resonant time-modulations. Adding inter-particle interactions to these engineered band systems is expected to lead to strongly-correlated states with topological features, such as fractional Chern insulators. However, the interplay between interactions and external time-periodic drives typically triggers violent instabilities and uncontrollable heating, hence potentially ruling out the possibility of accessing such intriguing states of matter in experiments. In this work, we study the early-stage parametric instabilities that occur in systems of resonantly-driven Bose–Einstein condensates in optical lattices. We apply and extend an approach based on Bogoliubov theory (Lellouch et al 2017 Phys. Rev. X 7 021015) to a variety of resonantly-driven band models, from a simple shaken Wannier–Stark ladder to the more intriguing driven-induced Harper–Hofstadter model. In particular, we provide ab initio numerical and analytical predictions for the stability properties of these topical models. This work sheds light on general features that could guide current experiments to stable regimes of operation.

Coupled Stochastic Time-Inverted Lagrangian Transport/Weather Forecast and Research/Vegetation Photosynthesis and Respiration Model. Part II; Simulations of Tower-Based and Airborne CO2 Measurements

NASA Technical Reports Server (NTRS)

Eluszkiewicz, Janusz; Nehrkorn, Thomas; Wofsy, Steven C.; Matross, Daniel; Gerbig, Christoph; Lin, John C.; Freitas, Saulo; Longo, Marcos; Andrews, Arlyn E.; Peters, Wouter

2007-01-01

This paper evaluates simulations of atmospheric CO2 measured in 2004 at continental surface and airborne receptors, intended to test the capability to use data with high temporal and spatial resolution for analyses of carbon sources and sinks at regional and continental scales. The simulations were performed using the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Forecast and Research (WRF) model, and linked to surface fluxes from the satellite-driven Vegetation Photosynthesis and Respiration Model (VPRM). The simulations provide detailed representations of hourly CO2 tower data and reproduce the shapes of airborne vertical profiles with high fidelity. WRF meteorology gives superior model performance compared with standard meteorological products, and the impact of including WRF convective mass fluxes in the STILT trajectory calculations is significant in individual cases. Important biases in the simulation are associated with the nighttime CO2 build-up and subsequent morning transition to convective conditions, and with errors in the advected lateral boundary condition. Comparison of STILT simulations driven by the WRF model against those driven by the Brazilian variant of the Regional Atmospheric Modeling System (BRAMS) shows that model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport. Future developments in the WRF model s data assimilation capabilities, basic research into the fundamental aspects of trajectory calculations, and intercomparison studies involving other transport models, are possible venues for reducing these errors. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates.

Relationships between aquatic vegetation and water turbidity: A field survey across seasons and spatial scales

PubMed Central

Austin, Åsa N.; Hansen, Joakim P.; Donadi, Serena; Eklöf, Johan S.

2017-01-01

Field surveys often show that high water turbidity limits cover of aquatic vegetation, while many small-scale experiments show that vegetation can reduce turbidity by decreasing water flow, stabilizing sediments, and competing with phytoplankton for nutrients. Here we bridged these two views by exploring the direction and strength of causal relationships between aquatic vegetation and turbidity across seasons (spring and late summer) and spatial scales (local and regional), using causal modeling based on data from a field survey along the central Swedish Baltic Sea coast. The two best-fitting regional-scale models both suggested that in spring, high cover of vegetation reduces water turbidity. In summer, the relationships differed between the two models; in the first model high vegetation cover reduced turbidity; while in the second model reduction of summer turbidity by high vegetation cover in spring had a positive effect on summer vegetation which suggests a positive feedback of vegetation on itself. Nitrogen load had a positive effect on turbidity in both seasons, which was comparable in strength to the effect of vegetation on turbidity. To assess whether the effect of vegetation was primarily caused by sediment stabilization or a reduction of phytoplankton, we also tested models where turbidity was replaced by phytoplankton fluorescence or sediment-driven turbidity. The best-fitting regional-scale models suggested that high sediment-driven turbidity in spring reduces vegetation cover in summer, which in turn has a negative effect on sediment-driven turbidity in summer, indicating a potential positive feedback of sediment-driven turbidity on itself. Using data at the local scale, few relationships were significant, likely due to the influence of unmeasured variables and/or spatial heterogeneity. In summary, causal modeling based on data from a large-scale field survey suggested that aquatic vegetation can reduce turbidity at regional scales, and that high vegetation cover vs. high sediment-driven turbidity may represent two self-enhancing, alternative states of shallow bay ecosystems. PMID:28854185

Relationships between aquatic vegetation and water turbidity: A field survey across seasons and spatial scales.

PubMed

Austin, Åsa N; Hansen, Joakim P; Donadi, Serena; Eklöf, Johan S

2017-01-01

Field surveys often show that high water turbidity limits cover of aquatic vegetation, while many small-scale experiments show that vegetation can reduce turbidity by decreasing water flow, stabilizing sediments, and competing with phytoplankton for nutrients. Here we bridged these two views by exploring the direction and strength of causal relationships between aquatic vegetation and turbidity across seasons (spring and late summer) and spatial scales (local and regional), using causal modeling based on data from a field survey along the central Swedish Baltic Sea coast. The two best-fitting regional-scale models both suggested that in spring, high cover of vegetation reduces water turbidity. In summer, the relationships differed between the two models; in the first model high vegetation cover reduced turbidity; while in the second model reduction of summer turbidity by high vegetation cover in spring had a positive effect on summer vegetation which suggests a positive feedback of vegetation on itself. Nitrogen load had a positive effect on turbidity in both seasons, which was comparable in strength to the effect of vegetation on turbidity. To assess whether the effect of vegetation was primarily caused by sediment stabilization or a reduction of phytoplankton, we also tested models where turbidity was replaced by phytoplankton fluorescence or sediment-driven turbidity. The best-fitting regional-scale models suggested that high sediment-driven turbidity in spring reduces vegetation cover in summer, which in turn has a negative effect on sediment-driven turbidity in summer, indicating a potential positive feedback of sediment-driven turbidity on itself. Using data at the local scale, few relationships were significant, likely due to the influence of unmeasured variables and/or spatial heterogeneity. In summary, causal modeling based on data from a large-scale field survey suggested that aquatic vegetation can reduce turbidity at regional scales, and that high vegetation cover vs. high sediment-driven turbidity may represent two self-enhancing, alternative states of shallow bay ecosystems.

Kinetic modeling of x-ray laser-driven solid Al plasmas via particle-in-cell simulation

NASA Astrophysics Data System (ADS)

Royle, R.; Sentoku, Y.; Mancini, R. C.; Paraschiv, I.; Johzaki, T.

2017-06-01

Solid-density plasmas driven by intense x-ray free-electron laser (XFEL) radiation are seeded by sources of nonthermal photoelectrons and Auger electrons that ionize and heat the target via collisions. Simulation codes that are commonly used to model such plasmas, such as collisional-radiative (CR) codes, typically assume a Maxwellian distribution and thus instantaneous thermalization of the source electrons. In this study, we present a detailed description and initial applications of a collisional particle-in-cell code, picls, that has been extended with a self-consistent radiation transport model and Monte Carlo models for photoionization and K L L Auger ionization, enabling the fully kinetic simulation of XFEL-driven plasmas. The code is used to simulate two experiments previously performed at the Linac Coherent Light Source investigating XFEL-driven solid-density Al plasmas. It is shown that picls-simulated pulse transmissions using the Ecker-Kröll continuum-lowering model agree much better with measurements than do simulations using the Stewart-Pyatt model. Good quantitative agreement is also found between the time-dependent picls results and those of analogous simulations by the CR code scfly, which was used in the analysis of the experiments to accurately reproduce the observed K α emissions and pulse transmissions. Finally, it is shown that the effects of the nonthermal electrons are negligible for the conditions of the particular experiments under investigation.

Inspired by design and driven by innovation. A conceptual model for radical design driven as a sustainable business model for Malaysian furniture design

NASA Astrophysics Data System (ADS)

Yusof, Wan Zaiyana Mohd; Fadzline Muhamad Tamyez, Puteri

2018-04-01

The definition of innovation does not help the entrepreneurs, business person or innovator to truly grasp what it means to innovate, hence we hear that government has spend millions of ringgit on “innovation” by doing R & D. However, the result has no avail in terms of commercial value. Innovation can be defined as the exploitation of commercialization of an idea or invention to create economic or social value. Most Entrepreneurs and business managers, regard innovation as creating economic value, while forgetting that innovation also create value for society or the environment. The ultimate goal as Entrepreneur, inventor or researcher is to exploit innovation to create value. As changes happen in society and economy, organizations and enterprises have to keep up and this requires innovation. This conceptual paper is to study the radical design driven innovation in the Malaysian furniture industry as a business model which the overall aim of the study is to examine the radical design driven innovation in Malaysia and how it compares with findings from Western studies. This paper will familiarize readers with the innovation and describe the radical design driven perspective that is adopted in its conceptual framework and design process.

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Testing collapse models by a thermometer

NASA Astrophysics Data System (ADS)

Bahrami, M.

2018-05-01

Collapse models postulate that space is filled with a collapse noise field, inducing quantum Brownian motions, which are dominant during the measurement, thus causing collapse of the wave function. An important manifestation of the collapse noise field, if any, is thermal energy generation, thus disturbing the temperature profile of a system. The experimental investigation of a collapse-driven heating effect has provided, so far, the most promising test of collapse models against standard quantum theory. In this paper, we calculate the collapse-driven heat generation for a three-dimensional multi-atomic Bravais lattice by solving stochastic Heisenberg equations. We perform our calculation for the mass-proportional continuous spontaneous localization collapse model with nonwhite noise. We obtain the temperature distribution of a sphere under stationary-state and insulated surface conditions. However, the exact quantification of the collapse-driven heat-generation effect highly depends on the actual value of cutoff in the collapse noise spectrum.

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

Formalism Challenges of the Cougaar Model Driven Architecture

NASA Technical Reports Server (NTRS)

Bohner, Shawn A.; George, Boby; Gracanin, Denis; Hinchey, Michael G.

2004-01-01

The Cognitive Agent Architecture (Cougaar) is one of the most sophisticated distributed agent architectures developed today. As part of its research and evolution, Cougaar is being studied for application to large, logistics-based applications for the Department of Defense (DoD). Anticipiting future complex applications of Cougaar, we are investigating the Model Driven Architecture (MDA) approach to understand how effective it would be for increasing productivity in Cougar-based development efforts. Recognizing the sophistication of the Cougaar development environment and the limitations of transformation technologies for agents, we have systematically developed an approach that combines component assembly in the large and transformation in the small. This paper describes some of the key elements that went into the Cougaar Model Driven Architecture approach and the characteristics that drove the approach.

Modeling and analysis of friction clutch at a driveline for suppressing car starting judder

NASA Astrophysics Data System (ADS)

Li, Liping; Lu, Zhaijun; Liu, Xue-Lai; Sun, Tao; Jing, Xingjian; Shangguan, Wen-Bin

2018-06-01

Car judder is a kind of back-forth vibration during vehicle starting which caused by the torsional oscillation of the driveline. This paper presents a systematic study on the dynamic response characteristics of the clutch driven disc for suppression of the judder during vehicle starting. Self-excited vibration behavior of the clutch driven disc is analyzed based on the developed 4DOF non-linear multi-body dynamic model of the clutch driving process considering stick-slip characteristics and using Karnopp friction models. Physical parameters of a clutch determining the generations of the judder behaviors are discussed and the revised designs of the driven disc of a clutch for suppression of the judder are consequently investigated and validated with experiments for two real cars.

Model driven development of clinical information sytems using openEHR.

PubMed

Atalag, Koray; Yang, Hong Yul; Tempero, Ewan; Warren, Jim

2011-01-01

openEHR and the recent international standard (ISO 13606) defined a model driven software development methodology for health information systems. However there is little evidence in the literature describing implementation; especially for desktop clinical applications. This paper presents an implementation pathway using .Net/C# technology for Microsoft Windows desktop platforms. An endoscopy reporting application driven by openEHR Archetypes and Templates has been developed. A set of novel GUI directives has been defined and presented which guides the automatic graphical user interface generator to render widgets properly. We also reveal the development steps and important design decisions; from modelling to the final software product. This might provide guidance for other developers and form evidence required for the adoption of these standards for vendors and national programs alike.

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

PubMed

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.

PubMed

Poleszczuk, Jan; Macklin, Paul; Enderling, Heiko

2016-01-01

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking

PubMed Central

Swan, Melanie

2009-01-01

A new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care. Patient-driven health care can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care though new services. This paper examines three categories of novel health services: health social networks, consumer personalized medicine and quantified self-tracking. PMID:19440396

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

DOE PAGES

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.; ...

2016-09-18

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

Critical exponents of the disorder-driven superfluid-insulator transition in one-dimensional Bose-Einstein condensates

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

Cestari, J. C. C.; Foerster, A.; Gusmao, M. A.

2011-11-15

We investigate the nature of the superfluid-insulator quantum phase transition driven by disorder for noninteracting ultracold atoms on one-dimensional lattices. We consider two different cases: Anderson-type disorder, with local energies randomly distributed, and pseudodisorder due to a potential incommensurate with the lattice, which is usually called the Aubry-Andre model. A scaling analysis of numerical data for the superfluid fraction for different lattice sizes allows us to determine quantum critical exponents characterizing the disorder-driven superfluid-insulator transition. We also briefly discuss the effect of interactions close to the noninteracting quantum critical point of the Aubry-Andre model.

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

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

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

A Hybrid Physics-Based Data-Driven Approach for Point-Particle Force Modeling

NASA Astrophysics Data System (ADS)

Moore, Chandler; Akiki, Georges; Balachandar, S.

2017-11-01

This study improves upon the physics-based pairwise interaction extended point-particle (PIEP) model. The PIEP model leverages a physical framework to predict fluid mediated interactions between solid particles. While the PIEP model is a powerful tool, its pairwise assumption leads to increased error in flows with high particle volume fractions. To reduce this error, a regression algorithm is used to model the differences between the current PIEP model's predictions and the results of direct numerical simulations (DNS) for an array of monodisperse solid particles subjected to various flow conditions. The resulting statistical model and the physical PIEP model are superimposed to construct a hybrid, physics-based data-driven PIEP model. It must be noted that the performance of a pure data-driven approach without the model-form provided by the physical PIEP model is substantially inferior. The hybrid model's predictive capabilities are analyzed using more DNS. In every case tested, the hybrid PIEP model's prediction are more accurate than those of physical PIEP model. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1315138 and the U.S. DOE, NNSA, ASC Program, as a Cooperative Agreement under Contract No. DE-NA0002378.

Quantum correlations and limit cycles in the driven-dissipative Heisenberg lattice

NASA Astrophysics Data System (ADS)

Owen, E. T.; Jin, J.; Rossini, D.; Fazio, R.; Hartmann, M. J.

2018-04-01

Driven-dissipative quantum many-body systems have attracted increasing interest in recent years as they lead to novel classes of quantum many-body phenomena. In particular, mean-field calculations predict limit cycle phases, slow oscillations instead of stationary states, in the long-time limit for a number of driven-dissipative quantum many-body systems. Using a cluster mean-field and a self-consistent Mori projector approach, we explore the persistence of such limit cycles as short range quantum correlations are taken into account in a driven-dissipative Heisenberg model.

Statistical and engineering methods for model enhancement

NASA Astrophysics Data System (ADS)

Chang, Chia-Jung

Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as “Minimal Adjustment”, which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

Use case driven approach to develop simulation model for PCS of APR1400 simulator

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

Dong Wook, Kim; Hong Soo, Kim; Hyeon Tae, Kang

2006-07-01

The full-scope simulator is being developed to evaluate specific design feature and to support the iterative design and validation in the Man-Machine Interface System (MMIS) design of Advanced Power Reactor (APR) 1400. The simulator consists of process model, control logic model, and MMI for the APR1400 as well as the Power Control System (PCS). In this paper, a use case driven approach is proposed to develop a simulation model for PCS. In this approach, a system is considered from the point of view of its users. User's view of the system is based on interactions with the system and themore » resultant responses. In use case driven approach, we initially consider the system as a black box and look at its interactions with the users. From these interactions, use cases of the system are identified. Then the system is modeled using these use cases as functions. Lower levels expand the functionalities of each of these use cases. Hence, starting from the topmost level view of the system, we proceeded down to the lowest level (the internal view of the system). The model of the system thus developed is use case driven. This paper will introduce the functionality of the PCS simulation model, including a requirement analysis based on use case and the validation result of development of PCS model. The PCS simulation model using use case will be first used during the full-scope simulator development for nuclear power plant and will be supplied to Shin-Kori 3 and 4 plant. The use case based simulation model development can be useful for the design and implementation of simulation models. (authors)« less

Optimizing nursing care by integrating theory-driven evidence-based practice.

PubMed

Pipe, Teri Britt

2007-01-01

An emerging challenge for nursing leadership is how to convey the importance of both evidence-based practice (EBP) and theory-driven care in ensuring patient safety and optimizing outcomes. This article describes a specific example of a leadership strategy based on Rosswurm and Larrabee's model for change to EBP, which was effective in aligning the processes of EBP and theory-driven care.

Data-driven modelling of social forces and collective behaviour in zebrafish.

PubMed

Zienkiewicz, Adam K; Ladu, Fabrizio; Barton, David A W; Porfiri, Maurizio; Bernardo, Mario Di

2018-04-14

Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations. Copyright © 2018 Elsevier Ltd. All rights reserved.

A video, text, and speech-driven realistic 3-d virtual head for human-machine interface.

PubMed

Yu, Jun; Wang, Zeng-Fu

2015-05-01

A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human-machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human-machine interaction.

Large eddy simulations of time-dependent and buoyancy-driven channel flows

NASA Technical Reports Server (NTRS)

Cabot, William H.

1993-01-01

The primary goal of this work has been to assess the performance of the dynamic SGS model in the large eddy simulation (LES) of channel flows in a variety of situations, viz., in temporal development of channel flow turned by a transverse pressure gradient and especially in buoyancy-driven turbulent flows such as Rayleigh-Benard and internally heated channel convection. For buoyancy-driven flows, there are additional buoyant terms that are possible in the base models, and one objective has been to determine if the dynamic SGS model results are sensitive to such terms. The ultimate goal is to determine the minimal base model needed in the dynamic SGS model to provide accurate results in flows with more complicated physical features. In addition, a program of direct numerical simulation (DNS) of fully compressible channel convection has been undertaken to determine stratification and compressibility effects. These simulations are intended to provide a comparative base for performing the LES of compressible (or highly stratified, pseudo-compressible) convection at high Reynolds number in the future.

Nonlinear optimal control policies for buoyancy-driven flows in the built environment

NASA Astrophysics Data System (ADS)

Nabi, Saleh; Grover, Piyush; Caulfield, Colm

2017-11-01

We consider optimal control of turbulent buoyancy-driven flows in the built environment, focusing on a model test case of displacement ventilation with a time-varying heat source. The flow is modeled using the unsteady Reynolds-averaged equations (URANS). To understand the stratification dynamics better, we derive a low-order partial-mixing ODE model extending the buoyancy-driven emptying filling box problem to the case of where both the heat source and the (controlled) inlet flow are time-varying. In the limit of a single step-change in the heat source strength, our model is consistent with that of Bower et al.. Our model considers the dynamics of both `filling' and `intruding' added layers due to a time-varying source and inlet flow. A nonlinear direct-adjoint-looping optimal control formulation yields time-varying values of temperature and velocity of the inlet flow that lead to `optimal' time-averaged temperature relative to appropriate objective functionals in a region of interest.

Magnetically-driven medical robots: An analytical magnetic model for endoscopic capsules design

NASA Astrophysics Data System (ADS)

Li, Jing; Barjuei, Erfan Shojaei; Ciuti, Gastone; Hao, Yang; Zhang, Peisen; Menciassi, Arianna; Huang, Qiang; Dario, Paolo

2018-04-01

Magnetic-based approaches are highly promising to provide innovative solutions for the design of medical devices for diagnostic and therapeutic procedures, such as in the endoluminal districts. Due to the intrinsic magnetic properties (no current needed) and the high strength-to-size ratio compared with electromagnetic solutions, permanent magnets are usually embedded in medical devices. In this paper, a set of analytical formulas have been derived to model the magnetic forces and torques which are exerted by an arbitrary external magnetic field on a permanent magnetic source embedded in a medical robot. In particular, the authors modelled cylindrical permanent magnets as general solution often used and embedded in magnetically-driven medical devices. The analytical model can be applied to axially and diametrically magnetized, solid and annular cylindrical permanent magnets in the absence of the severe calculation complexity. Using a cylindrical permanent magnet as a selected solution, the model has been applied to a robotic endoscopic capsule as a pilot study in the design of magnetically-driven robots.

A frictionally and hydraulically constrained model of the convectively driven mean flow in partially enclosed seas

NASA Astrophysics Data System (ADS)

Maxworthy, T.

1997-08-01

A simple three-layer model of the dynamics of partially enclosed seas, driven by a surface buoyancy flux, is presented. It contains two major elements, a hydraulic constraint at the exit contraction and friction in the interior of the main body of the sea; both together determine the vertical structure and magnitudes of the interior flow variables, i.e. velocity and density. Application of the model to the large-scale dynamics of the Red Sea gives results that are not in disagreement with observation once the model is applied, also, to predict the dense outflow from the Gulf of Suez. The latter appears to be the agent responsible for the formation of dense bottom water in this system. Also, the model is reasonably successful in predicting the density of the outflow from the Persian Gulf, and can be applied to any number of other examples of convectively driven flow in long, narrow channels, with or without sills and constrictions at their exits.

Using Distractor-Driven Standards-Based Multiple-Choice Assessments and Rasch Modeling to Investigate Hierarchies of Chemistry Misconceptions and Detect Structural Problems with Individual Items

ERIC Educational Resources Information Center

Herrmann-Abell, Cari F.; DeBoer, George E.

2011-01-01

Distractor-driven multiple-choice assessment items and Rasch modeling were used as diagnostic tools to investigate students' understanding of middle school chemistry ideas. Ninety-one items were developed according to a procedure that ensured content alignment to the targeted standards and construct validity. The items were administered to 13360…

Is Slow Slip a Cause or a Result of Tremor?

NASA Astrophysics Data System (ADS)

Luo, Y.; Ampuero, J. P.

2017-12-01

While various modeling efforts have been conducted to reproduce subsets of observations of tremor and slow-slip events (SSE), a fundamental but yet unanswered question is whether slow slip is a cause or a result of tremor. Tremor is commonly regarded as driven by SSE. This view is mainly based on observations of SSE without detected tremors and on (frequency-limited) estimates of total tremor seismic moment being lower than 1% of their concomitant SSE moment. In previous studies we showed that models of heterogeneous faults, composed of seismic asperities embedded in an aseismic fault zone matrix, reproduce quantitatively the hierarchical patterns of tremor migration observed in Cascadia and Shikoku. To address the title question, we design two end-member models of a heterogeneous fault. In the SSE-driven-tremor model, slow slip events are spontaneously generated by the matrix (even in the absence of seismic asperities) and drive tremor. In the Tremor-driven-SSE model the matrix is stable (it slips steadily in the absence of asperities) and slow slip events result from the collective behavior of tremor asperities interacting via transient creep (local afterslip fronts). We study these two end-member models through 2D quasi-dynamic multi-cycle simulations of faults governed by rate-and-state friction with heterogeneous frictional properties and effective normal stress, using the earthquake simulation software QDYN (https://zenodo.org/record/322459). We find that both models reproduce first-order observations of SSE and tremor and have very low seismic to aseismic moment ratio. However, the Tremor-driven-SSE model assumes a simpler rheology than the SSE-driven-tremor model and matches key observations better and without fine tuning, including the ratio of propagation speeds of forward SSE and rapid tremor reversals and the decay of inter-event times of Low Frequency Earthquakes. These modeling results indicate that, in contrast to a common view, SSE could be a result of tremor activity. We also find that, despite important interactions between asperities, tremor activity rates are proportional to the underlying aseismic slip rate, supporting an approach to estimate SSE properties with high spatial-temporal resolutions via tremor activity.

Data integrity systems for organ contours in radiation therapy planning.

PubMed

Shah, Veeraj P; Lakshminarayanan, Pranav; Moore, Joseph; Tran, Phuoc T; Quon, Harry; Deville, Curtiland; McNutt, Todd R

2018-06-12

The purpose of this research is to develop effective data integrity models for contoured anatomy in a radiotherapy workflow for both real-time and retrospective analysis. Within this study, two classes of contour integrity models were developed: data driven models and contiguousness models. The data driven models aim to highlight contours which deviate from a gross set of contours from similar disease sites and encompass the following regions of interest (ROI): bladder, femoral heads, spinal cord, and rectum. The contiguousness models, which individually analyze the geometry of contours to detect possible errors, are applied across many different ROI's and are divided into two metrics: Extent and Region Growing over volume. After analysis, we found that 70% of detected bladder contours were verified as suspicious. The spinal cord and rectum models verified that 73% and 80% of contours were suspicious respectively. The contiguousness models were the most accurate models and the Region Growing model was the most accurate submodel. 100% of the detected noncontiguous contours were verified as suspicious, but in the cases of spinal cord, femoral heads, bladder, and rectum, the Region Growing model detected additional two to five suspicious contours that the Extent model failed to detect. When conducting a blind review to detect false negatives, it was found that all the data driven models failed to detect all suspicious contours. The Region Growing contiguousness model produced zero false negatives in all regions of interest other than prostate. With regards to runtime, the contiguousness via extent model took an average of 0.2 s per contour. On the other hand, the region growing method had a longer runtime which was dependent on the number of voxels in the contour. Both contiguousness models have potential for real-time use in clinical radiotherapy while the data driven models are better suited for retrospective use. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

A miniature cable-driven robot for crawling on the heart.

PubMed

Patronik, N A; Zenati, M A; Riviere, C N

2005-01-01

This document describes the design and preliminary testing of a cable-driven robot for the purpose of traveling on the surface of the beating heart to administer therapy. This methodology obviates mechanical stabilization and lung deflation, which are typically required during minimally invasive cardiac surgery. Previous versions of the robot have been remotely actuated through push-pull wires, while visual feedback was provided by fiber optic transmission. Although these early models were able to perform locomotion in vivo on porcine hearts, the stiffness of the wire-driven transmission and fiber optic camera limited the mobility of the robots. The new prototype described in this document is actuated by two antagonistic cable pairs, and contains a color CCD camera located in the front section of the device. These modifications have resulted in superior mobility and visual feedback. The cable-driven prototype has successfully demonstrated prehension, locomotion, and tissue dye injection during in vitro testing with a poultry model.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

New perspectives on adolescent motivated behavior: attention and conditioning

PubMed Central

Ernst, Monique; Daniele, Teresa; Frantz, Kyle

2011-01-01

Adolescence is a critical transition period, during which fundamental changes prepare the adolescent for becoming an adult. Heuristic models of the neurobiology of adolescent behavior have emerged, promoting the central role of reward and motivation, coupled with cognitive immaturities. Here, we bring focus to two basic sets of processes, attention and conditioning, which are essential for adaptive behavior. Using the dual-attention model developed by Corbetta and Shulman (2002), which identifies a stimulus-driven and a goal-driven attention network, we propose a balance that favors stimulus-driven attention over goal-driven attention in youth. Regarding conditioning, we hypothesize that stronger associations tend to be made between environmental cues and appetitive stimuli, and weaker associations with aversive stimuli, in youth relative to adults. An attention system geared to prioritize stimulus-driven attention, together with more powerful associative learning with appetitive incentives, contribute to shape patterns of adolescent motivated behavior. This proposed bias in attention and conditioning function could facilitate the impulsive, novelty-seeking and risk-taking behavior that is typical of many adolescents. PMID:21977221

Vector Autoregression, Structural Equation Modeling, and Their Synthesis in Neuroimaging Data Analysis

PubMed Central

Chen, Gang; Glen, Daniel R.; Saad, Ziad S.; Hamilton, J. Paul; Thomason, Moriah E.; Gotlib, Ian H.; Cox, Robert W.

2011-01-01

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoids some prevalent pitfalls that can occur when VAR and SEM are utilized separately. PMID:21975109

Data-driven integration of genome-scale regulatory and metabolic network models

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

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Data-driven integration of genome-scale regulatory and metabolic network models

DOE PAGES

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

2015-05-05

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Input-driven versus turnover-driven controls of simulated changes in soil carbon due to land-use change

NASA Astrophysics Data System (ADS)

Nyawira, S. S.; Nabel, J. E. M. S.; Brovkin, V.; Pongratz, J.

2017-08-01

Historical changes in soil carbon associated with land-use change (LUC) result mainly from the changes in the quantity of litter inputs to the soil and the turnover of carbon in soils. We use a factor separation technique to assess how the input-driven and turnover-driven controls, as well as their synergies, have contributed to historical changes in soil carbon associated with LUC. We apply this approach to equilibrium simulations of present-day and pre-industrial land use performed using the dynamic global vegetation model JSBACH. Our results show that both the input-driven and turnover-driven changes generally contribute to a gain in soil carbon in afforested regions and a loss in deforested regions. However, in regions where grasslands have been converted to croplands, we find an input-driven loss that is partly offset by a turnover-driven gain, which stems from a decrease in the fire-related carbon losses. Omitting land management through crop and wood harvest substantially reduces the global losses through the input-driven changes. Our study thus suggests that the dominating control of soil carbon losses is via the input-driven changes, which are more directly accessible to human management than the turnover-driven ones.

Traffic Flow of Interacting Self-Driven Particles: Rails and Trails, Vehicles and Vesicles

NASA Astrophysics Data System (ADS)

Chowdhury, Debashish

One common feature of a vehicle, an ant and a kinesin motor is that they all convert chemical energy, derived from fuel or food, into mechanical energy required for their forward movement; such objects have been modelled in recent years as self-driven particles. Cytoskeletal filaments, e.g., microtubules, form a rail network for intra-cellular transport of vesicular cargo by molecular motors like, for example, kinesins. Similarly, ants move along trails while vehicles move along lanes. Therefore, the traffic of vehicles and organisms as well as that of molecular motors can be modelled as systems of interacting self-driven particles; these are of current interest in non-equilibrium statistical mechanics. In this paper we point out the common features of these model systems and emphasize the crucial differences in their physical properties.

Phase field model of fluid-driven fracture in elastic media: Immersed-fracture formulation and validation with analytical solutions

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

Santillán, David; Juanes, Ruben; Cueto-Felgueroso, Luis

Propagation of fluid-driven fractures plays an important role in natural and engineering processes, including transport of magma in the lithosphere, geologic sequestration of carbon dioxide, and oil and gas recovery from low-permeability formations, among many others. The simulation of fracture propagation poses a computational challenge as a result of the complex physics of fracture and the need to capture disparate length scales. Phase field models represent fractures as a diffuse interface and enjoy the advantage that fracture nucleation, propagation, branching, or twisting can be simulated without ad hoc computational strategies like remeshing or local enrichment of the solution space. Heremore » we propose a new quasi-static phase field formulation for modeling fluid-driven fracturing in elastic media at small strains. The approach fully couples the fluid flow in the fracture (described via the Reynolds lubrication approximation) and the deformation of the surrounding medium. The flow is solved on a lower dimensionality mesh immersed in the elastic medium. This approach leads to accurate coupling of both physics. We assessed the performance of the model extensively by comparing results for the evolution of fracture length, aperture, and fracture fluid pressure against analytical solutions under different fracture propagation regimes. Thus, the excellent performance of the numerical model in all regimes builds confidence in the applicability of phase field approaches to simulate fluid-driven fracture.« less

Phase field model of fluid-driven fracture in elastic media: Immersed-fracture formulation and validation with analytical solutions

DOE PAGES

Santillán, David; Juanes, Ruben; Cueto-Felgueroso, Luis

2017-04-20

Propagation of fluid-driven fractures plays an important role in natural and engineering processes, including transport of magma in the lithosphere, geologic sequestration of carbon dioxide, and oil and gas recovery from low-permeability formations, among many others. The simulation of fracture propagation poses a computational challenge as a result of the complex physics of fracture and the need to capture disparate length scales. Phase field models represent fractures as a diffuse interface and enjoy the advantage that fracture nucleation, propagation, branching, or twisting can be simulated without ad hoc computational strategies like remeshing or local enrichment of the solution space. Heremore » we propose a new quasi-static phase field formulation for modeling fluid-driven fracturing in elastic media at small strains. The approach fully couples the fluid flow in the fracture (described via the Reynolds lubrication approximation) and the deformation of the surrounding medium. The flow is solved on a lower dimensionality mesh immersed in the elastic medium. This approach leads to accurate coupling of both physics. We assessed the performance of the model extensively by comparing results for the evolution of fracture length, aperture, and fracture fluid pressure against analytical solutions under different fracture propagation regimes. Thus, the excellent performance of the numerical model in all regimes builds confidence in the applicability of phase field approaches to simulate fluid-driven fracture.« less

Event-driven management algorithm of an Engineering documents circulation system

NASA Astrophysics Data System (ADS)

Kuzenkov, V.; Zebzeev, A.; Gromakov, E.

2015-04-01

Development methodology of an engineering documents circulation system in the design company is reviewed. Discrete event-driven automatic models using description algorithms of project management is offered. Petri net use for dynamic design of projects is offered.

Efficient GIS-based model-driven method for flood risk management and its application in central China

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhou, J.; Song, L.; Zou, Q.; Guo, J.; Wang, Y.

2014-02-01

In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems.

PubMed

Pan, Shaoming; Chong, Yanwen; Zhang, Hang; Tan, Xicheng

2017-01-01

A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client's requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users' access behaviors and all tiles' relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users' access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods.

A review of surrogate models and their application to groundwater modeling

NASA Astrophysics Data System (ADS)

Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.; Peeters, L. J. M.

2015-08-01

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context.

A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth.

PubMed

Armstrong, Michelle Hine; Buganza Tepole, Adrián; Kuhl, Ellen; Simon, Bruce R; Vande Geest, Jonathan P

2016-01-01

The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS) to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS). To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug) fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.

A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth

PubMed Central

Armstrong, Michelle Hine; Buganza Tepole, Adrián; Kuhl, Ellen; Simon, Bruce R.; Vande Geest, Jonathan P.

2016-01-01

The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS) to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS). To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug) fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues. PMID:27078495

Open-ocean boundary conditions from interior data: Local and remote forcing of Massachusetts Bay

USGS Publications Warehouse

Bogden, P.S.; Malanotte-Rizzoli, P.; Signell, R.

1996-01-01

Massachusetts and Cape Cod Bays form a semienclosed coastal basin that opens onto the much larger Gulf of Maine. Subtidal circulation in the bay is driven by local winds and remotely driven flows from the gulf. The local-wind forced flow is estimated with a regional shallow water model driven by wind measurements. The model uses a gravity wave radiation condition along the open-ocean boundary. Results compare reasonably well with observed currents near the coast. In some offshore regions however, modeled flows are an order of magnitude less energetic than the data. Strong flows are observed even during periods of weak local wind forcing. Poor model-data comparisons are attributable, at least in part, to open-ocean boundary conditions that neglect the effects of remote forcing. Velocity measurements from within Massachusetts Bay are used to estimate the remotely forced component of the flow. The data are combined with shallow water dynamics in an inverse-model formulation that follows the theory of Bennett and McIntosh [1982], who considered tides. We extend their analysis to consider the subtidal response to transient forcing. The inverse model adjusts the a priori open-ocean boundary condition, thereby minimizing a combined measure of model-data misfit and boundary condition adjustment. A "consistency criterion" determines the optimal trade-off between the two. The criterion is based on a measure of plausibility for the inverse solution. The "consistent" inverse solution reproduces 56% of the average squared variation in the data. The local-wind-driven flow alone accounts for half of the model skill. The other half is attributable to remotely forced flows from the Gulf of Maine. The unexplained 44% comes from measurement errors and model errors that are not accounted for in the analysis. 

Library Catalog Log Analysis in E-Book Patron-Driven Acquisitions (PDA): A Case Study

ERIC Educational Resources Information Center

Urbano, Cristóbal; Zhang, Yin; Downey, Kay; Klingler, Thomas

2015-01-01

Patron-Driven Acquisitions (PDA) is a new model used for e-book acquisition by academic libraries. A key component of this model is to make records of ebooks available in a library catalog and let actual patron usage decide whether or not an item is purchased. However, there has been a lack of research examining the role of the library catalog as…

Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fan-Wei; Collatz, G. James; Pinzon, Jorge; Ivanoff, Alvaro

2011-01-01

interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

Spectrum Analysis of Inertial and Subinertial Motions Based on Analyzed Winds and Wind-Driven Currents from a Primitive Equation General Ocean Circulation Model.

DTIC Science & Technology

1982-12-01

1Muter.Te Motions Based on Ana lyzed Winds and wind-driven December 1982 Currents from. a Primitive Squat ion General a.OW -love"*..* Oean Circulation...mew se"$ (comeS.... do oISN..u am ae~ 00do OWaor NUN Fourier and Rotary Spc , Analysis Modeled Inertial and Subinrtial Motion 4 Primitive Equation

Evaluation of Normalization Methods to Pave the Way Towards Large-Scale LC-MS-Based Metabolomics Profiling Experiments

PubMed Central

Valkenborg, Dirk; Baggerman, Geert; Vanaerschot, Manu; Witters, Erwin; Dujardin, Jean-Claude; Burzykowski, Tomasz; Berg, Maya

2013-01-01

Abstract Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments. PMID:23808607

Active galactic nucleus outflows in galaxy discs

NASA Astrophysics Data System (ADS)

Hartwig, Tilman; Volonteri, Marta; Dashyan, Gohar

2018-05-01

Galactic outflows, driven by active galactic nuclei (AGNs), play a crucial role in galaxy formation and in the self-regulated growth of supermassive black holes (BHs). AGN feedback couples to and affects gas, rather than stars, and in many, if not most, gas-rich galaxies cold gas is rotationally supported and settles in a disc. We present a 2D analytical model for AGN-driven outflows in a gaseous disc and demonstrate the main improvements, compared to existing 1D solutions. We find significant differences for the outflow dynamics and wind efficiency. The outflow is energy-driven due to inefficient cooling up to a certain AGN luminosity (˜1043 erg s-1 in our fiducial model), above which the outflow remains momentum-driven in the disc up to galactic scales. We reproduce results of 3D simulations that gas is preferentially ejected perpendicular to the disc and find that the fraction of ejected interstellar medium is lower than in 1D models. The recovery time of gas in the disc, defined as the free-fall time from the radius to which the AGN pushes the ISM at most, is remarkably short, of the order 1 Myr. This indicates that AGN-driven winds cannot suppress BH growth for long. Without the inclusion of supernova feedback, we find a scaling of the BH mass with the halo velocity dispersion of MBH ∝ σ4.8.

Interplay of interfacial noise and curvature-driven dynamics in two dimensions

NASA Astrophysics Data System (ADS)

Roy, Parna; Sen, Parongama

2017-02-01

We explore the effect of interplay of interfacial noise and curvature-driven dynamics in a binary spin system. An appropriate model is the generalized two-dimensional voter model proposed earlier [M. J. de Oliveira, J. F. F. Mendes, and M. A. Santos, J. Phys. A: Math. Gen. 26, 2317 (1993), 10.1088/0305-4470/26/10/006], where the flipping probability of a spin depends on the state of its neighbors and is given in terms of two parameters, x and y . x =0.5 andy =1 correspond to the conventional voter model which is purely interfacial noise driven, while x =1 and y =1 correspond to the Ising model, where coarsening is fully curvature driven. The coarsening phenomena for 0.5 0.5 ; the effect of x appears in altering the value of the parameter occurring in the scaling function only.

Epidemic cycles driven by host behaviour

PubMed Central

Althouse, Benjamin M.; Hébert-Dufresne, Laurent

2014-01-01

Host immunity and demographics (the recruitment of susceptibles via birthrate) have been demonstrated to be a key determinant of the periodicity of measles, pertussis and dengue epidemics. However, not all epidemic cycles are from pathogens inducing sterilizing immunity or are driven by demographics. Many sexually transmitted infections are driven by sexual behaviour. We present a mathematical model of disease transmission where individuals can disconnect and reconnect depending on the infectious status of their contacts. We fit the model to historic syphilis (Treponema pallidum) and gonorrhea (Neisseria gonorrhoeae) incidence in the USA and explore potential intervention strategies against syphilis. We find that cycles in syphilis incidence can be driven solely by changing sexual behaviour in structured populations. Our model also explains the lack of similar cycles in gonorrhea incidence even if the two infections share the same propagation pathways. Our model similarly illustrates how sudden epidemic outbreaks can occur on time scales smaller than the characteristic demographic time scale of the population and that weaker infections can lead to more violent outbreaks. Behaviour also appears to be critical for control strategies as we found a bigger sensitivity to behavioural interventions than antibiotic treatment. Thus, behavioural interventions may play a larger role than previously thought, especially in the face of antibiotic resistance and low intervention efficacies. PMID:25100316

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

2017-12-01

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

NASA Technical Reports Server (NTRS)

Goodman, Michael L.; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

2017-01-01

There are many flare forecasting models. For an excellent review and comparison of some of them see Barnes et al. (2016). All these models are successful to some degree, but there is a need for better models. We claim the most successful models explicitly or implicitly base their forecasts on various estimates of components of the photospheric current density J, based on observations of the photospheric magnetic field B. However, none of the models we are aware of compute the complete J. We seek to develop a better model based on computing the complete photospheric J. Initial results from this model are presented in this talk. We present a data driven, near photospheric, 3 D, non-force free magnetohydrodynamic (MHD) model that computes time series of the total J, and associated resistive heating rate in each pixel at the photosphere in the neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of B measured by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series of B in every AR pixel. Errors in B due to these periods can be significant.

Statistical modeling of interannual shoreline change driven by North Atlantic climate variability spanning 2000-2014 in the Bay of Biscay

NASA Astrophysics Data System (ADS)

Robinet, A.; Castelle, B.; Idier, D.; Le Cozannet, G.; Déqué, M.; Charles, E.

2016-12-01

Modeling studies addressing daily to interannual coastal evolution typically relate shoreline change with waves, currents and sediment transport through complex processes and feedbacks. For wave-dominated environments, the main driver (waves) is controlled by the regional atmospheric circulation. Here a simple weather regime-driven shoreline model is developed for a 15-year shoreline dataset (2000-2014) collected at Truc Vert beach, Bay of Biscay, SW France. In all, 16 weather regimes (four per season) are considered. The centroids and occurrences are computed using the ERA-40 and ERA-Interim reanalyses, applying k-means and EOF methods to the anomalies of the 500-hPa geopotential height over the North Atlantic Basin. The weather regime-driven shoreline model explains 70% of the observed interannual shoreline variability. The application of a proven wave-driven equilibrium shoreline model to the same period shows that both models have similar skills at the interannual scale. Relation between the weather regimes and the wave climate in the Bay of Biscay is investigated and the primary weather regimes impacting shoreline change are identified. For instance, the winter zonal regime characterized by a strengthening of the pressure gradient between the Iceland low and the Azores high is associated with high-energy wave conditions and is found to drive an increase in the shoreline erosion rate. The study demonstrates the predictability of interannual shoreline change from a limited number of weather regimes, which opens new perspectives for shoreline change modeling and encourages long-term shoreline monitoring programs.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

NASA Astrophysics Data System (ADS)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

Partition-based discrete-time quantum walks

NASA Astrophysics Data System (ADS)

Konno, Norio; Portugal, Renato; Sato, Iwao; Segawa, Etsuo

2018-04-01

We introduce a family of discrete-time quantum walks, called two-partition model, based on two equivalence-class partitions of the computational basis, which establish the notion of local dynamics. This family encompasses most versions of unitary discrete-time quantum walks driven by two local operators studied in literature, such as the coined model, Szegedy's model, and the 2-tessellable staggered model. We also analyze the connection of those models with the two-step coined model, which is driven by the square of the evolution operator of the standard discrete-time coined walk. We prove formally that the two-step coined model, an extension of Szegedy model for multigraphs, and the two-tessellable staggered model are unitarily equivalent. Then, selecting one specific model among those families is a matter of taste not generality.

Profile modifications in laser-driven temperature fronts using flux-limiters and delocalization models

NASA Astrophysics Data System (ADS)

Colombant, Denis; Manheimer, Wallace; Busquet, Michel

2004-11-01

A simple steady-state model using flux-limiters by Day et al [1] showed that temperature profiles could formally be double-valued. Stability of temperature profiles in laser-driven temperature fronts using delocalization models was also discussed by Prasad and Kershaw [2]. We have observed steepening of the front and flattening of the maximum temperature in laser-driven implosions [3]. Following the simple model first proposed in [1], we solve for a two-boundary value steady-state heat flow problem for various non-local heat transport models. For the more complicated models [4,5], we obtain the steady-state solution as the asymptotic limit of the time-dependent solution. Solutions will be shown and compared for these various models. 1.M.Day, B.Merriman, F.Najmabadi and R.W.Conn, Contrib. Plasma Phys. 36, 419 (1996) 2.M.K.Prasad and D.S.Kershaw, Phys. Fluids B3, 3087 (1991) 3.D.Colombant, W.Manheimer and M.Busquet, Bull. Amer. Phys. Soc. 48, 326 (2003) 4.E.M.Epperlein and R.W.Short, Phys. Fluids B3, 3092 (1991) 5.W.Manheimer and D.Colombant, Phys. Plasmas 11, 260 (2004)

CONFOLD2: improved contact-driven ab initio protein structure modeling.

PubMed

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: Initial investigation of a combined model- and image-driven approach

PubMed Central

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J. Webster; Kleinszig, Gerhard; Sussman, Marc S.; Prince, Jerry L.; Siewerdsen, Jeffrey H.

2013-01-01

Purpose: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. Methods: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. Results: The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3–5 mm within the target wedge) and critical structure avoidance (∼1–2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. Conclusions: The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1–2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system. PMID:23298134

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.

PubMed

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J Webster; Kleinszig, Gerhard; Sussman, Marc S; Prince, Jerry L; Siewerdsen, Jeffrey H

2013-01-01

Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.

Synchronization of autonomous objects in discrete event simulation

NASA Technical Reports Server (NTRS)

Rogers, Ralph V.

1990-01-01

Autonomous objects in event-driven discrete event simulation offer the potential to combine the freedom of unrestricted movement and positional accuracy through Euclidean space of time-driven models with the computational efficiency of event-driven simulation. The principal challenge to autonomous object implementation is object synchronization. The concept of a spatial blackboard is offered as a potential methodology for synchronization. The issues facing implementation of a spatial blackboard are outlined and discussed.

An integrated, ethically driven environmental model of clinical decision making in emergency settings.

PubMed

Wolf, Lisa

2013-02-01

To explore the relationship between multiple variables within a model of critical thinking and moral reasoning. A quantitative descriptive correlational design using a purposive sample of 200 emergency nurses. Measured variables were accuracy in clinical decision-making, moral reasoning, perceived care environment, and demographics. Analysis was by bivariate correlation using Pearson's product-moment correlation coefficients, chi square and multiple linear regression analysis. The elements as identified in the integrated ethically-driven environmental model of clinical decision-making (IEDEM-CD) corrected depict moral reasoning and environment of care as factors significantly affecting accuracy in decision-making. The integrated, ethically driven environmental model of clinical decision making is a framework useful for predicting clinical decision making accuracy for emergency nurses in practice, with further implications in education, research and policy. A diagnostic and therapeutic framework for identifying and remediating individual and environmental challenges to accurate clinical decision making. © 2012, The Author. International Journal of Nursing Knowledge © 2012, NANDA International.

Origin of the main r-process elements

NASA Astrophysics Data System (ADS)

Otsuki, K.; Truran, J.; Wiescher, M.; Gorres, J.; Mathews, G.; Frekers, D.; Mengoni, A.; Bartlett, A.; Tostevin, J.

2006-07-01

The r-process is supposed to be a primary process which assembles heavy nuclei from a photo-dissociated nucleon gas. Hence, the reaction flow through light elements can be important as a constraint on the conditions for the r-process. We have studied the impact of di-neutron capture and the neutron-capture of light (Z<10) elements on r-process nucleosynthesis in three different environments: neutrino-driven winds in Type II supernovae; the prompt explosion of low mass supernovae; and neutron star mergers. Although the effect of di-neutron capture is not significant for the neutrino-driven wind model or low-mass supernovae, it becomes significant in the neutron-star merger model. The neutron-capture of light elements, which has been studied extensively for neutrino-driven wind models, also impacts the other two models. We show that it may be possible to identify the astrophysical site for the main r-process if the nuclear physics uncertainties in current r-process calculations could be reduced.

Model-Driven Engineering of Machine Executable Code

NASA Astrophysics Data System (ADS)

Eichberg, Michael; Monperrus, Martin; Kloppenburg, Sven; Mezini, Mira

Implementing static analyses of machine-level executable code is labor intensive and complex. We show how to leverage model-driven engineering to facilitate the design and implementation of programs doing static analyses. Further, we report on important lessons learned on the benefits and drawbacks while using the following technologies: using the Scala programming language as target of code generation, using XML-Schema to express a metamodel, and using XSLT to implement (a) transformations and (b) a lint like tool. Finally, we report on the use of Prolog for writing model transformations.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

Imaging plus X: multimodal models of neurodegenerative disease.

PubMed

Oxtoby, Neil P; Alexander, Daniel C

2017-08-01

This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns. Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression. The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

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

Zhou, Ping; Lv, Youbin; Wang, Hong

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation basedmore » robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.« less

Limitations of demand- and pressure-driven modeling for large deficient networks

NASA Astrophysics Data System (ADS)

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

2017-10-01

The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

NASA Astrophysics Data System (ADS)

Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

2016-09-01

We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

Modeling Particle Acceleration and Transport at a 2-D CME-Driven Shock

NASA Astrophysics Data System (ADS)

Hu, Junxiang; Li, Gang; Ao, Xianzhi; Zank, Gary P.; Verkhoglyadova, Olga

2017-11-01

We extend our earlier Particle Acceleration and Transport in the Heliosphere (PATH) model to study particle acceleration and transport at a coronal mass ejection (CME)-driven shock. We model the propagation of a CME-driven shock in the ecliptic plane using the ZEUS-3D code from 20 solar radii to 2 AU. As in the previous PATH model, the initiation of the CME-driven shock is simplified and modeled as a disturbance at the inner boundary. Different from the earlier PATH model, the disturbance is now longitudinally dependent. Particles are accelerated at the 2-D shock via the diffusive shock acceleration mechanism. The acceleration depends on both the parallel and perpendicular diffusion coefficients κ|| and κ⊥ and is therefore shock-obliquity dependent. Following the procedure used in Li, Shalchi, et al. (k href="#jgra53857-bib-0045"/>), we obtain the particle injection energy, the maximum energy, and the accelerated particle spectra at the shock front. Once accelerated, particles diffuse and convect in the shock complex. The diffusion and convection of these particles are treated using a refined 2-D shell model in an approach similar to Zank et al. (k href="#jgra53857-bib-0089"/>). When particles escape from the shock, they propagate along and across the interplanetary magnetic field. The propagation is modeled using a focused transport equation with the addition of perpendicular diffusion. We solve the transport equation using a backward stochastic differential equation method where adiabatic cooling, focusing, pitch angle scattering, and cross-field diffusion effects are all included. Time intensity profiles and instantaneous particle spectra as well as particle pitch angle distributions are shown for two example CME shocks.

Indicators of ecosystem function identify alternate states in the sagebrush steppe.

PubMed

Kachergis, Emily; Rocca, Monique E; Fernandez-Gimenez, Maria E

2011-10-01

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.

A Multi-Mode Shock Tube for Investigation of Blast-Induced Traumatic Brain Injury

PubMed Central

Reneer, Dexter V.; Hisel, Richard D.; Hoffman, Joshua M.; Kryscio, Richard J.; Lusk, Braden T.

2011-01-01

Abstract Blast-induced mild traumatic brain injury (bTBI) has become increasingly common in recent military conflicts. The mechanisms by which non-impact blast exposure results in bTBI are incompletely understood. Current small animal bTBI models predominantly utilize compressed air-driven membrane rupture as their blast wave source, while large animal models use chemical explosives. The pressure-time signature of each blast mode is unique, making it difficult to evaluate the contributions of the different components of the blast wave to bTBI when using a single blast source. We utilized a multi-mode shock tube, the McMillan blast device, capable of utilizing compressed air- and compressed helium-driven membrane rupture, and the explosives oxyhydrogen and cyclotrimethylenetrinitramine (RDX, the primary component of C-4 plastic explosives) as the driving source. At similar maximal blast overpressures, the positive pressure phase of compressed air-driven blasts was longer, and the positive impulse was greater, than those observed for shockwaves produced by other driving sources. Helium-driven shockwaves more closely resembled RDX blasts, but by displacing air created a hypoxic environment within the shock tube. Pressure-time traces from oxyhydrogen-driven shockwaves were very similar those produced by RDX, although they resulted in elevated carbon monoxide levels due to combustion of the polyethylene bag used to contain the gases within the shock tube prior to detonation. Rats exposed to compressed air-driven blasts had more pronounced vascular damage than those exposed to oxyhydrogen-driven blasts of the same peak overpressure, indicating that differences in blast wave characteristics other than peak overpressure may influence the extent of bTBI. Use of this multi-mode shock tube in small animal models will enable comparison of the extent of brain injury with the pressure-time signature produced using each blast mode, facilitating evaluation of the blast wave components contributing to bTBI. PMID:21083431

A multi-mode shock tube for investigation of blast-induced traumatic brain injury.

PubMed

Reneer, Dexter V; Hisel, Richard D; Hoffman, Joshua M; Kryscio, Richard J; Lusk, Braden T; Geddes, James W

2011-01-01

Blast-induced mild traumatic brain injury (bTBI) has become increasingly common in recent military conflicts. The mechanisms by which non-impact blast exposure results in bTBI are incompletely understood. Current small animal bTBI models predominantly utilize compressed air-driven membrane rupture as their blast wave source, while large animal models use chemical explosives. The pressure-time signature of each blast mode is unique, making it difficult to evaluate the contributions of the different components of the blast wave to bTBI when using a single blast source. We utilized a multi-mode shock tube, the McMillan blast device, capable of utilizing compressed air- and compressed helium-driven membrane rupture, and the explosives oxyhydrogen and cyclotrimethylenetrinitramine (RDX, the primary component of C-4 plastic explosives) as the driving source. At similar maximal blast overpressures, the positive pressure phase of compressed air-driven blasts was longer, and the positive impulse was greater, than those observed for shockwaves produced by other driving sources. Helium-driven shockwaves more closely resembled RDX blasts, but by displacing air created a hypoxic environment within the shock tube. Pressure-time traces from oxyhydrogen-driven shockwaves were very similar those produced by RDX, although they resulted in elevated carbon monoxide levels due to combustion of the polyethylene bag used to contain the gases within the shock tube prior to detonation. Rats exposed to compressed air-driven blasts had more pronounced vascular damage than those exposed to oxyhydrogen-driven blasts of the same peak overpressure, indicating that differences in blast wave characteristics other than peak overpressure may influence the extent of bTBI. Use of this multi-mode shock tube in small animal models will enable comparison of the extent of brain injury with the pressure-time signature produced using each blast mode, facilitating evaluation of the blast wave components contributing to bTBI.

Observations and simulations of microplastic marine debris in the ocean surface boundary layer

NASA Astrophysics Data System (ADS)

Kukulka, T.; Brunner, K.; Proskurowski, G. K.; Lavender Law, K. L.

2016-02-01

Motivated by observations of buoyant microplastic marine debris (MPMD) in the ocean surface boundary layer (OSBL), this study applies a large eddy simulation model and a parametric one-dimensional column model to examine vertical distributions of MPMD. MPMD is widely distributed in vast regions of the subtropical gyres and has emerged as a major open ocean pollutant whose distribution is subject to upper ocean turbulence. The models capture wind-driven turbulence, Langmuir turbulence (LT), and enhanced turbulent kinetic energy input due to breaking waves (BW). Model results are only consistent with MPMD observations if LT effects are included. Neither BW nor shear-driven turbulence is capable of deeply submerging MPMD, suggesting that the observed vertical MPMD distributions are a characteristic signature of wave-driven LT. Thus, this study demonstrates that LT substantially increases turbulent transport in the OSBL, resulting in deep submergence of buoyant tracers. The parametric model is applied to eleven years of observations in the North Atlantic and North Pacific subtropical gyres to show that surface measurements substantially underestimate MPMD concentrations by a factor of three to thirteen.

Modelling a demand driven biogas system for production of electricity at peak demand and for production of biomethane at other times.

PubMed

O'Shea, R; Wall, D; Murphy, J D

2016-09-01

Four feedstocks were assessed for use in a demand driven biogas system. Biomethane potential (BMP) assays were conducted for grass silage, food waste, Laminaria digitata and dairy cow slurry. Semi-continuous trials were undertaken for all feedstocks, assessing biogas and biomethane production. Three kinetic models of the semi-continuous trials were compared. A first order model most accurately correlated with gas production in the pulse fed semi-continuous system. This model was developed for production of electricity on demand, and biomethane upgrading. The model examined a theoretical grass silage digester that would produce 435kWe in a continuous fed system. Adaptation to demand driven biogas required 187min to produce sufficient methane to run a 2MWe combined heat and power (CHP) unit for 60min. The upgrading system was dispatched 71min following CHP shutdown. Of the biogas produced 21% was used in the CHP and 79% was used in the upgrading system. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining Domain-driven Design and Mashups for Service Development

NASA Astrophysics Data System (ADS)

Iglesias, Carlos A.; Fernández-Villamor, José Ignacio; Del Pozo, David; Garulli, Luca; García, Boni

This chapter presents the Romulus project approach to Service Development using Java-based web technologies. Romulus aims at improving productivity of service development by providing a tool-supported model to conceive Java-based web applications. This model follows a Domain Driven Design approach, which states that the primary focus of software projects should be the core domain and domain logic. Romulus proposes a tool-supported model, Roma Metaframework, that provides an abstraction layer on top of existing web frameworks and automates the application generation from the domain model. This metaframework follows an object centric approach, and complements Domain Driven Design by identifying the most common cross-cutting concerns (security, service, view, ...) of web applications. The metaframework uses annotations for enriching the domain model with these cross-cutting concerns, so-called aspects. In addition, the chapter presents the usage of mashup technology in the metaframework for service composition, using the web mashup editor MyCocktail. This approach is applied to a scenario of the Mobile Phone Service Portability case study for the development of a new service.

Swash saturation: an assessment of available models

NASA Astrophysics Data System (ADS)

Hughes, Michael G.; Baldock, Tom E.; Aagaard, Troels

2018-06-01

An extensive previously published (Hughes et al. Mar Geol 355, 88-97, 2014) field data set representing the full range of micro-tidal beach states (reflective, intermediate and dissipative) is used to investigate swash saturation. Two models that predict the behavior of saturated swash are tested: one driven by standing waves and the other driven by bores. Despite being based on entirely different premises, they predict similar trends in the limiting (saturated) swash height with respect to dependency on frequency and beach gradient. For a given frequency and beach gradient, however, the bore-driven model predicts a larger saturated swash height by a factor 2.5. Both models broadly predict the general behavior of swash saturation evident in the data, but neither model is accurate in detail. While swash saturation in the short-wave frequency band is common on some beach types, it does not always occur across all beach types. Further work is required on wave reflection/breaking and the role of wave-wave and wave-swash interactions to determine limiting swash heights on natural beaches.

Cathode fall model and current-voltage characteristics of field emission driven direct current microplasmas

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

Venkattraman, Ayyaswamy

2013-11-15

The post-breakdown characteristics of field emission driven microplasma are studied theoretically and numerically. A cathode fall model assuming a linearly varying electric field is used to obtain equations governing the operation of steady state field emission driven microplasmas. The results obtained from the model by solving these equations are compared with particle-in-cell with Monte Carlo collisions simulation results for parameters including the plasma potential, cathode fall thickness, ion number density in the cathode fall, and current density vs voltage curves. The model shows good overall agreement with the simulations but results in slightly overpredicted values for the plasma potential andmore » the cathode fall thickness attributed to the assumed electric field profile. The current density vs voltage curves obtained show an arc region characterized by negative slope as well as an abnormal glow discharge characterized by a positive slope in gaps as small as 10 μm operating at atmospheric pressure. The model also retrieves the traditional macroscale current vs voltage theory in the absence of field emission.« less

Nonlinear Tracking Control of a Conductive Supercoiled Polymer Actuator.

PubMed

Luong, Tuan Anh; Cho, Kyeong Ho; Song, Min Geun; Koo, Ja Choon; Choi, Hyouk Ryeol; Moon, Hyungpil

2018-04-01

Artificial muscle actuators made from commercial nylon fishing lines have been recently introduced and shown as a new type of actuator with high performance. However, the actuators also exhibit significant nonlinearities, which make them difficult to control, especially in precise trajectory-tracking applications. In this article, we present a nonlinear mathematical model of a conductive supercoiled polymer (SCP) actuator driven by Joule heating for model-based feedback controls. Our efforts include modeling of the hysteresis behavior of the actuator. Based on nonlinear modeling, we design a sliding mode controller for SCP actuator-driven manipulators. The system with proposed control law is proven to be asymptotically stable using the Lyapunov theory. The control performance of the proposed method is evaluated experimentally and compared with that of a proportional-integral-derivative (PID) controller through one-degree-of-freedom SCP actuator-driven manipulators. Experimental results show that the proposed controller's performance is superior to that of a PID controller, such as the tracking errors are nearly 10 times smaller compared with those of a PID controller, and it is more robust to external disturbances such as sensor noise and actuator modeling error.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Maternal depressive symptoms, dysfunctional cognitions, and infant night waking: the role of maternal nighttime behavior.

PubMed

Teti, Douglas M; Crosby, Brian

2012-01-01

Mechanisms were examined to clarify relations between maternal depressive symptoms, dysfunctional cognitions, and infant night waking among 45 infants (1-24 months) and their mothers. A mother-driven mediational model was tested in which maternal depressive symptoms and dysfunctional cognitions about infant sleep predicted infant night waking via their impact on mothers' bedtime and nighttime behavior with infants (from video). Two infant-driven mediational models were also examined, in which infant night waking predicted maternal depressive symptoms, or dysfunctional cognitions, via their impact on nighttime maternal behavior. Stronger support for the mother-driven model was obtained, which was further supported by qualitative observations from video-recordings. This study provides important insights about maternal depression's effects on nighttime parenting, and how such parenting affects infant sleep. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Stellar winds driven by Alfven waves

NASA Technical Reports Server (NTRS)

Belcher, J. W.; Olbert, S.

1973-01-01

Models of stellar winds were considered in which the dynamic expansion of a corona is driven by Alfven waves propagating outward along radial magnetic field lines. In the presence of Alfven waves, a coronal expansion can exist for a broad range of reference conditions which would, in the absence of waves, lead to static configurations. Wind models in which the acceleration mechanism is due to Alfven waves alone and exhibit lower mass fluxes and higher energies per particle are compared to wind models in which the acceleration is due to thermal processes. For example, winds driven by Alfven waves exhibit streaming velocities at infinity which may vary between the escape velocity at the coronal base and the geometrical mean of the escape velocity and the speed of light. Upper and lower limits were derived for the allowed energy fluxes and mass fluxes associated with these winds.

Active machine learning-driven experimentation to determine compound effects on protein patterns.

PubMed

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Generalized two-temperature model for coupled phonon-magnon diffusion.

PubMed

Liao, Bolin; Zhou, Jiawei; Chen, Gang

2014-07-11

We generalize the two-temperature model [Sanders and Walton, Phys. Rev. B 15, 1489 (1977)] for coupled phonon-magnon diffusion to include the effect of the concurrent magnetization flow, with a particular emphasis on the thermal consequence of the magnon flow driven by a nonuniform magnetic field. Working within the framework of the Boltzmann transport equation, we derive the constitutive equations for coupled phonon-magnon transport driven by gradients of both temperature and external magnetic fields, and the corresponding conservation laws. Our equations reduce to the original Sanders-Walton two-temperature model under a uniform external field, but predict a new magnon cooling effect driven by a nonuniform magnetic field in a homogeneous single-domain ferromagnet. We estimate the magnitude of the cooling effect in an yttrium iron garnet, and show it is within current experimental reach. With properly optimized materials, the predicted cooling effect can potentially supplement the conventional magnetocaloric effect in cryogenic applications in the future.

Modeling Dynamic Evolution of Online Friendship Network

NASA Astrophysics Data System (ADS)

Wu, Lian-Ren; Yan, Qiang

2012-10-01

In this paper, we study the dynamic evolution of friendship network in SNS (Social Networking Site). Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community, but also on the friendship network generated by those friends. In addition, we propose a model which is based on two processes: first, connecting nearest neighbors; second, strength driven attachment mechanism. The model reflects two facts: first, in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor; second, new nodes connect more likely to nodes which have larger weights and interactions, a phenomenon called strength driven attachment (also called weight driven attachment). From the simulation results, we find that degree distribution P(k), strength distribution P(s), and degree-strength correlation are all consistent with empirical data.

Simulation of high-energy radiation belt electron fluxes using NARMAX-VERB coupled codes

PubMed Central

Pakhotin, I P; Drozdov, A Y; Shprits, Y Y; Boynton, R J; Subbotin, D A; Balikhin, M A

2014-01-01

This study presents a fusion of data-driven and physics-driven methodologies of energetic electron flux forecasting in the outer radiation belt. Data-driven NARMAX (Nonlinear AutoRegressive Moving Averages with eXogenous inputs) model predictions for geosynchronous orbit fluxes have been used as an outer boundary condition to drive the physics-based Versatile Electron Radiation Belt (VERB) code, to simulate energetic electron fluxes in the outer radiation belt environment. The coupled system has been tested for three extended time periods totalling several weeks of observations. The time periods involved periods of quiet, moderate, and strong geomagnetic activity and captured a range of dynamics typical of the radiation belts. The model has successfully simulated energetic electron fluxes for various magnetospheric conditions. Physical mechanisms that may be responsible for the discrepancies between the model results and observations are discussed. PMID:26167432

Inter-species activity correlations reveal functional correspondences between monkey and human brain areas

PubMed Central

Mantini, Dante; Hasson, Uri; Betti, Viviana; Perrucci, Mauro G.; Romani, Gian Luca; Corbetta, Maurizio; Orban, Guy A.; Vanduffel, Wim

2012-01-01

Evolution-driven functional changes in the primate brain are typically assessed by aligning monkey and human activation maps using cortical surface expansion models. These models use putative homologous areas as registration landmarks, assuming they are functionally correspondent. In cases where functional changes have occurred in an area, this assumption prohibits to reveal whether other areas may have assumed lost functions. Here we describe a method to examine functional correspondences across species. Without making spatial assumptions, we assess similarities in sensory-driven functional magnetic resonance imaging responses between monkey (Macaca mulatta) and human brain areas by means of temporal correlation. Using natural vision data, we reveal regions for which functional processing has shifted to topologically divergent locations during evolution. We conclude that substantial evolution-driven functional reorganizations have occurred, not always consistent with cortical expansion processes. This novel framework for evaluating changes in functional architecture is crucial to building more accurate evolutionary models. PMID:22306809

Biomimetics and the case of the remarkable ragworms.

PubMed

Hesselberg, Thomas

2007-08-01

Biomimetics is a rapidly growing field both as an academic and as an applied discipline. This paper gives a short introduction to the current status of the discipline before it describes three approaches to biomimetics: the mechanism-driven, which is based on the study of a specific mechanism; the focused organism-driven, which is based on the study of one function in a model organism; and the integrative organism-driven approach, where multiple functions of a model organism provide inspiration. The first two are established approaches and include many modern studies and the famous biomimetic discoveries of Velcro and the Lotus-Effect, whereas the last approach is not yet well recognized. The advantages of the integrative organism-driven approach are discussed using the ragworms as a case study. A morphological and locomotory study of these marine polychaetes reveals their biomimetic potential, which includes using their ability to move in slippery substrates as inspiration for novel endoscopes, using their compound setae as models for passive friction structures and using their three gaits, slow crawling, fast crawling, and swimming as well as their rapid burrowing technique to provide inspiration for the design of displacement pumps and multifunctional robots.

Edge-driven microplate kinematics

USGS Publications Warehouse

Schouten, Hans; Klitgord, Kim D.; Gallo, David G.

1993-01-01

It is known from plate tectonic reconstructions that oceanic microplates undergo rapid rotation about a vertical axis and that the instantaneous rotation axes describing the microplate's motion relative to the bounding major plates are frequently located close to its margins with those plates, close to the tips of propagating rifts. We propose a class of edge-driven block models to illustrate how slip across the microplate margins, block rotation, and propagation of rifting may be related to the relative motion of the plates on either side. An important feature of these edge-driven models is that the instantaneous rotation axes are always located on the margins between block and two bounding plates. According to those models the pseudofaults or traces of disrupted seafloor resulting from the propagation of rifting between microplate and major plates may be used independently to approximately trace the continuous kinematic evolution of the microplate back in time. Pseudofault geometries and matching rotations of the Easter microplate show that for most of its 5 m.y. history, block rotation could be driven by the drag of the Nazca and Pacific plates on the microplate's edges rather than by a shear flow of mantle underneath.

Biomimetics and the case of the remarkable ragworms

NASA Astrophysics Data System (ADS)

Hesselberg, Thomas

2007-08-01

Biomimetics is a rapidly growing field both as an academic and as an applied discipline. This paper gives a short introduction to the current status of the discipline before it describes three approaches to biomimetics: the mechanism-driven, which is based on the study of a specific mechanism; the focused organism-driven, which is based on the study of one function in a model organism; and the integrative organism-driven approach, where multiple functions of a model organism provide inspiration. The first two are established approaches and include many modern studies and the famous biomimetic discoveries of Velcro and the Lotus-Effect, whereas the last approach is not yet well recognized. The advantages of the integrative organism-driven approach are discussed using the ragworms as a case study. A morphological and locomotory study of these marine polychaetes reveals their biomimetic potential, which includes using their ability to move in slippery substrates as inspiration for novel endoscopes, using their compound setae as models for passive friction structures and using their three gaits, slow crawling, fast crawling, and swimming as well as their rapid burrowing technique to provide inspiration for the design of displacement pumps and multifunctional robots.

Sensor Web Dynamic Measurement Techniques and Adaptive Observing Strategies

NASA Technical Reports Server (NTRS)

Talabac, Stephen J.

2004-01-01

Sensor Web observing systems may have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable environmental features and events. This improvement will come about by integrating novel data collection techniques, new or improved instruments, emerging communications technologies and protocols, sensor mark-up languages, and interoperable planning and scheduling systems. In contrast to today's observing systems, "event-driven" sensor webs will synthesize real- or near-real time measurements and information from other platforms and then react by reconfiguring the platforms and instruments to invoke new measurement modes and adaptive observation strategies. Similarly, "model-driven" sensor webs will utilize environmental prediction models to initiate targeted sensor measurements or to use a new observing strategy. The sensor web concept contrasts with today's data collection techniques and observing system operations concepts where independent measurements are made by remote sensing and in situ platforms that do not share, and therefore cannot act upon, potentially useful complementary sensor measurement data and platform state information. This presentation describes NASA's view of event-driven and model-driven Sensor Webs and highlights several research and development activities at the Goddard Space Flight Center.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PubMed Central

Hagmann, Patric; Deco, Gustavo

2015-01-01

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information. PMID:26317432

Microscopic origin and macroscopic implications of lane formation in mixtures of oppositely driven particles

NASA Astrophysics Data System (ADS)

Klymko, Katherine; Geissler, Phillip L.; Whitelam, Stephen

2016-08-01

Colloidal particles of two types, driven in opposite directions, can segregate into lanes [Vissers et al., Soft Matter 7, 2352 (2011), 10.1039/c0sm01343a]. This phenomenon can be reproduced by two-dimensional Brownian dynamics simulations of model particles [Dzubiella et al., Phys. Rev. E 65, 021402 (2002), 10.1103/PhysRevE.65.021402]. Here we use computer simulation to assess the generality of lane formation with respect to variation of particle type and dynamical protocol. We find that laning results from rectification of diffusion on the scale of a particle diameter: oppositely driven particles must, in the time taken to encounter each other in the direction of the drive, diffuse in the perpendicular direction by about one particle diameter. This geometric constraint implies that the diffusion constant of a particle, in the presence of those of the opposite type, grows approximately linearly with the Péclet number, a prediction confirmed by our numerics over a range of model parameters. Such environment-dependent diffusion is statistically similar to an effective interparticle attraction; consistent with this observation, we find that oppositely driven nonattractive colloids display features characteristic of the simplest model system possessing both interparticle attractions and persistent motion, the driven Ising lattice gas [Katz, Leibowitz, and Spohn, J. Stat. Phys. 34, 497 (1984), 10.1007/BF01018556]. These features include long-ranged correlations in the disordered regime, a critical regime characterized by a change in slope of the particle current with the Péclet number, and fluctuations that grow with system size. By analogy, we suggest that lane formation in the driven colloid system is a phase transition in the macroscopic limit, but that macroscopic phase separation would not occur in finite time upon starting from disordered initial conditions.

Chronic paroxetine treatment prevents disruption of methamphetamine-sensitive circadian oscillator in a transgenic mouse model of Huntington's disease.

PubMed

Ouk, Koliane; Aungier, Juliet; Cuesta, Marc; Morton, A Jennifer

2018-03-15

Circadian abnormalities seen in Huntington's disease (HD) patients are recapitulated in several HD transgenic mouse models. In mice, alongside the master clock located in the suprachiasmatic nucleus (SCN), two other oscillators may influence circadian behaviour. These are the food-entrainable oscillator (FEO) and the methamphetamine-sensitive circadian oscillator (MASCO). SCN- and MASCO- (but not FEO-) driven rhythms are progressively disrupted in the R6/2 mouse model of HD. MASCO-driven rhythms are induced by chronic treatment with low dose of methamphetamine and characterised by an increase in period length to greater than 24 h. Interestingly, the rhythms mediated by MASCO deteriorate earlier than those mediated by the SCN in R6/2 mice. Here, we used a pharmacological strategy to investigate the mechanisms underlying MASCO-driven rhythms in WT mice. In contrast to methamphetamine, chronic cocaine was ineffective in generating a MASCO-like component of activity although it markedly increased locomotion. Furthermore, neither blocking dopamine (DA) receptors (with the DA antagonist haloperidol) nor blocking neurotransmission by inhibiting the activity of vesicular monoamine transporter (with reserpine) prevented the expression of the MASCO-driven rhythms, although both treatments downregulated locomotor activity. Interestingly, chronic treatment with paroxetine, a serotonin-specific reuptake inhibitor commonly used as antidepressant in HD, was able to restore the expression of MASCO-driven rhythms in R6/2 mice. Thus, MASCO-driven rhythms appear to be mediated by both serotoninergic and dopaminergic systems. This supports the idea that abnormalities in MASCO output may contribute to both the HD circadian and psychiatric phenotype. Copyright © 2017 Elsevier Ltd. All rights reserved.

Non-daily pre-exposure prophylaxis for HIV prevention

PubMed Central

Anderson, Peter L.; García-Lerma, J. Gerardo; Heneine, Walid

2015-01-01

Purpose of review To discuss non-daily pre-exposure prophylaxis (PrEP) modalities that may provide advantages compared with daily PrEP in cost and cumulative toxicity, but may have lower adherence forgiveness. Recent Findings Animal models have informed our understanding of early viral transmission events, which help guide event-driven PrEP dosing strategies. These models indicate early establishment of viral replication in rectal or cervicovaginal tissues, so event-driven PrEP should rapidly deliver high mucosal drug concentrations within hours of the potential exposure event. Macaque models have demonstrated the high biological efficacy for event-driven dosing of oral tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) against both vaginal and rectal virus transmission. In humans, the IPERGAY study demonstrated 86% efficacy for event-driven oral TDF/FTC dosing among men who have sex with men (MSM), while no similar efficacy data are available on women or heterosexual men. The HPTN 067 study showed that certain MSM populations adhere well to non-daily PrEP while other populations of women adhere more poorly to non-daily versus daily regimens. Pharmacokinetic studies following oral TDF/FTC dosing in humans, indicate that TFV-diphosphate (the active form of TFV) accumulates to higher concentrations in rectal versus cervicovaginal tissue but non-adherence in trials complicates the interpretation of differential mucosal drug concentrations. Summary Event-driven dosing for TFV-based PrEP has promise for HIV prevention in MSM. Future research of event-driven PrEP in women and heterosexual men should be guided by a better understanding of the importance of mucosal drug concentrations for PrEP efficacy and its sensitivity to adherence. PMID:26633641

A new model of strabismic amblyopia: Loss of spatial acuity due to increased temporal dispersion of geniculate X-cell afferents on to cortical neurons.

PubMed

Crewther, D P; Crewther, S G

2015-09-01

Although the neural locus of strabismic amblyopia has been shown to lie at the first site of binocular integration, first in cat and then in primate, an adequate mechanism is still lacking. Here we hypothesise that increased temporal dispersion of LGN X-cell afferents driven by the deviating eye onto single cortical neurons may provide a neural mechanism for strabismic amblyopia. This idea was investigated via single cell extracellular recordings of 93 X and 50 Y type LGN neurons from strabismic and normal cats. Both X and Y neurons driven by the non-deviating eye showed shorter latencies than those driven by either the strabismic or normal eyes. Also the mean latency difference between X and Y neurons was much greater for the strabismic cells compared with the other two groups. The incidence of lagged X-cells driven by the deviating eye of the strabismic cats was higher than that of LGN X-cells from normal animals. Remarkably, none of the cells recorded from the laminae driven by the non-deviating eye were of the lagged class. A simple computational model was constructed in which a mixture of lagged and non-lagged afferents converge on to single cortical neurons. Model cut-off spatial frequencies to a moving grating stimulus were sensitive to the temporal dispersion of the geniculate afferents. Thus strabismic amblyopia could be viewed as a lack of developmental tuning of geniculate lags for neurons driven by the amblyopic eye. Monocular control of fixation by the non-deviating eye is associated with reduced incidence of lagged neurons, suggesting that in normal vision, lagged neurons might play a role in maintaining binocular connections for cortical neurons. Copyright © 2014 Elsevier Ltd. All rights reserved.

Measuring Energy Scaling of Laser Driven Magnetic Fields

NASA Astrophysics Data System (ADS)

Williams, Jackson; Goyon, Clement; Mariscal, Derek; Pollock, Brad; Patankar, Siddharth; Moody, John

2016-10-01

Laser-driven magnetic fields are of interest in particle confinement, fast ignition, and ICF platforms as an alternative to pulsed power systems to achieve many times higher fields. A comprehensive model describing the mechanism responsible for creating and maintaining magnetic fields from laser-driven coils has not yet been established. Understanding the scaling of key experimental parameters such as spatial and temporal uniformity and duration are necessary to implement coil targets in practical applications yet these measurements prove difficult due to the highly transient nature of the fields. We report on direct voltage measurements of laser-driven coil targets in which the laser energy spans more than four orders of magnitude. Results suggest that at low energies, laser-driven coils can be modeled as an electric circuit; however, at higher energies plasma effects dominate and a simple circuit treatment is insufficient to describe all observed phenomenon. The favorable scaling with laser power and pulse duration, observed in the present study and others at kilojoule energies, has positive implications for sustained, large magnetic fields for applications on the NIF. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Effect of Schmidt number on mass transfer across a sheared gas-liquid interface in a wind-driven turbulence.

PubMed

Takagaki, Naohisa; Kurose, Ryoichi; Kimura, Atsushi; Komori, Satoru

2016-11-14

The mass transfer across a sheared gas-liquid interface strongly depends on the Schmidt number. Here we investigate the relationship between mass transfer coefficient on the liquid side, k L , and Schmidt number, Sc, in the wide range of 0.7 ≤ Sc ≤ 1000. We apply a three-dimensional semi direct numerical simulation (SEMI-DNS), in which the mass transfer is solved based on an approximated deconvolution model (ADM) scheme, to wind-driven turbulence with mass transfer across a sheared wind-driven wavy gas-liquid interface. In order to capture the deforming gas-liquid interface, an arbitrary Lagrangian-Eulerian (ALE) method is employed. Our results show that similar to the case for flat gas-liquid interfaces, k L for the wind-driven wavy gas-liquid interface is generally proportional to Sc -0.5 , and can be roughly estimated by the surface divergence model. This trend is endorsed by the fact that the mass transfer across the gas-liquid interface is controlled mainly by streamwise vortices on the liquid side even for the wind-driven turbulence under the conditions of low wind velocities without wave breaking.

Effect of Schmidt number on mass transfer across a sheared gas-liquid interface in a wind-driven turbulence

PubMed Central

Takagaki, Naohisa; Kurose, Ryoichi; Kimura, Atsushi; Komori, Satoru

2016-01-01

The mass transfer across a sheared gas-liquid interface strongly depends on the Schmidt number. Here we investigate the relationship between mass transfer coefficient on the liquid side, kL, and Schmidt number, Sc, in the wide range of 0.7 ≤ Sc ≤ 1000. We apply a three-dimensional semi direct numerical simulation (SEMI-DNS), in which the mass transfer is solved based on an approximated deconvolution model (ADM) scheme, to wind-driven turbulence with mass transfer across a sheared wind-driven wavy gas-liquid interface. In order to capture the deforming gas-liquid interface, an arbitrary Lagrangian-Eulerian (ALE) method is employed. Our results show that similar to the case for flat gas-liquid interfaces, kL for the wind-driven wavy gas-liquid interface is generally proportional to Sc−0.5, and can be roughly estimated by the surface divergence model. This trend is endorsed by the fact that the mass transfer across the gas-liquid interface is controlled mainly by streamwise vortices on the liquid side even for the wind-driven turbulence under the conditions of low wind velocities without wave breaking. PMID:27841325

NORDA’s Pattern Analysis Laboratory: Current Contributions to Naval Mapping, Charting, and Geodesy

DTIC Science & Technology

1989-04-01

magnetic observatories (McLeod, 1988). Using system integrates a suite of sensors and control devices the PAL’s VAX 11/780, spherical harmonic models to...DJAO:[FPS]*.OLB 5. Miscellaneous Utilities CALENDAR (NORDA events) 780 $ CALENDAR (menu-driven) DIALER modem controller 780 $ R AUTO DIAL:DIALER DTC...Utilities CALENDAR (NORDA events) 780 CALENDAR (menu-driven) DIALER modem controller 780 $ R AUTO DIAL:DIALER DTC Desk Top Calendar 780 $ DTC (menu-driven

Nova-driven winds in globular clusters

NASA Technical Reports Server (NTRS)

Scott, E. H.; Durisen, R. H.

1978-01-01

Recent sensitive searches for H-alpha emission from ionized intracluster gas in globular clusters have set upper limits that conflict with theoretical predictions. It is suggested that nova outbursts heat the gas, producing winds that resolve this discrepancy. The incidence of novae in globular clusters, the conversion of kinetic energy of the nova shell to thermal energy of the intracluster gas, and the characteristics of the resultant winds are discussed. Calculated emission from the nova-driven models does not conflict with any observations to date. Some suggestions are made concerning the most promising approaches for future detection of intracluster gas on the basis of these models. The possible relationship of nova-driven winds to globular cluster X-ray sources is also considered.

Aspects of the BPRIM Language for Risk Driven Process Engineering

NASA Astrophysics Data System (ADS)

Sienou, Amadou; Lamine, Elyes; Pingaud, Hervé; Karduck, Achim

Nowadays organizations are exposed to frequent changes in business environment requiring continuous alignment of business processes on business strategies. This agility requires methods promoted in enterprise engineering approaches. Risk consideration in enterprise engineering is getting important since the business environment is becoming more and more competitive and unpredictable. Business processes are subject to the same quality requirements as material and human resources. Thus, process management is supposed to tackle value creation challenges but also the ones related to value preservation. Our research considers risk driven business process design as an integral part of enterprise engineering. A graphical modelling language for risk driven business process engineering was introduced in former research. This paper extends the language and handles questions related to modelling risk in organisational context.

Some Recent Developments in Turbulence Closure Modeling

NASA Astrophysics Data System (ADS)

Durbin, Paul A.

2018-01-01

Turbulence closure models are central to a good deal of applied computational fluid dynamical analysis. Closure modeling endures as a productive area of research. This review covers recent developments in elliptic relaxation and elliptic blending models, unified rotation and curvature corrections, transition prediction, hybrid simulation, and data-driven methods. The focus is on closure models in which transport equations are solved for scalar variables, such as the turbulent kinetic energy, a timescale, or a measure of anisotropy. Algebraic constitutive representations are reviewed for their role in relating scalar closures to the Reynolds stress tensor. Seamless and nonzonal methods, which invoke a single closure model, are reviewed, especially detached eddy simulation (DES) and adaptive DES. Other topics surveyed include data-driven modeling and intermittency and laminar fluctuation models for transition prediction. The review concludes with an outlook.

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Evaluating the utility of dynamical downscaling in agricultural impacts projections

PubMed Central

Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.

2014-01-01

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455

The application of data mining and cloud computing techniques in data-driven models for structural health monitoring

NASA Astrophysics Data System (ADS)

Khazaeli, S.; Ravandi, A. G.; Banerji, S.; Bagchi, A.

2016-04-01

Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.

Combining Load and Motor Encoders to Compensate Nonlinear Disturbances for High Precision Tracking Control of Gear-Driven Gimbal

PubMed Central

Tang, Tao; Chen, Sisi; Huang, Xuanlin; Yang, Tao; Qi, Bo

2018-01-01

High-performance position control can be improved by the compensation of disturbances for a gear-driven control system. This paper presents a mode-free disturbance observer (DOB) based on sensor-fusion to reduce some errors related disturbances for a gear-driven gimbal. This DOB uses the rate deviation to detect disturbances for implementation of a high-gain compensator. In comparison with the angular position signal the rate deviation between load and motor can exhibits the disturbances exiting in the gear-driven gimbal quickly. Due to high bandwidth of the motor rate closed loop, the inverse model of the plant is not necessary to implement DOB. Besides, this DOB requires neither complex modeling of plant nor the use of additive sensors. Without rate sensors providing angular rate, the rate deviation is easily detected by encoders mounted on the side of motor and load, respectively. Extensive experiments are provided to demonstrate the benefits of the proposed algorithm. PMID:29498643

Combining Load and Motor Encoders to Compensate Nonlinear Disturbances for High Precision Tracking Control of Gear-Driven Gimbal.

PubMed

Tang, Tao; Chen, Sisi; Huang, Xuanlin; Yang, Tao; Qi, Bo

2018-03-02

High-performance position control can be improved by the compensation of disturbances for a gear-driven control system. This paper presents a mode-free disturbance observer (DOB) based on sensor-fusion to reduce some errors related disturbances for a gear-driven gimbal. This DOB uses the rate deviation to detect disturbances for implementation of a high-gain compensator. In comparison with the angular position signal the rate deviation between load and motor can exhibits the disturbances exiting in the gear-driven gimbal quickly. Due to high bandwidth of the motor rate closed loop, the inverse model of the plant is not necessary to implement DOB. Besides, this DOB requires neither complex modeling of plant nor the use of additive sensors. Without rate sensors providing angular rate, the rate deviation is easily detected by encoders mounted on the side of motor and load, respectively. Extensive experiments are provided to demonstrate the benefits of the proposed algorithm.

[Mandibular-driven simultaneous maxillo-mandibular distraction for hemifacial microsomia with rapid prototyping technology].

PubMed

Gao, Quan-Wen; Song, Hui-Feng; Xu, Ming-Huo; Liu, Chun-Ming; Chai, Jia-Ke

2013-11-01

To explore the clinical application of mandibular-driven simultaneous maxillo-mandihular distraction to correct hemifacial microsomia with rapid prototyping technology. The patient' s skull resin model was manufactured with rapid prototyping technology. The osteotomy was designed on skull resin model. According to the preoperative design, the patients underwent Le Fort I osteotomy and mandibular ramus osteotomy. The internal mandible distractor was embedded onto the osteotomy position. The occlusal titanium pin was implanted. Distraction were carried out by mandibular-driven simultaneous maxillo-mandihular distraction 5 days after operation. The distraction in five patients was complete as designed. No infection and dysosteogenesis happened. The longest distance of distraction was 28 mm, and the shortest distance was 16 mm. The facial asymmetry deformity was significantly improved at the end of distraction. The ocelusal plane of patients obviously improved. Rapid prototyping technology is helpful to design precisely osteotomy before operation. Mandibular-driven simultaneous maxillo-mandibular distraction can correct hemifacial microsomia. It is worth to clinical application.

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

PubMed Central

Pan, Shaoming; Chong, Yanwen; Zhang, Hang; Tan, Xicheng

2017-01-01

A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client’s requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users’ access behaviors and all tiles’ relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users’ access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods. PMID:28085937

A model of nitrous oxide evolution from soil driven by rainfall events. I - Model structure and sensitivity. II - Model applications

NASA Technical Reports Server (NTRS)

Changsheng, LI; Frolking, Steve; Frolking, Tod A.

1992-01-01

Simulations of N2O and CO2 emissions from soils were conducted with a rain-event driven, process-oriented model (DNDC) of nitrogen and carbon cycling processes in soils. The magnitude and trends of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of these emissions from the range of soil types examined demonstrates that the DNDC will be a useful tool for the study of linkages among climate, soil-atmosphere interactions, land use, and trace gas fluxes.

Interest-Driven Model for Human Dynamics

NASA Astrophysics Data System (ADS)

Shang, Ming-Sheng; Chen, Guan-Xiong; Dai, Shuang-Xing; Wang, Bing-Hong; Zhou, Tao

2010-04-01

Empirical observations indicate that the interevent time distribution of human actions exhibits heavy-tailed features. The queuing model based on task priorities is to some extent successful in explaining the origin of such heavy tails, however, it cannot explain all the temporal statistics of human behavior especially for the daily entertainments. We propose an interest-driven model, which can reproduce the power-law distribution of interevent time. The exponent can be analytically obtained and is in good accordance with the simulations. This model well explains the observed relationship between activities and power-law exponents, as reported recently for web-based behavior and the instant message communications.

Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator.

PubMed

Huang, Weidong; Li, Kun; Wang, Gan; Wang, Yingzhe

2013-11-01

In this article, we present a newly designed inverse umbrella surface aerator, and tested its performance in driving flow of an oxidation ditch. Results show that it has a better performance in driving the oxidation ditch than the original one with higher average velocity and more uniform flow field. We also present a computational fluid dynamics model for predicting the flow field in an oxidation ditch driven by a surface aerator. The improved momentum source term approach to simulate the flow field of the oxidation ditch driven by an inverse umbrella surface aerator was developed and validated through experiments. Four kinds of turbulent models were investigated with the approach, including the standard k - ɛ model, RNG k - ɛ model, realizable k - ɛ model, and Reynolds stress model, and the predicted data were compared with those calculated with the multiple rotating reference frame approach (MRF) and sliding mesh approach (SM). Results of the momentum source term approach are in good agreement with the experimental data, and its prediction accuracy is better than MRF, close to SM. It is also found that the momentum source term approach has lower computational expenses, is simpler to preprocess, and is easier to use.

A phenomenological continuum model for force-driven nano-channel liquid flows

NASA Astrophysics Data System (ADS)

Ghorbanian, Jafar; Celebi, Alper T.; Beskok, Ali

2016-11-01

A phenomenological continuum model is developed using systematic molecular dynamics (MD) simulations of force-driven liquid argon flows confined in gold nano-channels at a fixed thermodynamic state. Well known density layering near the walls leads to the definition of an effective channel height and a density deficit parameter. While the former defines the slip-plane, the latter parameter relates channel averaged density with the desired thermodynamic state value. Definitions of these new parameters require a single MD simulation performed for a specific liquid-solid pair at the desired thermodynamic state and used for calibration of model parameters. Combined with our observations of constant slip-length and kinematic viscosity, the model accurately predicts the velocity distribution and volumetric and mass flow rates for force-driven liquid flows in different height nano-channels. Model is verified for liquid argon flow at distinct thermodynamic states and using various argon-gold interaction strengths. Further verification is performed for water flow in silica and gold nano-channels, exhibiting slip lengths of 1.2 nm and 15.5 nm, respectively. Excellent agreements between the model and the MD simulations are reported for channel heights as small as 3 nm for various liquid-solid pairs.

Perspectives on continuum flow models for force-driven nano-channel liquid flows

NASA Astrophysics Data System (ADS)

Beskok, Ali; Ghorbanian, Jafar; Celebi, Alper

2017-11-01

A phenomenological continuum model is developed using systematic molecular dynamics (MD) simulations of force-driven liquid argon flows confined in gold nano-channels at a fixed thermodynamic state. Well known density layering near the walls leads to the definition of an effective channel height and a density deficit parameter. While the former defines the slip-plane, the latter parameter relates channel averaged density with the desired thermodynamic state value. Definitions of these new parameters require a single MD simulation performed for a specific liquid-solid pair at the desired thermodynamic state and used for calibration of model parameters. Combined with our observations of constant slip-length and kinematic viscosity, the model accurately predicts the velocity distribution and volumetric and mass flow rates for force-driven liquid flows in different height nano-channels. Model is verified for liquid argon flow at distinct thermodynamic states and using various argon-gold interaction strengths. Further verification is performed for water flow in silica and gold nano-channels, exhibiting slip lengths of 1.2 nm and 15.5 nm, respectively. Excellent agreements between the model and the MD simulations are reported for channel heights as small as 3 nm for various liquid-solid pairs.

Just-in-time Database-Driven Web Applications

PubMed Central

2003-01-01

"Just-in-time" database-driven Web applications are inexpensive, quickly-developed software that can be put to many uses within a health care organization. Database-driven Web applications garnered 73873 hits on our system-wide intranet in 2002. They enabled collaboration and communication via user-friendly Web browser-based interfaces for both mission-critical and patient-care-critical functions. Nineteen database-driven Web applications were developed. The application categories that comprised 80% of the hits were results reporting (27%), graduate medical education (26%), research (20%), and bed availability (8%). The mean number of hits per application was 3888 (SD = 5598; range, 14-19879). A model is described for just-in-time database-driven Web application development and an example given with a popular HTML editor and database program. PMID:14517109

Demand-driven care and hospital choice. Dutch health policy toward demand-driven care: results from a survey into hospital choice.

PubMed

Lako, Christiaan J; Rosenau, Pauline

2009-03-01

In the Netherlands, current policy opinion emphasizes demand-driven health care. Central to this model is the view, advocated by some Dutch health policy makers, that patients should be encouraged to be aware of and make use of health quality and health outcomes information in making personal health care provider choices. The success of the new health care system in the Netherlands is premised on this being the case. After a literature review and description of the new Dutch health care system, the adequacy of this demand-driven health policy is tested. The data from a July 2005, self-administered questionnaire survey of 409 patients (response rate of 94%) as to how they choose a hospital are presented. Results indicate that most patients did not choose by actively employing available quality and outcome information. They were, rather, referred by their general practitioner. Hospital choice is highly related to the importance a patient attaches to his or her physician's opinion about a hospital. Some patients indicated that their hospital choice was affected by the reputation of the hospital, by the distance they lived from the hospital, etc. but physician's advice was, by far, the most important factor. Policy consequences are important; the assumptions underlying the demand-driven model of patient health provider choice are inadequate to explain the pattern of observed responses. An alternative, more adequate model is required, one that takes into account the patient's confidence in physician referral and advice.

How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

PubMed

Duarte, Jorge; Rodrigues, Carla; Januário, Cristina; Martins, Nuno; Sardanyés, Josep

2015-12-01

Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable.

Targeting MYCN-Driven Transcription By BET-Bromodomain Inhibition.

PubMed

Henssen, Anton; Althoff, Kristina; Odersky, Andrea; Beckers, Anneleen; Koche, Richard; Speleman, Frank; Schäfers, Simon; Bell, Emma; Nortmeyer, Maike; Westermann, Frank; De Preter, Katleen; Florin, Alexandra; Heukamp, Lukas; Spruessel, Annika; Astrahanseff, Kathy; Lindner, Sven; Sadowski, Natalie; Schramm, Alexander; Astorgues-Xerri, Lucile; Riveiro, Maria E; Eggert, Angelika; Cvitkovic, Esteban; Schulte, Johannes H

2016-05-15

Targeting BET proteins was previously shown to have specific antitumoral efficacy against MYCN-amplified neuroblastoma. We here assess the therapeutic efficacy of the BET inhibitor, OTX015, in preclinical neuroblastoma models and extend the knowledge on the role of BRD4 in MYCN-driven neuroblastoma. The efficacy of OTX015 was assessed in in vitro and in vivo models of human and murine MYCN-driven neuroblastoma. To study the effects of BET inhibition in the context of high MYCN levels, MYCN was ectopically expressed in human and murine cells. The effect of OTX015 on BRD4-regulated transcriptional pause release was analyzed using BRD4 and H3K27Ac chromatin immunoprecipitation coupled with DNA sequencing (ChIP-Seq) and gene expression analysis in neuroblastoma cells treated with OTX015 compared with vehicle control. OTX015 showed therapeutic efficacy against preclinical MYCN-driven neuroblastoma models. Similar to previously described BET inhibitors, concurrent MYCN repression was observed in OTX015-treated samples. Ectopic MYCN expression, however, did not abrogate effects of OTX015, indicating that MYCN repression is not the only target of BET proteins in neuroblastoma. When MYCN was ectopically expressed, BET inhibition still disrupted MYCN target gene transcription without affecting MYCN expression. We found that BRD4 binds to super-enhancers and MYCN target genes, and that OTX015 specifically disrupts BRD4 binding and transcription of these genes. We show that OTX015 is effective against mouse and human MYCN-driven tumor models and that BRD4 not only targets MYCN, but specifically occupies MYCN target gene enhancers as well as other genes associated with super-enhancers. Clin Cancer Res; 22(10); 2470-81. ©2015 AACR. ©2015 American Association for Cancer Research.

A hydrogeologic model of stratiform copper mineralization in the Midcontinent Rift System, Northern Michigan, USA

USGS Publications Warehouse

Swenson, J.B.; Person, M.; Raffensperger, Jeff P.; Cannon, W.F.; Woodruff, L.G.; Berndt, M.E.

2004-01-01

This paper presents a suite of two-dimensional mathematical models of basin-scale groundwater flow and heat transfer for the middle Proterozoic Midcontinent Rift System. The models were used to assess the hydrodynamic driving mechanisms responsible for main-stage stratiform copper mineralization of the basal Nonesuch Formation during the post-volcanic/pre-compressional phase of basin evolution. Results suggest that compaction of the basal aquifer (Copper Harbor Formation), in response to mechanical loading during deposition of the overlying Freda Sandstone, generated a pulse of marginward-directed, compaction-driven discharge of cupriferous brines from within the basal aquifer. The timing of this pulse is consistent with the radiometric dates for the timing of mineralization. Thinning of the basal aquifer near White Pine, Michigan, enhanced stratiform copper mineralization. Focused upward leakage of copper-laden brines into the lowermost facies of the pyrite-rich Nonesuch Formation resulted in copper sulfide mineralization in response to a change in oxidation state. Economic-grade mineralization within the White Pine ore district is a consequence of intense focusing of compaction-driven discharge, and corresponding amplification of leakage into the basal Nonesuch Formation, where the basal aquifer thins dramatically atop the Porcupine Mountains volcanic structure. Equilibrium geochemical modeling and mass-balance calculations support this conclusion. We also assessed whether topography and density-driven flow systems could have caused ore genesis at White Pine. Topography-driven flow associated with the Ottawan orogeny was discounted because it post-dates main-stage ore genesis and because recent seismic interpretations of basin inversion indicates that basin geometry would not be conductive to ore genesis. Density-driven flow systems did not produce focused discharge in the vicinity of the White Pine ore district.

Diffusive smoothing of surfzone bathymetry by gravity-driven sediment transport

NASA Astrophysics Data System (ADS)

Moulton, M. R.; Elgar, S.; Raubenheimer, B.

2012-12-01

Gravity-driven sediment transport often is assumed to have a small effect on the evolution of nearshore morphology. Here, it is shown that down-slope gravity-driven sediment transport is an important process acting to smooth steep bathymetric features in the surfzone. Gravity-driven transport can be modeled as a diffusive term in the sediment continuity equation governing temporal (t) changes in bed level (h): ∂h/∂t ≈ κ ▽2h, where κ is a sediment diffusion coefficient that is a function of the bed shear stress (τb) and sediment properties, such as the grain size and the angle of repose. Field observations of waves, currents, and the evolution of large excavated holes (initially 10-m wide and 2-m deep, with sides as steep as 35°) in an energetic surfzone are consistent with diffusive smoothing by gravity. Specifically, comparisons of κ estimated from the measured bed evolution with those estimated with numerical model results for several transport theories suggest that gravity-driven sediment transport dominates the bed evolution, with κ proportional to a power of τb. The models are initiated with observed bathymetry and forced with observed waves and currents. The diffusion coefficients from the measurements and from the model simulations were on average of order 10-5 m2/s, implying evolution time scales of days for features with length scales of 10 m. The dependence of κ on τb varies for different transport theories and for high and low shear stress regimes. The US Army Corps of Engineers Field Research Facility, Duck, NC provided excellent logistical support. Funded by a National Security Science and Engineering Faculty Fellowship, a National Defense Science and Engineering Graduate Fellowship, and the Office of Naval Research.

Data-driven Model of the ICME Propagation through the Solar Corona and Inner Heliosphere

NASA Astrophysics Data System (ADS)

Yalim, M. S.; Pogorelov, N.; Singh, T.; Liu, Y.

2017-12-01

The solar wind (SW) emerging from the Sun is the main driving mechanism of solar events which may lead to geomagnetic storms that are the primary causes of space weather disturbances that affect the magnetic environment of Earth and may have hazardous effects on the space-borne and ground-based technological systems as well as human health. Therefore, accurate modeling of the SW is very important to understand the underlying mechanisms of such storms.Getting ready for the Parker Solar Probe mission, we have developed a data-driven magnetohydrodynamic (MHD) model of the global solar corona which utilizes characteristic boundary conditions implemented within the Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS) - a collection of problem oriented routines incorporated into the Chombo adaptive mesh refinement framework developed at Lawrence Berkeley National Laboratory. Our global solar corona model can be driven by both synoptic and synchronic vector magnetogram data obtained by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) and the horizontal velocity data on the photosphere obtained by applying the Differential Affine Velocity Estimatorfor Vector Magnetograms (DAVE4VM) method on the HMI-observed vector magnetic fields.Our CME generation model is based on Gibson-Low-type flux ropes the parameters of which are determined from analysis of observational data from STEREO/SECCHI, SDO/AIA and SOHO/LASCO, and by applying the Graduate Cylindrical Shell model for the flux rope reconstruction.In this study, we will present the results of three-dimensional global simulations of ICME propagation through our characteristically-consistent MHD model of the background SW from the Sun to Earth driven by HMI-observed vector magnetic fields and validate our results using multiple spacecraft data at 1 AU.

Ergodicity-breaking bifurcations and tunneling in hyperbolic transport models

NASA Astrophysics Data System (ADS)

Giona, M.; Brasiello, A.; Crescitelli, S.

2015-11-01

One of the main differences between parabolic transport, associated with Langevin equations driven by Wiener processes, and hyperbolic models related to generalized Kac equations driven by Poisson processes, is the occurrence in the latter of multiple stable invariant densities (Frobenius multiplicity) in certain regions of the parameter space. This phenomenon is associated with the occurrence in linear hyperbolic balance equations of a typical bifurcation, referred to as the ergodicity-breaking bifurcation, the properties of which are thoroughly analyzed.

Identifying Androgen Receptor-Independent Mechanisms of Prostate Cancer Resistance to Second-Generation Antiandrogen Therapy

DTIC Science & Technology

2016-08-01

expanded upon the relationship between GR and SGK1 in the context of enzalutamide-driven prostate cancer. We have generated CRISPR /Cas9 cell lines...Complete Generate SGK1 overexpressing cell models 50% Ongoing Clone SGK1 CRISPR 100% Complete Generate SGK1-deficient cell models 75% Ongoing Test...driven enzalutamide resistance, GR-expressing enzalutamide-resistant prostate cancer cells expressing CRISPR /Cas9 and a guide targeting SGK1 (sgSGK1

Application-Driven No-Reference Quality Assessment for Dermoscopy Images With Multiple Distortions.

PubMed

Xie, Fengying; Lu, Yanan; Bovik, Alan C; Jiang, Zhiguo; Meng, Rusong

2016-06-01

Dermoscopy images often suffer from blur and uneven illumination distortions that occur during acquisition, which can adversely influence consequent automatic image analysis results on potential lesion objects. The purpose of this paper is to deploy an algorithm that can automatically assess the quality of dermoscopy images. Such an algorithm could be used to direct image recapture or correction. We describe an application-driven no-reference image quality assessment (IQA) model for dermoscopy images affected by possibly multiple distortions. For this purpose, we created a multiple distortion dataset of dermoscopy images impaired by varying degrees of blur and uneven illumination. The basis of this model is two single distortion IQA metrics that are sensitive to blur and uneven illumination, respectively. The outputs of these two metrics are combined to predict the quality of multiply distorted dermoscopy images using a fuzzy neural network. Unlike traditional IQA algorithms, which use human subjective score as ground truth, here ground truth is driven by the application, and generated according to the degree of influence of the distortions on lesion analysis. The experimental results reveal that the proposed model delivers accurate and stable quality prediction results for dermoscopy images impaired by multiple distortions. The proposed model is effective for quality assessment of multiple distorted dermoscopy images. An application-driven concept for IQA is introduced, and at the same time, a solution framework for the IQA of multiple distortions is proposed.

Origin of the pulse-like signature of shallow long-period volcano seismicity

USGS Publications Warehouse

Chouet, Bernard A.; Dawson, Phillip B.

2016-01-01

Short-duration, pulse-like long-period (LP) events are a characteristic type of seismicity accompanying eruptive activity at Mount Etna in Italy in 2004 and 2008 and at Turrialba Volcano in Costa Rica and Ubinas Volcano in Peru in 2009. We use the discrete wave number method to compute the free surface response in the near field of a rectangular tensile crack embedded in a homogeneous elastic half space and to gain insights into the origin of the LP pulses. Two source models are considered, including (1) a vertical fluid-driven crack and (2) a unilateral tensile rupture growing at a fixed sub-Rayleigh velocity with constant opening on a vertical crack. We apply cross correlation to the synthetics and data to demonstrate that a fluid-driven crack provides a natural explanation for these data with realistic source sizes and fluid properties. Our modeling points to shallow sources (<1 km depth), whose signatures are representative of the Rayleigh pulse sampled at epicentral distances >∼1 km. While a slow-rupture failure provides another potential model for these events, the synthetics and resulting fits to the data are not optimal in this model compared to a fluid-driven source. We infer that pulse-like LP signatures are parts of the continuum of responses produced by shallow fluid-driven sources in volcanoes.

Data-Driven Instructional Leadership

ERIC Educational Resources Information Center

Blink, Rebecca

2006-01-01

With real-world examples from actual schools, this book illustrates how to nurture a culture of continuous improvement, meet the needs of individual students, foster an environment of high expectations, and meet the requirements of NCLB. Each component of the Data-Driven Instructional Leadership (DDIS) model represents several branches of…

Mechanics of Interrill Erosion with Wind-Driven Rain (WDR)

USDA-ARS?s Scientific Manuscript database

This article provides an evaluation analysis for the performance of the interrill component of the Water Erosion Prediction Project (WEPP) model for Wind-Driven Rain (WDR) events. The interrill delivery rates (Di) were collected in the wind tunnel rainfall simulator facility of the International Cen...

A Simple and Accurate Rate-Driven Infiltration Model

NASA Astrophysics Data System (ADS)

Cui, G.; Zhu, J.

2017-12-01

In this study, we develop a novel Rate-Driven Infiltration Model (RDIMOD) for simulating infiltration into soils. Unlike traditional methods, RDIMOD avoids numerically solving the highly non-linear Richards equation or simply modeling with empirical parameters. RDIMOD employs infiltration rate as model input to simulate one-dimensional infiltration process by solving an ordinary differential equation. The model can simulate the evolutions of wetting front, infiltration rate, and cumulative infiltration on any surface slope including vertical and horizontal directions. Comparing to the results from the Richards equation for both vertical infiltration and horizontal infiltration, RDIMOD simply and accurately predicts infiltration processes for any type of soils and soil hydraulic models without numerical difficulty. Taking into account the accuracy, capability, and computational effectiveness and stability, RDIMOD can be used in large-scale hydrologic and land-atmosphere modeling.

A Model-Driven Architecture Approach for Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems

NASA Technical Reports Server (NTRS)

Pena, Joaquin; Hinchey, Michael G.; Sterritt, Roy; Ruiz-Cortes, Antonio; Resinas, Manuel

2006-01-01

Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based Self-Management. The Model Driven Architecture (MDA) approach focuses on building models that can be transformed into code in an automatic manner. In this paper, we look at ways to implement Policy-Based Self-Management by means of models that can be converted to code using transformations that follow the MDA philosophy. We propose a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system.

Retrospective cost adaptive Reynolds-averaged Navier-Stokes k-ω model for data-driven unsteady turbulent simulations

NASA Astrophysics Data System (ADS)

Li, Zhiyong; Hoagg, Jesse B.; Martin, Alexandre; Bailey, Sean C. C.

2018-03-01

This paper presents a data-driven computational model for simulating unsteady turbulent flows, where sparse measurement data is available. The model uses the retrospective cost adaptation (RCA) algorithm to automatically adjust the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k- ω turbulence equations to improve agreement between the simulated flow and the measurements. The RCA-RANS k- ω model is verified for steady flow using a pipe-flow test case and for unsteady flow using a surface-mounted-cube test case. Measurements used for adaptation of the verification cases are obtained from baseline simulations with known closure coefficients. These verification test cases demonstrate that the RCA-RANS k- ω model can successfully adapt the closure coefficients to improve agreement between the simulated flow field and a set of sparse flow-field measurements. Furthermore, the RCA-RANS k- ω model improves agreement between the simulated flow and the baseline flow at locations at which measurements do not exist. The RCA-RANS k- ω model is also validated with experimental data from 2 test cases: steady pipe flow, and unsteady flow past a square cylinder. In both test cases, the adaptation improves agreement with experimental data in comparison to the results from a non-adaptive RANS k- ω model that uses the standard values of the k- ω closure coefficients. For the steady pipe flow, adaptation is driven by mean stream-wise velocity measurements at 24 locations along the pipe radius. The RCA-RANS k- ω model reduces the average velocity error at these locations by over 35%. For the unsteady flow over a square cylinder, adaptation is driven by time-varying surface pressure measurements at 2 locations on the square cylinder. The RCA-RANS k- ω model reduces the average surface-pressure error at these locations by 88.8%.

Metric-driven harm: an exploration of unintended consequences of performance measurement.

PubMed

Rambur, Betty; Vallett, Carol; Cohen, Judith A; Tarule, Jill Mattuck

2013-11-01

Performance measurement is an increasingly common element of the US health care system. Typically a proxy for high quality outcomes, there has been little systematic investigation of the potential negative unintended consequences of performance metrics, including metric-driven harm. This case study details an incidence of post-surgical metric-driven harm and offers Smith's 1995 work and a patient centered, context sensitive metric model for potential adoption by nurse researchers and clinicians. Implications for further research are discussed. © 2013.

Radiation-driven winds of hot stars. V - Wind models for central stars of planetary nebulae

NASA Technical Reports Server (NTRS)

Pauldrach, A.; Puls, J.; Kudritzki, R. P.; Mendez, R. H.; Heap, S. R.

1988-01-01

Wind models using the recent improvements of radiation driven wind theory by Pauldrach et al. (1986) and Pauldrach (1987) are presented for central stars of planetary nebulae. The models are computed along evolutionary tracks evolving with different stellar mass from the Asymptotic Giant Branch. We show that the calculated terminal wind velocities are in agreement with the observations and allow in principle an independent determination of stellar masses and radii. The computed mass-loss rates are in qualitative agreement with the occurrence of spectroscopic stellar wind features as a function of stellar effective temperature and gravity.

Validity of thermally-driven small-scale ventilated filling box models

NASA Astrophysics Data System (ADS)

Partridge, Jamie L.; Linden, P. F.

2013-11-01

The majority of previous work studying building ventilation flows at laboratory scale have used saline plumes in water. The production of buoyancy forces using salinity variations in water allows dynamic similarity between the small-scale models and the full-scale flows. However, in some situations, such as including the effects of non-adiabatic boundaries, the use of a thermal plume is desirable. The efficacy of using temperature differences to produce buoyancy-driven flows representing natural ventilation of a building in a small-scale model is examined here, with comparison between previous theoretical and new, heat-based, experiments.

Musculoskeletal model-based control interface mimics physiologic hand dynamics during path tracing task

NASA Astrophysics Data System (ADS)

Crouch, Dustin L.; (Helen Huang, He

2017-06-01

Objective. We investigated the feasibility of a novel, customizable, simplified EMG-driven musculoskeletal model for estimating coordinated hand and wrist motions during a real-time path tracing task. Approach. A two-degree-of-freedom computational musculoskeletal model was implemented for real-time EMG-driven control of a stick figure hand displayed on a computer screen. After 5-10 minutes of undirected practice, subjects were given three attempts to trace 10 straight paths, one at a time, with the fingertip of the virtual hand. Able-bodied subjects completed the task on two separate test days. Main results. Across subjects and test days, there was a significant linear relationship between log-transformed measures of accuracy and speed (Pearson’s r  =  0.25, p  <  0.0001). The amputee subject could coordinate movement between the wrist and MCP joints, but favored metacarpophalangeal joint motion more highly than able-bodied subjects in 8 of 10 trials. For able-bodied subjects, tracing accuracy was lower at the extremes of the model’s range of motion, though there was no apparent relationship between tracing accuracy and fingertip location for the amputee. Our result suggests that, unlike able-bodied subjects, the amputee’s motor control patterns were not accustomed to the multi-joint dynamics of the wrist and hand, possibly as a result of post-amputation cortical plasticity, disuse, or sensory deficits. Significance. To our knowledge, our study is one of very few that have demonstrated the real-time simultaneous control of multi-joint movements, especially wrist and finger movements, using an EMG-driven musculoskeletal model, which differs from the many data-driven algorithms that dominate the literature on EMG-driven prosthesis control. Real-time control was achieved with very little training and simple, quick (~15 s) calibration. Thus, our model is potentially a practical and effective control platform for multifunctional myoelectric prostheses that could restore more life-like hand function for individuals with upper limb amputation.

Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis.

PubMed

Barman, Rahul; Jain, Atul K; Liang, Miaoling

2014-05-01

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2)  yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework. © 2013 John Wiley & Sons Ltd.

An Hypothesis-Driven, Molecular Phylogenetics Exercise for College Biology Students

ERIC Educational Resources Information Center

Parker, Joel D.; Ziemba, Robert E.; Cahan, Sara Helms; Rissing, Steven W.

2004-01-01

This hypothesis-driven laboratory exercise teaches how DNA evidence can be used to investigate an organism's evolutionary history while providing practical modeling of the fundamental processes of gene transcription and translation. We used an inquiry-based approach to construct a laboratory around a nontrivial, open-ended evolutionary question…

Evaluation of Interrill Erosion Under Wind-Driven Rain Events in Northern Burkina Faso

USDA-ARS?s Scientific Manuscript database

Wind changes the velocity, frequency and angle of raindrop impact and hence affects rain splash detachment rates. Many soil erosion models underpredict interrill erosion because the contribution of the wind to raindrop detachment and wind-driven transport processes are not taken into account. In thi...

Current uses of 2.0 applications in transportation : case studies of select state departments of transportation.

DOT National Transportation Integrated Search

2010-03-01

Web 2.0 is an umbrella term for websites or online applications that are user-driven and emphasize collaboration and user interactivity. The trend away from static web pages to a more user-driven Internet model has also occurred in the public s...

Constraint-Driven Software Design: An Escape from the Waterfall Model.

ERIC Educational Resources Information Center

de Hoog, Robert; And Others

1994-01-01

Presents the principles of a development methodology for software design based on a nonlinear, product-driven approach that integrates quality aspects. Two examples are given to show that the flexibility needed for building high quality systems leads to integrated development environments in which methodology, product, and tools are closely…

Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management

USDA-ARS?s Scientific Manuscript database

Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limit...

Raindrop and flow interactions for interrill erosion with wind-driven rain

USDA-ARS?s Scientific Manuscript database

Wind-driven rain (WDR) experiments were conducted to evaluate interrill component of the Water Erosion Prediction Project (WEPP) model with two-dimensional experimental set-up in wind tunnel. Synchronized wind and rain simulations were applied to soil surfaces on windward and leeward slopes of 7, 15...

Mechanics of interrill erosion with wind-driven rain

USDA-ARS?s Scientific Manuscript database

The vector physics of wind-driven rain (WDR) differs from that of wind-free rain, and the interrill soil detachment equations in the Water Erosion Prediction Project (WEPP) model were not originally developed to deal with this phenomenon. This article provides an evaluation of the performance of the...

Simplified subsurface modelling: data assimilation and violated model assumptions

NASA Astrophysics Data System (ADS)

Erdal, Daniel; Lange, Natascha; Neuweiler, Insa

2017-04-01

Integrated models are gaining more and more attention in hydrological modelling as they can better represent the interaction between different compartments. Naturally, these models come along with larger numbers of unknowns and requirements on computational resources compared to stand-alone models. If large model domains are to be represented, e.g. on catchment scale, the resolution of the numerical grid needs to be reduced or the model itself needs to be simplified. Both approaches lead to a reduced ability to reproduce the present processes. This lack of model accuracy may be compensated by using data assimilation methods. In these methods observations are used to update the model states, and optionally model parameters as well, in order to reduce the model error induced by the imposed simplifications. What is unclear is whether these methods combined with strongly simplified models result in completely data-driven models or if they can even be used to make adequate predictions of the model state for times when no observations are available. In the current work we consider the combined groundwater and unsaturated zone, which can be modelled in a physically consistent way using 3D-models solving the Richards equation. For use in simple predictions, however, simpler approaches may be considered. The question investigated here is whether a simpler model, in which the groundwater is modelled as a horizontal 2D-model and the unsaturated zones as a few sparse 1D-columns, can be used within an Ensemble Kalman filter to give predictions of groundwater levels and unsaturated fluxes. This is tested under conditions where the feedback between the two model-compartments are large (e.g. shallow groundwater table) and the simplification assumptions are clearly violated. Such a case may be a steep hill-slope or pumping wells, creating lateral fluxes in the unsaturated zone, or strong heterogeneous structures creating unaccounted flows in both the saturated and unsaturated compartments. Under such circumstances, direct modelling using a simplified model will not provide good results. However, a more data driven (e.g. grey box) approach, driven by the filter, may still provide an improved understanding of the system. Comparisons between full 3D simulations and simplified filter driven models will be shown and the resulting benefits and drawbacks will be discussed.

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.

Protein-Protein Interface Predictions by Data-Driven Methods: A Review

PubMed Central

Xue, Li C; Dobbs, Drena; Bonvin, Alexandre M.J.J.; Honavar, Vasant

2015-01-01

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction. PMID:26460190

Initial conditions and modeling for simulations of shock driven turbulent material mixing

DOE PAGES

Grinstein, Fernando F.

2016-11-17

Here, we focus on the simulation of shock-driven material mixing driven by flow instabilities and initial conditions (IC). Beyond complex multi-scale resolution issues of shocks and variable density turbulence, me must address the equally difficult problem of predicting flow transition promoted by energy deposited at the material interfacial layer during the shock interface interactions. Transition involves unsteady large-scale coherent-structure dynamics capturable by a large eddy simulation (LES) strategy, but not by an unsteady Reynolds-Averaged Navier–Stokes (URANS) approach based on developed equilibrium turbulence assumptions and single-point-closure modeling. On the engineering end of computations, such URANS with reduced 1D/2D dimensionality and coarsermore » grids, tend to be preferred for faster turnaround in full-scale configurations.« less

Shock dynamics of two-lane driven lattice gases

NASA Astrophysics Data System (ADS)

Schiffmann, Christoph; Appert-Rolland, Cécile; Santen, Ludger

2010-06-01

Driven lattice gases such as those of the ASEP model are useful tools for the modelling of various stochastic transport processes carried out by self-driven particles, such as molecular motors or vehicles in road traffic. Often these processes take place in one-dimensional systems offering several tracks to the particles, and in many cases the particles are able to change track with a given rate. In this work we consider the case of strong coupling where the rate of hopping along the tracks and the exchange rates are of the same order, and show how a phenomenological approach based on a domain wall theory can be used to describe the dynamics of the system. In particular, the domain walls on the different tracks form pairs, whose dynamics dominate the behaviour of the system.

Pyrotechnic modeling for the NSI and pin puller

NASA Technical Reports Server (NTRS)

Powers, Joseph M.; Gonthier, Keith A.

1993-01-01

A discussion concerning the modeling of pyrotechnically driven actuators is presented in viewgraph format. The following topics are discussed: literature search, constitutive data for full-scale model, simple deterministic model, observed phenomena, and results from simple model.

A method for computing ion energy distributions for multifrequency capacitive discharges

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

Wu, Alan C. F.; Lieberman, M. A.; Verboncoeur, J. P.

2007-03-01

The ion energy distribution (IED) at a surface is an important parameter for processing in multiple radio frequency driven capacitive discharges. An analytical model is developed for the IED in a low pressure discharge based on a linear transfer function that relates the time-varying sheath voltage to the time-varying ion energy response at the surface. This model is in good agreement with particle-in-cell simulations over a wide range of single, dual, and triple frequency driven capacitive discharge excitations.

Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

NASA Astrophysics Data System (ADS)

Postnikov, Eugene B.

2016-01-01

This work presents a comparative analysis of Influenzanet data for influenza itself and common cold in the Netherlands during the last 5 years, from the point of view of modelling by linearised SIRS equations parametrically driven by the ambient temperature. It is argued that this approach allows for the forecast of common cold, but not of influenza in a strict sense. The difference in their kinetic models is discussed with reference to the clinical background.

A nanojet: propulsion of a molecular machine by an asymmetric distribution of reaction--products

NASA Astrophysics Data System (ADS)

Liverpool, Tanniemola; Golestanian, Ramin; Ajdari, Armand

2006-03-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. Motion of the device is driven by an asymmetric distribution of reaction products. We calculate the propulsive velocity of the device as well as the scale of the velocity fluctuations. We also consider the effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction.

Propulsion of a Molecular Machine by Asymmetric Distribution of Reaction Products

NASA Astrophysics Data System (ADS)

Golestanian, Ramin; Liverpool, Tanniemola B.; Ajdari, Armand

2005-06-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Propulsion of a molecular machine by asymmetric distribution of reaction products.

PubMed

Golestanian, Ramin; Liverpool, Tanniemola B; Ajdari, Armand

2005-06-10

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Use of UUVs to Evaluate and Improve Model Performance Within a Tidally-Dominated Bay

DTIC Science & Technology

2008-09-30

Sequim Bay Road Sequim , WA 98382 Phone: (360) 681-3616 Fax: (360) 681-3699 Email: lyle.hibler@pnl.gov Grant Number: N00014-07-1-1113 LONG-TERM...releasing rhodamine dye on the surface of Sequim Bay ( Sequim , Washington) from an anchored vessel in 2006. Concurrently collected data from the...advective transport from a point release in Sequim Bay , Washington. Tidal, wind-driven and density-driven circulation were accounted for in the model. The

CFD simulation of an unbaffled stirred tank reactor driven by a magnetic rod: assessment of turbulence models.

PubMed

Li, Jiajia; Deng, Baoqing; Zhang, Bing; Shen, Xiuzhong; Kim, Chang Nyung

2015-01-01

A simulation of an unbaffled stirred tank reactor driven by a magnetic stirring rod was carried out in a moving reference frame. The free surface of unbaffled stirred tank was captured by Euler-Euler model coupled with the volume of fluid (VOF) method. The re-normalization group (RNG) k-ɛ model, large eddy simulation (LES) model and detached eddy simulation (DES) model were evaluated for simulating the flow field in the stirred tank. All turbulence models can reproduce the tangential velocity in an unbaffled stirred tank with a rotational speed of 150 rpm, 250 rpm and 400 rpm, respectively. Radial velocity is underpredicted by the three models. LES model and RNG k-ɛ model predict the better tangential velocity and axial velocity, respectively. RNG k-ɛ model is recommended for the simulation of the flow in an unbaffled stirred tank with magnetic rod due to its computational effort.

Threat driven modeling framework using petri nets for e-learning system.

PubMed

Khamparia, Aditya; Pandey, Babita

2016-01-01

Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a normalized method for rating vulnerabilities which will be used as basis in metric definitions and calculations. A case study has been also proposed which shows the need and feasibility of using aspect oriented stochastic petri net models for threat modeling which improves reliability, consistency and robustness of the e-learning system.

Cycle-averaged dynamics of a periodically driven, closed-loop circulation model

NASA Technical Reports Server (NTRS)

Heldt, T.; Chang, J. L.; Chen, J. J. S.; Verghese, G. C.; Mark, R. G.

2005-01-01

Time-varying elastance models have been used extensively in the past to simulate the pulsatile nature of cardiovascular waveforms. Frequently, however, one is interested in dynamics that occur over longer time scales, in which case a detailed simulation of each cardiac contraction becomes computationally burdensome. In this paper, we apply circuit-averaging techniques to a periodically driven, closed-loop, three-compartment recirculation model. The resultant cycle-averaged model is linear and time invariant, and greatly reduces the computational burden. It is also amenable to systematic order reduction methods that lead to further efficiencies. Despite its simplicity, the averaged model captures the dynamics relevant to the representation of a range of cardiovascular reflex mechanisms. c2004 Elsevier Ltd. All rights reserved.

Solving Immunology?

PubMed Central

Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L.; Bassaganya-Riera, Josep; Hafler, David A.; Sontag, Eduardo; Wang, Jin; Tsang, John S.; Day, Judy D.; Kleinstein, Steven; Butte, Atul J.; Altman, Matthew C; Hammond, Ross; Sealfon, Stuart C.

2016-01-01

Emergent responses of the immune system result from integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the NIAID workshop “Complex Systems Science, Modeling and Immunity” and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies. PMID:27986392

Nonlinear response and bistability of driven ion acoustic waves

NASA Astrophysics Data System (ADS)

Akbari-Moghanjoughi, M.

2017-08-01

The hydrodynamic model is used to obtain a generalized pseudoforce equation through which the nonlinear response of periodically driven ion acoustic waves is studied in an electron-ion plasma with isothermal and adiabatic ion fluids. The pseudotime series, corresponding to different driving frequencies, indicates that nonlinearity effects appear more strongly for smaller frequency values. The existence of extra harmonic resonances in the nonlinear amplitude spectrum is a clear indication of the interaction of an external force with harmonic components of the nonlinear ion acoustic waves. It is shown that many plasma parameters significantly and differently affect the nonlinear resonance spectrum of ion acoustic excitations. A heuristic but accurate model for the foldover effect is used which quite satisfactorily predicts the bistability of driven plasma oscillations. It is remarked that the characteristic resonance peak of isothermal ion plasma oscillations appears at lower frequencies but is stronger compared to that of adiabatic ions. Comparison of the exact numerical results for fully nonlinear and approximate (weakly nonlinear) models indicates that a weakly nonlinear model exaggerates the hysteresis and jump phenomenon for higher values of the external force amplitude.

A Saturnian cam current system driven by asymmetric thermospheric heating

NASA Astrophysics Data System (ADS)

Smith, C. G. A.

2011-02-01

We show that asymmetric heating of Saturn's thermosphere can drive a current system consistent with the magnetospheric ‘cam’ proposed by Espinosa, Southwood & Dougherty. A geometrically simple heating distribution is imposed on the Northern hemisphere of a simplified three-dimensional global circulation model of Saturn's thermosphere. Currents driven by the resulting winds are calculated using a globally averaged ionosphere model. Using a simple assumption about how divergences in these currents close by flowing along dipolar field lines between the Northern and Southern hemispheres, we estimate the magnetic field perturbations in the equatorial plane and show that they are broadly consistent with the proposed cam fields, showing a roughly uniform field implying radial and azimuthal components in quadrature. We also identify a small longitudinal phase drift in the cam current with radial distance as a characteristic of a thermosphere-driven current system. However, at present our model does not produce magnetic field perturbations of the required magnitude, falling short by a factor of ˜100, a discrepancy that may be a consequence of an incomplete model of the ionospheric conductance.

Data-driven train set crash dynamics simulation

NASA Astrophysics Data System (ADS)

Tang, Zhao; Zhu, Yunrui; Nie, Yinyu; Guo, Shihui; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2017-02-01

Traditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force-displacement curves and predicts a force-displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency.

Wind-invariant saltation heights imply linear scaling of aeolian saltation flux with shear stress.

PubMed

Martin, Raleigh L; Kok, Jasper F

2017-06-01

Wind-driven sand transport generates atmospheric dust, forms dunes, and sculpts landscapes. However, it remains unclear how the flux of particles in aeolian saltation-the wind-driven transport of sand in hopping trajectories-scales with wind speed, largely because models do not agree on how particle speeds and trajectories change with wind shear velocity. We present comprehensive measurements, from three new field sites and three published studies, showing that characteristic saltation layer heights remain approximately constant with shear velocity, in agreement with recent wind tunnel studies. These results support the assumption of constant particle speeds in recent models predicting linear scaling of saltation flux with shear stress. In contrast, our results refute widely used older models that assume that particle speed increases with shear velocity, thereby predicting nonlinear 3/2 stress-flux scaling. This conclusion is further supported by direct field measurements of saltation flux versus shear stress. Our results thus argue for adoption of linear saltation flux laws and constant saltation trajectories for modeling saltation-driven aeolian processes on Earth, Mars, and other planetary surfaces.

Realistic simulations of the coupling between the protomotive force and the mechanical rotation of the F0-ATPase

PubMed Central

Mukherjee, Shayantani; Warshel, Arieh

2012-01-01

The molecular origin of the action of the F0 proton gradient-driven rotor presents a major puzzle despite significant structural advances. Although important conceptual models have provided guidelines of how such systems should work, it has been challenging to generate a structure-based molecular model using physical principles that will consistently lead to the unidirectional proton-driven rotational motion during ATP synthesis. This work uses a coarse-grained (CG) model to simulate the energetics of the F0-ATPase system in the combined space defined by the rotational coordinate and the proton transport (PTR) from the periplasmic side (P) to the cytoplasmic side (N). The model establishes the molecular origin of the rotation, showing that this effect is due to asymmetry in the energetics of the proton path rather than only the asymmetry of the interaction of the Asp on the c-ring helices and Arg on the subunit-a. The simulation provides a clear conceptual background for further exploration of the electrostatic basis of proton-driven mechanochemical systems. PMID:22927379

Density-driven transport process following the seawater indundation of the freshwater lens of the island of Baltrum, Germany

NASA Astrophysics Data System (ADS)

Post, Vincent E. A.; Houben, Georg J.

2017-04-01

Due to the growing vulnerability of low-lying coastal zones to flooding by seawater, there is a need for more studies of the impact of inundations on fresh groundwater resources. We present previously unpublished data collected on the island of Baltrum following a devastating storm in 1962, which uniquely show the impact of seawater inundation on a freshwater lens in a siliciclastic aquifer. The field data show that elevated salinities persisted for at least 4 years at the measurement depths of 4 and 6 m, and at least for 6 years at greater depths. Numerical models confirm the importance of density-driven salt fingering. Models that did not consider density effects failed to simulate the observed breakthrough curves. Transient recharge, model dimension and lateral flow modify the details of the simulation results, but in all models density-driven flow dominates the overall system behaviour. The sequestration of intruded seawater into the deeper parts of the flow system, prolongs recovery and enhances the risk of upconing when pumping is resumed too early.

Wind-invariant saltation heights imply linear scaling of aeolian saltation flux with shear stress

PubMed Central

Martin, Raleigh L.; Kok, Jasper F.

2017-01-01

Wind-driven sand transport generates atmospheric dust, forms dunes, and sculpts landscapes. However, it remains unclear how the flux of particles in aeolian saltation—the wind-driven transport of sand in hopping trajectories—scales with wind speed, largely because models do not agree on how particle speeds and trajectories change with wind shear velocity. We present comprehensive measurements, from three new field sites and three published studies, showing that characteristic saltation layer heights remain approximately constant with shear velocity, in agreement with recent wind tunnel studies. These results support the assumption of constant particle speeds in recent models predicting linear scaling of saltation flux with shear stress. In contrast, our results refute widely used older models that assume that particle speed increases with shear velocity, thereby predicting nonlinear 3/2 stress-flux scaling. This conclusion is further supported by direct field measurements of saltation flux versus shear stress. Our results thus argue for adoption of linear saltation flux laws and constant saltation trajectories for modeling saltation-driven aeolian processes on Earth, Mars, and other planetary surfaces. PMID:28630907

Ionization and thermal equilibrium models for O star winds based on time-independent radiation-driven wind theory

NASA Technical Reports Server (NTRS)

Drew, J. E.

1989-01-01

Ab initio ionization and thermal equilibrium models are calculated for the winds of O stars using the results of steady state radiation-driven wind theory to determine the input parameters. Self-consistent methods are used for the roles of H, He, and the most abundant heavy elements in both the statistical and the thermal equilibrium. The model grid was chosen to encompass all O spectral subtypes and the full range of luminosity classes. Results of earlier modeling of O star winds by Klein and Castor (1978) are reproduced and used to motivate improvements in the treatment of the hydrogen equilibrium. The wind temperature profile is revealed to be sensitive to gross changes in the heavy element abundances, but insensitive to other factors considered such as the mass-loss rate and velocity law. The reduced wind temperatures obtained in observing the luminosity dependence of the Si IV lambda 1397 wind absorption profile are shown to eliminate any prospect of explaining the observed O VI lambda 1036 line profiles in terms of time-independent radiation-driven wind theory.

Prediction of gravity-driven fingering in porous media

NASA Astrophysics Data System (ADS)

Beljadid, Abdelaziz; Cueto-Felgueroso, Luis; Juanes, Ruben

2017-11-01

Gravity-driven displacement of one fluid by another in porous media is often subject to a hydrodynamic instability, whereby fluid invasion takes the form of preferential flow paths-examples include secondary oil migration in reservoir rocks, and infiltration of rainfall water in dry soil. Here, we develop a continuum model of gravity-driven two-phase flow in porous media within the phase-field framework (Cueto-Felgueroso and Juanes, 2008). We employ pore-scale physics arguments to design the free energy of the system, which notably includes a nonlinear formulation of the high-order (square-gradient) term based on equilibrium considerations in the direction orthogonal to gravity. This nonlocal term plays the role of a macroscopic surface tension, which exhibits a strong link with capillary pressure. Our theoretical analysis shows that the proposed model enforces that fluid saturations are bounded between 0 and 1 by construction, therefore overcoming a serious limitation of previous models. Our numerical simulations show that the proposed model also resolves the pinning behavior at the base of the infiltration front, and the asymmetric behavior of the fingers at material interfaces observed experimentally.

A data-driven prediction method for fast-slow systems

NASA Astrophysics Data System (ADS)

Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

2016-04-01

In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

Validation of buoyancy driven spectral tensor model using HATS data

NASA Astrophysics Data System (ADS)

Chougule, A.; Mann, J.; Kelly, M.; Larsen, G. C.

2016-09-01

We present a homogeneous spectral tensor model for wind velocity and temperature fluctuations, driven by mean vertical shear and mean temperature gradient. Results from the model, including one-dimensional velocity and temperature spectra and the associated co-spectra, are shown in this paper. The model also reproduces two-point statistics, such as coherence and phases, via cross-spectra between two points separated in space. Model results are compared with observations from the Horizontal Array Turbulence Study (HATS) field program (Horst et al. 2004). The spectral velocity tensor in the model is described via five parameters: the dissipation rate (ɛ), length scale of energy-containing eddies (L), a turbulence anisotropy parameter (Γ), gradient Richardson number (Ri) representing the atmospheric stability and the rate of destruction of temperature variance (ηθ).

Over-time associations among parental self-efficacy, promotive parenting practices, and adolescents' externalizing behaviors.

PubMed

Glatz, Terese; Buchanan, Christy M

2015-06-01

Parental self-efficacy (PSE) is defined as parents' beliefs about their abilities to influence their children in a way that fosters their children's positive development. Research has shown links among PSE, parenting, and children's behavior (Jones & Prinz, 2005), but there are still questions concerning the associations over time. Theory predicts 3 types of processes relevant to these associations: a PSE-driven process, a parent-behavior-driven process, and a child-driven process. In this study, we tested these processes during early to middle adolescence using reports from 401 parents (286 mothers, 115 fathers) from 305 families, and their adolescents (Mage = 11.5 years), at 3 time points. Cross-lagged panel models were used to examine the associations among PSE, promotive parenting practices, and adolescents' externalizing. Results supported a PSE-driven process for mothers within early adolescence. In addition, evidence for parent-behavior-driven and child-driven processes emerged at different times within this developmental period. (c) 2015 APA, all rights reserved).

Climate-change driven range shifts of anchovy biomass projected by bio-physical coupling individual based model in the marginal seas of East Asia

NASA Astrophysics Data System (ADS)

Jung, Sukgeun; Pang, Ig-Chan; Lee, Joon-ho; Lee, Kyunghwan

2016-12-01

Recent studies in the western North Pacific reported a declining standing stock biomass of anchovy ( Engraulis japonicus) in the Yellow Sea and a climate-driven southward shift of anchovy catch in Korean waters. We investigated the effects of a warming ocean on the latitudinal shift of anchovy catch by developing and applying individual-based models (IBMs) based on a regional ocean circulation model and an IPCC climate change scenario. Despite the greater uncertainty, our two IBMs projected that, by the 2030s, the strengthened Tsushima warm current in the Korea Strait and the East Sea, driven by global warming, and the subsequent confinement of the relatively cold water masses within the Yellow Sea will decrease larval anchovy biomass in the Yellow Sea, but will increase it in the Korea Strait and the East Sea. The decreasing trend of anchovy biomass in the Yellow Sea was reproduced by our models, but further validation and enhancement of the models is required together with extended ichthyoplankton surveys to understand and reliably project range shifts of anchovy and the impacts such range shifts will have on the marine ecosystems and fisheries in the region.

A zebrafish model of chordoma initiated by notochord-driven expression of HRASV12

PubMed Central

Burger, Alexa; Vasilyev, Aleksandr; Tomar, Ritu; Selig, Martin K.; Nielsen, G. Petur; Peterson, Randall T.; Drummond, Iain A.; Haber, Daniel A.

2014-01-01

Chordoma is a malignant tumor thought to arise from remnants of the embryonic notochord, with its origin in the bones of the axial skeleton. Surgical resection is the standard treatment, usually in combination with radiation therapy, but neither chemotherapeutic nor targeted therapeutic approaches have demonstrated success. No animal model and only few chordoma cell lines are available for preclinical drug testing, and, although no druggable genetic drivers have been identified, activation of EGFR and downstream AKT-PI3K pathways have been described. Here, we report a zebrafish model of chordoma, based on stable transgene-driven expression of HRASV12 in notochord cells during development. Extensive intra-notochordal tumor formation is evident within days of transgene expression, ultimately leading to larval death. The zebrafish tumors share characteristics of human chordoma as demonstrated by immunohistochemistry and electron microscopy. The mTORC1 inhibitor rapamycin, which has some demonstrated activity in a chordoma cell line, delays the onset of tumor formation in our zebrafish model, and improves survival of tumor-bearing fish. Consequently, the HRASV12-driven zebrafish model of chordoma could enable high-throughput screening of potential therapeutic agents for the treatment of this refractory cancer. PMID:24311731

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education.

PubMed

Hervatis, Vasilis; Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-10-06

Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. A deductive case study approach was applied to develop the conceptual model. The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education

PubMed Central

Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-01-01

Background Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. Objective The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. Methods A deductive case study approach was applied to develop the conceptual model. Results The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. Conclusions The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach. PMID:27731840

The beam-driven chromospheric evaporation model of solar flares - A model not supported by observations from nonimpulsive large flares

NASA Technical Reports Server (NTRS)

Feldman, U.

1990-01-01

Most large solar flares exhibit hard X-ray emission which is usually impulsive, as well as thermal soft X-ray emission, which is gradual. The beam-driven chromospheric evaporation model of solar flares was proposed to explain the origin of the soft X-ray emitting flare plasma. A careful evaluation of the issue under discussion reveals contradictions between predictions from the theoretical chromospheric evaporation model and actual observations from a set of large X- and M-type flares. It is shown that although the soft X-ray and hard X-ray emissions are a result of the same flare, one is not a result of the other.

Prediction of the interior noise levels of high-speed propeller-driven aircraft

NASA Technical Reports Server (NTRS)

Rennison, D. C.; Wilby, J. F.; Wilby, E. G.

1980-01-01

The theoretical basis for an analytical model developed to predict the interior noise levels of high-speed propeller-driven airplanes is presented. Particular emphasis is given to modeling the transmission of discrete tones through a fuselage element into a cavity, estimates for the mean and standard deviation of the acoustic power flow, the coupling between a non-homogeneous excitation and the fuselage vibration response, and the prediction of maximum interior noise levels. The model allows for convenient examination of the various roles of the excitation and fuselage structural characteristics on the fuselage vibration response and the interior noise levels, as is required for the design of model or prototype noise control validation tests.

A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs

PubMed Central

de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

2014-01-01

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. PMID:25036766

A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

PubMed

de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

2014-01-01

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

USGS Publications Warehouse

Friedel, Michael J.

2011-01-01

This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization

PubMed Central

Marai, G. Elisabeta

2018-01-01

Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage—and its evaluation—of existing, higher-level visualization design models. In a departure from existing visualization design models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature. PMID:28866550

To react or not to react? Intrinsic stochasticity of human control in virtual stick balancing

PubMed Central

Zgonnikov, Arkady; Lubashevsky, Ihor; Kanemoto, Shigeru; Miyazawa, Toru; Suzuki, Takashi

2014-01-01

Understanding how humans control unstable systems is central to many research problems, with applications ranging from quiet standing to aircraft landing. Increasingly, much evidence appears in favour of event-driven control hypothesis: human operators only start actively controlling the system when the discrepancy between the current and desired system states becomes large enough. The event-driven models based on the concept of threshold can explain many features of the experimentally observed dynamics. However, much still remains unclear about the dynamics of human-controlled systems, which likely indicates that humans use more intricate control mechanisms. This paper argues that control activation in humans may be not threshold-driven, but instead intrinsically stochastic, noise-driven. Specifically, we suggest that control activation stems from stochastic interplay between the operator's need to keep the controlled system near the goal state, on the one hand, and the tendency to postpone interrupting the system dynamics, on the other hand. We propose a model capturing this interplay and show that it matches the experimental data on human balancing of virtual overdamped stick. Our results illuminate that the noise-driven activation mechanism plays a crucial role at least in the considered task, and, hypothetically, in a broad range of human-controlled processes. PMID:25056217

Plasma Radiation and Acceleration Effectiveness of CME-driven Shocks

NASA Astrophysics Data System (ADS)

Gopalswamy, N.; Schmidt, J. M.

2008-05-01

CME-driven shocks are effective radio radiation generators and accelerators for Solar Energetic Particles (SEPs). We present simulated 3 D time-dependent radio maps of second order plasma radiation generated by CME- driven shocks. The CME with its shock is simulated with the 3 D BATS-R-US CME model developed at the University of Michigan. The radiation is simulated using a kinetic plasma model that includes shock drift acceleration of electrons and stochastic growth theory of Langmuir waves. We find that in a realistic 3 D environment of magnetic field and solar wind outflow of the Sun the CME-driven shock shows a detailed spatial structure of the density, which is responsible for the fine structure of type II radio bursts. We also show realistic 3 D reconstructions of the magnetic cloud field of the CME, which is accelerated outward by magnetic buoyancy forces in the diverging magnetic field of the Sun. The CME-driven shock is reconstructed by tomography using the maximum jump in the gradient of the entropy. In the vicinity of the shock we determine the Alfven speed of the plasma. This speed profile controls how steep the shock can grow and how stable the shock remains while propagating away from the Sun. Only a steep shock can provide for an effective particle acceleration.

Plasma radiation and acceleration effectiveness of CME-driven shocks

NASA Astrophysics Data System (ADS)

Schmidt, Joachim

CME-driven shocks are effective radio radiation generators and accelerators for Solar Energetic Particles (SEPs). We present simulated 3 D time-dependent radio maps of second order plasma radiation generated by CME-driven shocks. The CME with its shock is simulated with the 3 D BATS-R-US CME model developed at the University of Michigan. The radiation is simulated using a kinetic plasma model that includes shock drift acceleration of electrons and stochastic growth theory of Langmuir waves. We find that in a realistic 3 D environment of magnetic field and solar wind outflow of the Sun the CME-driven shock shows a detailed spatial structure of the density, which is responsible for the fine structure of type II radio bursts. We also show realistic 3 D reconstructions of the magnetic cloud field of the CME, which is accelerated outward by magnetic buoyancy forces in the diverging magnetic field of the Sun. The CME-driven shock is reconstructed by tomography using the maximum jump in the gradient of the entropy. In the vicinity of the shock we determine the Alfven speed of the plasma. This speed profile controls how steep the shock can grow and how stable the shock remains while propagating away from the Sun. Only a steep shock can provide for an effective particle acceleration.

Nation-scale adoption of new medicines by doctors: an application of the Bass diffusion model

PubMed Central

2012-01-01

Background The adoption of new medicines is influenced by a complex set of social processes that have been widely examined in terms of individual prescribers’ information-seeking and decision-making behaviour. However, quantitative, population-wide analyses of how long it takes for new healthcare practices to become part of mainstream practice are rare. Methods We applied a Bass diffusion model to monthly prescription volumes of 103 often-prescribed drugs in Australia (monthly time series data totalling 803 million prescriptions between 1992 and 2010), to determine the distribution of adoption rates. Our aim was to test the utility of applying the Bass diffusion model to national-scale prescribing volumes. Results The Bass diffusion model was fitted to the adoption of a broad cross-section of drugs using national monthly prescription volumes from Australia (median R2 = 0.97, interquartile range 0.95 to 0.99). The median time to adoption was 8.2 years (IQR 4.9 to 12.1). The model distinguished two classes of prescribing patterns – those where adoption appeared to be driven mostly by external forces (19 drugs) and those driven mostly by social contagion (84 drugs). Those driven more prominently by internal forces were found to have shorter adoption times (p = 0.02 in a non-parametric analysis of variance by ranks). Conclusion The Bass diffusion model may be used to retrospectively represent the patterns of adoption exhibited in prescription volumes in Australia, and distinguishes between adoption driven primarily by external forces such as regulation, or internal forces such as social contagion. The eight-year delay between the introduction of a new medicine and the adoption of the prescribing practice suggests the presence of system inertia in Australian prescribing practices. PMID:22876867

The design, hysteresis modeling and control of a novel SMA-fishing-line actuator

NASA Astrophysics Data System (ADS)

Xiang, Chaoqun; Yang, Hui; Sun, Zhiyong; Xue, Bangcan; Hao, Lina; Asadur Rahoman, M. D.; Davis, Steve

2017-03-01

Fishing line can be combined with shape memory alloy (SMA) to form novel artificial muscle actuators which have low cost, are lightweight and soft. They can be applied in bionic, wearable and rehabilitation robots, and can reduce system weight and cost, increase power-to-weight ratio and offer safer physical human-robot interaction. However, these actuators possess several disadvantages, for example fishing line based actuators possess low strength and are complex to drive, and SMA possesses a low percentage contraction and has high hysteresis. This paper presents a novel artificial actuator (known as an SMA-fishing-line) made of fishing line and SMA twisted then coiled together, which can be driven directly by an electrical voltage. Its output force can reach 2.65 N at 7.4 V drive voltage, and the percentage contraction at 4 V driven voltage with a 3 N load is 7.53%. An antagonistic bionic joint driven by the novel SMA-fishing-line actuators is presented, and based on an extended unparallel Prandtl-Ishlinskii (EUPI) model, its hysteresis behavior is established, and the error ratio of the EUPI model is determined to be 6.3%. A Joule heat model of the SMA-fishing-line is also presented, and the maximum error of the established model is 0.510 mm. Based on this accurate hysteresis model, a composite PID controller consisting of PID and an integral inverse (I-I) compensator is proposed and its performance is compared with a traditional PID controller through simulations and experimentation. These results show that the composite PID controller possesses higher control precision than basic PID, and is feasible for implementation in an SMA-fishing-line driven antagonistic bionic joint.

Fault kinematics and active tectonics of the Sabah margin: Insights from the 2015, Mw 6.0, Mt. Kinabalu earthquake

NASA Astrophysics Data System (ADS)

Wang, Y.; Wei, S.; Tapponnier, P.; WANG, X.; Lindsey, E.; Sieh, K.

2016-12-01

A gravity-driven "Mega-Landslide" model has been evoked to explain the shortening seen offshore Sabah and Brunei in oil-company seismic data. Although this model is considered to account simultaneously for recent folding at the edge of the submarine NW Sabah trough and normal faulting on the Sabah shelf, such a gravity-driven model is not consistent with geodetic data or critical examination of extant structural restorations. The rupture that produced the 2015 Mw6.0 Mt. Kinabalu earthquake is also inconsistent with the gravity-driven model. Our teleseismic analysis shows that the centroid depth of that earthquake's mainshock was 13 to 14 km, and its favored fault-plane solution is a 60° NW-dipping normal fault. Our finite-rupture model exhibits major fault slip between 5 and 15 km depth, in keeping with our InSAR analysis, which shows no appreciable surface deformation. Both the hypocentral depth and the depth of principal slip are far too deep to be explained by gravity-driven failure, as such a model would predict a listric normal fault connecting at a much shallower depth with a very gentle detachment. Our regional mapping of tectonic landforms also suggests the recent rupture is part of a 200-km long system of narrowly distributed active extension in northern Sabah. Taken together, the nature of the 2015 rupture, the belt of active normal faults, and structural consideration indicate that active tectonic shortening plays the leading role in controlling the overall deformation of northern Sabah and that deep-seated, onland normal faulting likely results from an abrupt change in the dip-angle of the collision interface beneath the Sabah accretionary prism.

Analytic expressions for ULF wave radiation belt radial diffusion coefficients

PubMed Central

Ozeke, Louis G; Mann, Ian R; Murphy, Kyle R; Jonathan Rae, I; Milling, David K

2014-01-01

We present analytic expressions for ULF wave-derived radiation belt radial diffusion coefficients, as a function of L and Kp, which can easily be incorporated into global radiation belt transport models. The diffusion coefficients are derived from statistical representations of ULF wave power, electric field power mapped from ground magnetometer data, and compressional magnetic field power from in situ measurements. We show that the overall electric and magnetic diffusion coefficients are to a good approximation both independent of energy. We present example 1-D radial diffusion results from simulations driven by CRRES-observed time-dependent energy spectra at the outer boundary, under the action of radial diffusion driven by the new ULF wave radial diffusion coefficients and with empirical chorus wave loss terms (as a function of energy, Kp and L). There is excellent agreement between the differential flux produced by the 1-D, Kp-driven, radial diffusion model and CRRES observations of differential electron flux at 0.976 MeV—even though the model does not include the effects of local internal acceleration sources. Our results highlight not only the importance of correct specification of radial diffusion coefficients for developing accurate models but also show significant promise for belt specification based on relatively simple models driven by solar wind parameters such as solar wind speed or geomagnetic indices such as Kp. Key Points Analytic expressions for the radial diffusion coefficients are presented The coefficients do not dependent on energy or wave m value The electric field diffusion coefficient dominates over the magnetic PMID:26167440

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

NASA Astrophysics Data System (ADS)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

Consistent model driven architecture

NASA Astrophysics Data System (ADS)

Niepostyn, Stanisław J.

2015-09-01

The goal of the MDA is to produce software systems from abstract models in a way where human interaction is restricted to a minimum. These abstract models are based on the UML language. However, the semantics of UML models is defined in a natural language. Subsequently the verification of consistency of these diagrams is needed in order to identify errors in requirements at the early stage of the development process. The verification of consistency is difficult due to a semi-formal nature of UML diagrams. We propose automatic verification of consistency of the series of UML diagrams originating from abstract models implemented with our consistency rules. This Consistent Model Driven Architecture approach enables us to generate automatically complete workflow applications from consistent and complete models developed from abstract models (e.g. Business Context Diagram). Therefore, our method can be used to check practicability (feasibility) of software architecture models.

Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

NASA Technical Reports Server (NTRS)

Boulatov, Alexei; Smelyanskiy, Vadier N.

2004-01-01

We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models

NASA Astrophysics Data System (ADS)

Booth, B. B. B.; Bernie, D.; McNeall, D.; Hawkins, E.; Caesar, J.; Boulton, C.; Friedlingstein, P.; Sexton, D. M. H.

2013-04-01

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10-90th percentile ranges of 1.7 K for the aggressive mitigation scenario, up to 3.9 K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climate-sensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work.

Aspect-Oriented Model-Driven Software Product Line Engineering

NASA Astrophysics Data System (ADS)

Groher, Iris; Voelter, Markus

Software product line engineering aims to reduce development time, effort, cost, and complexity by taking advantage of the commonality within a portfolio of similar products. The effectiveness of a software product line approach directly depends on how well feature variability within the portfolio is implemented and managed throughout the development lifecycle, from early analysis through maintenance and evolution. This article presents an approach that facilitates variability implementation, management, and tracing by integrating model-driven and aspect-oriented software development. Features are separated in models and composed of aspect-oriented composition techniques on model level. Model transformations support the transition from problem to solution space models. Aspect-oriented techniques enable the explicit expression and modularization of variability on model, template, and code level. The presented concepts are illustrated with a case study of a home automation system.

Statistical distributions of avalanche size and waiting times in an inter-sandpile cascade model

NASA Astrophysics Data System (ADS)

Batac, Rene; Longjas, Anthony; Monterola, Christopher

2012-02-01

Sandpile-based models have successfully shed light on key features of nonlinear relaxational processes in nature, particularly the occurrence of fat-tailed magnitude distributions and exponential return times, from simple local stress redistributions. In this work, we extend the existing sandpile paradigm into an inter-sandpile cascade, wherein the avalanches emanating from a uniformly-driven sandpile (first layer) is used to trigger the next (second layer), and so on, in a successive fashion. Statistical characterizations reveal that avalanche size distributions evolve from a power-law p(S)≈S-1.3 for the first layer to gamma distributions p(S)≈Sαexp(-S/S0) for layers far away from the uniformly driven sandpile. The resulting avalanche size statistics is found to be associated with the corresponding waiting time distribution, as explained in an accompanying analytic formulation. Interestingly, both the numerical and analytic models show good agreement with actual inventories of non-uniformly driven events in nature.

Cross-species identification of genomic drivers of squamous cell carcinoma development across preneoplastic intermediates

PubMed Central

Chitsazzadeh, Vida; Coarfa, Cristian; Drummond, Jennifer A.; Nguyen, Tri; Joseph, Aaron; Chilukuri, Suneel; Charpiot, Elizabeth; Adelmann, Charles H.; Ching, Grace; Nguyen, Tran N.; Nicholas, Courtney; Thomas, Valencia D.; Migden, Michael; MacFarlane, Deborah; Thompson, Erika; Shen, Jianjun; Takata, Yoko; McNiece, Kayla; Polansky, Maxim A.; Abbas, Hussein A.; Rajapakshe, Kimal; Gower, Adam; Spira, Avrum; Covington, Kyle R.; Xiao, Weimin; Gunaratne, Preethi; Pickering, Curtis; Frederick, Mitchell; Myers, Jeffrey N.; Shen, Li; Yao, Hui; Su, Xiaoping; Rapini, Ronald P.; Wheeler, David A.; Hawk, Ernest T.; Flores, Elsa R.; Tsai, Kenneth Y.

2016-01-01

Cutaneous squamous cell carcinoma (cuSCC) comprises 15–20% of all skin cancers, accounting for over 700,000 cases in USA annually. Most cuSCC arise in association with a distinct precancerous lesion, the actinic keratosis (AK). To identify potential targets for molecularly targeted chemoprevention, here we perform integrated cross-species genomic analysis of cuSCC development through the preneoplastic AK stage using matched human samples and a solar ultraviolet radiation-driven Hairless mouse model. We identify the major transcriptional drivers of this progression sequence, showing that the key genomic changes in cuSCC development occur in the normal skin to AK transition. Our data validate the use of this ultraviolet radiation-driven mouse cuSCC model for cross-species analysis and demonstrate that cuSCC bears deep molecular similarities to multiple carcinogen-driven SCCs from diverse sites, suggesting that cuSCC may serve as an effective, accessible model for multiple SCC types and that common treatment and prevention strategies may be feasible. PMID:27574101

Microwave Driven Actuators Power Allocation and Distribution

NASA Technical Reports Server (NTRS)

Forbes, Timothy; Song, Kyo D.

2000-01-01

Design, fabrication and test of a power allocation and distribution (PAD) network for microwave driven actuators is presented in this paper. Development of a circuit that would collect power from a rectenna array amplify and distribute the power to actuators was designed and fabricated for space application in an actuator array driven by a microwave. A P-SPICE model was constructed initially for data reduction purposes, and was followed by a working real-world model. A voltage up - converter (VUC) is used to amplify the voltage from the individual rectenna. The testing yielded a 26:1 voltage amplification ratio with input voltage at 9 volts and a measured output voltage 230VDC. Future work includes the miniaturization of the circuitry, the use of microwave remote control, and voltage amplification technology for each voltage source. The objective of this work is to develop a model system that will collect DC voltage from an array of rectenna and propagate the voltage to an array of actuators.

Non-equilibrium phase transitions in a driven-dissipative system of interacting bosons

NASA Astrophysics Data System (ADS)

Young, Jeremy T.; Foss-Feig, Michael; Gorshkov, Alexey V.; Maghrebi, Mohammad F.

2017-04-01

Atomic, molecular, and optical systems provide unique opportunities to study simple models of driven-dissipative many-body quantum systems. Typically, one is interested in the resultant steady state, but the non-equilibrium nature of the physics involved presents several problems in understanding its behavior theoretically. Recently, it has been shown that in many of these models, it is possible to map the steady-state phase transitions onto classical equilibrium phase transitions. In the language of Keldysh field theory, this relation typically only becomes apparent after integrating out massive fields near the critical point, leaving behind a single massless field undergoing near-equilibrium dynamics. In this talk, we study a driven-dissipative XXZ bosonic model and discover critical points at which two fields become gapless. Each critical point separates three different possible phases: a uniform phase, an anti-ferromagnetic phase, and a limit cycle phase. Furthermore, a description in terms of an equilibrium phase transition does not seem possible, so the associated phase transitions appear to be inherently non-equilibrium.

Autonomy and Non-autonomy of Angiogenic Cell Movements Revealed by Experiment-Driven Mathematical Modeling.

PubMed

Sugihara, Kei; Nishiyama, Koichi; Fukuhara, Shigetomo; Uemura, Akiyoshi; Arima, Satoshi; Kobayashi, Ryo; Köhn-Luque, Alvaro; Mochizuki, Naoki; Suda, Toshio; Ogawa, Hisao; Kurihara, Hiroki

2015-12-01

Angiogenesis is a multicellular phenomenon driven by morphogenetic cell movements. We recently reported morphogenetic vascular endothelial cell (EC) behaviors to be dynamic and complex. However, the principal mechanisms orchestrating individual EC movements in angiogenic morphogenesis remain largely unknown. Here we present an experiment-driven mathematical model that enables us to systematically dissect cellular mechanisms in branch elongation. We found that cell-autonomous and coordinated actions governed these multicellular behaviors, and a cell-autonomous process sufficiently illustrated essential features of the morphogenetic EC dynamics at both the single-cell and cell-population levels. Through refining our model and experimental verification, we further identified a coordinated mode of tip EC behaviors regulated via a spatial relationship between tip and follower ECs, which facilitates the forward motility of tip ECs. These findings provide insights that enhance our mechanistic understanding of not only angiogenic morphogenesis, but also other types of multicellular phenomenon. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Active machine learning-driven experimentation to determine compound effects on protein patterns

PubMed Central

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-01-01

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance. DOI: http://dx.doi.org/10.7554/eLife.10047.001 PMID:26840049

Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator

PubMed Central

Huang, Weidong; Li, Kun; Wang, Gan; Wang, Yingzhe

2013-01-01

Abstract In this article, we present a newly designed inverse umbrella surface aerator, and tested its performance in driving flow of an oxidation ditch. Results show that it has a better performance in driving the oxidation ditch than the original one with higher average velocity and more uniform flow field. We also present a computational fluid dynamics model for predicting the flow field in an oxidation ditch driven by a surface aerator. The improved momentum source term approach to simulate the flow field of the oxidation ditch driven by an inverse umbrella surface aerator was developed and validated through experiments. Four kinds of turbulent models were investigated with the approach, including the standard k−ɛ model, RNG k−ɛ model, realizable k−ɛ model, and Reynolds stress model, and the predicted data were compared with those calculated with the multiple rotating reference frame approach (MRF) and sliding mesh approach (SM). Results of the momentum source term approach are in good agreement with the experimental data, and its prediction accuracy is better than MRF, close to SM. It is also found that the momentum source term approach has lower computational expenses, is simpler to preprocess, and is easier to use. PMID:24302850

A data driven nonlinear stochastic model for blood glucose dynamics.

PubMed

Zhang, Yan; Holt, Tim A; Khovanova, Natalia

2016-03-01

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

A Comparison and Evaluation of Real-Time Software Systems Modeling Languages

NASA Technical Reports Server (NTRS)

Evensen, Kenneth D.; Weiss, Kathryn Anne

2010-01-01

A model-driven approach to real-time software systems development enables the conceptualization of software, fostering a more thorough understanding of its often complex architecture and behavior while promoting the documentation and analysis of concerns common to real-time embedded systems such as scheduling, resource allocation, and performance. Several modeling languages have been developed to assist in the model-driven software engineering effort for real-time systems, and these languages are beginning to gain traction with practitioners throughout the aerospace industry. This paper presents a survey of several real-time software system modeling languages, namely the Architectural Analysis and Design Language (AADL), the Unified Modeling Language (UML), Systems Modeling Language (SysML), the Modeling and Analysis of Real-Time Embedded Systems (MARTE) UML profile, and the AADL for UML profile. Each language has its advantages and disadvantages, and in order to adequately describe a real-time software system's architecture, a complementary use of multiple languages is almost certainly necessary. This paper aims to explore these languages in the context of understanding the value each brings to the model-driven software engineering effort and to determine if it is feasible and practical to combine aspects of the various modeling languages to achieve more complete coverage in architectural descriptions. To this end, each language is evaluated with respect to a set of criteria such as scope, formalisms, and architectural coverage. An example is used to help illustrate the capabilities of the various languages.

Systematic features of axisymmetric neutrino-driven core-collapse supernova models in multiple progenitors

NASA Astrophysics Data System (ADS)

Nakamura, Ko; Takiwaki, Tomoya; Kuroda, Takami; Kotake, Kei

2015-12-01

We present an overview of two-dimensional (2D) core-collapse supernova simulations employing a neutrino transport scheme by the isotropic diffusion source approximation. We study 101 solar-metallicity, 247 ultra metal-poor, and 30 zero-metal progenitors covering zero-age main sequence mass from 10.8 M⊙ to 75.0 M⊙. Using the 378 progenitors in total, we systematically investigate how the differences in the structures of these multiple progenitors impact the hydrodynamics evolution. By following a long-term evolution over 1.0 s after bounce, most of the computed models exhibit neutrino-driven revival of the stalled bounce shock at ˜200-800 ms postbounce, leading to the possibility of explosion. Pushing the boundaries of expectations in previous one-dimensional studies, our results confirm that the compactness parameter ξ that characterizes the structure of the progenitors is also a key in 2D to diagnosing the properties of neutrino-driven explosions. Models with high ξ undergo high ram pressure from the accreting matter onto the stalled shock, which affects the subsequent evolution of the shock expansion and the mass of the protoneutron star under the influence of neutrino-driven convection and the standing accretion-shock instability. We show that the accretion luminosity becomes higher for models with high ξ, which makes the growth rate of the diagnostic explosion energy higher and the synthesized nickel mass bigger. We find that these explosion characteristics tend to show a monotonic increase as a function of the compactness parameter ξ.

A homeostatic-driven turnover remodelling constitutive model for healing in soft tissues

PubMed Central

Gasser, T. Christian; Bellomo, Facundo J.

2016-01-01

Remodelling of soft biological tissue is characterized by interacting biochemical and biomechanical events, which change the tissue's microstructure, and, consequently, its macroscopic mechanical properties. Remodelling is a well-defined stage of the healing process, and aims at recovering or repairing the injured extracellular matrix. Like other physiological processes, remodelling is thought to be driven by homeostasis, i.e. it tends to re-establish the properties of the uninjured tissue. However, homeostasis may never be reached, such that remodelling may also appear as a continuous pathological transformation of diseased tissues during aneurysm expansion, for example. A simple constitutive model for soft biological tissues that regards remodelling as homeostatic-driven turnover is developed. Specifically, the recoverable effective tissue damage, whose rate is the sum of a mechanical damage rate and a healing rate, serves as a scalar internal thermodynamic variable. In order to integrate the biochemical and biomechanical aspects of remodelling, the healing rate is, on the one hand, driven by mechanical stimuli, but, on the other hand, subjected to simple metabolic constraints. The proposed model is formulated in accordance with continuum damage mechanics within an open-system thermodynamics framework. The numerical implementation in an in-house finite-element code is described, particularized for Ogden hyperelasticity. Numerical examples illustrate the basic constitutive characteristics of the model and demonstrate its potential in representing aspects of remodelling of soft tissues. Simulation results are verified for their plausibility, but also validated against reported experimental data. PMID:27009177

A homeostatic-driven turnover remodelling constitutive model for healing in soft tissues.

PubMed

Comellas, Ester; Gasser, T Christian; Bellomo, Facundo J; Oller, Sergio

2016-03-01

Remodelling of soft biological tissue is characterized by interacting biochemical and biomechanical events, which change the tissue's microstructure, and, consequently, its macroscopic mechanical properties. Remodelling is a well-defined stage of the healing process, and aims at recovering or repairing the injured extracellular matrix. Like other physiological processes, remodelling is thought to be driven by homeostasis, i.e. it tends to re-establish the properties of the uninjured tissue. However, homeostasis may never be reached, such that remodelling may also appear as a continuous pathological transformation of diseased tissues during aneurysm expansion, for example. A simple constitutive model for soft biological tissues that regards remodelling as homeostatic-driven turnover is developed. Specifically, the recoverable effective tissue damage, whose rate is the sum of a mechanical damage rate and a healing rate, serves as a scalar internal thermodynamic variable. In order to integrate the biochemical and biomechanical aspects of remodelling, the healing rate is, on the one hand, driven by mechanical stimuli, but, on the other hand, subjected to simple metabolic constraints. The proposed model is formulated in accordance with continuum damage mechanics within an open-system thermodynamics framework. The numerical implementation in an in-house finite-element code is described, particularized for Ogden hyperelasticity. Numerical examples illustrate the basic constitutive characteristics of the model and demonstrate its potential in representing aspects of remodelling of soft tissues. Simulation results are verified for their plausibility, but also validated against reported experimental data. © 2016 The Author(s).

Enrichment Clusters: A Practical Plan for Real-World, Student-Driven Learning.

ERIC Educational Resources Information Center

Renzulli, Joseph S.; Gentry, Marcia; Reis, Sally M.

This guidebook provides a rationale and guidelines for implementing a student-driven learning approach using enrichment clusters. Enrichment clusters allow students who share a common interest to meet each week to produce a product, performance, or targeted service based on that common interest. Chapter 1 discusses different models of learning.…

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

Reanalysis of Clause Boundaries in Japanese as a Constraint-Driven Process.

ERIC Educational Resources Information Center

Miyamoto, Edson T.

2003-01-01

Reports on two experiments that focus on clause boundaries in Japanese that suggest that minimal change restriction is unnecessary to characterize reanalysis. Proposes that the data and previous observations are more naturally explained by a constraint-driven model in which revisions are performed only when required by parsing constraints.…

Feature Integration Theory Revisited: Dissociating Feature Detection and Attentional Guidance in Visual Search

ERIC Educational Resources Information Center

Chan, Louis K. H.; Hayward, William G.

2009-01-01

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed…

The Future of Family Engagement in Residential Care Settings

ERIC Educational Resources Information Center

Affronti, Melissa L.; Levison-Johnson, Jody

2009-01-01

Residential programs for children and youth are increasingly implementing engagement strategies to promote family-centered and family-driven models of care (Leichtman, 2008). The practice of engagement is a fairly new area of research, especially in residential care. Driven by their goal to increase the use of state-of-the-art family engagement…

Current uses of Web 2.0 applications in transportation : case studies of select state departments of transportation

DOT National Transportation Integrated Search

2010-03-01

Web 2.0 is an umbrella term for websites or online applications that are user-driven and emphasize collaboration and user interactivity. The trend away from static web pages to a more user-driven Internet model has also occurred in the public s...

Closing the Loop: How We Better Serve Our Students through a Comprehensive Assessment Process

ERIC Educational Resources Information Center

Arcario, Paul; Eynon, Bret; Klages, Marisa; Polnariev, Bernard A.

2013-01-01

Outcomes assessment is often driven by demands for accountability. LaGuardia Community College's outcomes assessment model has advanced student learning, shaped academic program development, and created an impressive culture of faculty-driven assessment. Our inquiry-based approach uses ePortfolios for collection of student work and demonstrates…

Application-Driven Educational Game to Assist Young Children in Learning English Vocabulary

ERIC Educational Resources Information Center

Chen, Zhi-Hong; Lee, Shu-Yu

2018-01-01

This paper describes the development of an educational game, named My-Pet-Shop, to enhance young children's learning of English vocabulary. The educational game is underpinned by an application-driven model, which consists of three components: application scenario, subject learning, and learning regulation. An empirical study is further conducted…

Using Flexible Data-Driven Frameworks to Enhance School Psychology Training and Practice

ERIC Educational Resources Information Center

Coleman, Stephanie L.; Hendricker, Elise

2016-01-01

While a great number of scientific advances have been made in school psychology, the research to practice gap continues to exist, which has significant implications for training future school psychologists. Training in flexible, data-driven models may help school psychology trainees develop important competencies that will benefit them throughout…

Spin and topological order in a periodically driven spin chain

NASA Astrophysics Data System (ADS)

Russomanno, Angelo; Friedman, Bat-el; Dalla Torre, Emanuele G.

2017-07-01

The periodically driven quantum Ising chain has recently attracted a large attention in the context of Floquet engineering. In addition to the common paramagnet and ferromagnet, this driven model can give rise to new topological phases. In this work, we systematically explore its quantum phase diagram by examining the properties of its Floquet ground state. We specifically focus on driving protocols with time-reversal invariant points, and demonstrate the existence of an infinite number of distinct phases. These phases are separated by second-order quantum phase transitions, accompanied by continuous changes of local and string order parameters, as well as sudden changes of a topological winding number and of the number of protected edge states. When one of these phase transitions is adiabatically crossed, the correlator associated to the order parameter is nonvanishing over a length scale which shows a Kibble-Zurek scaling. In some phases, the Floquet ground state spontaneously breaks the discrete time-translation symmetry of the Hamiltonian. Our findings provide a better understanding of topological phases in periodically driven clean integrable models.

Space-based laser-driven MHD generator: Feasibility study

NASA Technical Reports Server (NTRS)

Choi, S. H.

1986-01-01

The feasibility of a laser-driven MHD generator, as a candidate receiver for a space-based laser power transmission system, was investigated. On the basis of reasonable parameters obtained in the literature, a model of the laser-driven MHD generator was developed with the assumptions of a steady, turbulent, two-dimensional flow. These assumptions were based on the continuous and steady generation of plasmas by the exposure of the continuous wave laser beam thus inducing a steady back pressure that enables the medium to flow steadily. The model considered here took the turbulent nature of plasmas into account in the two-dimensional geometry of the generator. For these conditions with the plasma parameters defining the thermal conductivity, viscosity, electrical conductivity for the plasma flow, a generator efficiency of 53.3% was calculated. If turbulent effects and nonequilibrium ionization are taken into account, the efficiency is 43.2%. The study shows that the laser-driven MHD system has potential as a laser power receiver for space applications because of its high energy conversion efficiency, high energy density and relatively simple mechanism as compared to other energy conversion cycles.

Continuous Energy Improvement in Motor Driven Systems - A Guidebook for Industry

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

Gilbert A. McCoy and John G. Douglass

2014-02-01

This guidebook provides a step-by-step approach to developing a motor system energy-improvement action plan. An action plan includes which motors should be repaired or replaced with higher efficiency models, recommendations on maintaining a spares inventory, and discussion of improvements in maintenance practices. The guidebook is the successor to DOE’s 1997 Energy Management for Motor Driven Systems. It builds on its predecessor publication by including topics such as power transmission systems and matching driven equipment to process requirements in addition to motors.

Research on influence factor about the dynamic characteristic of armored vehicle hydraulic-driven fan system

NASA Astrophysics Data System (ADS)

Chao, Zhiqiang; Mao, Feiyue; Liu, Xiangbo; Li, Huaying; Han, Shousong

2017-01-01

In view of the large power of armored vehicle cooling system, the demand for high fan speed control and energy saving, this paper expounds the basic composition and principle of hydraulic-driven fan system and establishes the mathematical model of the system. Through the simulation analysis of different parameters, such as displacement of motor and working volume of fan system, the influences of performance parameters on the dynamic characteristic of hydraulic-driven fan system are obtained, which can provide theoretical guidance for system optimization design.

Design and experiment of data-driven modeling and flutter control of a prototype wing

NASA Astrophysics Data System (ADS)

Lum, Kai-Yew; Xu, Cai-Lin; Lu, Zhenbo; Lai, Kwok-Leung; Cui, Yongdong

2017-06-01

This paper presents an approach for data-driven modeling of aeroelasticity and its application to flutter control design of a wind-tunnel wing model. Modeling is centered on system identification of unsteady aerodynamic loads using computational fluid dynamics data, and adopts a nonlinear multivariable extension of the Hammerstein-Wiener system. The formulation is in modal coordinates of the elastic structure, and yields a reduced-order model of the aeroelastic feedback loop that is parametrized by airspeed. Flutter suppression is thus cast as a robust stabilization problem over uncertain airspeed, for which a low-order H∞ controller is computed. The paper discusses in detail parameter sensitivity and observability of the model, the former to justify the chosen model structure, and the latter to provide a criterion for physical sensor placement. Wind tunnel experiments confirm the validity of the modeling approach and the effectiveness of the control design.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

The Role of Emotion-Driven Impulse Control Difficulties in the Relation Between Social Anxiety and Aggression.

PubMed

Dixon, Laura J; Tull, Matthew T; Lee, Aaron A; Kimbrel, Nathan A; Gratz, Kim L

2017-06-01

To enhance our understanding of the factors that may account for increased aggression in socially anxious individuals, this study examined associations among emotion-driven impulse control difficulties, social anxiety, and dimensions of aggression (i.e., hostility, anger, physical aggression, verbal aggression). Individuals (N = 107; 73.8% male; M age = 40.8 years) receiving residential substance abuse treatment participated in this cross-sectional study. Social anxiety symptoms were significantly positively correlated with emotion-driven impulse control difficulties, anger, and hostility, but not verbal or physical aggression. Separate models for each aggression facet were examined to test the direct and indirect paths. Bootstrapped mediation analyses indicated a significant indirect path from social anxiety symptoms to each facet of aggression through emotion-driven impulse control difficulties (ps < .05). Results highlight the potential utility of targeting emotion-driven impulse control difficulties to decrease aggression among socially anxious individuals. © 2016 Wiley Periodicals, Inc.

Application of Bounded Linear Stability Analysis Method for Metrics-Driven Adaptive Control

NASA Technical Reports Server (NTRS)

Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje

2009-01-01

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.

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

Klein, Steven Karl; Determan, John C.

Dynamic System Simulation (DSS) models of fissile solution systems have been developed and verified against a variety of historical configurations. DSS techniques have been applied specifically to subcritical accelerator-driven systems using fissile solution fuels of uranium. Initial DSS models were developed in DESIRE, a specialized simulation scripting language. In order to tailor the DSS models to specifically meet needs of system designers they were converted to a Visual Studio implementation, and one of these subsequently to National Instrument’s LabVIEW for human factors engineering and operator training. Specific operational characteristics of subcritical accelerator-driven systems have been examined using a DSS modelmore » tailored to this particular class using fissile fuel.« less

ODISEES: Ontology-Driven Interactive Search Environment for Earth Sciences

NASA Technical Reports Server (NTRS)

Rutherford, Matthew T.; Huffer, Elisabeth B.; Kusterer, John M.; Quam, Brandi M.

2015-01-01

This paper discusses the Ontology-driven Interactive Search Environment for Earth Sciences (ODISEES) project currently being developed to aid researchers attempting to find usable data among an overabundance of closely related data. ODISEES' ontological structure relies on a modular, adaptable concept modeling approach, which allows the domain to be modeled more or less as it is without worrying about terminology or external requirements. In the model, variables are individually assigned semantic content based on the characteristics of the measurements they represent, allowing intuitive discovery and comparison of data without requiring the user to sift through large numbers of data sets and variables to find the desired information.

Theoretical regime diagrams for thermally driven flows in a beta-plane channel. [in atmosphere

NASA Technical Reports Server (NTRS)

Geisler, J. E.; Fowlis, W. W.

1979-01-01

It is noted that thermally driven flows in rotating laboratory containers with cylindrical geometry can be axially symmetric or wavelike depending on the experimental parameters. In anticipation that rotating fluid experiments might soon be done in spherical shell geometry, Barcilon's model has been extended to a beta-plane channel in order to gain a rough understanding of the effects of rotating spherical geometry. An incompressible fluid version of the Charney (1947) model of baroclinic instability, modified to include Ekman pumping at rigid horizontal boundaries is used. With this model, stability boundaries are mapped out for individual zonal wavenumbers in the parameter space used by Barcilon.

Analysis of dynamic behavior of multiple-stage planetary gear train used in wind driven generator.

PubMed

Wang, Jungang; Wang, Yong; Huo, Zhipu

2014-01-01

A dynamic model of multiple-stage planetary gear train composed of a two-stage planetary gear train and a one-stage parallel axis gear is proposed to be used in wind driven generator to analyze the influence of revolution speed and mesh error on dynamic load sharing characteristic based on the lumped parameter theory. Dynamic equation of the model is solved using numerical method to analyze the uniform load distribution of the system. It is shown that the load sharing property of the system is significantly affected by mesh error and rotational speed; load sharing coefficient and change rate of internal and external meshing of the system are of obvious difference from each other. The study provides useful theoretical guideline for the design of the multiple-stage planetary gear train of wind driven generator.

A necessary condition for dispersal driven growth of populations with discrete patch dynamics.

PubMed

Guiver, Chris; Packman, David; Townley, Stuart

2017-07-07

We revisit the question of when can dispersal-induced coupling between discrete sink populations cause overall population growth? Such a phenomenon is called dispersal driven growth and provides a simple explanation of how dispersal can allow populations to persist across discrete, spatially heterogeneous, environments even when individual patches are adverse or unfavourable. For two classes of mathematical models, one linear and one non-linear, we provide necessary conditions for dispersal driven growth in terms of the non-existence of a common linear Lyapunov function, which we describe. Our approach draws heavily upon the underlying positive dynamical systems structure. Our results apply to both discrete- and continuous-time models. The theory is illustrated with examples and both biological and mathematical conclusions are drawn. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Kähler-driven tribrid inflation

NASA Astrophysics Data System (ADS)

Antusch, Stefan; Nolde, David

2012-11-01

We discuss a new class of tribrid inflation models in supergravity, where the shape of the inflaton potential is dominated by effects from the Kähler potential. Tribrid inflation is a variant of hybrid inflation which is particularly suited for connecting inflation with particle physics, since the inflaton can be a D-flat combination of charged fields from the matter sector. In models of tribrid inflation studied so far, the inflaton potential was dominated by either loop corrections or by mixing effects with the waterfall field (as in "pseudosmooth" tribrid inflation). Here we investigate the third possibility, namely that tribrid inflation is dominantly driven by effects from higher-dimensional operators of the Kähler potential. We specify for which superpotential parameters the new regime is realized and show how it can be experimentally distinguished from the other two (loop-driven and "pseudosmooth") regimes.

Analysis of Dynamic Behavior of Multiple-Stage Planetary Gear Train Used in Wind Driven Generator

PubMed Central

Wang, Jungang; Wang, Yong; Huo, Zhipu

2014-01-01

A dynamic model of multiple-stage planetary gear train composed of a two-stage planetary gear train and a one-stage parallel axis gear is proposed to be used in wind driven generator to analyze the influence of revolution speed and mesh error on dynamic load sharing characteristic based on the lumped parameter theory. Dynamic equation of the model is solved using numerical method to analyze the uniform load distribution of the system. It is shown that the load sharing property of the system is significantly affected by mesh error and rotational speed; load sharing coefficient and change rate of internal and external meshing of the system are of obvious difference from each other. The study provides useful theoretical guideline for the design of the multiple-stage planetary gear train of wind driven generator. PMID:24511295

Spectral functions of a time-periodically driven Falicov-Kimball model: Real-space Floquet dynamical mean-field theory study

NASA Astrophysics Data System (ADS)

Qin, Tao; Hofstetter, Walter

2017-08-01

We present a systematic study of the spectral functions of a time-periodically driven Falicov-Kimball Hamiltonian. In the high-frequency limit, this system can be effectively described as a Harper-Hofstadter-Falicov-Kimball model. Using real-space Floquet dynamical mean-field theory (DMFT), we take into account the interaction effects and contributions from higher Floquet bands in a nonperturbative way. Our calculations show a high degree of similarity between the interacting driven system and its effective static counterpart with respect to spectral properties. However, as also illustrated by our results, one should bear in mind that Floquet DMFT describes a nonequilibrium steady state, while an effective static Hamiltonian describes an equilibrium state. We further demonstrate the possibility of using real-space Floquet DMFT to study edge states on a cylinder geometry.

Modeling of fast neutral-beam-generated ions and rotation effects on RWM stability in DIII-D plasmas

DOE PAGES

Turco, Francesca; Turnbull, Alan D.; Hanson, Jeremy M.; ...

2015-10-15

Here, validation results for the MARS-K code for DIII-D equilibria, predict that the absence of fast Neutral Beam (NB) generated ions leads to a plasma response ~40–60% higher than in NB-sustained H-mode plasmas when the no-wall β N limit is reached. In a β N scan, the MARS-K model with thermal and fast-ions, reproduces the experimental measurements above the no-wall limit, except at the highest β N where the phase of the plasma response is overestimated. The dependencies extrapolate unfavorably to machines such as ITER with smaller fast ion fractions since elevated responses in the absence of fast ions indicatemore » the potential onset of a resistive wall mode (RWM). The model was also tested for the effects of rotation at high β N, and recovers the measured response even when fast-ions are neglected, reversing the effect found in lower β N cases, but consistent with the higher β N results above the no-wall limit. The agreement in the response amplitude and phase for the rotation scan is not as good, and additional work will be needed to reproduce the experimental trends. In the case of current-driven instabilities, the magnetohydrodynamic spectroscopy system used to measure the plasma response reacts differently from that for pressure driven instabilities: the response amplitude remains low up to ~93% of the current limit, showing an abrupt increase only in the last ~5% of the current ramp. This makes it much less effective as a diagnostic for the approach to an ideal limit. However, the mode structure of the current driven RWM extends radially inwards, consistent with that in the pressure driven case for plasmas with q edge~2. This suggests that previously developed RWM feedback techniques together with the additional optimizations that enabled q edge~2 operation, can be applied to control of both current-driven and pressure-driven modes at high β N.« less

Driven tracer with absolute negative mobility

NASA Astrophysics Data System (ADS)

Cividini, J.; Mukamel, D.; Posch, H. A.

2018-02-01

Instances of negative mobility, where a system responds to a perturbation in a way opposite to naive expectation, have been studied theoretically and experimentally in numerous nonequilibrium systems. In this work we show that absolute negative mobility (ANM), whereby current is produced in a direction opposite to the drive, can occur around equilibrium states. This is demonstrated with a simple one-dimensional lattice model with a driven tracer. We derive analytical predictions in the linear response regime and elucidate the mechanism leading to ANM by studying the high-density limit. We also study numerically a model of hard Brownian disks in a narrow planar channel, for which the lattice model can be viewed as a toy model. We find that the model exhibits negative differential mobility (NDM), but no ANM.

Semi-empirical "leaky-bucket" model of laser-driven x-ray cavities

NASA Astrophysics Data System (ADS)

Moody, J. D.; Landen, O. L.; Divol, L.; LePape, S.; Michel, P.; Town, R. P. J.; Hall, G.; Widmann, K.; Moore, A.

2017-04-01

A semi-empirical analytical model is shown to approximately describe the energy balance in a laser-driven x-ray cavity, such as a hohlraum, for general laser pulse-shapes. Agreement between the model and measurements relies on two scalar parameters, one characterizes the efficiency of x-ray generation for a given laser power and the other represents a characteristic power-loss rate. These parameters, once obtained through estimation or optimization for a particular hohlraum design, can be used to predict either the x-ray flux or the coupled laser power time-history in terms of other quantities for similar hohlraum designs. The value of the model is that it can be used as an approximate "first-look" at hohlraum energy balance prior to a more detailed radiation hydrodynamic modeling.

Driven Metadynamics: Reconstructing Equilibrium Free Energies from Driven Adaptive-Bias Simulations

PubMed Central

2013-01-01

We present a novel free-energy calculation method that constructively integrates two distinct classes of nonequilibrium sampling techniques, namely, driven (e.g., steered molecular dynamics) and adaptive-bias (e.g., metadynamics) methods. By employing nonequilibrium work relations, we design a biasing protocol with an explicitly time- and history-dependent bias that uses on-the-fly work measurements to gradually flatten the free-energy surface. The asymptotic convergence of the method is discussed, and several relations are derived for free-energy reconstruction and error estimation. Isomerization reaction of an atomistic polyproline peptide model is used to numerically illustrate the superior efficiency and faster convergence of the method compared with its adaptive-bias and driven components in isolation. PMID:23795244

Improved understanding of geologic CO{sub 2} storage processes requires risk-driven field experiments

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

Oldenburg, C.M.

2011-06-01

The need for risk-driven field experiments for CO{sub 2} geologic storage processes to complement ongoing pilot-scale demonstrations is discussed. These risk-driven field experiments would be aimed at understanding the circumstances under which things can go wrong with a CO{sub 2} capture and storage (CCS) project and cause it to fail, as distinguished from accomplishing this end using demonstration and industrial scale sites. Such risk-driven tests would complement risk-assessment efforts that have already been carried out by providing opportunities to validate risk models. In addition to experimenting with high-risk scenarios, these controlled field experiments could help validate monitoring approaches to improvemore » performance assessment and guide development of mitigation strategies.« less

Improving the Fitness of High-Dimensional Biomechanical Models via Data-Driven Stochastic Exploration

PubMed Central

Bustamante, Carlos D.; Valero-Cuevas, Francisco J.

2010-01-01

The field of complex biomechanical modeling has begun to rely on Monte Carlo techniques to investigate the effects of parameter variability and measurement uncertainty on model outputs, search for optimal parameter combinations, and define model limitations. However, advanced stochastic methods to perform data-driven explorations, such as Markov chain Monte Carlo (MCMC), become necessary as the number of model parameters increases. Here, we demonstrate the feasibility and, what to our knowledge is, the first use of an MCMC approach to improve the fitness of realistically large biomechanical models. We used a Metropolis–Hastings algorithm to search increasingly complex parameter landscapes (3, 8, 24, and 36 dimensions) to uncover underlying distributions of anatomical parameters of a “truth model” of the human thumb on the basis of simulated kinematic data (thumbnail location, orientation, and linear and angular velocities) polluted by zero-mean, uncorrelated multivariate Gaussian “measurement noise.” Driven by these data, ten Markov chains searched each model parameter space for the subspace that best fit the data (posterior distribution). As expected, the convergence time increased, more local minima were found, and marginal distributions broadened as the parameter space complexity increased. In the 36-D scenario, some chains found local minima but the majority of chains converged to the true posterior distribution (confirmed using a cross-validation dataset), thus demonstrating the feasibility and utility of these methods for realistically large biomechanical problems. PMID:19272906

On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao

2018-02-01

Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.

Modeling Cable and Guide Channel Interaction in a High-Strength Cable-Driven Continuum Manipulator

PubMed Central

Moses, Matthew S.; Murphy, Ryan J.; Kutzer, Michael D. M.; Armand, Mehran

2016-01-01

This paper presents several mechanical models of a high-strength cable-driven dexterous manipulator designed for surgical procedures. A stiffness model is presented that distinguishes between contributions from the cables and the backbone. A physics-based model incorporating cable friction is developed and its predictions are compared with experimental data. The data show that under high tension and high curvature, the shape of the manipulator deviates significantly from a circular arc. However, simple parametric models can fit the shape with good accuracy. The motivating application for this study is to develop a model so that shape can be predicted using easily measured quantities such as tension, so that real-time navigation may be performed, especially in minimally-invasive surgical procedures, while reducing the need for hazardous imaging methods such as fluoroscopy. PMID:27818607

Modeling Cable and Guide Channel Interaction in a High-Strength Cable-Driven Continuum Manipulator.

PubMed

Moses, Matthew S; Murphy, Ryan J; Kutzer, Michael D M; Armand, Mehran

2015-12-01

This paper presents several mechanical models of a high-strength cable-driven dexterous manipulator designed for surgical procedures. A stiffness model is presented that distinguishes between contributions from the cables and the backbone. A physics-based model incorporating cable friction is developed and its predictions are compared with experimental data. The data show that under high tension and high curvature, the shape of the manipulator deviates significantly from a circular arc. However, simple parametric models can fit the shape with good accuracy. The motivating application for this study is to develop a model so that shape can be predicted using easily measured quantities such as tension, so that real-time navigation may be performed, especially in minimally-invasive surgical procedures, while reducing the need for hazardous imaging methods such as fluoroscopy.

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

PubMed Central

Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

2016-01-01

The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS) and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP) congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data. PMID:27690035

Quasi-monoenergetic ion beam acceleration by laser-driven shock and solitary waves in near-critical plasmas

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

Zhang, W. L.; Qiao, B., E-mail: bqiao@pku.edu.cn; Huang, T. W.

2016-07-15

Ion acceleration in near-critical plasmas driven by intense laser pulses is investigated theoretically and numerically. A theoretical model has been given for clarification of the ion acceleration dynamics in relation to different laser and target parameters. Two distinct regimes have been identified, where ions are accelerated by, respectively, the laser-induced shock wave in the weakly driven regime (comparatively low laser intensity) and the nonlinear solitary wave in the strongly driven regime (comparatively high laser intensity). Two-dimensional particle-in-cell simulations show that quasi-monoenergetic proton beams with a peak energy of 94.6 MeV and an energy spread 15.8% are obtained by intense laser pulsesmore » at intensity I{sub 0} = 3 × 10{sup 20 }W/cm{sup 2} and pulse duration τ = 0.5 ps in the strongly driven regime, which is more advantageous than that got in the weakly driven regime. In addition, 233 MeV proton beams with narrow spread can be produced by extending τ to 1.0 ps in the strongly driven regime.« less

The transferability of safety-driven access management models for application to other sites.

DOT National Transportation Integrated Search

2001-01-01

Several research studies have produced mathematical models that predict the safety impacts of selected access management techniques. Since new models require substantial resources to construct, this study evaluated five existing models with regard to...

Intelligent fuzzy controller for event-driven real time systems

NASA Technical Reports Server (NTRS)

Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

1992-01-01

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Data-driven indexing mechanism for the recognition of polyhedral objects

NASA Astrophysics Data System (ADS)

McLean, Stewart; Horan, Peter; Caelli, Terry M.

1992-02-01

This paper is concerned with the problem of searching large model databases. To date, most object recognition systems have concentrated on the problem of matching using simple searching algorithms. This is quite acceptable when the number of object models is small. However, in the future, general purpose computer vision systems will be required to recognize hundreds or perhaps thousands of objects and, in such circumstances, efficient searching algorithms will be needed. The problem of searching a large model database is one which must be addressed if future computer vision systems are to be at all effective. In this paper we present a method we call data-driven feature-indexed hypothesis generation as one solution to the problem of searching large model databases.

Two species drag/diffusion model for energetic particle driven modes

NASA Astrophysics Data System (ADS)

Aslanyan, V.; Sharapov, S. E.; Spong, D. A.; Porkolab, M.

2017-12-01

A nonlinear bump-on-tail model for the growth and saturation of energetic particle driven plasma waves has been extended to include two populations of fast particles—one dominated by dynamical friction at the resonance and the other by velocity space diffusion. The resulting temporal evolution of the wave amplitude and frequency depends on the relative weight of the two populations. The two species model is applied to burning plasma with drag-dominated alpha particles and diffusion-dominated ICRH accelerated minority ions, showing the stabilization of bursting modes. The model also suggests an explanation for the recent observations on the TJ-II stellarator, where Alfvén Eigenmodes transition between steady state and bursting as the magnetic configuration varied.

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

Riensche, Roderick M.; Paulson, Patrick R.; Danielson, Gary R.

We describe a methodology and architecture to support the development of games in a predictive analytics context. These games serve as part of an overall family of systems designed to gather input knowledge, calculate results of complex predictive technical and social models, and explore those results in an engaging fashion. The games provide an environment shaped and driven in part by the outputs of the models, allowing users to exert influence over a limited set of parameters, and displaying the results when those actions cause changes in the underlying model. We have crafted a prototype system in which we aremore » implementing test versions of games driven by models in such a fashion, using a flexible architecture to allow for future continuation and expansion of this work.« less

A comparison of fisheries biological reference points estimated from temperature-specific multi-species and single-species climate-enhanced stock assessment models

NASA Astrophysics Data System (ADS)

Holsman, Kirstin K.; Ianelli, James; Aydin, Kerim; Punt, André E.; Moffitt, Elizabeth A.

2016-12-01

Multi-species statistical catch at age models (MSCAA) can quantify interacting effects of climate and fisheries harvest on species populations, and evaluate management trade-offs for fisheries that target several species in a food web. We modified an existing MSCAA model to include temperature-specific growth and predation rates and applied the modified model to three fish species, walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus) and arrowtooth flounder (Atheresthes stomias), from the eastern Bering Sea (USA). We fit the model to data from 1979 through 2012, with and without trophic interactions and temperature effects, and use projections to derive single- and multi-species biological reference points (BRP and MBRP, respectively) for fisheries management. The multi-species model achieved a higher over-all goodness of fit to the data (i.e. lower negative log-likelihood) for pollock and Pacific cod. Variability from water temperature typically resulted in 5-15% changes in spawning, survey, and total biomasses, but did not strongly impact recruitment estimates or mortality. Despite this, inclusion of temperature in projections did have a strong effect on BRPs, including recommended yield, which were higher in single-species models for Pacific cod and arrowtooth flounder that included temperature compared to the same models without temperature effects. While the temperature-driven multi-species model resulted in higher yield MBPRs for arrowtooth flounder than the same model without temperature, we did not observe the same patterns in multi-species models for pollock and Pacific cod, where variability between harvest scenarios and predation greatly exceeded temperature-driven variability in yield MBRPs. Annual predation on juvenile pollock (primarily cannibalism) in the multi-species model was 2-5 times the annual harvest of adult fish in the system, thus predation represents a strong control on population dynamics that exceeds temperature-driven changes to growth and is attenuated through harvest-driven reductions in predator populations. Additionally, although we observed differences in spawning biomasses at the accepted biological catch (ABC) proxy between harvest scenarios and single- and multi-species models, discrepancies in spawning stock biomass estimates did not translate to large differences in yield. We found that multi-species models produced higher estimates of combined yield for aggregate maximum sustainable yield (MSY) targets than single species models, but were more conservative than single-species models when individual MSY targets were used, with the exception of scenarios where minimum biomass thresholds were imposed. Collectively our results suggest that climate and trophic drivers can interact to affect MBRPs, but for prey species with high predation rates, trophic- and management-driven changes may exceed direct effects of temperature on growth and predation. Additionally, MBRPs are not inherently more conservative than single-species BRPs. This framework provides a basis for the application of MSCAA models for tactical ecosystem-based fisheries management decisions under changing climate conditions.

Ontology-Driven Information Integration

NASA Technical Reports Server (NTRS)

Tissot, Florence; Menzel, Chris

2005-01-01

Ontology-driven information integration (ODII) is a method of computerized, automated sharing of information among specialists who have expertise in different domains and who are members of subdivisions of a large, complex enterprise (e.g., an engineering project, a government agency, or a business). In ODII, one uses rigorous mathematical techniques to develop computational models of engineering and/or business information and processes. These models are then used to develop software tools that support the reliable processing and exchange of information among the subdivisions of this enterprise or between this enterprise and other enterprises.

Modeling laser-driven electron acceleration using WARP with Fourier decomposition

DOE PAGES

Lee, P.; Audet, T. L.; Lehe, R.; ...

2015-12-31

WARP is used with the recent implementation of the Fourier decomposition algorithm to model laser-driven electron acceleration in plasmas. Simulations were carried out to analyze the experimental results obtained on ionization-induced injection in a gas cell. The simulated results are in good agreement with the experimental ones, confirming the ability of the code to take into account the physics of electron injection and reduce calculation time. We present a detailed analysis of the laser propagation, the plasma wave generation and the electron beam dynamics.

Modeling laser-driven electron acceleration using WARP with Fourier decomposition

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

Lee, P.; Audet, T. L.; Lehe, R.

WARP is used with the recent implementation of the Fourier decomposition algorithm to model laser-driven electron acceleration in plasmas. Simulations were carried out to analyze the experimental results obtained on ionization-induced injection in a gas cell. The simulated results are in good agreement with the experimental ones, confirming the ability of the code to take into account the physics of electron injection and reduce calculation time. We present a detailed analysis of the laser propagation, the plasma wave generation and the electron beam dynamics.

Data-Driven Boundary Correction and Optimization of a Nearshore Wave and Hydrodynamic Model to Enable Rapid Environmental Assessment

DTIC Science & Technology

2011-09-30

Number : N00014 N00014-09-1-0503 http://ceprofs.civil.tamu.edu/jkaihatu/research/proj.html LONG-TERM GOALS The present project is part of a... number . 1. REPORT DATE 30 SEP 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE Data-Driven Boundary...Correction and Optimization of a Nearshore Wave and Hydrodynamic Model to Enable Rapid Environmental Assessment 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

The wind of the M-type AGB star RT Virginis probed by VLTI/MIDI

NASA Astrophysics Data System (ADS)

Sacuto, S.; Ramstedt, S.; Höfner, S.; Olofsson, H.; Bladh, S.; Eriksson, K.; Aringer, B.; Klotz, D.; Maercker, M.

2013-03-01

Aims: We study the circumstellar environment of the M-type AGB star RT Vir using mid-infrared high spatial resolution observations from the ESO-VLTI focal instrument MIDI. The aim of this study is to provide observational constraints on theoretical prediction that the winds of M-type AGB objects can be driven by photon scattering on iron-free silicate grains located in the close environment (about 2 to 3 stellar radii) of the star. Methods: We interpreted spectro-interferometric data, first using wavelength-dependent geometric models. We then used a self-consistent dynamic model atmosphere containing a time-dependent description of grain growth for pure forsterite dust particles to reproduce the photometric, spectrometric, and interferometric measurements of RT Vir. Since the hydrodynamic computation needs stellar parameters as input, a considerable effort was first made to determine these parameters. Results: MIDI differential phases reveal the presence of an asymmetry in the stellar vicinity. Results from the geometrical modeling give us clues to the presence of aluminum and silicate dust in the close circumstellar environment (<5 stellar radii). Comparison between spectro-interferometric data and a self-consistent dust-driven wind model reveals that silicate dust has to be present in the region between 2 to 3 stellar radii to reproduce the 59 and 63 m baseline visibility measurements around 9.8 μm. This gives additional observational evidence in favor of winds driven by photon scattering on iron-free silicate grains located in the close vicinity of an M-type star. However, other sources of opacity are clearly missing to reproduce the 10-13 μm visibility measurements for all baselines. Conclusions: This study is a first attempt to understand the wind mechanism of M-type AGB stars by comparing photometric, spectrometric, and interferometric measurements with state-of-the-art, self-consistent dust-driven wind models. The agreement of the dynamic model atmosphere with interferometric measurements in the 8-10 μm spectral region gives additional observational evidence that the winds of M-type stars can be driven by photon scattering on iron-free silicate grains. Finally, a larger statistical study and progress in advanced self-consistent 3D modeling are still required to solve the remaining problems. Based on observations made with the Very Large Telescope Interferometer at Paranal Observatory under programs 083.D-0234 and 086.D-0737 (Open Time Observations).

Drivers of Arctic Ocean warming in CMIP5 models

NASA Astrophysics Data System (ADS)

Burgard, Clara; Notz, Dirk

2017-05-01

We investigate changes in the Arctic Ocean energy budget simulated by 26 general circulation models from the Coupled Model Intercomparison Project Phase 5 framework. Our goal is to understand whether the Arctic Ocean warming between 1961 and 2099 is primarily driven by changes in the net atmospheric surface flux or by changes in the meridional oceanic heat flux. We find that the simulated Arctic Ocean warming is driven by positive anomalies in the net atmospheric surface flux in 11 models, by positive anomalies in the meridional oceanic heat flux in 11 models, and by positive anomalies in both energy fluxes in four models. The different behaviors are mainly characterized by the different changes in meridional oceanic heat flux that lead to different changes in the turbulent heat loss to the atmosphere. The multimodel ensemble mean is hence not representative of a consensus across the models in Arctic climate projections.

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

NASA Astrophysics Data System (ADS)

Yao, Bing; Yang, Hui

2016-12-01

This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

Energy modelling in sensor networks

NASA Astrophysics Data System (ADS)

Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.

2007-06-01

Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.

Cross-stream diffusion under pressure-driven flow in microchannels with arbitrary aspect ratios: a phase diagram study using a three-dimensional analytical model

PubMed Central

Song, Hongjun; Wang, Yi; Pant, Kapil

2011-01-01

This article presents a three-dimensional analytical model to investigate cross-stream diffusion transport in rectangular microchannels with arbitrary aspect ratios under pressure-driven flow. The Fourier series solution to the three-dimensional convection–diffusion equation is obtained using a double integral transformation method and associated eigensystem calculation. A phase diagram derived from the dimensional analysis is presented to thoroughly interrogate the characteristics in various transport regimes and examine the validity of the model. The analytical model is verified against both experimental and numerical models in terms of the concentration profile, diffusion scaling law, and mixing efficiency with excellent agreement (with <0.5% relative error). Quantitative comparison against other prior analytical models in extensive parameter space is also performed, which demonstrates that the present model accommodates much broader transport regimes with significantly enhanced applicability. PMID:22247719

Cross-stream diffusion under pressure-driven flow in microchannels with arbitrary aspect ratios: a phase diagram study using a three-dimensional analytical model.

PubMed

Song, Hongjun; Wang, Yi; Pant, Kapil

2012-01-01

This article presents a three-dimensional analytical model to investigate cross-stream diffusion transport in rectangular microchannels with arbitrary aspect ratios under pressure-driven flow. The Fourier series solution to the three-dimensional convection-diffusion equation is obtained using a double integral transformation method and associated eigensystem calculation. A phase diagram derived from the dimensional analysis is presented to thoroughly interrogate the characteristics in various transport regimes and examine the validity of the model. The analytical model is verified against both experimental and numerical models in terms of the concentration profile, diffusion scaling law, and mixing efficiency with excellent agreement (with <0.5% relative error). Quantitative comparison against other prior analytical models in extensive parameter space is also performed, which demonstrates that the present model accommodates much broader transport regimes with significantly enhanced applicability.

Task-Driven Tube Current Modulation and Regularization Design in Computed Tomography with Penalized-Likelihood Reconstruction.

PubMed

Gang, G J; Siewerdsen, J H; Stayman, J W

2016-02-01

This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison. We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index ( d' ). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom. The task-driven design yielded the best performance, improving d' by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d' by 16% and 9%, respectively. This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

Enhancement of the Acquisition Process for a Combat System-A Case Study to Model the Workflow Processes for an Air Defense System Acquisition

DTIC Science & Technology

2009-12-01

Business Process Modeling BPMN Business Process Modeling Notation SoA Service-oriented Architecture UML Unified Modeling Language CSP...system developers. Supporting technologies include Business Process Modeling Notation ( BPMN ), Unified Modeling Language (UML), model-driven architecture

First Principles Modeling of the Performance of a Hydrogen-Peroxide-Driven Chem-E-Car

ERIC Educational Resources Information Center

Farhadi, Maryam; Azadi, Pooya; Zarinpanjeh, Nima

2009-01-01

In this study, performance of a hydrogen-peroxide-driven car has been simulated using basic conservation laws and a few numbers of auxiliary equations. A numerical method was implemented to solve sets of highly non-linear ordinary differential equations. Transient pressure and the corresponding traveled distance for three different car weights are…

EVALUATION OF TOXICITY BY OIL FLY-ASHES IN RAT ALVEOLAR EPITHELIAL CELLS: ROLE OF FENTON DRIVEN OH-RADICAL GENERATION AND PARATICLE FRACTION

EPA Science Inventory

Leachable transition metals are considered to be important mediators in the biological effects of ambient particulate matter (PM) and model particles such as oil-fly ashes (OFA). We determined metal driven Fenton activity in 10 OFA in the presence of hydrogen peroxide using elect...

A variable-instar climate-driven individual beetle-based phenology model for the invasive Asian longhorned beetle (Coleoptera: Cerambycidae)

Treesearch

R. Talbot Trotter, III; Melody A. Keena

2016-01-01

Efforts to manage and eradicate invasive species can benefit from an improved understanding of the physiology, biology, and behavior of the target species, and ongoing efforts to eradicate the Asian longhorned beetle (Anoplophora glabripennis Motschulsky) highlight the roles this information may play. Here, we present a climate-driven phenology...

Water Network Tool for Resilience v. 1.0

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

2015-12-09

WNTR is a python package designed to simulate and analyze resilience of water distribution networks. The software includes: - Pressure driven and demand driven hydraulic simulation - Water quality simulation to track concentration, trace, and water age - Conditional controls to simulate power outages - Models to simulate pipe breaks - A wide range of resilience metrics - Analysis and visualization tools

Sensory processing during viewing of cinematographic material: Computational modeling and functional neuroimaging

PubMed Central

Bordier, Cecile; Puja, Francesco; Macaluso, Emiliano

2013-01-01

The investigation of brain activity using naturalistic, ecologically-valid stimuli is becoming an important challenge for neuroscience research. Several approaches have been proposed, primarily relying on data-driven methods (e.g. independent component analysis, ICA). However, data-driven methods often require some post-hoc interpretation of the imaging results to draw inferences about the underlying sensory, motor or cognitive functions. Here, we propose using a biologically-plausible computational model to extract (multi-)sensory stimulus statistics that can be used for standard hypothesis-driven analyses (general linear model, GLM). We ran two separate fMRI experiments, which both involved subjects watching an episode of a TV-series. In Exp 1, we manipulated the presentation by switching on-and-off color, motion and/or sound at variable intervals, whereas in Exp 2, the video was played in the original version, with all the consequent continuous changes of the different sensory features intact. Both for vision and audition, we extracted stimulus statistics corresponding to spatial and temporal discontinuities of low-level features, as well as a combined measure related to the overall stimulus saliency. Results showed that activity in occipital visual cortex and the superior temporal auditory cortex co-varied with changes of low-level features. Visual saliency was found to further boost activity in extra-striate visual cortex plus posterior parietal cortex, while auditory saliency was found to enhance activity in the superior temporal cortex. Data-driven ICA analyses of the same datasets also identified “sensory” networks comprising visual and auditory areas, but without providing specific information about the possible underlying processes, e.g., these processes could relate to modality, stimulus features and/or saliency. We conclude that the combination of computational modeling and GLM enables the tracking of the impact of bottom–up signals on brain activity during viewing of complex and dynamic multisensory stimuli, beyond the capability of purely data-driven approaches. PMID:23202431

Designing an optimal software intensive system acquisition: A game theoretic approach

NASA Astrophysics Data System (ADS)

Buettner, Douglas John

The development of schedule-constrained software-intensive space systems is challenging. Case study data from national security space programs developed at the U.S. Air Force Space and Missile Systems Center (USAF SMC) provide evidence of the strong desire by contractors to skip or severely reduce software development design and early defect detection methods in these schedule-constrained environments. The research findings suggest recommendations to fully address these issues at numerous levels. However, the observations lead us to investigate modeling and theoretical methods to fundamentally understand what motivated this behavior in the first place. As a result, Madachy's inspection-based system dynamics model is modified to include unit testing and an integration test feedback loop. This Modified Madachy Model (MMM) is used as a tool to investigate the consequences of this behavior on the observed defect dynamics for two remarkably different case study software projects. Latin Hypercube sampling of the MMM with sample distributions for quality, schedule and cost-driven strategies demonstrate that the higher cost and effort quality-driven strategies provide consistently better schedule performance than the schedule-driven up-front effort-reduction strategies. Game theory reasoning for schedule-driven engineers cutting corners on inspections and unit testing is based on the case study evidence and Austin's agency model to describe the observed phenomena. Game theory concepts are then used to argue that the source of the problem and hence the solution to developers cutting corners on quality for schedule-driven system acquisitions ultimately lies with the government. The game theory arguments also lead to the suggestion that the use of a multi-player dynamic Nash bargaining game provides a solution for our observed lack of quality game between the government (the acquirer) and "large-corporation" software developers. A note is provided that argues this multi-player dynamic Nash bargaining game also provides the solution to Freeman Dyson's problem, for a way to place a label of good or bad on systems.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

PubMed Central

2011-01-01

Background Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge. PMID:22165854

Two-fluid flowing equilibria of spherical torus sustained by coaxial helicity injection

NASA Astrophysics Data System (ADS)

Kanki, Takashi; Steinhauer, Loren; Nagata, Masayoshi

2007-11-01

Two-dimensional equilibria in helicity-driven systems using two-fluid model were previously computed, showing the existence of an ultra-low-q spherical torus (ST) configuration with diamagnetism and higher beta. However, this computation assumed purely toroidal ion flow and uniform density. The purpose of the present study is to apply the two-fluid model to the two-dimensional equilibria of helicity-driven ST with non-uniform density and both toroidal and poloidal flows for each species by means of the nearby-fluids procedure, and to explore their properties. We focus our attention on the equilibria relevant to the HIST device, which are characterized by either driven or decaying λ profiles. The equilibrium for the driven λ profile has a diamagnetic toroidal field, high-β (βt = 32%), and centrally broad density. By contrast, the decaying equilibrium has a paramagnetic toroidal field, low-β (βt = 10%), and centrally peaked density with a steep gradient in the outer edge region. In the driven case, the toroidal ion and electron flows are in the same direction, and two-fluid effects are less important since the ExB drift is dominant. In the decaying case, the toroidal ion and electron flows are opposite in the outer edge region, and two-fluid effects are significant locally in the edge due to the ion diamagnetic drift.

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource.

PubMed

Shim, Hongseok; Kim, Ji Hyun; Kim, Chan Yeong; Hwang, Sohyun; Kim, Hyojin; Yang, Sunmo; Lee, Ji Eun; Lee, Insuk

2016-11-16

Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Climate-driven vital rates do not always mean climate-driven population.

PubMed

Tavecchia, Giacomo; Tenan, Simone; Pradel, Roger; Igual, José-Manuel; Genovart, Meritxell; Oro, Daniel

2016-12-01

Current climatic changes have increased the need to forecast population responses to climate variability. A common approach to address this question is through models that project current population state using the functional relationship between demographic rates and climatic variables. We argue that this approach can lead to erroneous conclusions when interpopulation dispersal is not considered. We found that immigration can release the population from climate-driven trajectories even when local vital rates are climate dependent. We illustrated this using individual-based data on a trans-equatorial migratory seabird, the Scopoli's shearwater Calonectris diomedea, in which the variation of vital rates has been associated with large-scale climatic indices. We compared the population annual growth rate λ i , estimated using local climate-driven parameters with ρ i , a population growth rate directly estimated from individual information and that accounts for immigration. While λ i varied as a function of climatic variables, reflecting the climate-dependent parameters, ρ i did not, indicating that dispersal decouples the relationship between population growth and climate variables from that between climatic variables and vital rates. Our results suggest caution when assessing demographic effects of climatic variability especially in open populations for very mobile organisms such as fish, marine mammals, bats, or birds. When a population model cannot be validated or it is not detailed enough, ignoring immigration might lead to misleading climate-driven projections. © 2016 John Wiley & Sons Ltd.

Electric-field-driven electron-transfer in mixed-valence molecules.

PubMed

Blair, Enrique P; Corcelli, Steven A; Lent, Craig S

2016-07-07

Molecular quantum-dot cellular automata is a computing paradigm in which digital information is encoded by the charge configuration of a mixed-valence molecule. General-purpose computing can be achieved by arranging these compounds on a substrate and exploiting intermolecular Coulombic coupling. The operation of such a device relies on nonequilibrium electron transfer (ET), whereby the time-varying electric field of one molecule induces an ET event in a neighboring molecule. The magnitude of the electric fields can be quite large because of close spatial proximity, and the induced ET rate is a measure of the nonequilibrium response of the molecule. We calculate the electric-field-driven ET rate for a model mixed-valence compound. The mixed-valence molecule is regarded as a two-state electronic system coupled to a molecular vibrational mode, which is, in turn, coupled to a thermal environment. Both the electronic and vibrational degrees-of-freedom are treated quantum mechanically, and the dissipative vibrational-bath interaction is modeled with the Lindblad equation. This approach captures both tunneling and nonadiabatic dynamics. Relationships between microscopic molecular properties and the driven ET rate are explored for two time-dependent applied fields: an abruptly switched field and a linearly ramped field. In both cases, the driven ET rate is only weakly temperature dependent. When the model is applied using parameters appropriate to a specific mixed-valence molecule, diferrocenylacetylene, terahertz-range ET transfer rates are predicted.

Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

NASA Astrophysics Data System (ADS)

Ibañez, Ruben; Borzacchiello, Domenico; Aguado, Jose Vicente; Abisset-Chavanne, Emmanuelle; Cueto, Elias; Ladeveze, Pierre; Chinesta, Francisco

2017-11-01

The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\\ldots ), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.

Theoretical modeling of a gas clearance phase regulation mechanism for a pneumatically-driven split-Stirling-cycle cryocooler

NASA Astrophysics Data System (ADS)

Zhang, Cun-quan; Zhong, Cheng

2015-03-01

The concept of a new type of pneumatically-driven split-Stirling-cycle cryocooler with clearance-phase-adjustor is proposed. In this implementation, the gap between the phase-adjusting part and the cylinder of the spring chamber is used, instead of dry friction acting on the pneumatically-driven rod to control motion damping of the displacer and to adjust the phase difference between the compression piston and displacer. It has the advantages of easy damping adjustment, low cost, and simplified manufacturing and assembly. A theoretical model has been established to simulate its dynamic performance. The linear compressor is modeled under adiabatic conditions, and the displacement of the compression piston is experimentally rectified. The working characteristics of the compressor motor and the principal losses of cooling, including regenerator inefficiency loss, solid conduction loss, shuttle loss, pump loss and radiation loss, are taken into account. The displacer motion was modeled as a single-degree-of-freedom (SDOF) forced system. A set of governing equations can be solved numerically to simulate the cooler's performance. The simulation is useful for understanding the physical processes occurring in the cooler and for predicting the cooler's performance.

Quantitative theory of driven nonlinear brain dynamics.

PubMed

Roberts, J A; Robinson, P A

2012-09-01

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f(α), subharmonic entrainment near 2f(α), and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory. Copyright © 2012 Elsevier Inc. All rights reserved.

Reliable fuzzy H∞ control for active suspension of in-wheel motor driven electric vehicles with dynamic damping

NASA Astrophysics Data System (ADS)

Shao, Xinxin; Naghdy, Fazel; Du, Haiping

2017-03-01

A fault-tolerant fuzzy H∞ control design approach for active suspension of in-wheel motor driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The controller is designed based on the quarter-car active suspension model with a dynamic-damping-in-wheel-motor-driven-system, in which the suspended motor is operated as a dynamic absorber. The Takagi-Sugeno (T-S) fuzzy model is used to model this suspension with possible sprung mass variation. The parallel-distributed compensation (PDC) scheme is deployed to derive a fault-tolerant fuzzy controller for the T-S fuzzy suspension model. In order to reduce the motor wear caused by the dynamic force transmitted to the in-wheel motor, the dynamic force is taken as an additional controlled output besides the traditional optimization objectives such as sprung mass acceleration, suspension deflection and actuator saturation. The H∞ performance of the proposed controller is derived as linear matrix inequalities (LMIs) comprising three equality constraints which are solved efficiently by means of MATLAB LMI Toolbox. The proposed controller is applied to an electric vehicle suspension and its effectiveness is demonstrated through computer simulation.

Depinning and heterogeneous dynamics of colloidal crystal layers under shear flow

NASA Astrophysics Data System (ADS)

Gerloff, Sascha; Klapp, Sabine H. L.

2016-12-01

Using Brownian dynamics (BD) simulations and an analytical approach we investigate the shear-induced, nonequilibrium dynamics of dense colloidal suspensions confined to a narrow slit-pore. Focusing on situations where the colloids arrange in well-defined layers with solidlike in-plane structure, the confined films display complex, nonlinear behavior such as collective depinning and local transport via density excitations. These phenomena are reminiscent of colloidal monolayers driven over a periodic substrate potential. In order to deepen this connection, we present an effective model that maps the dynamics of the shear-driven colloidal layers to the motion of a single particle driven over an effective substrate potential. This model allows us to estimate the critical shear rate of the depinning transition based on the equilibrium configuration, revealing the impact of important parameters, such as the slit-pore width and the interaction strength. We then turn to heterogeneous systems where a layer of small colloids is sheared with respect to bottom layers of large particles. For these incommensurate systems we find that the particle transport is dominated by density excitations resembling the so-called "kink" solutions of the Frenkel-Kontorova (FK) model. In contrast to the FK model, however, the corresponding "antikinks" do not move.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Quantum simulations and many-body physics with light.

PubMed

Noh, Changsuk; Angelakis, Dimitris G

2017-01-01

In this review we discuss the works in the area of quantum simulation and many-body physics with light, from the early proposals on equilibrium models to the more recent works in driven dissipative platforms. We start by describing the founding works on Jaynes-Cummings-Hubbard model and the corresponding photon-blockade induced Mott transitions and continue by discussing the proposals to simulate effective spin models and fractional quantum Hall states in coupled resonator arrays (CRAs). We also analyse the recent efforts to study out-of-equilibrium many-body effects using driven CRAs, including the predictions for photon fermionisation and crystallisation in driven rings of CRAs as well as other dynamical and transient phenomena. We try to summarise some of the relatively recent results predicting exotic phases such as super-solidity and Majorana like modes and then shift our attention to developments involving 1D nonlinear slow light setups. There the simulation of strongly correlated phases characterising Tonks-Girardeau gases, Luttinger liquids, and interacting relativistic fermionic models is described. We review the major theory results and also briefly outline recent developments in ongoing experimental efforts involving different platforms in circuit QED, photonic crystals and nanophotonic fibres interfaced with cold atoms.

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering.

PubMed

Sangavai, C; Chellapandi, P

2017-12-01

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n -butanol, n -butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

Three-dimensional simulation of pseudopod-driven swimming of amoeboid cells

NASA Astrophysics Data System (ADS)

Campbell, Eric; Bagchi, Prosenjit

2016-11-01

Pseudopod-driven locomotion is common in eukaryotic cells, such as amoeba, neutrophils, and cancer cells. Pseudopods are protrusions of the cell body that grow, bifurcate, and retract. Due to the dynamic nature of pseudopods, the shape of a motile cell constantly changes. The actin-myosin protein dynamics is a likely mechanism for pseudopod growth. Existing theoretical models often focus on the acto-myosin dynamics, and not the whole cell shape dynamics. Here we present a full 3D simulation of pseudopod-driven motility by coupling a surface-bound reaction-diffusion (RD) model for the acto-myosin dynamics, a continuum model for the cell membrane deformation, and flow of the cytoplasmic and extracellular fluids. The whole cell is represented as a viscous fluid surrounded by a membrane. A finite-element method is used to solve the membrane deformation, and the RD model on the deforming membrane, while a finite-difference/spectral method is used to solve the flow fields inside and outside the cell. The fluid flow and cell deformation are coupled by the immersed-boundary method. The model predicts pseudopod growth, bifurcation, and retraction as observed for a swimming amoeba. The work provides insights on the role of membrane stiffness and cytoplasmic viscosity on amoeboid swimming. Funded by NSF CBET 1438255.

A zebrafish model of chordoma initiated by notochord-driven expression of HRASV12.

PubMed

Burger, Alexa; Vasilyev, Aleksandr; Tomar, Ritu; Selig, Martin K; Nielsen, G Petur; Peterson, Randall T; Drummond, Iain A; Haber, Daniel A

2014-07-01

Chordoma is a malignant tumor thought to arise from remnants of the embryonic notochord, with its origin in the bones of the axial skeleton. Surgical resection is the standard treatment, usually in combination with radiation therapy, but neither chemotherapeutic nor targeted therapeutic approaches have demonstrated success. No animal model and only few chordoma cell lines are available for preclinical drug testing, and, although no druggable genetic drivers have been identified, activation of EGFR and downstream AKT-PI3K pathways have been described. Here, we report a zebrafish model of chordoma, based on stable transgene-driven expression of HRASV12 in notochord cells during development. Extensive intra-notochordal tumor formation is evident within days of transgene expression, ultimately leading to larval death. The zebrafish tumors share characteristics of human chordoma as demonstrated by immunohistochemistry and electron microscopy. The mTORC1 inhibitor rapamycin, which has some demonstrated activity in a chordoma cell line, delays the onset of tumor formation in our zebrafish model, and improves survival of tumor-bearing fish. Consequently, the HRASV12-driven zebrafish model of chordoma could enable high-throughput screening of potential therapeutic agents for the treatment of this refractory cancer. © 2014. Published by The Company of Biologists Ltd.

Design and modeling of magnetically driven electric-field sensor for non-contact DC voltage measurement in electric power systems.

PubMed

Wang, Decai; Li, Ping; Wen, Yumei

2016-10-01

In this paper, the design and modeling of a magnetically driven electric-field sensor for non-contact DC voltage measurement are presented. The magnetic drive structure of the sensor is composed of a small solenoid and a cantilever beam with a cylindrical magnet mounted on it. The interaction of the magnet and the solenoid provides the magnetic driving force for the sensor. Employing magnetic drive structure brings the benefits of low driving voltage and large vibrating displacement, which consequently results in less interference from the drive signal. In the theoretical analyses, the capacitance calculation model between the wire and the sensing electrode is built. The expression of the magnetic driving force is derived by the method of linear fitting. The dynamical model of the magnetic-driven cantilever beam actuator is built by using Euler-Bernoulli theory and distributed parameter method. Taking advantage of the theoretical model, the output voltage of proposed sensor can be predicted. The experimental results are in good agreement with the theoretical results. The proposed sensor shows a favorable linear response characteristic. The proposed sensor has a measuring sensitivity of 9.87 μV/(V/m) at an excitation current of 37.5 mA. The electric field intensity resolution can reach 10.13 V/m.

A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots.

PubMed

Ma, Ye; Xie, Shengquan; Zhang, Yanxin

2016-03-01

A patient-specific electromyography (EMG)-driven neuromuscular model (PENm) is developed for the potential use of human-inspired gait rehabilitation robots. The PENm is modified based on the current EMG-driven models by decreasing the calculation time and ensuring good prediction accuracy. To ensure the calculation efficiency, the PENm is simplified into two EMG channels around one joint with minimal physiological parameters. In addition, a dynamic computation model is developed to achieve real-time calculation. To ensure the calculation accuracy, patient-specific muscle kinematics information, such as the musculotendon lengths and the muscle moment arms during the entire gait cycle, are employed based on the patient-specific musculoskeletal model. Moreover, an improved force-length-velocity relationship is implemented to generate accurate muscle forces. Gait analysis data including kinematics, ground reaction forces, and raw EMG signals from six adolescents at three different speeds were used to evaluate the PENm. The simulation results show that the PENm has the potential to predict accurate joint moment in real-time. The design of advanced human-robot interaction control strategies and human-inspired gait rehabilitation robots can benefit from the application of the human internal state provided by the PENm. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

PubMed

Quo, Chang F; Kaddi, Chanchala; Phan, John H; Zollanvari, Amin; Xu, Mingqing; Wang, May D; Alterovitz, Gil

2012-07-01

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

Model-Driven Energy Intelligence

DTIC Science & Technology

2015-03-01

building information model ( BIM ) for operations...estimate of the potential impact on energy performance at Fort Jackson. 15. SUBJECT TERMS Building Information Modeling ( BIM ), Energy, ECMs, monitoring...dimensional AHU Air Handling Unit API Application Programming Interface BIM building information model BLCC Building Life Cycle Cost

Modeling radiation belt electron dynamics during GEM challenge intervals with the DREAM3D diffusion model

NASA Astrophysics Data System (ADS)

Tu, Weichao; Cunningham, G. S.; Chen, Y.; Henderson, M. G.; Camporeale, E.; Reeves, G. D.

2013-10-01

a response to the Geospace Environment Modeling (GEM) "Global Radiation Belt Modeling Challenge," a 3D diffusion model is used to simulate the radiation belt electron dynamics during two intervals of the Combined Release and Radiation Effects Satellite (CRRES) mission, 15 August to 15 October 1990 and 1 February to 31 July 1991. The 3D diffusion model, developed as part of the Dynamic Radiation Environment Assimilation Model (DREAM) project, includes radial, pitch angle, and momentum diffusion and mixed pitch angle-momentum diffusion, which are driven by dynamic wave databases from the statistical CRRES wave data, including plasmaspheric hiss, lower-band, and upper-band chorus. By comparing the DREAM3D model outputs to the CRRES electron phase space density (PSD) data, we find that, with a data-driven boundary condition at Lmax = 5.5, the electron enhancements can generally be explained by radial diffusion, though additional local heating from chorus waves is required. Because the PSD reductions are included in the boundary condition at Lmax = 5.5, our model captures the fast electron dropouts over a large L range, producing better model performance compared to previous published results. Plasmaspheric hiss produces electron losses inside the plasmasphere, but the model still sometimes overestimates the PSD there. Test simulations using reduced radial diffusion coefficients or increased pitch angle diffusion coefficients inside the plasmasphere suggest that better wave models and more realistic radial diffusion coefficients, both inside and outside the plasmasphere, are needed to improve the model performance. Statistically, the results show that, with the data-driven outer boundary condition, including radial diffusion and plasmaspheric hiss is sufficient to model the electrons during geomagnetically quiet times, but to best capture the radiation belt variations during active times, pitch angle and momentum diffusion from chorus waves are required.

A new concept for simulation of vegetated land surface dynamics - Part 1: The event driven phenology model

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Henebry, G. M.

2012-01-01

Phenologies of the vegetated land surface are being used increasingly for diagnosis and prognosis of climate change consequences. Current prospective and retrospective phenological models stand far apart in their approaches to the subject. We report on an exploratory attempt to implement a phenological model based on a new event driven concept which has both diagnostic and prognostic capabilities in the same modeling framework. This Event Driven Phenological Model (EDPM) is shown to simulate land surface phenologies and phenophase transition dates in agricultural landscapes based on assimilation of weather data and land surface observations from spaceborne sensors. The model enables growing season phenologies to develop in response to changing environmental conditions and disturbance events. It also has the ability to ingest remotely sensed data to adjust its output to improve representation of the modeled variable. We describe the model and report results of initial testing of the EDPM using Level 2 flux tower records from the Ameriflux sites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulating the dynamics of normalized difference vegetation index based on flux tower data, the predictions by the EDPM show good agreement (RMSE < 0.08; r2 > 0.8) for maize and soybean during several growing seasons at different locations. This study presents the EDPM used in the companion paper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate daily actual evapotranspiration over multiple growing seasons.

A new concept for simulation of vegetated land surface dynamics - Part 1: The event driven phenology model

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Henebry, G. M.

2011-05-01

Phenologies of the vegetated land surface are being used increasingly for diagnosis and prognosis of climate change consequences. Current prospective and retrospective phenological models stand far apart in their approaches to the subject. We report on an exploratory attempt to implement a phenological model based on a new event driven concept which has both diagnostic and prognostic capabilities in the same modeling framework. This Event Driven Phenological Model (EDPM) is shown to simulate land surface phenologies and phenophase transition dates in agricultural landscapes based on assimilation of weather data and land surface observations from spaceborne sensors. The model enables growing season phenologies to develop in response to changing environmental conditions and disturbance events. It also has the ability to ingest remotely sensed data to adjust its output to improve representation of the modeled variable. We describe the model and report results of initial testing of the EDPM using Level 2 flux tower records from the Ameriflux sites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulating the dynamics of normalized difference vegetation index based on flux tower data, the predictions by the EDPM show good agreement (RMSE < 0.08; r2>0.8) for maize and soybean during several growing seasons at different locations. This study presents the EDPM used in the companion paper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate daily actual evapotranspiration over multiple growing seasons.

Modular, Semantics-Based Composition of Biosimulation Models

ERIC Educational Resources Information Center

Neal, Maxwell Lewis

2010-01-01

Biosimulation models are valuable, versatile tools used for hypothesis generation and testing, codification of biological theory, education, and patient-specific modeling. Driven by recent advances in computational power and the accumulation of systems-level experimental data, modelers today are creating models with an unprecedented level of…

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models.

PubMed

Nazem-Zadeh, Mohammad-Reza; Elisevich, Kost V; Schwalb, Jason M; Bagher-Ebadian, Hassan; Mahmoudi, Fariborz; Soltanian-Zadeh, Hamid

2014-12-15

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Data-driven modeling, control and tools for cyber-physical energy systems

NASA Astrophysics Data System (ADS)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Contrasting impacts of local and non-local anthropogenic aerosols detected on 20th century monsoon precipitation over West Africa and South Asia

NASA Astrophysics Data System (ADS)

Hegerl, G. C.; Polson, D.; Bollasina, M. A.; Ming, Y.

2015-12-01

Anthropogenic aerosols are a key driver 4 of historical changes in Summer monsoon precipition in the Northern Hemisphere. Detection and attribution studies have shown that the reduction in Northern Hemisphere precipitation over the second half of the 20th century is driven by anthropogenic aerosol emissions. Here we apply these same methods to investigate changes in the West African and South Asian monsoons and identify the source regions of the anthropogenic aerosols that drive the observed changes. Historical climate model simulations are used to derive fingerprints of aerosol forcing for different regions of the globe. Comparing model changes with observations show that the changes in West African monsoon preciptiation are driven by remote aerosol emissions from North America and Europe, while changes in South Asian monsoon precipitation are driven by local aerosol emissions.

Preliminary Results from a Model-Driven Architecture Methodology for Development of an Event-Driven Space Communications Service Concept

NASA Technical Reports Server (NTRS)

Roberts, Christopher J.; Morgenstern, Robert M.; Israel, David J.; Borky, John M.; Bradley, Thomas H.

2017-01-01

NASA's next generation space communications network will involve dynamic and autonomous services analogous to services provided by current terrestrial wireless networks. This architecture concept, known as the Space Mobile Network (SMN), is enabled by several technologies now in development. A pillar of the SMN architecture is the establishment and utilization of a continuous bidirectional control plane space link channel and a new User Initiated Service (UIS) protocol to enable more dynamic and autonomous mission operations concepts, reduced user space communications planning burden, and more efficient and effective provider network resource utilization. This paper provides preliminary results from the application of model driven architecture methodology to develop UIS. Such an approach is necessary to ensure systematic investigation of several open questions concerning the efficiency, robustness, interoperability, scalability and security of the control plane space link and UIS protocol.

Insight from animal models of environmentally-driven epigenetic changes in the developing and adult brain

PubMed Central

Doherty, Tiffany S.; Roth, Tania L.

2017-01-01

The efforts of many neuroscientists are directed toward understanding the appreciable plasticity of the brain and behavior. In recent years, epigenetics has become a core of this focus as a prime mechanistic candidate for behavioral modifications. Animal models have been instrumental in advancing our understanding of environmentally-driven changes to the epigenome in the developing and adult brain. This review focuses mainly on such discoveries driven by adverse environments along with their associated behavioral outcomes. While much of the evidence discussed focuses on epigenetics within the central nervous system, several peripheral studies in humans who have experienced significant adversity are also highlighted. As we continue to unravel the link between epigenetics and phenotype, discerning the complexity and specificity of epigenetic changes induced by environments is an important step toward understanding optimal development and how to prevent or ameliorate behavioral deficits bred by disruptive environments. PMID:27687803

On the efficiency of the golf swing

NASA Astrophysics Data System (ADS)

White, Rod

2006-12-01

A non-driven double pendulum model is used to explain the principle underlying the surprising efficiency of the golf swing. The principle can be described as a parametric energy transfer between the arms and the club head due to the changing moment of inertia of the club. The transfer is a consequence of conservation of energy and angular momentum. Because the pendulum is not driven by an external force, it shows that the golfer need do little more than accelerate the arms with the wrists cocked and let the double pendulum transfer kinetic energy to the club head. A driven double pendulum model is used to study factors affecting the efficiency of a real golf swing. It is concluded that the wrist-cock angle is the most significant efficiency-determining parameter under the golfer's control and that improvements in golf technology have had a significant impact on driving distance.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

NASA Astrophysics Data System (ADS)

Kerekes, John P.; Presnar, Michael D.; Fourspring, Kenneth D.; Ninkov, Zoran; Pogorzala, David R.; Raisanen, Alan D.; Rice, Andrew C.; Vasquez, Juan R.; Patel, Jeffrey P.; MacIntyre, Robert T.; Brown, Scott D.

2009-05-01

A novel multi-object spectrometer (MOS) is being explored for use as an adaptive performance-driven sensor that tracks moving targets. Developed originally for astronomical applications, the instrument utilizes an array of micromirrors to reflect light to a panchromatic imaging array. When an object of interest is detected the individual micromirrors imaging the object are tilted to reflect the light to a spectrometer to collect a full spectrum. This paper will present example sensor performance from empirical data collected in laboratory experiments, as well as our approach in designing optical and radiometric models of the MOS channels and the micromirror array. Simulation of moving vehicles in a highfidelity, hyperspectral scene is used to generate a dynamic video input for the adaptive sensor. Performance-driven algorithms for feature-aided target tracking and modality selection exploit multiple electromagnetic observables to track moving vehicle targets.

Role of large-scale velocity fluctuations in a two-vortex kinematic dynamo.

PubMed

Kaplan, E J; Brown, B P; Rahbarnia, K; Forest, C B

2012-06-01

This paper presents an analysis of the Dudley-James two-vortex flow, which inspired several laboratory-scale liquid-metal experiments, in order to better demonstrate its relation to astrophysical dynamos. A coordinate transformation splits the flow into components that are axisymmetric and nonaxisymmetric relative to the induced magnetic dipole moment. The reformulation gives the flow the same dynamo ingredients as are present in more complicated convection-driven dynamo simulations. These ingredients are currents driven by the mean flow and currents driven by correlations between fluctuations in the flow and fluctuations in the magnetic field. The simple model allows us to isolate the dynamics of the growing eigenvector and trace them back to individual three-wave couplings between the magnetic field and the flow. This simple model demonstrates the necessity of poloidal advection in sustaining the dynamo and points to the effect of large-scale flow fluctuations in exciting a dynamo magnetic field.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PubMed Central

Cruchet, Steeve; Gustafson, Kyle; Benton, Richard; Floreano, Dario

2015-01-01

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. PMID:26600381

Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?

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

Granderson, Jessica; Bonvini, Marco; Piette, Mary Ann

We present that analytics software is increasingly used to improve and maintain operational efficiency in commercial buildings. Energy managers, owners, and operators are using a diversity of commercial offerings often referred to as Energy Information Systems, Fault Detection and Diagnostic (FDD) systems, or more broadly Energy Management and Information Systems, to cost-effectively enable savings on the order of ten to twenty percent. Most of these systems use data from meters and sensors, with rule-based and/or data-driven models to characterize system and building behavior. In contrast, physics-based modeling uses first-principles and engineering models (e.g., efficiency curves) to characterize system and buildingmore » behavior. Historically, these physics-based approaches have been used in the design phase of the building life cycle or in retrofit analyses. Researchers have begun exploring the benefits of integrating physics-based models with operational data analytics tools, bridging the gap between design and operations. In this paper, we detail the development and operator use of a software tool that uses hybrid data-driven and physics-based approaches to cooling plant FDD and optimization. Specifically, we describe the system architecture, models, and FDD and optimization algorithms; advantages and disadvantages with respect to purely data-driven approaches; and practical implications for scaling and replicating these techniques. Finally, we conclude with an evaluation of the future potential for such tools and future research opportunities.« less

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

NASA Astrophysics Data System (ADS)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Modeling dynamic plasmas driven by ultraintense nano-focused x-ray laser pulses in solid iron targets

NASA Astrophysics Data System (ADS)

Royle, Ryan; Sentoku, Yasuhiko; Mancini, Roberto

2017-10-01

The hard x-ray free electron laser has proven to be a valuable tool for high energy density (HED) physics as it is able to produce well-characterized samples of HED matter at exactly solid density and homogeneous temperatures. However, if the x-ray pulses are focused to sub-micron spot sizes, where peak intensities can exceed 1020 W/cm2, the plasmas driven by sources of non-thermal photoelectrons and Auger electrons can be highly dynamic and so cannot be modeled by atomic kinetics or fluid codes. We apply the 2D/3D particle-in-cell code, PICLS-which has been extended with numerous physics models to enable the simulation of XFEL-driven plasmas-to the modeling of such dynamic plasmas driven by nano-focused XFEL pulses in solid iron targets. In the case of the smallest focal spot investigated of just 100 nm in diameter, keV plasmas induce strong radial E-fields that accelerate keV ions radially as well as sheath fields that accelerate surface ions to hundreds of keV. The heated spot, which is initially larger than the laser spot due to the kinetic nature of the fast Auger electrons, expands as ion and electron waves propagate radially, leaving a low density region along the laser axis. This research was supported by the US DOE-OFES under Grant No. DE-SC0008827, the DOE-NNSA under Grant No. DE-NA0002075, and the JSPS KAKENHI under Grant No. JP15K21767.

Light-powered micromotor driven by geometry-assisted, asymmetric photon-heating and subsequent gas convection

NASA Astrophysics Data System (ADS)

Han, Li-Hsin; Wu, Shaomin; Condit, J. Christopher; Kemp, Nate J.; Milner, Thomas E.; Feldman, Marc D.; Chen, Shaochen

2010-05-01

We report on the design, fabrication, and analysis of a light-driven micromotor. The micromotor was created from a nanoporous polymer with close-packed gold nanoparticles which generate heat by absorbing light. The blades of the micromotor were curved, forming convex and concave sides. Upon lateral irradiation, by geometric effect the convex side transfers more photon-generated heat to the surrounding gas molecules, causing a convective motion of gas and leading to the rotation of the micromotor. The light-driven motions of gas molecules were analyzed using molecular dynamics modeling.

The effect of sheared toroidal rotation on pressure driven magnetic islands in toroidal plasmas

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

Hegna, C. C.

2016-05-15

The impact of sheared toroidal rotation on the evolution of pressure driven magnetic islands in tokamak plasmas is investigated using a resistive magnetohydrodynamics model augmented by a neoclassical Ohm's law. Particular attention is paid to the asymptotic matching data as the Mercier indices are altered in the presence of sheared flow. Analysis of the nonlinear island Grad-Shafranov equation shows that sheared flows tend to amplify the stabilizing pressure/curvature contribution to pressure driven islands in toroidal tokamaks relative to the island bootstrap current contribution. As such, sheared toroidal rotation tends to reduce saturated magnetic island widths.

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.

Generic analysis of kinetically driven inflation

NASA Astrophysics Data System (ADS)

Saitou, Rio

2018-04-01

We perform a model-independent analysis of kinetically driven inflation (KDI) which (partially) includes generalized G-inflation and ghost inflation. We evaluate the background evolution splitting into the inflationary attractor and the perturbation around it. We also consider the quantum fluctuation of the scalar mode with a usual scaling and derive the spectral index, ignoring the contribution from the second-order products of slow-roll parameters. Using these formalisms, we find that within our generic framework the models of KDI which possess the shift symmetry of scalar field cannot create the quantum fluctuation consistent with the observation. Breaking the shift symmetry, we obtain a few essential conditions for viable models of KDI associated with the graceful exit.

Control of Tollmien-Schlichting instabilities by finite distributed wall actuation

NASA Astrophysics Data System (ADS)

Losse, Nikolas R.; King, Rudibert; Zengl, Marcus; Rist, Ulrich; Noack, Bernd R.

2011-06-01

Tollmien-Schlichting waves are one of the key mechanisms triggering the laminar-turbulent transition in a flat-plate boundary-layer flow. By damping these waves and thus delaying transition, skin friction drag can be significantly decreased. In this simulation study, a wall segment is actuated according to a control scheme based on a POD-Galerkin model driven extended Kalman filter for state estimation and a model predictive controller to dampen TS waves by negative superposition based on this information. The setup of the simulation is chosen to resemble actuation with a driven compliant wall, such as a membrane actuator. Most importantly, a method is proposed to integrate such a localized wall actuation into a Galerkin model.

Physics of Efficiency Droop in GaN:Eu Light-Emitting Diodes.

PubMed

Fragkos, Ioannis E; Dierolf, Volkmar; Fujiwara, Yasufumi; Tansu, Nelson

2017-12-01

The internal quantum efficiency (IQE) of an electrically-driven GaN:Eu based device for red light emission is analyzed in the framework of a current injection efficiency model (CIE). The excitation path of the Eu +3 ion is decomposed in a multiple level system, which includes the carrier transport phenomena across the GaN/GaN:Eu/GaN active region of the device, and the interactions among traps, Eu +3 ions and the GaN host. The identification and analysis of the limiting factors of the IQE are accomplished through the CIE model. The CIE model provides a guidance for high IQE in the electrically-driven GaN:Eu based red light emitters.

The Distribution and Abundance of Bird Species: Towards a Satellite, Data Driven Avian Energetics and Species Richness Model

NASA Technical Reports Server (NTRS)

Smith, James A.

2003-01-01

This paper addresses the fundamental question of why birds occur where and when they do, i.e., what are the causative factors that determine the spatio-temporal distributions, abundance, or richness of bird species? In this paper we outline the first steps toward building a satellite, data-driven model of avian energetics and species richness based on individual bird physiology, morphology, and interaction with the spatio-temporal habitat. To evaluate our model, we will use the North American Breeding Bird Survey and Christmas Bird Count data for species richness, wintering and breeding range. Long term and current satellite data series include AVHRR, Landsat, and MODIS.

Particle drift model for Z-pinch-driven magneto-Rayleigh-Taylor instability

NASA Astrophysics Data System (ADS)

Dan, Jia Kun; Xu, Qiang; Wang, Kun Lun; Ren, Xiao Dong; Huang, Xian Bin

2016-09-01

A theoretical model of Z-pinch driven magneto-Rayleigh-Taylor instability is proposed based on the particle drift point of view, which can explain the helical instability structure observed in premagnetized imploding liner experiments. It is demonstrated that all possible drift motions, including polarization drift, gradient drift, and curvature drift, which can lead to charge separations, each will attribute to an effective gravity acceleration. Theoretical predictions given by this model are dramatically different from those given by previous theories which have been readily recovered in the theory presented here as a limiting case. The theory shows qualitative agreement with available experimental data of the pitch angle and provides certain predictions to be verified.

Modelling of piezoelectric actuator dynamics for active structural control

NASA Technical Reports Server (NTRS)

Hagood, Nesbitt W.; Chung, Walter H.; Von Flotow, Andreas

1990-01-01

The paper models the effects of dynamic coupling between a structure and an electrical network through the piezoelectric effect. The coupled equations of motion of an arbitrary elastic structure with piezoelectric elements and passive electronics are derived. State space models are developed for three important cases: direct voltage driven electrodes, direct charge driven electrodes, and an indirect drive case where the piezoelectric electrodes are connected to an arbitrary electrical circuit with embedded voltage and current sources. The equations are applied to the case of a cantilevered beam with surface mounted piezoceramics and indirect voltage and current drive. The theoretical derivations are validated experimentally on an actively controlled cantilevered beam test article with indirect voltage drive.

Generating a fractal butterfly Floquet spectrum in a class of driven SU(2) systems

NASA Astrophysics Data System (ADS)

Wang, Jiao; Gong, Jiangbin

2010-02-01

A scheme for generating a fractal butterfly Floquet spectrum, first proposed by Wang and Gong [Phys. Rev. A 77, 031405(R) (2008)], is extended to driven SU(2) systems such as a driven two-mode Bose-Einstein condensate. A class of driven systems without a link with the Harper-model context is shown to have an intriguing butterfly Floquet spectrum. The found butterfly spectrum shows remarkable deviations from the known Hofstadter’s butterfly. In addition, the level crossings between Floquet states of the same parity and between Floquet states of different parities are studied and highlighted. The results are relevant to studies of fractal statistics, quantum chaos, and coherent destruction of tunneling, as well as the validity of mean-field descriptions of Bose-Einstein condensates.

Current-driven plasma acceleration versus current-driven energy dissipation. I - Wave stability theory

NASA Technical Reports Server (NTRS)

Kelly, A. J.; Jahn, R. G.; Choueiri, E. Y.

1990-01-01

The dominant unstable electrostatic wave modes of an electromagnetically accelerated plasma are investigated. The study is the first part of a three-phase program aimed at characterizing the current-driven turbulent dissipation degrading the efficiency of Lorentz force plasma accelerators such as the MPD thruster. The analysis uses a kinetic theory that includes magnetic and thermal effects as well as those of an electron current transverse to the magnetic field and collisions, thus combining all the features of previous models. Analytical and numerical solutions allow a detailed description of threshold criteria, finite growth behavior, destabilization mechanisms and maximized-growth characteristics of the dominant unstable modes. The lower hybrid current-driven instability is implicated as dominant and was found to preserve its character in the collisional plasma regime.

Generating a fractal butterfly Floquet spectrum in a class of driven SU(2) systems

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

Wang Jiao; Temasek Laboratories, National University of Singapore, Singapore 117542; Gong Jiangbin

2010-02-15

A scheme for generating a fractal butterfly Floquet spectrum, first proposed by Wang and Gong [Phys. Rev. A 77, 031405(R) (2008)], is extended to driven SU(2) systems such as a driven two-mode Bose-Einstein condensate. A class of driven systems without a link with the Harper-model context is shown to have an intriguing butterfly Floquet spectrum. The found butterfly spectrum shows remarkable deviations from the known Hofstadter's butterfly. In addition, the level crossings between Floquet states of the same parity and between Floquet states of different parities are studied and highlighted. The results are relevant to studies of fractal statistics, quantummore » chaos, and coherent destruction of tunneling, as well as the validity of mean-field descriptions of Bose-Einstein condensates.« less

Solving Immunology?

PubMed

Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L; Bassaganya-Riera, Josep; Hafler, David A; Sontag, Eduardo; Wang, Jin; Tsang, John S; Day, Judy D; Kleinstein, Steven H; Butte, Atul J; Altman, Matthew C; Hammond, Ross; Sealfon, Stuart C

2017-02-01

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting the stochastic guiding of kinesin-driven microtubules in microfabricated tracks: a statistical-mechanics-based modeling approach.

PubMed

Lin, Chih-Tin; Meyhofer, Edgar; Kurabayashi, Katsuo

2010-01-01

Directional control of microtubule shuttles via microfabricated tracks is key to the development of controlled nanoscale mass transport by kinesin motor molecules. Here we develop and test a model to quantitatively predict the stochastic behavior of microtubule guiding when they mechanically collide with the sidewalls of lithographically patterned tracks. By taking into account appropriate probability distributions of microscopic states of the microtubule system, the model allows us to theoretically analyze the roles of collision conditions and kinesin surface densities in determining how the motion of microtubule shuttles is controlled. In addition, we experimentally observe the statistics of microtubule collision events and compare our theoretical prediction with experimental data to validate our model. The model will direct the design of future hybrid nanotechnology devices that integrate nanoscale transport systems powered by kinesin-driven molecular shuttles.

Data-driven process decomposition and robust online distributed modelling for large-scale processes

NASA Astrophysics Data System (ADS)

Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

2018-02-01

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

Convection- and SASI-driven flows in parametrized models of core-collapse supernova explosions

DOE PAGES

Endeve, E.; Cardall, C. Y.; Budiardja, R. D.; ...

2016-01-21

We present initial results from three-dimensional simulations of parametrized core-collapse supernova (CCSN) explosions obtained with our astrophysical simulation code General Astrophysical Simulation System (GenASIS). We are interested in nonlinear flows resulting from neutrino-driven convection and the standing accretion shock instability (SASI) in the CCSN environment prior to and during the explosion. By varying parameters in our model that control neutrino heating and shock dissociation, our simulations result in convection-dominated and SASI-dominated evolution. We describe this initial set of simulation results in some detail. To characterize the turbulent flows in the simulations, we compute and compare velocity power spectra from convection-dominatedmore » and SASI-dominated (both non-exploding and exploding) models. When compared to SASI-dominated models, convection-dominated models exhibit significantly more power on small spatial scales.« less

Relating coupled map lattices to integro-difference equations: dispersal-driven instabilities in coupled map lattices.

PubMed

White, Steven M; White, K A Jane

2005-08-21

Recently there has been a great deal of interest within the ecological community about the interactions of local populations that are coupled only by dispersal. Models have been developed to consider such scenarios but the theory needed to validate model outcomes has been somewhat lacking. In this paper, we present theory which can be used to understand these types of interaction when population exhibit discrete time dynamics. In particular, we consider a spatial extension to discrete-time models, known as coupled map lattices (CMLs) which are discrete in space. We introduce a general form of the CML and link this to integro-difference equations via a special redistribution kernel. General conditions are then derived for dispersal-driven instabilities. We then apply this theory to two discrete-time models; a predator-prey model and a host-pathogen model.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

Current Driven Instabilities and Anomalous Mobility in Hall-effect Thrusters

NASA Astrophysics Data System (ADS)

Tran, Jonathan; Eckhardt, Daniel; Martin, Robert

2017-10-01

Due to the extreme cost of fully resolving the Debye length and plasma frequency, hybrid plasma simulations utilizing kinetic ions and quasi-steady state fluid electrons have long been the principle workhorse methodology for Hall-effect thruster (HET) modeling. Plasma turbulence and the resulting anomalous electron transport in HETs is a promising candidate for developing predictive models for the observed anomalous transport. In this work, we investigate the implementation of an anomalous electron cross field transport model for hybrid HET simulations such a HPHall. A theory for anomalous transport in HETs and current driven instabilities has been recently studied by Lafleur et al. This work has shown collective electron-wave scattering due to large amplitude azimuthal fluctuations of the electric field. We will further adapt the previous results for related current driven instabilities to electric propulsion relevant mass ratios and conduct a preliminary study of resolving this instability with a modified hybrid (fluid electron and kinetic ion) simulation with the hope of integration with established hybrid HET simulations. This work is supported by the Air Force Office of Scientific Research award FA9950-17RQCOR465.

Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover change

USGS Publications Warehouse

Bouska, Kristen; Whitledge, Gregory W.; Lant, Christopher; Schoof, Justin

2018-01-01

Land cover is an important determinant of aquatic habitat and is projected to shift with climate changes, yet climate-driven land cover changes are rarely factored into climate assessments. To quantify impacts and uncertainty of coupled climate and land cover change on warm-water fish species’ distributions, we used an ensemble model approach to project distributions of 14 species. For each species, current range projections were compared to 27 scenario-based projections and aggregated to visualize uncertainty. Multiple regression and model selection techniques were used to identify drivers of range change. Novel, or no-analogue, climates were assessed to evaluate transferability of models. Changes in total probability of occurrence ranged widely across species, from a 63% increase to a 65% decrease. Distributional gains and losses were largely driven by temperature and flow variables and underscore the importance of habitat heterogeneity and connectivity to facilitate adaptation to changing conditions. Finally, novel climate conditions were driven by mean annual maximum temperature, which stresses the importance of understanding the role of temperature on fish physiology and the role of temperature-mitigating management practices.

Quasistationary magnetic field generation with a laser-driven capacitor-coil assembly.

PubMed

Tikhonchuk, V T; Bailly-Grandvaux, M; Santos, J J; Poyé, A

2017-08-01

Recent experiments are showing possibilities to generate strong magnetic fields on the excess of 500 T with high-energy nanosecond laser pulses in a compact setup of a capacitor connected to a single turn coil. Hot electrons ejected from the capacitor plate (cathode) are collected at the other plate (anode), thus providing the source of a current in the coil. However, the physical processes leading to generation of currents exceeding hundreds of kiloamperes in such a laser-driven diode are not sufficiently understood. Here we present a critical analysis of previous results and propose a self-consistent model for the high current generation in a laser-driven capacitor-coil assembly. It accounts for three major effects controlling the diode current: the space charge neutralization, the plasma magnetization between the capacitor plates, and the Ohmic heating of the external circuit-the coil-shaped connecting wire. The model provides the conditions necessary for transporting strongly super-Alfvenic currents through the diode on the time scale of a few nanoseconds. The model validity is confirmed by a comparison with the available experimental data.

Model-driven methodology for rapid deployment of smart spaces based on resource-oriented architectures.

PubMed

Corredor, Iván; Bernardos, Ana M; Iglesias, Josué; Casar, José R

2012-01-01

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym.

Swiss and Dutch "consumer-driven health care": ideal model or reality?

PubMed

Okma, Kieke G H; Crivelli, Luca

2013-02-01

This article addresses three topics. First, it reports on the international interest in the health care reforms of Switzerland and The Netherlands in the 1990s and early 2000s that operate under the label "managed competition" or "consumer-driven health care." Second, the article reviews the behavior assumptions that make plausible the case for the model of "managed competition." Third, it analyze the actual reform experience of Switzerland and Holland to assess to what extent they confirm the validity of those assumptions. The article concludes that there is a triple gap in understanding of those topics: a gap between the theoretical model of managed competition and the reforms as implemented in both Switzerland and The Netherlands; second, a gap between the expectations of policy-makers and the results of the reforms, and third, a gap between reform outcomes and the observations of external commentators that have embraced the reforms as the ultimate success of "consumer-driven health care." The article concludes with a discussion of the implications of this "triple gap". Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Probing short-range correlations in asymmetric nuclei with quasi-free pair knockout reactions

NASA Astrophysics Data System (ADS)

Stevens, Sam; Ryckebusch, Jan; Cosyn, Wim; Waets, Andreas

2018-02-01

Short-range correlations (SRC) in asymmetric nuclei with an unusual neutron-to-proton ratio can be studied with quasi-free two-nucleon knockout processes following the collision between accelerated ions and a proton target. We derive an approximate factorized cross section for those SRC-driven p (A ,p‧N1N2) reactions. Our reaction model hinges on the factorization properties of SRC-driven A (e ,e‧N1N2) reactions for which strong indications are found in theory-experiment comparisons. In order to put our model to the test we compare its predictions with results of 12C (p ,p‧ pn) measurements conducted at Brookhaven National Laboratory (BNL) and find a fair agreement. The model can also reproduce characteristic features of SRC-driven two-nucleon knockout reactions, like back-to-back emission of the correlated nucleons. We study the asymmetry dependence of nuclear SRC by providing predictions for the ratio of proton-proton to proton-neutron knockout cross sections for the carbon isotopes 9-15C thereby covering neutron excess values (N - Z) / Z between -0.5 and +0.5.

Aerodynamically and acoustically driven modes of vibration in a physical model of the vocal folds.

PubMed

Zhang, Zhaoyan; Neubauer, Juergen; Berry, David A

2006-11-01

In a single-layered, isotropic, physical model of the vocal folds, distinct phonation types were identified based on the medial surface dynamics of the vocal fold. For acoustically driven phonation, a single, in-phase, x-10 like eigenmode captured the essential dynamics, and coupled with one of the acoustic resonances of the subglottal tract. Thus, the fundamental frequency appeared to be determined primarily by a subglottal acoustic resonance. In contrast, aerodynamically driven phonation did not naturally appear in the single-layered model, but was facilitated by the introduction of a vertical constraint. For this phonation type, fundamental frequency was relatively independent of the acoustic resonances, and two eigenmodes were required to capture the essential dynamics of the vocal fold, including an out-of-phase x-11 like eigenmode and an in-phase x-10 like eigenmode, as described in earlier theoretical work. The two eigenmodes entrained to the same frequency, and were decoupled from subglottal acoustic resonances. With this independence from the acoustic resonances, vocal fold dynamics appeared to be determined primarily by near-field, fluid-structure interactions.

Intelligence rules of hysteresis in the feedforward trajectory control of piezoelectrically-driven nanostagers

NASA Astrophysics Data System (ADS)

Bashash, Saeid; Jalili, Nader

2007-02-01

Piezoelectrically-driven nanostagers have limited performance in a variety of feedforward and feedback positioning applications because of their nonlinear hysteretic response to input voltage. The hysteresis phenomenon is well known for its complex and multi-path behavior. To realize the underlying physics of this phenomenon and to develop an efficient compensation strategy, the intelligence properties of hysteresis with the effects of non-local memories are discussed here. Through performing a set of experiments on a piezoelectrically-driven nanostager with a high resolution capacitive position sensor, it is shown that for the precise prediction of the hysteresis path, certain memory units are required to store the previous hysteresis trajectory data. Based on the experimental observations, a constitutive memory-based mathematical modeling framework is developed and trained for the precise prediction of the hysteresis path for arbitrarily assigned input profiles. Using the inverse hysteresis model, a feedforward control strategy is then developed and implemented on the nanostager to compensate for the ever-present nonlinearity. Experimental results demonstrate that the controller remarkably eliminates the nonlinear effect, if memory units are sufficiently chosen for the inverse model.

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

Documentation Driven Development for Complex Real-Time Systems

DTIC Science & Technology

2004-12-01

This paper presents a novel approach for development of complex real - time systems , called the documentation-driven development (DDD) approach. This... time systems . DDD will also support automated software generation based on a computational model and some relevant techniques. DDD includes two main...stakeholders to be easily involved in development processes and, therefore, significantly improve the agility of software development for complex real

An Investigation of the Role of Grapheme Units in Word Recognition

ERIC Educational Resources Information Center

Lupker, Stephen J.; Acha, Joana; Davis, Colin J.; Perea, Manuel

2012-01-01

In most current models of word recognition, the word recognition process is assumed to be driven by the activation of letter units (i.e., that letters are the perceptual units in reading). An alternative possibility is that the word recognition process is driven by the activation of grapheme units, that is, that graphemes, rather than letters, are…

Impact of GODAE Products on Nested HYCOM Simulations of the West Florida Shelf

DTIC Science & Technology

2009-01-20

circulation and the Atlantic Meridional Overturning Circulation . For temperature, the non-assimilative outer model had a cold...associated with the basin-scale wind-driven gyres and with the Atlantic Meridional Overturning Circulation is incor- rectly represented. In contrast...not contain realistic LC transport variability associated with the wind-driven gyre circulation and the Atlantic Meridio- nal Overturning Circulation

Use of Theory-Driven Research in Counseling: Investigating Three Counseling Psychology Journals from 1990 to 1999

ERIC Educational Resources Information Center

Karr, Carolyn A.; Larson, Lisa M.

2005-01-01

Three major journals in counseling psychology were sampled from 1990 to 1999 to assess the percentage of quantitative, empirical articles that were theory driven. Only 43% of the studies utilized a theory or model, and 57% predicted the relation between the variables, with few studies specifying the strength of the relation. Studies sampled in the…

Argument-Driven Inquiry as a Way to Help Undergraduate Students Write to Learn by Learning to Write in Chemistry

ERIC Educational Resources Information Center

Sampson, Victor; Walker, Joi Phelps

2012-01-01

This exploratory study examined how undergraduate students' ability to write in science changed over time as they completed a series of laboratory activities designed using a new instructional model called argument-driven inquiry. The study was conducted in a single section of an undergraduate general chemistry lab course offered at a large…